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false | 2013-03-02 22:19:11 | Ruby/Haml: Maintaining white space/indentation in a <pre> tag | [
"ruby",
"haml"
] | [
"Ruby"
] | I've been writing a little web app in which I wanted to display cypher queries inside a
....
tag which was then prettified using SyntaxHighlighter but I was having problems with how code on new lines was being displayed.</p>
I had the following Haml code to display a query looking up Gareth Bale in a graph:
~~~haml
%pre{ :class => "brush: cypher; gutter: false; toolbar: false;"}
START player = node:players('name:"Gareth Bale"')
RETURN player.name
~~~
When I rendered the page it looked like this:
After a bit of googling I ended up on this Stack Overflow post which described the preserve helper which seems to do the job:
~~~haml
%pre{ :class => "brush: cypher; gutter: false; toolbar: false;"}
= preserve do
START player = node:players('name:"Gareth Bale"')
RETURN player.name
~~~
That part of the page now looks much better:
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] | rubyhaml-maintaining-white-spaceindentation-in-a-pre-tag | https://markhneedham.com/blog/2013/03/02/rubyhaml-maintaining-white-spaceindentation-in-a-pre-tag |
false | 2013-03-02 23:22:50 | Ruby/Haml: Conditionally/Optionally setting an attribute/class | [
"ruby",
"haml"
] | [
"Ruby"
] | One of the things that we want to do reasonably frequently is set an attribute (most often a class) on a HTML element depending on the value of a variable.
I always forget how to do this in Haml so I thought I better write it down so I'll remember next time!
Let's say we want to add a +++<cite>+++success+++</cite>+++ class to a paragraph if the variable +++<cite>+++correct+++</cite>+++ is true and not have any value if it's false.
The following code does what we want:
[source,haml]
----
- correct = true
%p{:class => (correct ? "success" : nil) }
important text
----
This generates the following HTML is +++<cite>+++correct+++</cite>+++ is true:
[source,html4strict]
----
<p class="success">
important text
</p>
----
And the following HTML if it's false
[source,html4strict]
----
<p>
important text
</p>
----
To summarise, http://stackoverflow.com/questions/3841116/conditionally-set-html-element-id-with-haml[if we set an attribute to nil in Haml it just won't be rendered at all] which is exactly what we want in this situation. | null | null | [
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] | rubyhaml-conditionallyoptionally-setting-an-attributeclass | https://markhneedham.com/blog/2013/03/02/rubyhaml-conditionallyoptionally-setting-an-attributeclass |
false | 2013-03-20 00:25:00 | neo4j/cypher: Getting the hang of the WITH statement | [
"neo4j",
"cypher"
] | [
"neo4j"
] | I wrote a post a few weeks ago http://www.markhneedham.com/blog/2013/02/24/neo4jcypher-combining-count-and-collect-in-one-query/[showing an example of a cypher query which made use of the WITH statement] but I still don't completely understand how it works so I thought I'd write some more queries that use it.
I wanted to find out whether Luis Suárez has a better scoring record depending on which day a match is played on.
We start out by finding all the matches that he's played in and which days those matches were on:
[source,cypher]
----
START player = node:players('name:"Luis Suárez"')
MATCH game-[:in]-stats-[:played]-player, game-[:on_day]-day
RETURN day.name, game.name
----
[source,text]
----
+---------------------------------------------------+
| day.name | game.name |
+---------------------------------------------------+
| "Saturday" | "Liverpool vs Southampton" |
| "Saturday" | "Southampton vs Liverpool" |
| "Saturday" | "Liverpool vs Reading" |
| "Saturday" | "West Bromwich Albion vs Liverpool" |
...
+---------------------------------------------------+
29 rows
----
We can then group those matches by day to find out how many games he played in on a particular day:
[source,cypher]
----
START player = node:players('name:"Luis Suárez"')
MATCH game-[:in]-stats-[:played]-player, game-[:on_day]-day
RETURN day.name, COUNT(game.name)
----
[source,text]
----
+--------------------------------+
| day.name | COUNT(game.name) |
+--------------------------------+
| "Sunday" | 13 |
| "Wednesday" | 4 |
| "Monday" | 1 |
| "Saturday" | 11 |
+--------------------------------+
4 rows
----
Now we want to find out which days the games that Suarez scored in were on so we start out by returning each day that Suarez played in a match and then return a collection containing information about the games he played on that day and whether he scored:
[source,cypher]
----
START player = node:players('name:"Luis Suárez"')
MATCH game-[:in]-stats-[:played]-player-[r?:scored_in]-game-[:on_day]-day
RETURN day, COLLECT(DISTINCT([type(r), game.name])) AS games
----
[source,text]
----
+----------------------------------------------------------------------------------------+
| day.name | games |
+----------------------------------------------------------------------------------------+
| "Sunday" | [["scored_in","Liverpool vs Manchester City"]…] |
| "Wednesday" | [[<null>,"Tottenham Hotspur vs Liverpool"],[<null>,"Stoke City vs Liverpool"]...] |
| "Monday" | [[<null>,"Liverpool vs West Bromwich Albion"]] |
| "Saturday" | [[<null>,"Liverpool vs Southampton"]…] |
+----------------------------------------------------------------------------------------+
4 rows
----
This query has got a little bit more complicated than our previous ones because we wanted to return all the days that Suarez played matches on even if he didn't score on that day.
The only interesting thing in the first couple of lines is that we match the 'scored_in' relationship http://www.markhneedham.com/blog/2012/06/24/neo4j-handling-optional-relationships/[optionally] so that we can handle the situation where Suarez didn't score while still returning a row.
On the third line we return the day and then we get a collection of tuples of the 'scored_in' relationship and the corresponding game.
We use +++<cite>+++DISTINCT+++</cite>+++ on this line to take care of the situation where Suarez scored multiple times in the same match. We're working out the number of games that Suarez scored in so counting multiple goals in the same match would ruin that count.
We then need to tweak that query slightly to get a count of the matches that Suarez scored in rather than just returning them. We end up with the following:
[source,cypher]
----
START player = node:players('name:"Luis Suárez"')
MATCH game-[:in]-stats-[:played]-player-[r?:scored_in]-game-[:on_day]-day
WITH day, COLLECT(DISTINCT([type(r), game.name])) AS games
RETURN day.name, REDUCE(totalGames = 0, game in FILTER(x in games : head(x) = "scored_in"): totalGames + 1) AS gamesScoredIn
----
[source,text]
----
+-----------------------------+
| day.name | gamesScoredIn |
+-----------------------------+
| "Wednesday" | 2 |
| "Saturday" | 6 |
| "Sunday" | 7 |
| "Monday" | 0 |
+-----------------------------+
4 rows
----
We start off by http://docs.neo4j.org/chunked/milestone/query-function.html#functions-filter[filtering] the games so that we only keep the ones that Suarez scored in. We then run a http://docs.neo4j.org/chunked/milestone/query-function.html#functions-reduce[+++<cite>+++REDUCE+++</cite>+++] over the resulting collection which just adds 1 to an accumulator for each record in the collection.
Now that we've got that the next step is to combine our games played and games scored in queries together so that we can see what % of games Suarez scores in on each day.
We end up with the following:
[source,cypher]
----
START player = node:players('name:"Luis Suárez"')
MATCH game-[:in]-stats-[:played]-player, game-[:on_day]-day
WITH player, day, COUNT(game) AS playedGames
MATCH game-[:in]-stats-[:played]-player-[r?:scored_in]-game-[:on_day]-day
WITH day, COLLECT(DISTINCT([type(r), game.name])) AS games, playedGames
WITH day, REDUCE(totalGames = 0, game in FILTER(x in games : head(x) = "scored_in"): totalGames + 1) AS scoredGames, playedGames
RETURN day.name, playedGames, scoredGames, (scoredGames*1.0/playedGames*1.0) * 100 AS percentage
----
[source,text]
----
+-------------------------------------------------------------+
| day.name | playedGames | scoredGames | percentage |
+-------------------------------------------------------------+
| "Saturday" | 11 | 6 | 54.54545454545454 |
| "Monday" | 1 | 0 | 0.0 |
| "Wednesday" | 4 | 2 | 50.0 |
| "Sunday" | 13 | 7 | 53.84615384615385 |
+-------------------------------------------------------------+
4 rows
----
One thing I kept getting confused about here is that we need to pass everything that we want to eventually return in each WITH statement otherwise it won't be available to us at the end.
If we're going to do multiple MATCH statements we need to pass the starting node in the preceding WITH statement which in this case means that we need to pass the player variable along.
Other than that this query is the amalgamation of the previous two except we've added some arithmetic on the final line to work out the % of matches that Suarez scores in. I had to multiply each number by 1.0 to force float based arithmetic rather than integer based.
In answer to our initial question it doesn't seem to matter which day a match is played on, Suarez scores in approximately every other game. | null | null | [
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] | neo4jcypher-getting-the-hang-of-the-with-statement | https://markhneedham.com/blog/2013/03/20/neo4jcypher-getting-the-hang-of-the-with-statement |
false | 2013-03-20 02:54:43 | neo4j/cypher: WITH, COLLECT & EXTRACT | [
"neo4j",
"cypher"
] | [
"neo4j"
] | As I http://www.markhneedham.com/blog/2013/03/20/neo4jcypher-getting-the-hang-of-the-with-statement/[mentioned in my last post] I'm trying to get the hang of the +++<cite>+++http://docs.neo4j.org/chunked/milestone/query-with.html[WITH]+++</cite>+++ statement in neo4j's http://docs.neo4j.org/chunked/milestone/cypher-query-lang.html[cypher] query language and I found another application when trying to work out which opponents teams played on certain days.
I started out with a query which grouped the data set by day and showed the opponents that were played on that day:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH team-[h:home_team|away_team]-game-[:on_day]-day
RETURN DISTINCT day.name, COLLECT(TRIM(REPLACE(REPLACE(game.name, "Manchester United", ""), "vs", "")))
----
[source,text]
----
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| day.name | opponents |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "Sunday" | ["Liverpool","Everton","Southampton","Liverpool","Newcastle United","Chelsea","Manchester City","Swansea City","Tottenham Hotspur"] |
| "Wednesday" | ["Southampton","West Ham United","Newcastle United"] |
| "Monday" | ["Everton"] |
| "Saturday" | ["Reading","Fulham","Wigan Athletic","Tottenham Hotspur","Stoke City","Arsenal","Queens Park Rangers","Sunderland","West Bromwich Albion","Norwich City","Reading","Aston Villa","Norwich City","Fulham","Queens Park Rangers"] |
| "Tuesday" | ["Wigan Athletic"] |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
5 rows
----
The way we've got the opponents is a bit of a hack - the name of the two teams is in the 'name' property of a game node and we've removed 'Manchester United' and the word 'vs' to get the opponent's name.
I thought it'd be cool if we could separate the games on each day based on whether Manchester United were playing at home or away.
With https://groups.google.com/forum/?fromgroups=#!topic/neo4j/D4M1gXKwQ3U[a lot of help from Wes Freeman] we ended up with the following query which does the job:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH team-[h:home_team|away_team]-game-[:on_day]-day
WITH day.name as d, game, team, h
MATCH team-[:home_team|away_team]-game-[:home_team|away_team]-opp
WITH d, COLLECT([type(h),opp.name]) AS games
RETURN d,
EXTRACT(c in FILTER(x in games: HEAD(x) = "home_team") : HEAD(TAIL(c))) AS home,
EXTRACT(c in FILTER(x in games: HEAD(x) = "away_team") : HEAD(TAIL(c))) AS away
----
We use a similar approach with COLLECT as in the http://www.markhneedham.com/blog/2013/03/20/neo4jcypher-getting-the-hang-of-the-with-statement/[previous post] whereby we have a collection of tuples describing whether Manchester United were at home or not and who they were playing.
A neat thing that Wes pointed out is that since there are only 2 teams per game we're able to get the opponent node easily because it's the only other node that can match the 'home_team|away_team" relationship since we've already matched our team.
If we run the query just up to the last WITH we get the following result:
[source,text]
----
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| d | games |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "Sunday" | [["home_team","Liverpool"],["home_team","Everton"],["away_team","Southampton"],["away_team","Liverpool"],["away_team","Newcastle United"],["away_team","Chelsea"],["away_team","Manchester City"],["away_team","Swansea City"],["away_team","Tottenham Hotspur"]] |
| "Wednesday" | [["home_team","Southampton"],["home_team","West Ham United"],["home_team","Newcastle United"]] |
| "Monday" | [["away_team","Everton"]] |
| "Saturday" | [["home_team","Reading"],["home_team","Fulham"],["home_team","Wigan Athletic"],["home_team","Tottenham Hotspur"],["home_team","Stoke City"],["home_team","Arsenal"],["home_team","Queens Park Rangers"],["home_team","Sunderland"],["home_team","West Bromwich Albion"],["home_team","Norwich City"],["away_team","Reading"],["away_team","Aston Villa"],["away_team","Norwich City"],["away_team","Fulham"],["away_team","Queens Park Rangers"]] |
| "Tuesday" | [["away_team","Wigan Athletic"]] |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
5 rows
----
We then use the http://docs.neo4j.org/chunked/milestone/query-function.html#functions-filter[FILTER] function to choose either the opponents Manchester United played at home or away and then we use the http://docs.neo4j.org/chunked/milestone/query-function.html#functions-extract[EXTRACT] function to get the opponent from the tuple:
[source,text]
----
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| d | home |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "Sunday" | ["Liverpool","Everton"] |
| "Wednesday" | ["Southampton","West Ham United","Newcastle United"] |
| "Monday" | [] |
| "Saturday" | ["Reading","Fulham","Wigan Athletic","Tottenham Hotspur","Stoke City","Arsenal","Queens Park Rangers","Sunderland","West Bromwich Albion","Norwich City"] |
| "Tuesday" | [] |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
5 rows
+-----------------------------------------------------------------------------------------------------------------------------+
| d | away |
+-----------------------------------------------------------------------------------------------------------------------------+
| "Sunday" | ["Southampton","Liverpool","Newcastle United","Chelsea","Manchester City","Swansea City","Tottenham Hotspur"] |
| "Wednesday" | [] |
| "Monday" | ["Everton"] |
| "Saturday" | ["Reading","Aston Villa","Norwich City","Fulham","Queens Park Rangers"] |
| "Tuesday" | ["Wigan Athletic"] |
+-----------------------------------------------------------------------------------------------------------------------------+
5 rows
----
_(I ran the query twice alternating between the last two lines so that it's readable here. In actual fact the away teams would be in a column next to the home teams)_
I thought it was quite interesting how many games Manchester United play away on a Sunday - I think all of those games were probably televised so I thought they'd be more evenly split between home and away matches. Adding televised matches is perhaps http://www.markhneedham.com/blog/2012/07/21/neo4j-embracing-the-sub-graph/[another layer to add to the graph].
It's probably more useful to summarise how many games were played on each day at home and away rather than who they're against and we can use the +++<cite>+++REDUCE+++</cite>+++ function to do this:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH team-[h:home_team|away_team]-game-[:on_day]-day
WITH day.name as dayName, game, team, h
MATCH team-[:home_team|away_team]-game-[:home_team|away_team]-opp
WITH dayName, COLLECT([type(h),opp.name]) AS games
RETURN dayName,
REDUCE(homeGames=0, game in EXTRACT(c in FILTER(x in games: head(x) = "home_team") : HEAD(TAIL(c))) : homeGames + 1) as home,
REDUCE(awayGames=0, game in EXTRACT(c in FILTER(x in games: head(x) = "away_team") : HEAD(TAIL(c))) : awayGames + 1) as away,
REDUCE(totalGames=0, game in games : totalGames + 1) as total
----
[source,text]
----
+-----------------------------------+
| dayName | home | away | total |
+-----------------------------------+
| "Sunday" | 2 | 7 | 9 |
| "Wednesday" | 3 | 0 | 3 |
| "Monday" | 0 | 1 | 1 |
| "Saturday" | 10 | 5 | 15 |
| "Tuesday" | 0 | 1 | 1 |
+-----------------------------------+
5 rows
----
An alternative way of writing the initial query would be the following which Michael Hunger suggested on the thread:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH p=team-[:home_team|away_team]-game-[:home_team|away_team]-(), game-[:on_day]-day
WITH day.name as dayName, COLLECT([LAST(p), HEAD(RELS(p))]) AS opponents
WITH dayName,
EXTRACT(y in FILTER(x in opponents: TYPE(HEAD(TAIL(x))) = "home_team") : HEAD(y)) AS home,
EXTRACT(y in FILTER(x in opponents : TYPE(HEAD(TAIL(x))) = "away_team") : HEAD(y)) AS away
RETURN dayName,
EXTRACT(team in home: team.name) AS homeOpponents,
EXTRACT(team in away: team.name) AS awayOpponents
ORDER BY dayName
----
Here we take a slightly different approach where we make use of functions that we can apply to a matching path. We create a collection of tuples where +++<cite>+++LAST(p)+++</cite>+++ matches the opponent node and +++<cite>+++HEAD(RELS(p))+++</cite>+++ matches the 'home_team' or 'away_team' relationship accordingly.
We then filter the collection to find the times that we played at home and away. This is done by taking the second value from the tuple and then calling +++<cite>+++TYPE+++</cite>+++ on it which either returns 'home_team' or 'away_team'. We then extract the first value from the tuple which is the opponent node.
In the last part of the query we extract the name from the opponent nodes. | null | null | [
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] | neo4jcypher-with-collect-extract | https://markhneedham.com/blog/2013/03/20/neo4jcypher-with-collect-extract |
false | 2013-03-18 23:19:36 | neo4j/cypher: SQL style GROUP BY WITH LIMIT query | [
"neo4j",
"cypher"
] | [
"neo4j"
] | A few weeks ago I wrote a blog post where I described how we could http://www.markhneedham.com/blog/2013/02/17/neo4jcypher-sql-style-group-by-functionality/[construct a SQL GROUP BY style query] in http://docs.neo4j.org/chunked/milestone/cypher-query-lang.html[cypher] and last week I wanted to write a similar query but with what I think would be a LIMIT clause in SQL.
I wanted to find the maximum number of goals that players had scored in a match for a specific team and started off with the following query to find all the matches that players had scored in:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH team-[:home_team|away_team]-game-[:scored_in]-player-[:played]-stats-[:for]-team,
stats-[:in]-game
RETURN DISTINCT player.name, stats.goals, game.name
----
We find all the matches where Manchester United were playing and then get a list of the players who scored for them in those games:
[source,text]
----
+--------------------------------------------------------------------------------+
| player.name | stats.goals | game.name |
+--------------------------------------------------------------------------------+
| "Javier Hernández" | 1 | "Manchester United vs Wigan Athletic" |
| "Robin Van Persie" | 1 | "Manchester United vs Sunderland" |
| "Danny Welbeck" | 1 | "Manchester United vs Stoke City" |
| "Rafael" | 1 | "Queens Park Rangers vs Manchester United" |
| "Wayne Rooney" | 1 | "Manchester United vs Norwich City" |
| "Shinji Kagawa" | 1 | "Manchester United vs Fulham" |
| "Shinji Kagawa" | 3 | "Manchester United vs Norwich City" |
...
+--------------------------------------------------------------------------------+
50 rows
----
Our next step would be to only return the unique combinations of players and goals:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH team-[:home_team|away_team]-game-[:scored_in]-player-[:played]-stats-[:for]-team,
stats-[:in]-game
RETURN DISTINCT player.name, stats.goals
----
[source,text]
----
+-----------------------------------+
| player.name | stats.goals |
+-----------------------------------+
| "Nemanja Vidic" | 1 |
| "Shinji Kagawa" | 1 |
| "Danny Welbeck" | 1 |
| "Darren Fletcher" | 1 |
| "Wayne Rooney" | 2 |
| "Javier Hernández" | 1 |
| "Nani" | 1 |
| "Tom Cleverley" | 1 |
| "Robin Van Persie" | 2 |
| "Shinji Kagawa" | 3 |
...
| "Robin Van Persie" | 3 |
+-----------------------------------+
21 rows
----
Since we only want to return each player once along with their maximum value from the 'stats.goals' column all we have to do now is make use of the +++<cite>+++http://docs.neo4j.org/chunked/milestone/query-aggregation.html#aggregation-max[MAX]+++</cite>+++ function:
[source,cypher]
----
START team = node:teams('name:"Manchester United"')
MATCH team-[:home_team|away_team]-game-[:scored_in]-player-[:played]-stats-[:for]-team,
stats-[:in]-game
RETURN DISTINCT player.name, MAX(stats.goals) AS goals
ORDER BY goals DESC
----
[source,text]
----
+-----------------------------+
| player.name | goals |
+-----------------------------+
| "Robin Van Persie" | 3 |
| "Shinji Kagawa" | 3 |
| "Javier Hernández" | 2 |
| "Wayne Rooney" | 2 |
| "Paul Scholes" | 1 |
| "Ryan Giggs" | 1 |
| "Tom Cleverley" | 1 |
...
| "Rafael" | 1 |
| "Darren Fletcher" | 1 |
| "Nani" | 1 |
+-----------------------------+
16 rows
---- | null | null | [
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] | neo4jcypher-sql-style-group-by-with-limit-query | https://markhneedham.com/blog/2013/03/18/neo4jcypher-sql-style-group-by-with-limit-query |
false | 2013-03-29 10:51:37 | Editing config files on a server & Ctrl-Z | [
"software-development"
] | [
"Software Development"
] | A couple of weeks ago https://twitter.com/timrgoodwin[Tim] and I were spinning up a new service on a machine which wasn't quite working so we were manually making changes to the +++<cite>+++/etc/nginx/nginx.conf+++</cite>+++ file and restarting nginx to try and sort it out.
This process is generally not that interesting - you open the file in vi, make some changes, close it, then restart nginx and see if it works. If not then you open the file again and repeat.
Except Tim had a slight variation on this workflow which is an improvement that I don't want to forget!
Once we'd finished making the changes to the file in vi Tim hit 'http://en.wikipedia.org/wiki/Control-Z[Ctrl + Z]' which suspended the vi process and put us back at the shell prompt.
We could then restart nginx or do whatever else we needed to do and then type 'http://en.wikipedia.org/wiki/Fg_(Unix)[fg]' to go back into vi again.
Not only is this workflow quicker, it also keeps the history of the changes that we've made to the file so if one of our changes really screws things up we can easily undo it. Previously we'd have to remember what changes we'd made and do that manually.
In summary this workflow is a pretty simple idea but nevertheless one I had never thought about or seen anyone else do and I'll be using it in future. | null | null | [
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] | editing-config-files-on-a-server-ctrl-z | https://markhneedham.com/blog/2013/03/29/editing-config-files-on-a-server-ctrl-z |
false | 2013-03-16 11:58:42 | A quick and dirty way of testing the performance of a service | [
"software-development"
] | [
"Software Development"
] | We had a power outage in our data centre yesterday and once it had recovered https://twitter.com/jasonneylon[Jason] and I wanted to do a quick check that one of our backend services was still responding in an acceptable amount of time.
Since this particular service only serves HTTP GET requests it was reasonably easy to http://unixbhaskar.wordpress.com/2010/11/12/measure-website-response-time-through-curl/[setup a cURL command to do this]:
[source,text]
----
while true; do curl -k -s -w %{time_total} https://serviceurl/whatever/something; -o /dev/null; printf "\n"; done > service.txt
----
The '-w' flag is one I haven't used before but it allows us to output a bunch of interesting things about the request.
In this case we're only interested in the end to end time of the request/response but we could be more fine grained if we wanted to be.
We ran that for a little while and then killed it and wrote a little Ruby script to get the times in a format that would make it easy to plug into R:
[source,ruby]
----
# output cut for brevity
$ p File.readlines("service.txt").map { |x| x.gsub(/\n/, "").to_f }.sort
[0.042, 0.043, 0.043, 0.045, 0.046...1.083]
----
We then plotted a Histogram in R:
[source,r]
----
> times = c(0.042, 0.043, 0.043, 0.045, 0.046, 0.046, 0.046, 0.047, 0.047, 0.047, 0.048, 0.048, 0.048, 0.048, 0.048, 0.048, 0.048, 0.049, 0.05, 0.05, 0.05, 0.051, 0.051, 0.051, 0.051, 0.051, 0.051, 0.051, 0.051, 0.052, 0.052, 0.052, 0.053, 0.053, 0.053, 0.054, 0.054, 0.055, 0.055, 0.056, 0.056, 0.056, 0.056, 0.056, 0.056, 0.056, 0.056, 0.057, 0.057, 0.057, 0.057, 0.058, 0.059, 0.06, 0.06, 0.06, 0.061, 0.061, 0.061, 0.061, 0.061, 0.061, 0.062, 0.062, 0.063, 0.063, 0.064, 0.066, 0.066, 0.067, 0.071, 0.071, 0.072, 0.073, 0.074, 0.079, 0.081, 0.084, 0.085, 0.085, 0.091, 0.108, 0.116, 0.14, 0.182, 0.26, 0.281, 1.083)
> hist(times)
----
image::{{<siteurl>}}/uploads/2013/03/service-tiff.jpg[Service tiff,600]
That roughly shows us that the massive majority of requests were being served in under 1/10th a second which was acceptable for this service.
We can also run a couple of other commands on the times to learn more about the distribution:
[source,r]
----
> summary(times)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.04200 0.05100 0.05600 0.07745 0.06325 1.08300
----
[source,r]
----
> quantile(times, 0.95)
95%
0.1316
----
From these functions we learn that the max value is an outlier compared to the others and 95% of the requests were served in 0.13 seconds or less which was enough evidence to convince us that the service hadn't degraded. | null | null | [
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] | a-quick-and-dirty-way-of-testing-the-performance-of-a-service | https://markhneedham.com/blog/2013/03/16/a-quick-and-dirty-way-of-testing-the-performance-of-a-service |
false | 2013-03-17 20:21:10 | clojure/Java Interop: The doto macro | [
"java",
"clojure",
"interop"
] | [
"Clojure"
] | I recently wrote about some http://www.markhneedham.com/blog/2013/03/17/clojurejava-interop-importing-neo4j-spatial-data/[code I've been playing with to import neo4j spatial data] and while looking to simplify the code I came across the +++<cite>+++http://clojure.org/java_interop#Java Interop-The Dot special form-(doto instance-expr (instanceMethodName-symbol args*)*)[doto]+++</cite>+++ macro.
The +++<cite>+++doto+++</cite>+++ macro allows us to chain method calls on an initial object and then returns the resulting object. e.g.
[source,lisp]
----
(doto (new java.util.HashMap) (.put "a" 1) (.put "b" 2))
-> {a=1, b=2}
----
In our case this comes in quite useful in the function used to create a stadium node which initially reads like this:~~~ ~~~lisp (defn create-stadium-node [db line] (let [stadium-node (.. db createNode)] (.. stadium-node (setProperty "wkt" (format "POINT(%s %s)" (:long line) (:lat line)))) (.. stadium-node (setProperty "name" (:stadium line))) stadium-node)) ~~~
Here we first create a node, set a couple of properties on the node and then return it.
Using the macro it would read like this: ~~~lisp (defn create-stadium-node [db line] (doto (.. db createNode) (.setProperty "wkt" (format "POINT(%s %s)" (:long line) (:lat line))) (.setProperty "name" (:stadium line)))) ~~~
We can also use it to close the transaction at the end of our function although we don't actually have a need for the transaction object which gets returned:~~~ ~~~lisp # the end of our main function (.. tx success) (.. tx finish) ~~~
\...becomes... ~~~lisp (doto tx (.success) (.finish)) ~~~
As far as I can tell this is pretty similar in functionality to the +++<cite>+++http://blog.moertel.com/posts/2007-02-07-ruby-1-9-gets-handy-new-method-object-tap.html[Object#tap]+++</cite>+++ function in Ruby: ~~~ruby {}.tap { |x| x[:a] = 1; x[:b] = 2 } \=> {:a\=>1, :b\=>2} ~~~
Either way it's a pretty neat way of simplifying code. | null | null | [
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] | clojurejava-interop-the-doto-macro | https://markhneedham.com/blog/2013/03/17/clojurejava-interop-the-doto-macro |
false | 2013-03-17 18:56:36 | clojure/Java Interop - Importing neo4j spatial data | [
"java",
"clojure",
"neo4j"
] | [
"neo4j"
] | I wrote a post about a week ago where I described how I'd http://www.markhneedham.com/blog/2013/03/10/neo4jcypher-finding-football-stadiums-near-a-city-using-spatial/[added football stadiums to my football graph using neo4j spatial] and after I'd done that I wanted to put it into my import script along with the rest of the data.
I thought https://github.com/technomancy/leiningen[leiningen] would probably work quite well for this as you can point it at a Java class and have it be executed.
To start with I had to change the import code slightly to link stadiums to teams which have already been added to the graph:
[source,java]
----
package main.java;
// imports excluded
public class StadiumsImport {
public static void main(String[] args) throws IOException {
List<String> lines = readFile("data/stadiums.csv");
EmbeddedGraphDatabase db = new EmbeddedGraphDatabase("neo4j-community-1.9.M04/data/graph.db");
Index<Node> stadiumsIndex = createSpatialIndex(db, "stadiumsLocation");
Transaction tx = db.beginTx();
for (String stadium : lines) {
String[] columns = stadium.split(",");
Index<Node> teamsIndex = db.index().forNodes("teams");
String team = columns[1].replaceAll("\"","");
Node teamNode = teamsIndex.get("name", team).getSingle();
if(teamNode != null) {
Node stadiumNode = db.createNode();
stadiumNode.setProperty("wkt", String.format("POINT(%s %s)", columns[4], columns[3]));
stadiumNode.setProperty("name", columns[0].replaceAll("\"",""));
stadiumsIndex.add(stadiumNode, "dummy", "value");
teamNode.createRelationshipTo(stadiumNode, DynamicRelationshipType.withName("play_at"));
}
}
tx.success();
tx.finish();
}
private static Index<Node> createSpatialIndex(EmbeddedGraphDatabase db, String indexName) {
return db.index().forNodes(indexName, SpatialIndexProvider.SIMPLE_WKT_CONFIG);
}
// readFile excluded
}
----
I've excluded some bits of the code for brevity but it's on https://gist.github.com/mneedham/5182948[this gist] if you're interested.
The only change from last week's version is that we're now looking up the team that a stadium belongs to and creating a 'play_at' relationship from the team to the stadium.
I was then able to execute that code by calling 'lein run' based on the following project.clj file:
[source,text]
----
(defproject neo4jfootball "1.0.0-SNAPSHOT"
:description "neo4j football project"
:main "main.java.StadiumsImport"
:dependencies [[org.clojure/clojure "1.4.0"]
[org.neo4j/neo4j-spatial "0.11-SNAPSHOT"]
[clojure-csv/clojure-csv "2.0.0-alpha1"]]
:jvm-opts ["-Xmx2g"]
:plugins [[lein-idea "1.0.1"]]
:repositories {"local" ~(str (.toURI (java.io.File. "maven_repository")))}
:java-source-paths ["src/main/java"] )
----
I'm using http://www.markhneedham.com/blog/2011/12/27/leiningen-using-goose-via-a-local-maven-repository/[a local Maven repository] to store the neo4j spatial JAR. The Maven entry was created by executed the following command from where I had the https://github.com/neo4j/spatial[neo4j spatial] project checked out:
[source,text]
----
mvn install:install-file -Dfile=target/neo4j-spatial-0.11-SNAPSHOT.jar -DartifactId=neo4j-spatial -Dversion=0.11-SNAPSHOT -DgroupId=org.neo4j -Dpackaging=jar -DlocalRepositoryPath=/path/to/neo4j-football/maven_repository -DpomFile=pom.xml
----
That worked reasonably well but I thought it'd be interesting to see what the above code would look like if it was written in clojure instead.
This is what I ended up with:
[source,lisp]
----
(ns neo4jfootball.core
(:require [clojure-csv.core :as csv])
(:use clojure.java.io)
(:import (org.neo4j.kernel EmbeddedGraphDatabase) (org.neo4j.gis.spatial.indexprovider SpatialIndexProvider) (org.neo4j.graphdb DynamicRelationshipType)))
(defn take-csv [fname]
(with-open [file (reader fname)]
(csv/parse-csv (slurp file))))
(defn transform [line] {:stadium (get line 0) :team (get line 1) :lat (get line 3) :long (get line 4)})
(def not-nil? (comp not nil?))
(defn create-stadium-node [db line]
(let [stadium-node (.. db createNode)]
(.. stadium-node (setProperty "wkt" (format "POINT(%s %s)" (:long line) (:lat line))))
(.. stadium-node (setProperty "name" (:stadium line)))
stadium-node))
(defn -main []
(do
(let [db (new EmbeddedGraphDatabase "neo4j-community-1.9.M04/data/graph.db")
tx (.beginTx db)
stadiums-index (.. db index (forNodes "stadiumsLocation" (SpatialIndexProvider/SIMPLE_WKT_CONFIG)))
teams-index (.. db index (forNodes "teams"))]
(doseq [line (drop 1 (map transform (take-csv "data/stadiums.csv")))]
(let [team-node (.. teams-index (get "name" (:team line)) getSingle)]
(if (not-nil? team-node)
(let [stadium-node (create-stadium-node db line)]
(.. stadiums-index (add stadium-node "dummy" "value"))
(.. team-node (createRelationshipTo stadium-node (DynamicRelationshipType/withName "play_at")))))))
(.. tx success)
(.. tx finish))))
----
The code is simplified quite a bit by using the https://github.com/davidsantiago/clojure-csv[clojure CSV] library so I could probably have achieved similar in the Java version by using an equivalent library.
It's a bit easier to see what properties of a row in the CSV file are being used where as a result of the +++<cite>+++transform+++</cite>+++ function where we convert the array into a map.
It would have taken quite a bit more code to achieve a similar thing in Java so I didn't bother.
The http://clojure.org/java_interop[Java Interop page on the clojure website] was quite useful for working out how to call the various methods on the Java API.
I'm mainly using the +++<cite>+++http://clojure.org/java_interop#Java Interop-The Dot special form-(.. Classname-symbol member+)[..]+++</cite>+++ macro which allows us to chain Java method calls together. In a couple of cases we could just as easily have used the +++<cite>+++http://clojure.org/java_interop#Java Interop-The Dot special form-(. Classname-symbol (method-symbol args*)) or[.]+++</cite>+++ macro instead.
We can then call this code from lein like so:
[source,text]
----
lein run -m neo4jfootball.core
---- | null | null | [
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] | clojurejava-interop-importing-neo4j-spatial-data | https://markhneedham.com/blog/2013/03/17/clojurejava-interop-importing-neo4j-spatial-data |
false | 2013-03-17 14:00:35 | Understanding what lsof socket/port aliases refer to | [
"software-development",
"devops-2"
] | [
"DevOps"
] | Earlier in the week we wanted to check which ports were being listened on and by what processes which we can do with the following command on Mac OS X:
[source,text]
----
$ lsof -ni | grep LISTEN
idea 2398 markhneedham 58u IPv6 0xac8f13f77b903331 0t0 TCP *:49410 (LISTEN)
idea 2398 markhneedham 65u IPv6 0xac8f13f7799a4af1 0t0 TCP *:58741 (LISTEN)
idea 2398 markhneedham 122u IPv6 0xac8f13f7799a4711 0t0 TCP 127.0.0.1:6942 (LISTEN)
idea 2398 markhneedham 249u IPv6 0xac8f13f777586711 0t0 TCP *:63342 (LISTEN)
idea 2398 markhneedham 253u IPv6 0xac8f13f777586331 0t0 TCP 127.0.0.1:63342 (LISTEN)
java 16973 markhneedham 152u IPv6 0xac8f13f777586af1 0t0 TCP *:56471 (LISTEN)
java 16973 markhneedham 154u IPv6 0xac8f13f779e6b711 0t0 TCP *:menandmice-dns (LISTEN)
java 16973 markhneedham 168u IPv6 0xac8f13f77b902f51 0t0 TCP 127.0.0.1:7474 (LISTEN)
java 16973 markhneedham 171u IPv6 0xac8f13f77b013711 0t0 TCP 127.0.0.1:7473 (LISTEN)
----
One of the interesting things about this output is that for the most part it shows the port number and which IPs it will accept a connection from but sometimes it uses a socket/port alias.
In this case we can see that the 3rd last line refers to 'menandmice-dns' but others could be 'http-alt' or 'mysql'.
We can find out what port those names refer to by looking in +++<cite>+++/etc/services+++</cite>+++:
[source,text]
----
$ cat /etc/services | grep menandmice-dns
menandmice-dns 1337/udp # menandmice DNS
menandmice-dns 1337/tcp # menandmice DNS
----
[source,text]
----
$ cat /etc/services | grep http-alt
http-alt 591/udp # FileMaker, Inc. - HTTP Alternate (see Port 80)
http-alt 591/tcp # FileMaker, Inc. - HTTP Alternate (see Port 80)
http-alt 8008/udp # HTTP Alternate
http-alt 8008/tcp # HTTP Alternate
http-alt 8080/udp # HTTP Alternate (see port 80)
http-alt 8080/tcp # HTTP Alternate (see port 80)
----
There's a http://www.iana.org/assignments/service-names-port-numbers/service-names-port-numbers.xml[massive XML document on the IANA website] with a full list of the port assignments which is presumably where +++<cite>+++/etc/services+++</cite>+++ is derived from. | null | null | [
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] | understanding-what-lsof-socketport-aliases-refer-to | https://markhneedham.com/blog/2013/03/17/understanding-what-lsof-socketport-aliases-refer-to |
false | 2013-03-10 22:13:41 | neo4j/cypher: Finding football stadiums near a city using spatial | [
"spatial",
"neo4j",
"cypher"
] | [
"neo4j"
] | One of the things that I wanted to add to my football graph was something location related so I could try out https://github.com/neo4j/spatial[neo4j spatial] and I thought the easiest way to do that was to model the location of football stadiums.
To start with I needed to add spatial as http://maxdemarzi.com/2012/11/26/extending-neo4j/[an unmanaged extension] to my neo4j plugins folder which involved doing the following:
[source,text]
----
$ git clone git://github.com/neo4j/spatial.git spatial
$ cd spatial
$ mvn clean package -Dmaven.test.skip=true install
$ unzip target/neo4j-spatial-0.11-SNAPSHOT-server-plugin.zip -d /path/to/neo4j-community-1.9.M04/plugins/
$ /path/to/neo4j-community-1.9.M04/bin/neo4j restart
----
If it's installed correctly then you should see this sort of output from issuing a 'curl' against the web interface:
[source,text]
----
$ curl -L http://localhost:7474/db/data
{
"extensions" : {
...
"SpatialPlugin" : {
"addEditableLayer" : "http://localhost:7474/db/data/ext/SpatialPlugin/graphdb/addEditableLayer",
"addCQLDynamicLayer" : "http://localhost:7474/db/data/ext/SpatialPlugin/graphdb/addCQLDynamicLayer",
"findGeometriesWithinDistance" : "http://localhost:7474/db/data/ext/SpatialPlugin/graphdb/findGeometriesWithinDistance",
"updateGeometryFromWKT" : "http://localhost:7474/db/data/ext/SpatialPlugin/graphdb/updateGeometryFromWKT",
"addGeometryWKTToLayer" : "http://localhost:7474/db/data/ext/SpatialPlugin/graphdb/addGeometryWKTToLayer",
"getLayer" : "http://localhost:7474/db/data/ext/SpatialPlugin/graphdb/getLayer",
"addSimplePointLayer" : "http://localhost:7474/db/data/ext/SpatialPlugin/graphdb/addSimplePointLayer",
"findGeometriesInBBox" : "http://localhost:7474/db/data/ext/SpatialPlugin/graphdb/findGeometriesInBBox",
"addNodeToLayer" : "http://localhost:7474/db/data/ext/SpatialPlugin/graphdb/addNodeToLayer"
},
…
},
...
"neo4j_version" : "1.9.M04"
----
The next step was to create a spatial index containing the stadiums latitudes/longitudes.
There's a good example in +++<cite>+++https://github.com/mneedham/spatial/blob/master/src/test/java/org/neo4j/gis/spatial/IndexProviderTest.java#L251[IndexProviderTest]+++</cite>+++ which I was able to adapt to do what I wanted.
I got a list of stadiums along with their locations as a CSV from http://www.doogal.co.uk/FootballStadiums.php[Chris Bell's blog].
The output looks like this:
[source,text]
----
Name,Team,Capacity,Latitude,Longitude
"Adams Park","Wycombe Wanderers",10284,51.6306,-0.800299
"Almondvale Stadium","Livingston",10122,55.8864,-3.52207
"Amex Stadium","Brighton and Hove Albion",22374,50.8609,-0.08014
"Anfield","Liverpool",45522,53.4308,-2.96096
"Ashton Gate","Bristol City",21497,51.44,-2.62021
"B2net Stadium","Chesterfield",10400,53.2535,-1.4272
----
I ended up with the following code to create nodes for each of the stadium and add them to the spatial index:
[source,java]
----
// imports excluded
public class SampleSpatialGraph {
public static void main(String[] args) throws IOException {
List<String> lines = readFile("/path/to/stadiums.csv");
EmbeddedGraphDatabase db = new EmbeddedGraphDatabase("/path/to/neo4j-community-1.9.M04/data/graph.db");
Index<Node> index = createSpatialIndex(db, "stadiumsLocation");
Transaction tx = db.beginTx();
for (String stadium : lines) {
String[] columns = stadium.split(",");
Node stadiumNode = db.createNode();
stadiumNode.setProperty("wkt", String.format("POINT(%s %s)", columns[4], columns[3]));
stadiumNode.setProperty("name", columns[0]);
index.add(stadiumNode, "dummy", "value");
}
tx.success();
tx.finish();
}
private static Index<Node> createSpatialIndex(EmbeddedGraphDatabase db, String indexName) {
return db.index().forNodes(indexName, SpatialIndexProvider.SIMPLE_WKT_CONFIG);
}
// readFile function excluded
}
----
The full code is on https://gist.github.com/mneedham/5130545[this gist] if you're interested.
We can now query the stadiums using cypher to find say the stadiums within 5 kilometres of https://maps.google.co.uk/maps?q=53.488454,-2.248764&hl=en&ll=53.488454,-2.248764&spn=0.027219,0.084028&sll=53.525411,-2.087402&sspn=0.108782,0.336113&t=m&z=14[Manchester]:
[source,cypher]
----
START n=node:stadiumsLocation('withinDistance:[53.489271, -2.246704, 5.0]')
RETURN n.name, n.wkt;
----
[source,text]
----
==> +------------------------------------------------+
==> | n.name | n.wkt |
==> +------------------------------------------------+
==> | ""Etihad Stadium"" | "POINT(-2.20024 53.483)" |
==> | ""Old Trafford"" | "POINT(-2.29139 53.4631)" |
==> +------------------------------------------------+
==> 2 rows
==> 214 ms
----
Or we could use a http://en.wikipedia.org/wiki/Minimum_bounding_rectangle[bounding box query] whereby we return all the stadiums within a virtual box based on coordinates. For example the following query returns all the stadiums which are within the M25:
[source,cypher]
----
START n=node:stadiumsLocation('bbox:[-0.519104,0.22934, 51.279958,51.69299]')
RETURN n.name, n.wkt;
----
[source,text]
----
==> +----------------------------------------------------+
==> | n.name | n.wkt |
==> +----------------------------------------------------+
==> | ""White Hart Lane"" | "POINT(-0.065684 51.6033)" |
==> | ""Wembley"" | "POINT(-0.279543 51.5559)" |
==> | ""Victoria Road"" | "POINT(0.159739 51.5478)" |
==> | ""Vicarage Road"" | "POINT(-0.401569 51.6498)" |
==> | ""Underhill Stadium"" | "POINT(-0.191789 51.6464)" |
==> | ""The Valley"" | "POINT(0.036757 51.4865)" |
==> | ""The Den"" | "POINT(-0.050743 51.4859)" |
==> | ""Stamford Bridge"" | "POINT(-0.191034 51.4816)" |
==> | ""Selhurst Park"" | "POINT(-0.085455 51.3983)" |
==> | ""Craven Cottage"" | "POINT(-0.221619 51.4749)" |
==> | ""Griffin Park"" | "POINT(-0.302621 51.4882)" |
==> | ""Loftus Road"" | "POINT(-0.232204 51.5093)" |
==> | ""Boleyn Ground"" | "POINT(0.039225 51.5321)" |
==> | ""Emirates Stadium"" | "POINT(-0.108436 51.5549)" |
==> | ""Brisbane Road"" | "POINT(-0.012551 51.5601)" |
==> +----------------------------------------------------+
==> 15 rows
==> 23 ms
----
Now I just need to wire the stadiums in with the rest of the graph and I'll be able to write queries based on players performance in different parts of the country. | null | null | [
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] | neo4jcypher-finding-football-stadiums-near-a-city-using-spatial | https://markhneedham.com/blog/2013/03/10/neo4jcypher-finding-football-stadiums-near-a-city-using-spatial |
false | 2013-03-21 22:39:02 | Wiring up an Amazon S3 bucket to a CNAME entry - The specified bucket does not exist | [
"software-development",
"aws"
] | [
"Software Development"
] | https://twitter.com/jasonneylon[Jason] and I were setting up an internal static website using an http://aws.amazon.com/s3/[S3] bucket a couple of days ago and wanted to point a more friendly domain name at it.
We initially called our bucket 'static-site' and then created a http://en.wikipedia.org/wiki/CNAME_record[CNAME] entry using http://www.zerigo.com/[zerigo] to point our sub domain at the bucket.
The mapping was something like this:
[source,text]
----
our-subdomain.somedomain.com -> static-site.s3-website-eu-west-1.amazonaws.com
----
When we tried to access the site through our-subdomain.somedomain.com we got the following error:
[source,text]
----
<Error>
<Code>NoSuchBucket</Code>
<Message>The specified bucket does not exist</Message>
<BucketName></BucketName>
<RequestId></RequestId>
<HostId>
----
A bit of googling led us to https://forums.aws.amazon.com/message.jspa?messageID=237562[this thread] which suggested that we needed to ensure that our bucket was named after the sub domain that we wanted to serve the site from.
In this case we needed to rename our bucket to 'our-subdomain.somedomain.com" and then our CNAME entry to:
[source,text]
----
our-subdomain.somedomain.com -> our-subdomain.somedomain.com.s3-website-eu-west-1.amazonaws.com
----
And then everything was happy. | null | null | [
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] | wiring-up-an-amazon-s3-bucket-to-a-cname-entry-the-specified-bucket-does-not-exist | https://markhneedham.com/blog/2013/03/21/wiring-up-an-amazon-s3-bucket-to-a-cname-entry-the-specified-bucket-does-not-exist |
false | 2013-03-21 23:25:09 | beanstalkd: Getting the status of the queue | [
"beanstalkd"
] | [
"Software Development"
] | For the last few days https://twitter.com/jasonneylon[Jason] and I have been porting a few of our applications across to a new puppet setup and one thing we needed to do was check that messages were passing through http://kr.github.com/beanstalkd/[beanstalkd] correctly.
We initially had the idea that it wasn't configured correctly so https://twitter.com/pingles[Paul] showed us a way of checking whether that was the case by connecting to the port it runs on like so:
[source,text]
----
$ telnet localhost 11300
stats
current-jobs-urgent: 0
current-jobs-ready: 0
current-jobs-reserved: 0
current-jobs-delayed: 0
current-jobs-buried: 0
cmd-put: 66
...
current-connections: 6
current-producers: 1
current-workers: 1
current-waiting: 1
total-connections: 58
pid: 15622
version: 1.4.6
rusage-utime: 0.000000
rusage-stime: 0.040002
uptime: 22740
binlog-oldest-index: 0
binlog-current-index: 0
binlog-max-size: 10485760
----
The way we'd setup our beanstalks consumer, if it wasn't able to process a message correctly we put the message back on the queue in a 'buried' state so we'd see a number greater than 0 for the 'current-jobs-buried' property.
I was already curious how we'd go about writing a one liner to get those stats using http://netcat.sourceforge.net/[netcat] and after a bit of fiddling to force the new line character to be sent properly I ended up with the following:
[source,text]
----
$ echo -e "stats\r\n" | nc localhost 11300
----
The key is the http://linux.about.com/library/cmd/blcmdl1_echo.htm['-e'] flag which I may well have written about before but had forgotten all about:
[source,text]
----
-e
enable interpretation of the backslash-escaped characters listed below
...
\NNN
the character whose ASCII code is NNN (octal)
\\
backslash
\a
alert (BEL)
\b
backspace
\c
suppress trailing newline
\f
form feed
\n
new line
\r
carriage return
\t
horizontal tab
\v
vertical tab
----
We can see how that works with the following example:
[source,text]
----
$ echo -e "mark\nmark"
mark
mark
----
[source,text]
----
$ echo "mark\nmark"
mark\nmark
----
Alternatively we can pass either the '-c' or '-C' flag depending on our version of netcat and a http://en.wikipedia.org/wiki/Newline[CRLF/newline] will be sent as the line ending:
[source,text]
----
# netcat-openbsd version
$ echo "stats" | nc -C localhost 11300
----
or
[source,text]
----
# one on Mac OS X by default
$ echo "stats" | nc -c localhost 11300
----
Going back to beanstalkd - there is actually a https://github.com/kr/beanstalkd/blob/master/doc/protocol.txt[pretty good document] explaining all the different commands that you can send to it, most of which I haven't tried yet!
I have come across some useful ones though:
[source,text]
----
$ telnet localhost 11300
----
== To see the names of the tubes (queues) where messages get put
[source,text]
----
list-tubes
OK 14
---
- default
----
== To use that tube
[source,text]
----
use default
USING DEFAULT
----
== To see if there are any ready jobs
[source,text]
----
peek-ready
NOT_FOUND
----
== To get the stats for that tube
[source,text]
----
stats-tube default
OK 253
---
name: default
current-jobs-urgent: 0
current-jobs-ready: 0
current-jobs-reserved: 0
current-jobs-delayed: 0
current-jobs-buried: 0
total-jobs: 155
current-using: 9
current-watching: 9
current-waiting: 1
cmd-pause-tube: 0
pause: 0
pause-time-left: 0
----
I came across https://github.com/dustin/beanstalk-tools[beanstalk-tools] which contains a bunch of tools for working with beanstalks but since our use is sporadic sending the commands over TCP every now and then will probably do! | null | null | [
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] | beanstalkd-getting-the-status-of-the-queue | https://markhneedham.com/blog/2013/03/21/beanstalkd-getting-the-status-of-the-queue |
false | 2013-03-31 21:08:22 | neo4j/cypher: Playing around with time | [
"neo4j",
"lucene"
] | [
"neo4j"
] | I've done a bit of modelling with years and months in neo4j graphs that I've worked on previously but I haven't ever done anything with time so I thought it'd be interesting to have a go with my football graph.
I came across http://stackoverflow.com/questions/9138857/in-neo4j-how-can-i-index-by-date-and-search-in-a-date-range[this StackOverflow post] on my travels which suggested that indexing nodes by time would be helpful and since I have a bunch of football matches with associated times I thought I'd try it out.
I created the key of the index by running code similar to the following:
[source,ruby]
----
> DateTime.now.strftime("%H%M")
=> "2200"
----
We can then write a query to show all the games at a certain time of day:
[source,cypher]
----
START game=node:times('time:*')
RETURN game.time, COUNT(game)
ORDER BY game.time
----
[source,text]
----
+-------------------------+
| game.time | COUNT(game) |
+-------------------------+
| 1245 | 21 |
| 1330 | 21 |
| 1500 | 163 |
| 1600 | 29 |
| 1730 | 22 |
| 1945 | 21 |
| 2000 | 19 |
+-------------------------+
7 rows
----
To be fair any index that referenced all the matches would allow us to do this. e.g.
[source,cypher]
----
START game=node:matches('match_id:*')
RETURN game.time, COUNT(game)
ORDER BY game.time
----
The time based indexing becomes more interesting when we use Lucene's http://lucene.apache.org/core/old_versioned_docs/versions/2_9_0/api/all/org/apache/lucene/search/NumericRangeQuery.html[numeric range query] syntax to only select matches which happened between certain times of day:
[source,cypher]
----
START game=node:times('time:[1600 TO 2000]')
RETURN game.time, COUNT(game)
ORDER BY game.time
----
[source,text]
----
+-------------------------+
| game.time | COUNT(game) |
+-------------------------+
| 1600 | 29 |
| 1730 | 22 |
| 1945 | 21 |
| 2000 | 19 |
+-------------------------+
4 rows
----
I couldn't see a way to set an open ended value either side of the 'TO' so if we want to do that we just need to set a really high maximum value or really low minimum value.
For example if we want to find all the evening matches we could use this query:
[source,cypher]
----
START game=node:times('time:[1730 TO 2359]')
RETURN game.time, COUNT(game)
ORDER BY game.time
----
[source,text]
----
+-------------------------+
| game.time | COUNT(game) |
+-------------------------+
| 1730 | 22 |
| 1945 | 21 |
| 2000 | 19 |
+-------------------------+
3 rows
----
I also indexed each match by its full timestamp so we could find all the evening games this year if we wanted as well:
[source,ruby]
----
> Time.new(2013,1,1).to_i
=> 1356998400
----
[source,cypher]
----
START game=node:times('time:[1730 TO 2359] AND date: [1356998400 TO 9999999999]')
RETURN game.time, game.name, game.friendly_date
----
[source,text]
----
+------------------------------------------------------------------------------------+
| game.time | game.name | game.friendly_date |
+------------------------------------------------------------------------------------+
| 1730 | "Wigan Athletic vs Liverpool" | "2013-03-02 17:30:00 +0000" |
| 2000 | "Aston Villa vs Manchester City" | "2013-03-04 20:00:00 +0000" |
| 2000 | "West Ham United vs Tottenham Hotspur" | "2013-02-25 20:00:00 +0000" |
| 2000 | "Liverpool vs West Bromwich Albion" | "2013-02-11 20:00:00 +0000" |
| 1730 | "Fulham vs Manchester United" | "2013-02-02 17:30:00 +0000" |
...
| 1945 | "Chelsea vs Southampton" | "2013-01-16 19:45:00 +0000" |
+------------------------------------------------------------------------------------+
25 rows
---- | null | null | [
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] | neo4jcypher-playing-around-with-time | https://markhneedham.com/blog/2013/03/31/neo4jcypher-playing-around-with-time |
false | 2013-03-31 21:44:19 | Embracing the logs | [
"logs"
] | [
"Software Development"
] | Despite the fact that I've been working full time in software for almost 8 years now every now and then I still need a reminder of how useful reading logs can be in helping solve problems.
I had a couple of such instances recently which I thought I'd document.
The first was a couple of weeks ago when https://twitter.com/timrgoodwin[Tim] and I were pairing on http://www.markhneedham.com/blog/2013/03/24/incrementally-rolling-out-machines-with-a-new-puppet-role/[moving some applications from Passenger to Unicorn] and were testing whether or not we'd done so successfully.
We were doing this by creating an +++<cite>+++/etc/hosts+++</cite>+++ entry from our top level domain to an nginx proxy node which was to forward on the request to the application server.
[source,text]
----
Request -> nginx on proxy node -> nginx on app server node -> unicorn on app server node
----
This didn't work and we got a 404 response code so I logged onto the server hosting the application server and started writing our a cURL command to simulate what the proxy should be doing to see if the problem was there.
After watching me do this a couple of times Tim suggested that we might be more successful if we opened a bunch of tabs on the shell tailing the various log files that the request should pass through.
We set up tail commands against the following files:
* nginx access log on proxy node
* nginx error log on proxy node
* nginx access log on the app server node
* unicorn log on the app server node
Immediately it became clear that we actually had a problem on the proxy node because we'd configured one of the nginx directives incorrectly.
Once we'd fixed this the request flowed through smoothly.
We extended this tailing of files idea when testing multiple nodes through a load balancer except this time we http://www.markhneedham.com/blog/2013/01/15/fabric-tailing-log-files-on-multiple-machines/[made use of Fabric] to make things a bit easier.
The second was earlier this week when https://twitter.com/barisbalic[Baris] and I were trying to configure puppet so that we could install different Ruby versions on different machines.
We were having trouble figuring out why the wrong version was getting installed so eventually we chatted to https://twitter.com/supersheep[Andy] who amongst other things had a look at the apt history log @ +++<cite>+++/var/log/apt/history.log+++</cite>+++ and was able to figure out how this was happening.
Lesson of the last two weeks: *embrace the logs*! | null | null | [
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] | embracing-the-logs | https://markhneedham.com/blog/2013/03/31/embracing-the-logs |
false | 2013-03-06 00:59:36 | neo4j: Make properties relationships | [
"neo4j",
"cypher"
] | [
"neo4j"
] | I spent some of the weekend working my way through https://twitter.com/jimwebber[Jim], https://twitter.com/iansrobinson[Ian] & https://twitter.com/emileifrem[Emil]'s book 'http://graphdatabases.com/[Graph Databases]' and one of the things that they emphasise is that graphs allow us to *make relationships first class citizens in our model*.
Looking back on a couple of the graphs that I modelled last year I realise that I didn't quite get this and although the graphs I modelled had some relationships a lot of the time I was defining things as properties on nodes.
While it's fine to do this I think we lose some of the power of a graph and it's not necessarily obvious what we've lost until we model a property as a relationship and see what possibilities open up.
For example in my football graph I wanted to record the date of matches and initially stored this as a property on the match before realising that modelling it as a relationship which might open up some interesting queries.
I created this relationship between a match and the month that the match took place in:
[source,text]
----
MATCH-[:in_month]->MONTH
----
As a result of having this relationship I can now really easily find out which matches Gareth Bale played in September for example:
[source,cypher]
----
START player = node:players('name:"Gareth Bale"'), month=node:months('name:September')
MATCH player-[:played_in]-game
WHERE game-[:in_month]-month
RETURN game.name, game.home_goals + "-" +game.away_goals AS score, game.date
----
[source,text]
----
+----------------------------------------------------------------------------------+
| game.name | score | game.date |
+----------------------------------------------------------------------------------+
| "Reading vs Tottenham Hotspur" | "1-3" | "2012-09-16 16:00:00 +0100" |
| "Tottenham Hotspur vs Norwich City" | "1-1" | "2012-09-01 15:00:00 +0100" |
| "Tottenham Hotspur vs Queens Park Rangers" | "2-1" | "2012-09-23 16:00:00 +0100" |
| "Manchester United vs Tottenham Hotspur" | "2-3" | "2012-09-29 17:30:00 +0100" |
+----------------------------------------------------------------------------------+
----
Or we could find all the matches in December where one of the teams won by more than 2 goals:
[source,cypher]
----
START month=node:months('name:December')
MATCH month-[:in_month]-game
WHERE ABS(game.home_goals - game.away_goals) > 2
RETURN game.name, game.home_goals + "-" +game.away_goals AS score, game.date
----
[source,text]
----
+----------------------------------------------------------------------------+
| game.name | score | game.date |
+----------------------------------------------------------------------------+
| "Sunderland vs Reading" | "3-0" | "2012-12-11 19:45:00 +0000" |
| "Reading vs Arsenal" | "2-5" | "2012-12-17 20:00:00 +0000" |
| "Newcastle United vs Wigan Athletic" | "3-0" | "2012-12-03 20:00:00 +0000" |
| "Fulham vs Tottenham Hotspur" | "0-3" | "2012-12-01 15:00:00 +0000" |
| "Liverpool vs Fulham" | "4-0" | "2012-12-22 17:30:00 +0000" |
| "Chelsea vs Aston Villa" | "8-0" | "2012-12-23 16:00:00 +0000" |
| "Aston Villa vs Tottenham Hotspur" | "0-4" | "2012-12-26 17:30:00 +0000" |
| "Aston Villa vs Wigan Athletic" | "0-3" | "2012-12-29 15:00:00 +0000" |
| "Arsenal vs Newcastle United" | "7-3" | "2012-12-29 17:30:00 +0000" |
| "Queens Park Rangers vs Liverpool" | "0-3" | "2012-12-30 16:00:00 +0000" |
+----------------------------------------------------------------------------+
----
There are certainly other things that we can find out now that we've got this relationship from months to matches explicit but it's not only dates where this idea comes in useful.
I already had players modelled in the data set but I thought it'd be interesting to find out more about the data set based on where players came from.
I therefore added the following relationships:
[source,text]
----
PLAYER-[:comes_from]->COUNTRY-[:is_in]->CONTINENT
----
We can now find the top scorers in the Premiership (accurate until before last weekend) who come from South America for example:
[source,cypher]
----
START continent = node:continents('name:"South America"')
MATCH continent-[:is_in]-country-[:comes_from]-player-[:played|subbed_on]-stats-[:in]-game
WHERE player-[:scored_in]-game
RETURN player.name, country.name, player.team, SUM(stats.goals) AS goals
ORDER BY goals DESC
LIMIT 5
----
[source,text]
----
+--------------------------------------------------------------+
| player.name | country.name | player.team | goals |
+--------------------------------------------------------------+
| "Luis Suárez" | "Uruguay" | "Liverpool" | 18 |
| "Sergio Agüero" | "Argentina" | "Manchester City" | 9 |
| "Carlos Tevez" | "Argentina" | "Manchester City" | 8 |
| "Franco Di Santo" | "Argentina" | "Wigan Athletic" | 5 |
| "Ramires" | "Brazil" | "Chelsea" | 4 |
+--------------------------------------------------------------+
----
Or we could find out how many goals have been scored by players from each continent:
[source,cypher]
----
START continent = node:continents('name:*')
MATCH continent-[:is_in]-country-[:comes_from]-player-[:played|subbed_on]-stats-[:in]-game
WHERE player-[:scored_in]-game
RETURN continent.name, SUM(stats.goals) AS goals
ORDER BY goals DESC
----
[source,text]
----
+-------------------------+
| continent.name | goals |
+-------------------------+
| "Europe" | 569 |
| "Africa" | 73 |
| "South America" | 62 |
| "North America" | 22 |
| "Asia" | 3 |
| "Oceania" | 3 |
+-------------------------+
----
I don't think every property needs to be a relationship but it can certainly be useful to think about doing so because it does allow you to think of interesting queries that you may not have previously thought about.
As an aside I'm working on putting this data set somewhere so people can play around with cypher queries on it so if you'd be interested let me know. | null | null | [
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] | neo4j-make-properties-relationships | https://markhneedham.com/blog/2013/03/06/neo4j-make-properties-relationships |
false | 2013-03-24 13:00:29 | neo4j/cypher: CypherTypeException: Failed merging Number with Relationship | [
"neo4j",
"cypher"
] | [
"neo4j"
] | The latest thing that I added to my football graph was the matches that are shown on TV as I have the belief that players who score on televised games get more attention than players who score in other games.
I thought it'd be interesting to work out who the top scorers are on each of these game types.
I added the following relationship type to allow me to do this:
[source,text]
----
game-[:on_tv]-channel
----
I then wrote a query to get a list of all the players along with a collection of the games they played in and whether or not this game was televised:
[source,cypher]
----
START player=node:players('name:*')
MATCH player-[:played|subbed_on]-stats-[:in]-game-[t?:on_tv]-channel
RETURN player.name, COLLECT([stats.goals, t]) AS games
LIMIT 10
----
Unfortunately when I ran this query I ended up with the following exception:
[source,text]
----
CypherTypeException: Failed merging Number with Relationship
----
From some previous conversations with http://wes.skeweredrook.com/[Wes] I'd noticed that this exception didn't seem to happen with the http://blog.neo4j.org/2013/03/neo4j-19m05-released-wrapping-up.html[1.9.M05] release but I was using http://blog.neo4j.org/2013/01/neo4j-milestone-19m04-released.html[1.9.M04].
Just to see what happened I tried returning the type of the relationship in the collection literal rather than the relationship and that worked:
[source,cypher]
----
START player=node:players('name:*')
MATCH player-[:played|subbed_on]-stats-[:in]-game-[t?:on_tv]-channel
RETURN player.name, COLLECT([stats.goals, TYPE(t)]) AS games
LIMIT 10
----
[source,text]
----
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| player.name | games |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "Djibril Cissé" | [[0,<null>],[1,"on_tv"],[0,<null>],[0,<null>],[0,"on_tv"],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[1,<null>],[0,"on_tv"],[0,"on_tv"],[0,<null>],[0,<null>],[0,"on_tv"],[0,<null>],[1,<null>]] |
| "Markus Rosenberg" | [[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,"on_tv"],[0,"on_tv"],[0,<null>],[0,"on_tv"],[0,<null>]] |
| "Gabriel Agbonlahor" | [[0,"on_tv"],[1,<null>],[0,"on_tv"],[0,<null>],[0,<null>],[0,"on_tv"],[1,<null>],[0,"on_tv"],[0,<null>],[0,"on_tv"],[0,<null>],[0,<null>],[1,"on_tv"],[1,<null>],[0,<null>],[0,"on_tv"],[1,<null>],[0,<null>],[0,<null>],[0,<null>],[0,<null>]] |
| "Shaun Derry" | [[0,<null>],[0,<null>],[0,<null>],[0,"on_tv"],[0,<null>],[0,<null>],[0,"on_tv"],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,"on_tv"]] |
| "Marouane Fellaini" | [[0,<null>],[0,"on_tv"],[0,<null>],[1,<null>],[1,"on_tv"],[1,<null>],[0,<null>],[0,"on_tv"],[1,"on_tv"],[0,<null>],[0,"on_tv"],[2,<null>],[1,<null>],[1,<null>],[1,<null>],[0,<null>],[0,<null>],[0,"on_tv"],[0,<null>],[0,<null>],[2,<null>],[0,"on_tv"],[0,<null>],[0,"on_tv"]] |
| "Jermaine Jenas" | [[0,<null>],[1,<null>],[0,<null>],[0,<null>],[0,"on_tv"],[0,<null>],[1,<null>]] |
| "Sean Morrison" | [[0,<null>],[0,"on_tv"],[0,<null>],[0,<null>],[1,<null>],[0,"on_tv"],[1,"on_tv"],[0,<null>],[0,"on_tv"]] |
| "Claudio Yacob" | [[0,"on_tv"],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,"on_tv"],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,"on_tv"],[0,<null>],[0,<null>],[0,"on_tv"],[0,"on_tv"],[0,"on_tv"],[0,<null>],[0,<null>],[0,<null>],[0,<null>],[0,<null>]] |
| "Michael Owen" | [[0,<null>],[0,<null>],[0,<null>],[0,<null>],[1,<null>],[0,<null>]] |
| "Tony Hibbert" | [[0,"on_tv"],[0,"on_tv"],[0,<null>],[0,<null>],[0,<null>]] |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
10 rows
----
The final query ends up looking like this out of interest:
[source,cypher]
----
START player=node:players('name:*')
MATCH player-[:played|subbed_on]-stats-[:in]-game-[t?:on_tv]-channel
WITH COLLECT([stats.goals, TYPE(t)]) AS games, player
RETURN player.name,
REDUCE(goals = 0, h IN FILTER(g IN games : HEAD(TAIL(g)) IS NULL): goals + HEAD(h)) AS nonTvGoals,
REDUCE(goals = 0, h IN FILTER(g IN games : HEAD(TAIL(g)) <> NULL): goals + HEAD(h)) AS tvGoals,
REDUCE(goals = 0, h in games : goals + HEAD(h)) AS totalGoals
ORDER BY tvGoals DESC
LIMIT 10
----
[source,text]
----
+--------------------------------------------------------+
| player.name | nonTvGoals | tvGoals | totalGoals |
+--------------------------------------------------------+
| "Gareth Bale" | 4 | 12 | 16 |
| "Robin Van Persie" | 8 | 11 | 19 |
| "Luis Suárez" | 12 | 10 | 22 |
| "Theo Walcott" | 3 | 8 | 11 |
| "Demba Ba" | 7 | 8 | 15 |
| "Santi Cazorla" | 4 | 7 | 11 |
| "Carlos Tevez" | 3 | 6 | 9 |
| "Edin Dzeko" | 6 | 6 | 12 |
| "Wayne Rooney" | 6 | 6 | 12 |
| "Juan Mata" | 4 | 6 | 10 |
+--------------------------------------------------------+
10 rows
----
So as we can see Gareth Bale pretty much only scores when TV cameras are about!
I was intrigued what had changed between 1.9.M04 and 1.9.M05 so I spent a few hours this morning browsing the https://github.com/neo4j/neo4j/tree/master/community/cypher[cypher part of the code base] and not really getting anywhere for the most part.
I thought that there had probably been a change around the way that https://github.com/neo4j/neo4j/blob/master/community/cypher/src/main/scala/org/neo4j/cypher/internal/parser/v1_9/Expressions.scala#L86[collection literals] were handled but a quick scan of git log suggested there hadn't been any changes:
[source,text]
----
$ git log -- community/cypher/src/main/scala/org/neo4j/cypher/internal/parser/v1_9
commit 7311bbe33bc06b346e60e12a4eee2a7173cbd317
Author: Andres Taylor <[email protected]>
Date: Tue Mar 19 06:42:49 2013 +0100
Handles single node patterns in MATCH
commit c9f580456572d3d267a15dc88b35e07fd450cf93
Author: Stefan Plantikow <[email protected]>
Date: Fri Jan 11 21:27:55 2013 +0100
scala 2.10 support: Kills type erasure warnings with a few casts and cleans up a bit
...
----
1.9.M04 was released on January 22nd and 1.9.M05 on March 6th and the only commit in this part of the code base in that time didn't touch the bit of code I'd been looking at.
Interestingly I couldn't find anywhere in the code base which had the string 'Failed merging' so I thought I'd do a http://stackoverflow.com/questions/4468361/search-all-of-git-history-for-string[quick scan of the diffs] to see if this had been deleted:
[source,text]
----
$ git log -S"Failed merging"
commit b6501aac03cf70419e94b4cfc160695e4950914a
Author: Andres Taylor <[email protected]>
Date: Sat Feb 16 19:30:00 2013 +0100
Changed how Cypher merges types
----
So in fact https://github.com/neo4j/neo4j/commit/b6501aac03cf70419e94b4cfc160695e4950914a[there was a commit] which changed the way that the collection type was determined so that rather than throwing an exception for clashing types a parent type would be used instead.
In any case this merging problem doesn't exist in 1.9.M05 and I've switched my graph to use that version of neo4j so I won't be see in this exception anymore! | null | null | [
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] | neo4jcypher-cyphertypeexception-failed-merging-number-with-relationship | https://markhneedham.com/blog/2013/03/24/neo4jcypher-cyphertypeexception-failed-merging-number-with-relationship |
false | 2013-03-24 21:20:35 | When nokogiri fails with 'Nokogiri::XML::SyntaxError: Element script embeds close tag' Web Driver to the rescue | [
"software-development"
] | [
"Software Development"
] | As I mentioned in http://www.markhneedham.com/blog/2013/03/24/neo4jcypher-cyphertypeexception-failed-merging-number-with-relationship/[my previous post] I wanted to add televised games to my football graph and the http://www.premierleague.com/en-gb/matchday/broadcast-schedules.tv.html?rangeType=.dateSeason&country=GB&clubId=ALL&season=2012-2013&isLive=true[Premier League website] seemed like the best case to find out which games those were.
I initially tried to use http://nokogiri.org/[Nokogiri] to grab the data that I wanted\...
[source,ruby]
----
> require 'nokogiri'
> require 'open-air'
> tv_times = Nokogiri::HTML(open('http://www.premierleague.com/en-gb/matchday/broadcast-schedules.tv.html?rangeType=.dateSeason&country=GB&clubId=ALL&season=2012-2013&isLive=true'))
----
\...but when I tried to query by CSS selector for all the matches nothing came back:
[source,ruby]
----
> tv_times.css(".broadcastschedule table.contentTable tbody tr")
=> []
----
I was a bit surprised but read somewhere that I should check if there were any errors while parsing the document. In fact there were quite a few!
[source,ruby]
----
> tv_times.errors
=> [#<Nokogiri::XML::SyntaxError: Element script embeds close tag>, #<Nokogiri::XML::SyntaxError: Element script embeds close tag>, #<Nokogiri::XML::SyntaxError: Element script embeds close tag>, #<Nokogiri::XML::SyntaxError: Element script embeds close tag>, #<Nokogiri::XML::SyntaxError: Element script embeds close tag>, #<Nokogiri::XML::SyntaxError: Element script embeds close tag>, #<Nokogiri::XML::SyntaxError: Element script embeds close tag>, #<Nokogiri::XML::SyntaxError: Element script embeds close tag>, #<Nokogiri::XML::SyntaxError: Element script embeds close tag>, #<Nokogiri::XML::SyntaxError: Element script embeds close tag>, #<Nokogiri::XML::SyntaxError: Element script embeds close tag>, #<Nokogiri::XML::SyntaxError: Element script embeds close tag>, ...]
----
I ran the document through the http://validator.w3.org/[W3C markup validation service] and it didn't seem to find any problem with it.
Next I tried stripping out all the script tags using https://github.com/flavorjones/loofah[loofah] before manually removing them but neither of those approaches helped.
I've previously used https://code.google.com/p/selenium/wiki/RubyBindings[Web Driver] to scrape web pages but I'd found that Nokogiri was much faster so I stopped using it.
Since my new library wasn't playing ball I thought I'd quickly see if Web Driver was up to the challenge and indeed it was:
[source,ruby]
----
require "selenium-webdriver"
driver = Selenium::WebDriver.for :chrome
driver.navigate.to "http://www.premierleague.com/en-gb/matchday/broadcast-schedules.tv.html?rangeType=.dateSeason&country=GB&clubId=ALL&season=2012-2013&isLive=true"
matches = driver.find_elements(:css, '.broadcastschedule table.contentTable tbody tr')
matches.each do|tr|
match = tr.find_element(:css, "td.show a").text
broadcaster = tr.find_element(:css, "td.broadcaster img").attribute("src")
tv_channel = broadcaster.include?("sky-sports") ? "Sky" : "ESPN"
puts "#{match},#{tv_channel}"
end
driver.quit
----
[source,ruby]
----
$ ruby tv_games.rb
Newcastle United vs Tottenham Hotspur,ESPN
Wigan Athletic vs Chelsea,Sky
Manchester City vs Southampton,Sky
Everton vs Manchester United,Sky
Swansea City vs West Ham United,Sky
Chelsea vs Newcastle United,ESPN
...
----
Ideally I'd like to use Nokogiri to do this job but it's decided that the document is invalid and it can't parse it properly so Web Driver is a pretty decent replacement I reckon! | null | null | [
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] | when-nokogiri-fails-with-nokogirixmlsyntaxerror-element-script-embeds-close-tag-web-driver-to-the-rescue | https://markhneedham.com/blog/2013/03/24/when-nokogiri-fails-with-nokogirixmlsyntaxerror-element-script-embeds-close-tag-web-driver-to-the-rescue |
false | 2013-03-24 22:01:12 | Best tool for the job/Learning new ways to do things | [
"learning"
] | [
"Learning"
] | I recently came across an interesting post written by https://twitter.com/Me1000[Randy Luecke] titled 'http://randyluecke.tumblr.com/post/45915323813/im-done-with-the-web[I'm done with the web]' in which he expresses his surprise that people often aren't willing to take the time out to learn something new.
In this context he's referring to javascript libraries but I think his thinking is generally applicable.
Having worked for a few years now I've played around with a reasonable number of programming languages/text editors/databases/etc to the point that I have favourites when it comes to solving certain problems.
If I come across a new problem and I want to solve it quickly my first instinct is to use one of the things that I know and am comfortable with as this will allow me to solve the problem most quickly.
The problem with this way of thinking is that it's restrictive - *what if there's some other way of solving the problem which is actually better than what I'm doing* but would take some practice for me to recognise that?
For example at the moment if I wanted to hack together a script which used a Java API I'd probably just crack open IntelliJ and write the code in Java even if http://www.markhneedham.com/blog/2013/03/17/clojurejava-interop-importing-neo4j-spatial-data/[clojure might be a better choice] if I knew it better.
If I want to quickly put together a simple web application I'd use http://www.sinatrarb.com/[Sinatra] but would http://flask.pocoo.org/[one] https://github.com/breily/juno[of the] http://bottlepy.org/docs/dev/index.html[Python equivalents] be better if I was more familiar with Python or does it not matter?
I'm reminded of conversations that I used to have with http://www.oshineye.com/[Ade] when we worked together and he was showing me http://www.amazon.co.uk/Apprenticeship-Patterns-Guidance-Aspiring-Craftsman/dp/0596518382/ref=sr_1_1?ie=UTF8&qid=1364161683&sr=8-1[Apprenticeship Patterns] and in particular the 'http://ofps.oreilly.com/titles/9780596518387/emptying_the_cup.html#confront_your_ignorance[Confront your ignorance]' pattern.
____
You have identified gaps in your skillset, gaps that are relevant to your daily work.
____
In this case the gap I'm talking about is relevant in as far as you can arguably do a better job if you have more tools to choose from.
A common pattern that I've noticed myself/others doing is to stick with the toolset we're comfortable with until it breaks down at which point we start looking around for something else.
While this approach does work it feels like we should be more proactive in learning new things so that we can question whether are current tools are actually the best ones for the job rather than just assuming they are.
I don't really have a conclusion other than that Randy's post struck a chord and I want to try and always challenge myself to keep learning things.
It'd be interesting to know if others have a similar experience or if I'm way off track? | null | null | [
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] | best-tool-for-the-joblearning-new-ways-to-do-things | https://markhneedham.com/blog/2013/03/24/best-tool-for-the-joblearning-new-ways-to-do-things |
false | 2013-03-24 22:52:19 | Incrementally rolling out machines with a new puppet role | [
"devops-2"
] | [
"DevOps"
] | Last week https://twitter.com/jasonneylon[Jason] and I with (a lot of) help from https://twitter.com/timrgoodwin[Tim] have been working on moving several of our applications from https://twitter.com/jasonneylon[Passenger] to https://github.com/blog/517-unicorn[Unicorn] and decided that the easiest way to do this was to create a new set of nodes with this setup.
The architecture we're working with looks like this at a VM level:
image::{{<siteurl>}}/uploads/2013/03/architecture.png[Architecture,600]
The 'nginx LB' nodes are responsible for routing all the requests to their appropriate application servers and the 'web' nodes serve the different applications initially using Passenger.
We started off by creating a new 'nginx LB' node which we pointed to a new 'web ELB' and just put one 'unicorn web' node behind it so that we could test everything was working.
We then pointed 'www.uswitch.com' at the IP of our new 'nginx LB' node in our +++<cite>+++/etc/hosts+++</cite>+++ file and checked that the main flows through the various applications were working correctly.
Once we were happy this was working correctly we increased the number of 'unicorn web' nodes to three and then repeated our previous checks while tailing the log files across the three machines to make sure everything was ok.
The next step was to send some of the real traffic to the new nodes and check whether they were able to handle it.
Initially we thought that we could put our 'unicorn web' nodes alongside the 'web' nodes but we realised that we'd made some changes on our new 'nginx LB' nodes which meant that the 'unicorn web' nodes needed to receive requests proxies through there rather than from the old style node.
A combination of Jason and https://twitter.com/siddharthdawara[Sid] came up with the idea of just plugging our new 'nginx LB' into the 'nginx ELB' and having the processing of the whole request treated separately.
Our intermediate architecture therefore looked like this:
image::{{<siteurl>}}/uploads/2013/03/arhictecture-rollover.png[Arhictecture rollover,600]
We initially served 1/4 of the requests from the Unicorn and watched the performance of the nodes via http://newrelic.com/[New Relic] to check that everything was working expected.
One thing we did notice was that the CPU usage on the Unicorn nodes was really high because we'd set up each Unicorn process with 5 workers which meant that we had 25 workers on the VM in total. In comparison our Passenger instances used 5 workers in total.
Once we'd sorted that out we removed one of the 'nginx LB' nodes from the 'nginx ELB' and served 1/3 of the traffic from our new stack.
We didn't see any problems so we removed all the 'nginx LB' nodes and served all the traffic from our new stack for half an hour.
Again we didn't notice any problems so our next step before we can decommission the old nodes is to run the new stack for a day and iron out any problems before using it for real. | null | null | [
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] | incrementally-rolling-out-machines-with-a-new-puppet-role | https://markhneedham.com/blog/2013/03/24/incrementally-rolling-out-machines-with-a-new-puppet-role |
false | 2013-04-18 23:36:32 | Puppet: Installing Oracle Java - oracle-license-v1-1 license could not be presented | [
"devops-2",
"vagrant"
] | [
"DevOps"
] | In order to run the neo4j server on my Ubuntu 12.04 Vagrant VM I needed to install the Oracle/Sun JDK which proved to be more difficult than I'd expected.
I initially tried to install it via the https://github.com/flexiondotorg/oab-java6[OAB-Java] script but was running into some dependency problems and eventually came across http://www.ubuntugeek.com/how-to-install-oracle-java-7-in-ubuntu-12-04.html[a post which specified a PPA] that had an installer I could use.
I wrote a little puppet Java module to wrap the commands in:
[source,text]
----
class java($version) {
package { "python-software-properties": }
exec { "add-apt-repository-oracle":
command => "/usr/bin/add-apt-repository -y ppa:webupd8team/java",
notify => Exec["apt_update"]
}
package { 'oracle-java7-installer':
ensure => "${version}",
require => [Exec['add-apt-repository-oracle']],
}
}
----
I then included this in my default node definition:
[source,text]
----
node default {
class { 'java': version => '7u21-0~webupd8~0', }
}
----
(as Dave Yeung points out in the comments, you may need to tweak the version. Running +++<cite>+++aptitude versions oracle-java7-installer+++</cite>+++ should indicate the latest version.)
Unfortunately when I ran that I ended up with the following error:
[source,text]
----
err: /Stage[main]/Java/Package[oracle-java7-installer]/ensure: change from purged to present failed: Execution of '/usr/bin/apt-get -q -y -o DPkg::Options::=--force-confold install oracle-java7-installer' returned 100: Reading package lists...
Building dependency tree...
Reading state information...
The following extra packages will be installed:
java-common
Suggested packages:
...
Unpacking oracle-java7-installer (from .../oracle-java7-installer_7u21-0~webupd8~0_all.deb) ...
oracle-license-v1-1 license could not be presented
try 'dpkg-reconfigure debconf' to select a frontend other than noninteractive
dpkg: error processing /var/cache/apt/archives/oracle-java7-installer_7u21-0~webupd8~0_all.deb (--unpack):
subprocess new pre-installation script returned error exit status 2
Processing triggers for man-db ...
Errors were encountered while processing:
/var/cache/apt/archives/oracle-java7-installer_7u21-0~webupd8~0_all.deb
E: Sub-process /usr/bin/dpkg returned an error code (1)
----
I came across http://askubuntu.com/questions/190582/installing-java-automatically-with-silent-option[this post on Ask Ubuntu] which explained a neat trick for getting around it by making it look like we've agreed to the licence. This is done by passing options to +++<cite>+++http://man.he.net/man1/debconf-set-selections[debconf-set-selections]+++<cite>+++.</p>
For a real server I guess you'd want some step where a person accepts the licence but since this is just for my hacking it seems to make sense.
My new Java manifest looks like this: ~~~text class java($version) { package { "python-software-properties": } exec { "add-apt-repository-oracle": command \=> "/usr/bin/add-apt-repository -y ppa:webupd8team/java", notify \=> Exec["apt_update"] } exec { 'set-licence-selected': command \=> '/bin/echo debconf shared/accepted-oracle-license-v1-1 select true | /usr/bin/debconf-set-selections'; 'set-licence-seen': command \=> '/bin/echo debconf shared/accepted-oracle-license-v1-1 seen true | /usr/bin/debconf-set-selections'; } package { 'oracle-java7-installer': ensure \=> "$\{version}", require \=> [Exec['add-apt-repository-oracle'], Exec['set-licence-selected'], Exec['set-licence-seen']], } } ~~~ +++</cite>++++++</cite>+++ | null | null | [
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] | puppet-installing-oracle-java-oracle-license-v1-1-license-could-not-be-presented | https://markhneedham.com/blog/2013/04/18/puppet-installing-oracle-java-oracle-license-v1-1-license-could-not-be-presented |
false | 2013-04-18 21:54:10 | dpkg/apt-cache: Useful commands | [
"devops-2",
"dpkg",
"apt"
] | [
"DevOps"
] | As I've mentioned in a couple of previous posts I've been playing around with creating a Vagrant VM that I can use for my neo4j hacking which has involved a lot of messing around with installing apt packages.
There are loads of different ways of working out what's going on when packages aren't installing as you'd expect so I thought it'd be good to document the ones I've been using so I can find them more easily next time.
== Finding reverse dependencies
A couple of times I found myself wondering how a certain package had ended up on the VM because I hadn't specified that it should be installed so I wanted to know who had!
I wanted to find out the reverse dependency for the package. e.g. to find out who depended on make which we can find out with the following command:
[source,text]
----
$ apt-cache rdepends make
make
Reverse Depends:
...
build-essential
make:i386
libc6-dev:i386
open-vm-dkms
mythbuntu-desktop
broadcom-sta-source
...
----
The nice thing about 'rdepends' is that it will tell us reverse dependencies even for a package that we haven't installed. This was helpful here as I had forgotten to install 'build-essential' and this made it obvious.
== Finding which version of a package is installed
I added one of the Brightbox repositories to get a more recent Ruby version and noticed that something weird was going on with the version of 'nginx-common' that puppet was trying to install.
It seemed like one one my dependencies was trying to pull in the 'latest' version of 'nginx-common' which I'd expected to be '1.1.19-1ubuntu0.1'.
By passing the 'policy' flag to apt-cache I was able to see that there was a recent version available via Brightbox:
[source,text]
----
$ apt-cache policy nginx-common
nginx-common:
Installed: 1.1.19-1ubuntu0.1
Candidate: 1:1.2.6-1~43~precise1
Version table:
1:1.2.6-1~43~precise1 0
500 http://ppa.launchpad.net/brightbox/ruby-ng/ubuntu/ precise/main amd64 Packages
*** 1.1.19-1ubuntu0.1 0
500 http://us.archive.ubuntu.com/ubuntu/ precise-updates/universe amd64 Packages
100 /var/lib/dpkg/status
1.1.19-1 0
500 http://us.archive.ubuntu.com/ubuntu/ precise/universe amd64 Packages
----
== Finding which versions of a package are available
Another flag that we can pass to apt-cache is 'madison' which shows us the available versions for a package but doesn't indicate which version is installed:
[source,text]
----
$ apt-cache madison nginx-common
nginx-common | 1:1.2.6-1~43~precise1 | http://ppa.launchpad.net/brightbox/ruby-ng/ubuntu/ precise/main amd64 Packages
nginx-common | 1.1.19-1ubuntu0.1 | http://us.archive.ubuntu.com/ubuntu/ precise-updates/universe amd64 Packages
nginx-common | 1.1.19-1 | http://us.archive.ubuntu.com/ubuntu/ precise/universe amd64 Packages
nginx | 1.1.19-1 | http://us.archive.ubuntu.com/ubuntu/ precise/universe Sources
nginx | 1.1.19-1ubuntu0.1 | http://us.archive.ubuntu.com/ubuntu/ precise-updates/universe Sources
nginx | 1:1.2.6-1~43~precise1 | http://ppa.launchpad.net/brightbox/ruby-ng/ubuntu/ precise/main Sources
----
== Finding which package a file belongs to
At some stage I wanted to check which exact package was installing nginx which I was able to do with the following command:
[source,text]
----
$ dpkg -S `which nginx`
nginx-extras: /usr/sbin/nginx
----
I had installed 'nginx-common' which I learn depends on 'nginx-extras' by using our 'rdepends' command:
[source,text]
----
$ apt-cache rdepends nginx-extras
nginx-extras
Reverse Depends:
nginx-naxsi:i386
...
nginx-common
----
== Finding the dependencies of a package
I wanted to check the dependencies of the 'ruby1.9.1' package to see whether or not I needed to explicitly install 'libruby1.9.1' or if that would be taken care of.
Passing the '-s' flag to dpkg let me check this:
[source,text]
----
$ dpkg -s ruby1.9.1
Package: ruby1.9.1
Status: install ok installed
Architecture: amd64
Version: 1:1.9.3.327-1bbox2~precise1
Replaces: irb1.9.1, rdoc1.9.1, rubygems1.9.1
Provides: irb1.9.1, rdoc1.9.1, ruby-interpreter, rubygems1.9.1
Depends: libruby1.9.1 (= 1:1.9.3.327-1bbox2~precise1), libc6 (>= 2.2.5)
Suggests: ruby1.9.1-examples, ri1.9.1, graphviz, ruby1.9.1-dev, ruby-switch
Conflicts: irb1.9.1 (<< 1.9.1.378-2~), rdoc1.9.1 (<< 1.9.1.378-2~), ri (<= 4.5), ri1.9.1 (<< 1.9.2.180-3~), ruby (<= 4.5), rubygems1.9.1
...
----
These are the ones that I've found useful so far. I'd love to here other people's favourites though as I'm undoubtably missing some. | null | null | [
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false | 2013-04-27 14:22:10 | Treat servers as cattle: Spin them up, tear them down | [
"devops-2"
] | [
"DevOps"
] | A few agos I wrote a post about http://www.markhneedham.com/blog/2013/04/07/treating-servers-as-cattle-not-as-pets/[treating servers as cattle, not as pets] in which I described an approach to managing virtual machines at uSwitch whereby we frequently spin up new ones and delete the existing ones.
I've worked on teams previously where we've also talked about this mentality but ended up not doing it because it was difficult, usually for one of two reasons:
* *Slow spin up* - this might be due to the cloud providers infrastructure, doing too much on spin up or I'm sure a variety of other reasons.
* *Manual steps involved in spin up* - the process isn't 100% automated so we have to do some manual tweaks. Once the machine is finally working we don't want to have to go through that again.
Martin Fowler http://martinfowler.com/bliki/FrequencyReducesDifficulty.html[wrote a post a couple of years ago] where he said the following:
____
One of my favorite soundbites is: *if it hurts, do it more often*. It has the happy property of seeming nonsensical on the surface, but yielding some valuable meaning when you dig deeper
____
I think it applies in this context too and I have noticed that the more frequently we tear down and spin up new nodes the easier it becomes to do so.
Part of this is because there's been less time for http://www.markhneedham.com/blog/2013/04/27/puppet-package-versions-to-pin-or-not-to-pin/[changes to have happened in package repositories] but we are also *more inclined to optimise things that we have to do frequently* so the whole process is faster as well.
For example in one of our sets of machines we need to give one machine a specific tag so that when the application is deployed it sets up a bunch of cron jobs to run each evening.
Initially this was done manually and we were quite reluctant to ever tear down that machine but we've now got it all automated and it's not a big deal anymore - it can be cattle just like the rest of them!
One neat rule of thumb https://twitter.com/philandstuff[Phil] taught me is that if we make major changes to our infrastructure we should spin up some new machines to check that it still actually works.
If we don't do this then when we actually need to spin up a new node because of a traffic spike or machine corruption problem it's not going to work and we're going to have to fix things in a much more stressful context.
For example we recently moved some repositories around in github and although it's a fairly simple change spinning up new nodes helped us see all the places where we'd failed to make the appropriate change.
While I appreciate taking this approach is more time consuming in the short term I'd argue that if we automate as much of the pain as possible in the long run it will probably be beneficial. | null | null | [
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] | treat-servers-as-cattle-spin-them-up-tear-them-down | https://markhneedham.com/blog/2013/04/27/treat-servers-as-cattle-spin-them-up-tear-them-down |
false | 2013-04-27 13:40:28 | Puppet: Package Versions - To pin or not to pin | [
"puppet"
] | [
"DevOps"
] | Over the last year or so I've spent quite a bit of time working with puppet and one of the things that we had to decide when installing packages was whether or not to specify a particular version.
On the first project I worked on we didn't bother and just let the package manager chose the most recent version.
Therefore if we were installing nginx the puppet code would read like this:
[source,puppet]
----
package { 'nginx':
ensure => 'present',
}
----
We can see which version that would install by checking the version table for the package:
[source,bash]
----
$ apt-cache policy nginx
nginx:
Installed: (none)
Candidate: 1:1.2.6-1~43~precise1
Version table:
1:1.2.6-1~43~precise1 0
500 http://ppa.launchpad.net/brightbox/ruby-ng/ubuntu/ precise/main amd64 Packages
1.4.0-1~precise 0
500 http://nginx.org/packages/ubuntu/ precise/nginx amd64 Packages
1.1.19-1ubuntu0.1 0
500 http://us.archive.ubuntu.com/ubuntu/ precise-updates/universe amd64 Packages
1.1.19-1 0
500 http://us.archive.ubuntu.com/ubuntu/ precise/universe amd64 Packages
----
In this case if we don't specify a version the Brightbox '1:1.2.6-1~43~precise1' version will be installed.
Running dpkg with the 'compare-versions' flag shows us that this version is considered higher than the nginx.org one:
[source,bash]
----
$ dpkg --compare-versions '1:1.2.6-1~43~precise1' gt '1.4.0-1~precise' ; echo $?
0
----
From what I understand you can http://wiki.debian.org/AptPreferences[pin versions higher up the list] by associating a higher number with them but given that all these versions are set to '500' I'm not sure how it decides on the order!
The problem with not specifying a version is that when a new version becomes available the next time puppet runs it will automatically upgrade the version for us.
Most of the time this isn't a problem but there were a couple of occasions when a version got bumped and something elsewhere stopped working and it took us quite a while to work out what had changed.
The alternative approach is to pin the package installation to a specific version. So if we want the http://nginx.org/[recent 1.4.0 version] installed we'd have the following code:
[source,puppet]
----
package { 'nginx':
ensure => '1.4.0-1~precise',
}
----
The nice thing about this approach is that we always know which version is going to be installed.
The problem we now introduce is that when an updated version is added to the repository the old one is typically removed which means a puppet run on a new machine will fail because it can't find the version.
After working with puppet for a few months it becomes quite easy to see when this is the reason for the failure but it creates the perception that 'puppet is always failing' for newer people which isn't so good.
I think on balance I prefer to have the versions explicitly defined because I find it easier to work out what's going on that way but I'm sure there's an equally strong argument for just picking the latest version. | null | null | [
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] | puppet-package-versions-to-pin-or-not-to-pin | https://markhneedham.com/blog/2013/04/27/puppet-package-versions-to-pin-or-not-to-pin |
false | 2013-04-16 20:57:53 | Puppet Debt | [
"devops-2",
"puppet"
] | [
"DevOps"
] | I've been playing around with a puppet configuration to run a neo4j server on an Ubuntu VM and one thing that has been quite tricky is getting the Sun/Oracle Java JDK to install repeatably.
I adapted Julian's https://github.com/neo4j-contrib/neo4j-puppet/blob/master/manifests/java.pp[Java module] which uses https://github.com/flexiondotorg/oab-java6[OAB-Java] and although it was certainly working cleanly at one stage I somehow ended up with it not working because of failed dependencies:
[source,text]
----
[2013-04-12 07:03:10] Notice: /Stage[main]/Java/Exec[install OAB repo]/returns: [x] Installing Java build requirements Ofailed
[2013-04-12 07:03:10] Notice: /Stage[main]/Java/Exec[install OAB repo]/returns: ^[[m^O [i] Showing the last 5 lines from the logfile (/root/oab-java.sh.log)...
[2013-04-12 07:03:10] Notice: /Stage[main]/Java/Exec[install OAB repo]/returns: nginx-common
[2013-04-12 07:03:10] Notice: /Stage[main]/Java/Exec[install OAB repo]/returns: nginx-extras
[2013-04-12 07:03:10] Notice: /Stage[main]/Java/Exec[install OAB repo]/returns: E: Sub-process /usr/bin/dpkg returned an error code (1)
...
[2013-04-12 07:03:10] Warning: /Stage[main]/Java/Package[sun-java6-jdk]: Skipping because of failed dependencies
[2013-04-12 07:03:10] Notice: /Stage[main]/Java/Exec[default JVM]: Dependency Exec[install OAB repo] has failures: true
[2013-04-12 07:03:10] Warning: /Stage[main]/Java/Exec[default JVM]: Skipping because of failed dependencies
----
I spent a few hours looking at this problem but couldn't quite figure out how to sort out the dependency problem and ended up running part one command manually after which applying puppet again worked.
Obviously this is a bit of a cop out because ideally I'd like it to be possible to spin up the VM in one puppet run without manual intervention.
A couple of days ago I was discussing the problem with https://twitter.com/a5hok[Ashok] and he suggested that it was probably good to know when I could defer fixing the problem to a later stage since having a completely automated spin up isn't my highest priority.
i.e. when I could take on what he referred to as '*Puppet debt*'
I think this is a reasonable way of looking at things and I have worked on projects where we've been baffled by puppet's dependency graph and have setup scripts which run puppet twice until we have time to sort it out.
If we're http://www.markhneedham.com/blog/2013/04/07/treating-servers-as-cattle-not-as-pets/[spinning up new instances frequently] then we have less ability to take on this type of debt because it's going to hurt us much more but if not then I think it is reasonable to defer the problem.
This feels like another type of http://martinfowler.com/bliki/TechnicalDebt.html[technical debt] to me but I'd be interested in others' thoughts and whether I'm just a complete cop out! | null | null | [
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] | puppet-debt | https://markhneedham.com/blog/2013/04/16/puppet-debt |
false | 2013-04-16 21:41:58 | neo4j/cypher: Redundant relationships | [
"neo4j",
"cypher"
] | [
"neo4j"
] | Last week I was writing a query to find the top scorers in the Premier League so far this season alongside the number of games they've played in which initially read like this:
[source,text]
----
START player = node:players('name:*')
MATCH player-[:started|as_sub]-playedLike-[:in]-game-[r?:scored_in]-player
WITH player, COUNT(DISTINCT game) AS games, COLLECT(r) AS allGoals
RETURN player.name, games, LENGTH(allGoals) AS goals
ORDER BY goals DESC
LIMIT 5
----
[source,text]
----
+------------------------------------+
| player.name | games | goals |
+------------------------------------+
| "Luis Suárez" | 30 | 22 |
| "Robin Van Persie" | 30 | 19 |
| "Gareth Bale" | 27 | 17 |
| "Michu" | 29 | 16 |
| "Demba Ba" | 28 | 15 |
+------------------------------------+
5 rows
1 ms
----
I modelled whether a player started a game or came on as a substitute with separate relationship types 'started' and 'as_sub' but in this query we're not interested in that, we just want to know whether they played.
In the world of relational database design we tend to try and avoid redundancy but http://www.markhneedham.com/blog/2012/07/21/neo4j-embracing-the-sub-graph/[with graphs this isn't such a big deal] so I thought I may as well add a 'played' relationship whenever a 'as_sub' or 'started' one was being created.
We can then simplify the above query to read like this:
[source,cypher]
----
START player = node:players('name:*')
MATCH player-[:played]-playedLike-[:in]-game-[r?:scored_in]-player
WITH player, COUNT(DISTINCT game) AS games, COLLECT(r) AS allGoals
RETURN player.name, games, LENGTH(allGoals) AS goals
ORDER BY goals DESC
LIMIT 5
----
[source,text]
----
+------------------------------------+
| player.name | games | goals |
+------------------------------------+
| "Luis Suárez" | 30 | 22 |
| "Robin Van Persie" | 30 | 19 |
| "Gareth Bale" | 27 | 17 |
| "Michu" | 29 | 16 |
| "Demba Ba" | 28 | 15 |
+------------------------------------+
5 rows
0 ms
----
When I'm querying I often forget that I modelled starting/substitute separately and think the data has screwed up and it's always because I've forgotten to include the 'as_sub' relationship.
Having the 'played' relationship means that no longer happens which is cool.
I have a reasonably small data set so I haven't seen any performance problems from creating this redundancy.
However, since the maximum number of relationships going out from a player would be unlikely to be more than 1000s I don't think it will become one either.
As always I'd be interested in thoughts from others who have come across similar problems or can see something that I've missed. | null | null | [
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] | neo4jcypher-redundant-relationships | https://markhneedham.com/blog/2013/04/16/neo4jcypher-redundant-relationships |
false | 2013-04-28 22:32:38 | A/B Testing: Reporting | [
"absplittesting"
] | [
"Software Development"
] | A few months ago I wrote about http://www.markhneedham.com/blog/2013/01/27/ab-testing-thoughts-so-far/[my initial experiences with A/B testing] and since then we've been working on another one and learnt some things around reporting on these types of tests that I thought was interesting.
== Reporting as a first class concern
One thing we changed from our previous test after a suggestion by https://twitter.com/michael_jones[Mike] was to start treating the reporting of data related to the test as a first class citizen.
To do this we created an end point which the main application could send POST requests to in order to record page views and various other information about users.
On our previous test we'd derived the various conversion rates from our main transactional data store but it was really slow and painful because the way we structure data in there is optimised for a completely different use case.
Having just the data we want to report on in a separate data store has massively reduced the time spent generating reports.
However, one thing that we learnt about this approach is that you need to spend some time thinking about what data is going to be needed up front.
If you don't then it will have to be added later on and the reporting on that metric won't cover the whole test duration.
== Drilling down to get insight
In the first test we ran we only really looked at conversion at quite a high level which is good for getting an overview but doesn't give much insight into what's going on.
For this test we started off with higher level metrics but a few days in became curious about what was going on between two of the pages and so created a report that segmented users based on an action they'd taken on the first page.
This allowed us to rule out a theory about a change in conversion which we had initially thought was down to a change we'd made but actually proved to be because of a change in an external factor.
The frustrating part of drilling down into the data is that you don't really know what is it you're going to want to zoom in on so you have to write code for the specific scenario each time!
== Detecting bugs
We generate browser specific metrics on each test that we run and while the conversion rate is generally similar between them there have been some times when there's a big drop in one browser.
More often than not when we've drilled into this we've found that there was actually a Javascript bug that we hadn't detected and we can then go back and sort that out.
An alternative approach would be to have an automated Javascript/Web Driver test suite which ran against each browser. We've effectively traded off the maintenance cost of that for what is usually a small period of inconvenience for some users. | null | null | [
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] | ab-testing-reporting | https://markhneedham.com/blog/2013/04/28/ab-testing-reporting |
false | 2013-04-10 07:03:15 | awk: Parsing 'free -m' output to get memory usage/consumption | [
"unix"
] | [
"Shell Scripting"
] | Although I know this problem is already solved by http://collectd.org/[collectd] and http://newrelic.com/[New Relic] I wanted to write a little shell script that showed me the memory usage on a bunch of VMs by parsing the output of +++<cite>+++http://linux.about.com/library/cmd/blcmdl1_free.htm[free]+++</cite>+++.
The output I was playing with looks like this:
[source,text]
----
$ free -m
total used free shared buffers cached
Mem: 365 360 5 0 59 97
-/+ buffers/cache: 203 161
Swap: 767 13 754
----
I wanted to find out what % of the memory on the machine was being used and as I understand it the numbers that we would use to calculate this are the 'total' value on the 'Mem' line and the 'used' value on the 'buffers/cache' line.
I initially thought that the 'used' value I was interested in should be the one on the 'Mem' line but this number includes memory that http://www.linuxatemyram.com/index.html[Linux has borrowed for disk caching] so it isn't the true number.
There's another http://www.linuxatemyram.com/play.html[quite interesting article showing some experiments you can do] to prove this.
So what I wanted to do was get the result of the calculation '203/365' which I wasn't sure how to do until I realised you can match multiple regular expressions with awk like so:
[source,text]
----
$ free -m | awk '/Mem:/ { print $2 } /buffers\/cache/ { print $3 }'
365
203
----
We've now filtered the output down to just our two numbers but another neat thing you can do with awk is change what it uses as its field and record separator.
In this case we want to change the field separator to be the new line character and we'll set the record separator to be nothing because otherwise it defaults to the new line character which will mess with our field separator.
Those two values are set by using http://www.thegeekstuff.com/2010/01/8-powerful-awk-built-in-variables-fs-ofs-rs-ors-nr-nf-filename-fnr/[the 'RS' and 'FS' variables]:
[source,text]
----
$ free -m |
awk '/Mem:/ { print $2 } /buffers\/cache/ { print $3 }' |
awk 'BEGIN { RS = "" ; FS = "\n" } { print $2 / $1 }'
0.556164
----
This is still sub optimal because we're using two awk commands rather than one! We can get around that by storing the two memory values in variables and printing them out in an END block:
[source,text]
----
$ free -m |
awk '/Mem:/ { total=$2 } /buffers\/cache/ { used=$3 } END { print used/total}'
0.556164
---- | null | null | [
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] | awk-parsing-free-m-output-to-get-memory-usageconsumption | https://markhneedham.com/blog/2013/04/10/awk-parsing-free-m-output-to-get-memory-usageconsumption |
false | 2013-04-07 11:41:34 | Treating servers as cattle, not as pets | [
"devops-2"
] | [
"DevOps"
] | Although I didn't go to http://www.devopsdays.org/events/2013-london/[Dev Ops Days London] earlier in the year I was following the hash tag on twitter and one of my favourites things that I read https://twitter.com/pikesley/statuses/312614061818904576[was the following]:
____
"`Treating servers as cattle, not as pets`" #DevOpsDays
____
I think this is particularly applicable now that a lot of the time we're using virtualised production environments via http://aws.amazon.com/[AWS], http://www.rackspace.co.uk/cloud-servers/[Rackspace] or +++<insert-cloud-provider-here>+++.</p>
At http://www.uswitch.com/[uSwitch] we use AWS and over the last week https://twitter.com/siddharthdawara[Sid] and I spent some time investigating a memory leak by running our applications against two different versions of Ruby.
One of them was from the https://launchpad.net/~brightbox/+archive/ppa[Brightbox repository] and the other was custom built but they had annoyingly different puppet configurations so we decided to treat them as separate machine types.
We spun up one of the custom built Ruby nodes and put it in the load balancer alongside 11 of the other node types and left it for the day serving traffic.
The next day we had look at the http://newrelic.com/[New Relic] memory consumption for both node types and it was clear that the custom built one's memory usage was climbing much more slowly than the other one.
Instead of trying to work out how to change the Ruby version of the 11 existing nodes we realised it would probably be quicker to just spin up 11 new ones with the custom built Ruby and swap them with the existing ones.
This was pretty much as easy as removing the existing nodes from the load balancer and putting the new ones in although we do have one 'special' machine which runs some background jobs.
We needed to make sure there weren't any jobs on its queue that hadn't been processed and then make sure that we tagged one of the new machines so that they could take over that role.
One thing that made it particularly easy for us to do this is that spin up of new VMs is extremely quick and completely automated including the installation and start up of applications.
The only manual step we have is to put the new nodes into the load balancer which I think works ok as a manual step because it gives us a chance to quickly scan the box and check everything spun up correctly.
We install all packages/configuration on nodes using http://docs.puppetlabs.com/man/apply.html[puppet headless] which makes spin up easier than if you use server/client mode where you have to coordinate node registration with the master on spin up.
I do like this philosophy to machines and although I'm sure it doesn't apply to all situations we're almost at the point where if something breaks on a node we might as well spin up a new one while we're investigating and see which finishes first!+++</insert-cloud-provider-here>+++ | null | null | [
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] | treating-servers-as-cattle-not-as-pets | https://markhneedham.com/blog/2013/04/07/treating-servers-as-cattle-not-as-pets |
false | 2013-04-07 01:00:08 | Sublime: Getting Textmate's Reveal/Select in Side Bar (Cmd + Ctrl + R) | [
"software-development",
"sublime"
] | [
"Software Development"
] | After coming across this post about http://delvarworld.github.io/blog/2013/03/16/just-use-sublime-text/[why you should use Sublime Text] I decided to try using it a bit more and one of the things that I missed from Textmate was the way you can select the current file on the sidebar.
In Textmate the shortcut to do that is 'Cmd + Ctrl + R' so I wanted to be able to do something similar or configure Sublime so it responded to the same shortcut.
The option to reveal a file in the side bar is accessible from the context menu by right clicking on the contents of a file after it's opening and selecting 'Reveal in Side Bar' which is a good start.
To map that to a key binding we need to go 'Preferences > Key Bindings (User)' and http://sublimetext.userecho.com/topic/41241-reveal-file-in-project-tree/[put the following into that file]:
[source,text]
----
[
{ "keys": ["ctrl+super+r"], "command": "reveal_in_side_bar" }
]
----
Of course if we already have other custom key bindings then we can just add it after those instead.
We can work out what the name of commands are by turning on command logging in the Sublime console.
We need to first open the console with 'Ctrl + `" and then http://www.sublimetext.com/forum/viewtopic.php?f=2&t=11217[paste the following]:
[source,text]
----
sublime.log_commands(True)
----
Any commands that we run will now have their name printed in the console window. e.g.
[source,text]
----
>>> sublime.log_commands(True)
command: context_menu {"event": {"button": 1, "x": 390.21484375, "y": 329.66796875}}
command: reveal_in_side_bar
command: rename_path {"paths": ["/Users/markhneedham/code/thinkingingraphs/public/js/bootstrap.js"]}
no command for selector: noop:
command: show_panel {"panel": "console", "toggle": true}
----
We can then setup appropriate key bindings for whichever commands we like. | null | null | [
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] | sublime-getting-textmates-revealselect-in-side-bar-cmd-ctrl-r | https://markhneedham.com/blog/2013/04/07/sublime-getting-textmates-revealselect-in-side-bar-cmd-ctrl-r |
false | 2013-04-09 07:23:59 | Python: Reading a JSON file | [
"python",
"json"
] | [
"Python"
] | I've been playing around with some code to spin up AWS instances using http://docs.fabfile.org/en/1.6/[Fabric] and https://github.com/boto/boto[Boto] and one thing that I wanted to do was define a bunch of default properties in a JSON file and then load this into a script.
I found it harder to work out how to do this than I expected to so I thought I'd document it for future me!
My JSON file looks like this:
_config/defaults.json_
[source,json]
----
{
"region" : "eu-west-1",
"instanceType": "m1.small"
}
----
To read that file we can do the following:
[source,python]
----
>>> open('config/defaults.json').read()
'{\n\t"region" : "eu-west-1",\n\t"instanceType": "m1.small"\n}'
----
We can then use the http://stackoverflow.com/questions/2835559/python-parsing-file-json[json.loads] function to convert that from a string into a Python object:
[source,python]
----
>>> import json
>>> config = json.loads(open('config/defaults.json').read())
>>> config
{u'region': u'eu-west-1', u'instanceType': u'm1.small'}
----
We'd write the following code to get the region:
[source,python]
----
>>> config["region"]
u'eu-west-1'
----
I guess we might want to use a different approach that didn't load the whole string into memory if we had a large JSON file but for my purposes this will do! | null | null | [
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] | python-reading-a-json-file | https://markhneedham.com/blog/2013/04/09/python-reading-a-json-file |
false | 2013-04-30 07:02:09 | neo4j/cypher: Returning a row with zero count when no relationship exists | [
"neo4j",
"cypher"
] | [
"neo4j"
] | I've been trying to see if I can match some of the football stats that https://twitter.com/OptaJoe[OptaJoe] posts on twitter and one that I was looking at yesterday was around the https://twitter.com/OptaJoe/status/328969438361690113[number of red cards different teams have received].
____
1 - Sunderland have picked up their first PL red card of the season. The only team without one now are Man Utd. Angels.
____
To refresh this is the sub graph that we'll need to look at to work it out:
image::{{<siteurl>}}/uploads/2013/04/sent_off.png[Sent off,242]
I started off with the following query which traverses out from each match, finds the players who were sent off in the match and then http://www.markhneedham.com/blog/2013/02/17/neo4jcypher-sql-style-group-by-functionality/[groups] the sendings off by the team they were playing for:
[source,cypher]
----
START game = node:matches('match_id:*')
MATCH game<-[:sent_off_in]-player-[:played]->likeThis-[:in]->game,
likeThis-[:for]->team
RETURN team.name, COUNT(game) AS redCards
ORDER BY redCards
LIMIT 5
----
When we run this we get the following results:
[source,text]
----
+------------------------------+
| team.name | redCards |
+------------------------------+
| "Sunderland" | 1 |
| "West Ham United" | 1 |
| "Norwich City" | 1 |
| "Reading" | 1 |
| "Liverpool" | 2 |
+------------------------------+
5 rows
----
The problem we have here is that it hasn't returned Manchester United because they haven't yet received any red cards and therefore none of their players match the 'sent_off_in' relationship.
I ran into something similar in a post I wrote about a month ago where I was http://www.markhneedham.com/blog/2013/03/20/neo4jcypher-getting-the-hang-of-the-with-statement/[working out which day of the week players scored on].
The first step towards getting Manchester United to return with a count of 0 is to make the 'sent_off_in' relationship optional.
However, that on its own that isn't enough because it now returns a count of all the player performances for each team:
[source,cypher]
----
START game = node:matches('match_id:*')
MATCH game<-[?:sent_off_in]-player-[:played]->likeThis-[:in]->game,
likeThis-[:for]->team
RETURN team.name, COUNT(game) AS redCards
ORDER BY redCards ASC
LIMIT 5
----
[source,text]
----
+-----------------------------+
| team.name | redCards |
+-----------------------------+
| "Chelsea" | 448 |
| "Wigan Athletic" | 459 |
| "Fulham" | 460 |
| "Liverpool" | 466 |
| "Everton" | 467 |
+-----------------------------+
5 rows
----
Instead what we need to do is collect up all the 'sent_off_in' relationships and sum them up.
We can use the http://www.markhneedham.com/blog/2013/03/20/neo4jcypher-with-collect-extract/[COLLECT] function to do that and the neat thing about COLLECT is that it doesn't bother collecting the empty relationships so we end up with exactly what we need:
[source,cypher]
----
START game = node:matches('match_id:*')
MATCH game<-[r?:sent_off_in]-player-[:played]->likeThis-[:in]->game,
likeThis-[:for]->team
RETURN team.name, COLLECT(r) AS redCards
LIMIT 5
----
[source,text]
----
+-----------------------------------------------------------------------------------------------------+
| team.name | redCards |
+-----------------------------------------------------------------------------------------------------+
| "Wigan Athletic" | [:sent_off_in[26443] {},:sent_off_in[37785] {}] |
| "Everton" | [:sent_off_in[6795] {minute:61},:sent_off_in[21735] {},:sent_off_in[34594] {}] |
| "Newcastle United" | [:sent_off_in[434] {minute:75},:sent_off_in[32389] {},:sent_off_in[34915] {}] |
| "Southampton" | [:sent_off_in[49393] {minute:70},:sent_off_in[49392] {minute:82}] |
| "West Ham United" | [:sent_off_in[21734] {minute:67}] |
+-----------------------------------------------------------------------------------------------------+
5 rows
----
We then just need to call the LENGTH function to work out how many red cards there are in each collection and then we're done:
[source,cypher]
----
START game = node:matches('match_id:*')
MATCH game<-[r?:sent_off_in]-player-[:played]->likeThis-[:in]->game,
likeThis-[:for]->team
RETURN team.name, LENGTH(COLLECT(r)) AS redCards
ORDER BY redCards
LIMIT 5
----
[source,text]
----
+--------------------------------+
| team.name | redCards |
+--------------------------------+
| "Manchester United" | 0 |
| "West Ham United" | 1 |
| "Sunderland" | 1 |
| "Norwich City" | 1 |
| "Reading" | 1 |
+--------------------------------+
5 rows
---- | null | null | [
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] | neo4jcypher-returning-a-row-with-zero-count-when-no-relationship-exists | https://markhneedham.com/blog/2013/04/30/neo4jcypher-returning-a-row-with-zero-count-when-no-relationship-exists |
false | 2013-04-30 20:07:57 | Mac OS X: A couple of neat tools | [
"software-development"
] | [
"Software Development"
] | When I first started working at http://www.uswitch.com/[uSwitch] https://twitter.com/siddharthdawara[Sid] installed a couple of 'productivity applications' on my Mac which I've found pretty useful but from talking to others I realised they aren't known/being used by everyone.
== Alfred
http://www.alfredapp.com/[Alfred] is a http://en.wikipedia.org/wiki/Quicksilver_(software)[Quick Silver] replacement which allows you to quickly open applications, find files, search Google and more. Even though we're not using half of its features it's still proved to be useful.
I quite like the calculator feature which we've been using for adhoc calculation like working out http://www.markhneedham.com/blog/2013/04/10/awk-parsing-free-m-output-to-get-memory-usageconsumption/[how much free memory there was on a server] or the http://www.markhneedham.com/blog/2013/04/28/ab-testing-reporting/[conversion rate on part of an A/B test].
image::{{<siteurl>}}/uploads/2013/04/calculator.png[Calculator,600]
== Moom
The other application is http://manytricks.com/moom/[Moom] which allows you to move/resize windows.
I didn't see the point when I first saw it but it's actually really useful when you're working on a big monitor and want to put say the terminal alongside the browser.
We have the following shortcuts set up:
image::{{<siteurl>}}/uploads/2013/04/moom1.png[Moom1,474]
That allows us to type 'Ctrl + Space' to make the window fill the left hand side of the screen, 'Alt + Space' to make it fill the right hand side of the screen and 'Alt + Ctrl + Space' to fill the whole screen.
You can also set up shortcuts to allow you to move a window between displays or to rearrange the windows based on certain events.
Highly recommended!
If anyone knows any other cool tools like this I'd love to hear about them. | null | null | [
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] | mac-os-x-a-couple-of-neat-tools | https://markhneedham.com/blog/2013/04/30/mac-os-x-a-couple-of-neat-tools |
false | 2013-04-30 21:37:11 | Ruby 1.9.3 p0: Investigating weirdness with HTTP POST request in net/http | [
"ruby"
] | [
"Ruby"
] | https://twitter.com/the_T_bot[Thibaut] and I spent the best part of the last couple of days trying to diagnose a problem we were having trying to make a POST request using https://www.ruby-toolbox.com/projects/rest-client[rest-client] to one of our services.
We have nginx fronting the application server so the request passes through there first:
image::{{<siteurl>}}/uploads/2013/04/post.png[Post,335]
The problem we were having was that the request was timing out on the client side before it had been processed and the request wasn't reaching the application server.
We initially thought there might be a problem with our nginx configuration because we don't have many POST requests with largish (40kb) payloads so we initially tried tweaking the http://wiki.nginx.org/HttpProxyModule#proxy_buffer_size[proxy buffer size].
It was a bit of a long shot because changing that setting only reduces the likelihood that nginx writes the request body to disc and then loads it later which shouldn't impact performance that much.
The next thing we tried was replicating the request using http://curl.haxx.se/[cURL] with a smaller payload which worked fine. cURL had no problem with the bigger payload either.
We therefore thought there must be a difference in the request headers being sent by rest-client and our initial investigation suggested that it might be to do with the 'http://stackoverflow.com/questions/2773396/whats-the-content-length-field-in-http-header[Content-Length]' header.
There was a 1 byte difference in the value being sent by cURL and the one being sent by rest-client which was to do with the last character of the payload being a http://homepage.smc.edu/morgan_david/CS41/lineterminators.htm[0A] (linefeed) character.
We changed the 'Content-Length' header on our cURL request to match that of the rest-client request (i.e. 1 byte too large) and were able to replicate the timeout problem.
At this stage we thought that calling 'strip' on the body of our rest-client request would solve the problem as the 'Content-Length' header would now be set to the correct value. It did set the 'Content-Length' header properly but unfortunately didn't get rid of the timeout.
Our next step was to check whether or not we could get any request to work from rest-client so we tried using a smaller payload which worked fine.
At this stage https://twitter.com/jasonneylon[Jason] heard us discussing what to do next and said that he'd come across it earlier and that upgrading our Ruby Version from '1.9.3p0' would solve all our woes.
That Ruby version is a couple of years old and most of our servers are running '1.9.3p392' but somehow this one had slipped through the net.
We http://www.markhneedham.com/blog/2013/04/27/treat-servers-as-cattle-spin-them-up-tear-them-down/[spun up a new server] with that version of Ruby installed and it did indeed fix the problem.
However, we were curious what the fix was and had a look at the http://svn.ruby-lang.org/repos/ruby/tags/v1_9_3_125/ChangeLog[change log of the first patch release after '1.9.3p0']. We noticed the following which seemed relevant:
____
Tue May 31 17:03:24 2011 Hiroshi Nakamura <[email protected]> * lib/net/http.rb, lib/net/protocol.rb: Allow to configure to wait server returning '100 continue' response before sending HTTP request body. See NEWS for more detail. See #3622. Original patch is made by Eric Hodel <[email protected]>. * test/net/http/test_http.rb: test it. * NEWS: Add new feature.
____
One thing we noticed from looking at the requests with http://vccv.posterous.com/use-ngrep-to-inspect-http-headers[ngrep] was that cURL was setting the http://www.w3.org/Protocols/rfc2616/rfc2616-sec8.html#sec8.2.3[100 Continue Expect request header] and rest-client wasn't.
When the payload size was small nginx didn't seem to send a '100 Continue' response which was presumably why we weren't seeing a problem with the small payloads.
I wasn't sure how to go about finding out exactly what was going wrong but given how long it took us to get to this point I thought I'd summarise what we tried and see if anyone could explain it to me.
So if you've come across this problem (probably 2 years ago!) it'd be cool to know exactly what the problem was. | null | null | [
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] | ruby-1-9-3-p0-investigating-weirdness-with-http-post-request-in-nethttp | https://markhneedham.com/blog/2013/04/30/ruby-1-9-3-p0-investigating-weirdness-with-http-post-request-in-nethttp |
false | 2013-04-06 17:26:20 | MySQL: Repairing broken tables/indices | [
"mysql"
] | [
"Software Development"
] | I part time administrate http://www.soccer24-7.com/forum/index.php[a football forum] that I used to run when I was at university and one problem we had recently was that some of the tables/indices had got corrupted when MySQL crashed due to a lack of disc space.
We weren't seeing any visible sign of a problem in any of the logs but whenever you tried to query one of the topics it wasn't returning any posts.
I eventually came across a useful article which explained http://www.softwareprojects.com/resources/programming/t-how-to-fix-mysql-database-myisam-innodb-1634.html[how to check whether some of the tables in a MySQL database had been corrupted and how to fix them].
I first shutdown the database using the following command:
[source,text]
----
mysqladmin shutdown
----
And then I ran this command to check on the status of each of the tables:
[source,text]
----
for path in `ls /var/lib/mysql/forum/*.MYI`; do echo $path; myisamchk $path; done
----
This gave an output like the following for each table:
[source,text]
----
Checking MyISAM file: /var/lib/mysql/forum/forum.MYI
Data records: 217 Deleted blocks: 4
myisamchk: warning: 1 client is using or hasn't closed the table properly
- check file-size
- check record delete-chain
- check key delete-chain
- check index reference
- check data record references index: 1
- check record links
MyISAM-table '/var/lib/mysql/forum/forum.MYI' is usable but should be fixed
----
If you pass the '--recover' flag to +++<cite>+++myisamchk+++</cite>+++ it will attempt to fix any problems it finds. I therefore ran the following command:
[source,text]
----
for path in `ls /var/lib/mysql/forum/*.MYI`; do echo $path; myisamchk --recover $path; done
----
After I'd run that it seemed to fix most of the problems we'd been experiencing. There are still a couple of edge cases left but at least the majority of the forum is now in a usable state.
I think we could just as easily run +++<cite>+++myisamchk+++</cite>+++ by passing a wildcard selection of files for it to run against but I didn't realise that until afterwards!
The following would therefore work just as well:~~~ ~~~text myisamchk --recover /var/lib/mysql/forum/*.MYI ~~~ | null | null | [
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] | mysql-repairing-broken-tablesindices | https://markhneedham.com/blog/2013/04/06/mysql-repairing-broken-tablesindices |
false | 2013-04-23 23:59:49 | Unix: Checking for open sockets on nginx | [
"unix"
] | [
"Shell Scripting"
] | https://twitter.com/timrgoodwin[Tim] and I were investigating a weird problem we were having with nginx where it was getting in a state where it had exceeded the number of open files allowed on the system and started rejecting requests.
We can find out the maximum number of open files that we're allowed on a system with the following command:
[source,bash]
----
$ ulimit -n
1024
----
Our hypothesis was that some socket connections were never being closed and therefore the number of open files was climbing slowly upwards until it exceeded the limit.
We wanted to check how many sockets nginx had open so to start with we needed to know the process IDs it was running under:
[source,bash]
----
$ ps aux | grep nginx | grep -v grep
root 1089 0.0 0.7 105152 2736 ? Ss 17:34 0:00 nginx: master process /usr/sbin/nginx
www-data 17474 0.0 0.6 105300 2296 ? S 21:49 0:04 nginx: worker process
www-data 17475 0.0 0.7 105300 2856 ? S 21:49 0:04 nginx: worker process
www-data 17476 0.0 0.7 105300 2792 ? S 21:49 0:03 nginx: worker process
www-data 17477 0.0 0.7 105300 2668 ? S 21:49 0:04 nginx: worker process
----
So the process IDs we're interested in are 1089, 17474, 17475, 17476 and 17477.
We can check which file descriptors they have open with the following command:
[source,bash]
----
$ sudo ls -alh /proc/{1089,17{474,475,476,477}}/fd
/proc/17476/fd:
total 0
dr-x------ 2 www-data www-data 0 Apr 23 23:40 .
...
l-wx------ 1 www-data www-data 64 Apr 23 23:40 6 -> /var/log/nginx/error.log
l-wx------ 1 www-data www-data 64 Apr 23 23:40 7 -> /var/www/thinkingingraphs/shared/log/nginx_access.log
l-wx------ 1 www-data www-data 64 Apr 23 23:40 8 -> /var/www/thinkingingraphs/shared/log/nginx_error.log
lrwx------ 1 www-data www-data 64 Apr 23 23:40 9 -> socket:[8910]
/proc/17477/fd:
total 0
...
lrwx------ 1 www-data www-data 64 Apr 23 23:40 56 -> socket:[52213]
lrwx------ 1 www-data www-data 64 Apr 23 23:40 57 -> anon_inode:[eventpoll]
l-wx------ 1 www-data www-data 64 Apr 23 23:40 6 -> /var/log/nginx/error.log
l-wx------ 1 www-data www-data 64 Apr 23 23:40 7 -> /var/www/thinkingingraphs/shared/log/nginx_access.log
l-wx------ 1 www-data www-data 64 Apr 23 23:40 8 -> /var/www/thinkingingraphs/shared/log/nginx_error.log
lrwx------ 1 www-data www-data 64 Apr 23 23:40 9 -> socket:[8910]
----
We can narrow that down to just show us how many sockets are open:
[source,bash]
----
$ sudo ls -alh /proc/{1089,17{474,475,476,477}}/fd | grep socket | wc -l
189
----
We could also use +++<cite>+++http://linux.die.net/man/8/lsof[lsof]+++</cite>+++ although for some reason that returns a slightly different number:
[source,bash]
----
$ sudo lsof -p 1089,17474,17475,17476,17477 | grep socket | wc -l
184
----
If we want to use brace expansion to do that it becomes a bit more tricky:
[source,bash]
----
$ sudo lsof -p `echo {1089,174{74,75,76,77}} | sed 's/ /,/g'` | grep socket | wc -l
184
----
Annoyingly we couldn't actually replicate the error but think that it's been solved in nginx 1.2.0 (we were using 1.1.19) by http://nginx.org/en/CHANGES[this change]:
[source,text]
----
Bugfix: a segmentation fault might occur in a worker process if the
"try_files" directive was used; the bug had appeared in 1.1.19.
---- | null | null | [
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] | unix-checking-for-open-sockets-on-nginx | https://markhneedham.com/blog/2013/04/23/unix-checking-for-open-sockets-on-nginx |
false | 2013-04-23 23:25:15 | No downtime deploy with capistrano, Thin and nginx | [
"capistrano",
"nginx",
"thin"
] | [
"Software Development"
] | As I mentioned a couple of weeks ago I've been working on a tutorial about http://www.markhneedham.com/blog/2013/04/13/capistrano-deploying-to-a-vagrant-vm/[thinking through problems in graphs] and since it's a Sinatra application I thought thin would be a decent choice for http://code.macournoyer.com/thin/[web server].
In my initial setup I had the following nginx config file which was used to proxy requests on to thin:
_/etc/nginx/sites-available/thinkingingraphs.conf_
[source,text]
----
upstream thin {
server 127.0.0.1:3000;
}
server {
listen 80 default;
server_name _;
charset utf-8;
rewrite ^\/status(.*)$ $1 last;
gzip on;
gzip_disable "MSIE [1-6]\.(?!.*SV1)";
gzip_types text/plain application/xml text/xml text/css application/x-javascript application/xml+rss text/javascript application/json;
gzip_vary on;
access_log /var/www/thinkingingraphs/shared/log/nginx_access.log;
error_log /var/www/thinkingingraphs/shared/log/nginx_error.log;
root /var/www/thinkingingraphs/current/public;
location / {
proxy_pass http://thin;
}
error_page 404 /404.html;
error_page 500 502 503 504 /500.html;
}
----
I had an http://upstart.ubuntu.com/[upstart] script which started the thin server\...
_/etc/init/thinkingingraphs.conf_
[source,text]
----
script
export RACK_ENV=production
export RUBY=ruby
cd /var/www/thinkingingraphs/current
exec su -s /bin/sh vagrant -c '$RUBY -S bundle exec thin -p 3000 start >> /var/www/thinkingingraphs/current/log/production.log 2>&1'
end script
----
\... and then I used the following https://github.com/capistrano/capistrano[capistrano] script to stop and start the server whenever I was deploying a new version of the application:
_config/deploy.rb_
[source,ruby]
----
namespace :deploy do
task(:start) {}
task(:stop) {}
desc "Restart Application"
task :restart do
sudo "stop thinkingingraphs || echo 0"
sudo "start thinkingingraphs"
end
end
----
The problem with this approach is that some requests receive a 502 response code while its restarting:
[source,bash]
----
$ bundle exec cap deploy
----
[source,bash]
----
$ while true; do curl -w %{http_code}:%{time_total} http://localhost/ -o /dev/null -s; printf "\n"; sleep 0.5; done
200:0.076
200:0.074
200:0.095
502:0.003
200:0.696
----
I wanted to try and make a no downtime deploy script and I came across http://jordanhollinger.com/2011/12/19/deploying-with-thin[a couple] http://www.treeder.com/2012/03/upstarting-thin-aka-using-upstart-to.html[of posts] which helped me work out how to do it.
The first step was to make sure that I had more than one thin instance running so that requests could be sent to one of the other ones while a restart was in progress.
I created the following config file:
_/etc/thin/thinkingingraphs.yml_
[source,text]
----
chdir: /var/www/thinkingingraphs/current
environment: production
address: 0.0.0.0
port: 3000
timeout: 30
log: log/thin.log
pid: tmp/pids/thin.pid
max_conns: 1024
max_persistent_conns: 100
require: []
wait: 30
servers: 3
daemonize: true
onebyone: true
----
One of the other properties that we need to set is 'onebyone' which means that when you restart thin it will take down the thin instances one at a time. This means one of the other two can handle incoming requests.
We've set the number of servers to 3 which will spin up 3 instances on ports 3000, 3001 and 3002.
I changed my upstart script to look like this:
_/etc/init/thinkingingraphs.conf_
[source,text]
----
script
export RACK_ENV=production
export RUBY=ruby
cd /var/www/thinkingingraphs/current
exec su -s /bin/sh vagrant -c '$RUBY -S bundle exec thin -C /etc/thin/thinkingingraphs.yml start >> /var/www/thinkingingraphs/current/log/production.log 2>&1'
end script
----
I also had to change the capistrano script to call 'thin restart' instead of stopping and starting the upstart script:
_config/deploy.rb_
[source,ruby]
----
namespace :deploy do
task(:start) {}
task(:stop) {}
desc "Restart Application"
task :restart do
run "cd #{current_path} && bundle exec thin restart -C /etc/thin/thinkingingraphs.yml"
end
end
----
Finally I had to make some changes to the nginx config file to send on requests to other thin instances if the first attempt failed (due to it being restarted) using the http://wiki.nginx.org/HttpProxyModule#proxy_next_upstream[proxy_next_upstream] method:
_/etc/nginx/sites-available/thinkingingraphs.conf_
[source,text]
----
upstream thin {
server 127.0.0.1:3000 max_fails=1 fail_timeout=15s;
server 127.0.0.1:3001 max_fails=1 fail_timeout=15s;
server 127.0.0.1:3002 max_fails=1 fail_timeout=15s;
}
server {
listen 80 default;
server_name _;
charset utf-8;
rewrite ^\/status(.*)$ $1 last;
gzip on;
gzip_disable "MSIE [1-6]\.(?!.*SV1)";
gzip_types text/plain application/xml text/xml text/css application/x-javascript application/xml+rss text/javascript application/json;
gzip_vary on;
access_log /var/www/thinkingingraphs/shared/log/nginx_access.log;
error_log /var/www/thinkingingraphs/shared/log/nginx_error.log;
root /var/www/thinkingingraphs/current/public;
location / {
proxy_pass http://thin;
proxy_next_upstream error timeout http_502 http_503;
}
error_page 404 /404.html;
error_page 500 502 503 504 /500.html;
}
----
We've also made a change to our upstream definition to proxy requests to one of the thin instances which will be running.
When I deploy the application now there is no downtime:
[source,bash]
----
$ bundle exec cap deploy
----
[source,bash]
----
$ while true; do curl -w %{http_code}:%{time_total} http://localhost/ -o /dev/null -s; printf "\n"; sleep 0.5; done
200:0.094
200:0.095
200:0.082
200:0.102
200:0.080
200:0.081
----
The only problem is that upstart now seems to have lost a handle on the thin processes and from what I can tell there isn't a master process which upstart could get a handle on so I'm not sure how to wire this up.
Any ideas welcome! | null | null | [
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] | no-downtime-deploy-with-capistrano-thin-and-nginx | https://markhneedham.com/blog/2013/04/23/no-downtime-deploy-with-capistrano-thin-and-nginx |
false | 2013-04-13 11:17:37 | Capistrano: Deploying to a Vagrant VM | [
"devops-2"
] | [
"DevOps"
] | I've been working on a tutorial around thinking through problems in graphs using my football graph and I wanted to deploy it on a local http://www.vagrantup.com/[vagrant] VM as a stepping stone to deploying it in a live environment.
My Vagrant file for the VM looks like this:
[source,text]
----
# -*- mode: ruby -*-
# vi: set ft=ruby :
Vagrant::Config.run do |config|
config.vm.box = "precise64"
config.vm.define :neo01 do |neo|
neo.vm.network :hostonly, "192.168.33.101"
neo.vm.host_name = 'neo01.local'
neo.vm.forward_port 7474, 57474
neo.vm.forward_port 80, 50080
end
config.vm.box_url = "http://files.vagrantup.com/precise64.box"
config.vm.provision :puppet do |puppet|
puppet.manifests_path = "puppet/manifests"
puppet.manifest_file = "site.pp"
puppet.module_path = "puppet/modules"
end
end
----
I'm port forwarding ports 80 and 7474 to 50080 and 57474 respectively so that I can access the web app and neo4j console from my browser.
There is a bunch of puppet code to configure the machine in the location specified.
Since the web app is written in Ruby/Sinatra the easiest deployment tool to use is probably capistrano and I found the http://guides.beanstalkapp.com/deployments/deploy-with-capistrano.html[tutorial on the beanstalk website] really helpful for getting me setup.
My +++<cite>+++config/deploy.rb+++</cite>+++ file which I've got Capistrano setup to read looks like this:
[source,text]
----
require 'capistrano/ext/multistage'
set :application, "thinkingingraphs"
set :scm, :git
set :repository, "[email protected]:markhneedham/thinkingingraphs.git"
set :scm_passphrase, ""
set :ssh_options, {:forward_agent => true}
set :default_run_options, {:pty => true}
set :stages, ["vagrant"]
set :default_stage, "vagrant"
----
In my +++<cite>+++config/deploy/vagrant.rb+++</cite>+++ file I have the following:
[source,text]
----
set :user, "vagrant"
server "192.168.33.101", :app, :web, :db, :primary => true
set :deploy_to, "/var/www/thinkingingraphs"
----
So that IP there is the same one that I assigned in Vagrantfile. If you didn't do that then you'd need to use 'vagrant ssh' to go onto the VM and then 'ifconfig' to grab the IP instead.
I figured there was probably another step required to tell Capistrano where it should get the vagrant public key from but I thought I'd try and deploy anyway just to see what would happen.
[source,text]
----
$ bundle exec cap deploy
----
It asked me to enter the vagrant user's password which is 'vagrant' by default and I eventually found http://stackoverflow.com/questions/10353530/is-there-a-way-to-deploy-into-a-vagrant-vm-using-capistrano[a post on StackOverflow] which suggested changing the 'ssh_options' to the following:
[source,text]
----
set :ssh_options, {:forward_agent => true, keys: ['~/.vagrant.d/insecure_private_key']}
----
And with that the deployment worked flawlessly! Happy days. | null | null | [
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] | capistrano-deploying-to-a-vagrant-vm | https://markhneedham.com/blog/2013/04/13/capistrano-deploying-to-a-vagrant-vm |
false | 2013-04-14 18:18:32 | Capistrano: Host key verification failed. ** [err] fatal: The remote end hung up unexpectedly | [
"capistrano"
] | [
"DevOps"
] | As I mentioned in http://www.markhneedham.com/blog/2013/04/13/capistrano-deploying-to-a-vagrant-vm/[my previous post] I've been deploying a web application to a vagrant VM using Capistrano and my initial configuration was like so:
[source,text]
----
require 'capistrano/ext/multistage'
set :application, "thinkingingraphs"
set :scm, :git
set :repository, "[email protected]:markhneedham/thinkingingraphs.git"
set :scm_passphrase, ""
set :ssh_options, {:forward_agent => true, :paranoid => false, keys: ['~/.vagrant.d/insecure_private_key']}
set :stages, ["vagrant"]
set :default_stage, "vagrant"
set :user, "vagrant"
server "192.168.33.101", :app, :web, :db, :primary => true
set :deploy_to, "/var/www/thinkingingraphs"
----
When I ran 'cap deploy' I ended up with the following error:
[source,text]
----
* executing "git clone -q [email protected]:markhneedham/thinkingingraphs.git /var/www/thinkingingraphs/releases/20130414171523 && cd /var/www/thinkingingraphs/releases/20130414171523 && git checkout -q -b deploy 6dcbf945ef5b8a5d5d39784800f4a6b7731c7d8a && (echo 6dcbf945ef5b8a5d5d39784800f4a6b7731c7d8a > /var/www/thinkingingraphs/releases/20130414171523/REVISION)"
servers: ["192.168.33.101"]
[192.168.33.101] executing command
** [192.168.33.101 :: err] Host key verification failed.
** [192.168.33.101 :: err] fatal: The remote end hung up unexpectedly
----
As far as I can tell the reason for this is that bitbucket hasn't been verified as a host by the VM and therefore the equivalent of the following happens when it tries to clone the repository:
[source,text]
----
$ ssh [email protected]
The authenticity of host 'bitbucket.org (207.223.240.182)' can't be established.
RSA key fingerprint is 97:8c:1b:f2:6f:14:6b:5c:3b:ec:aa:46:46:74:7c:40.
Are you sure you want to continue connecting (yes/no)?
----
Since we aren't answering 'yes' to that question and bitbucket isn't in our +++<cite>+++~/.ssh/known_hosts+++</cite>+++ file it's not able to continue.
One solution to this problem is to run the ssh command above and then answer 'yes' to the question which will add bitbucket to our known_hosts file and we can then run 'cap deploy' again.
It's a bit annoying to have that manual step though so another way is to set cap to use pty by putting the following line in our config file:
[source,text]
----
set :default_run_options, {:pty => true}
----
Now when we run 'cap deploy' we can see that bitbucket automatically gets added to the known_hosts file:
[source,text]
----
servers: ["192.168.33.101"]
[192.168.33.101] executing command
** [192.168.33.101 :: out] The authenticity of host 'bitbucket.org (207.223.240.181)' can't be established.
** RSA key fingerprint is 97:8c:1b:f2:6f:14:6b:5c:3b:ec:aa:46:46:74:7c:40.
** Are you sure you want to continue connecting (yes/no)?
** [192.168.33.101 :: out] yes
** [192.168.33.101 :: out] Warning: Permanently added 'bitbucket.org,207.223.240.181' (RSA) to the list of known hosts.
----
As far as I can tell this runs the command using a pseudo terminal and then automatically adds bitbucket into the known_hosts file but I'm not entirely sure how that works. My google skillz have also failed me so if anyone can explain it to me that'd be cool | null | null | [
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] | capistrano-host-key-verification-failed-err-fatal-the-remote-end-hung-up-unexpectedly | https://markhneedham.com/blog/2013/04/14/capistrano-host-key-verification-failed-err-fatal-the-remote-end-hung-up-unexpectedly |
false | 2013-05-05 00:03:17 | Sublime: Overriding default file type/Assigning specific files to a file type | [
"sublime"
] | [
"Software Development"
] | I've been using link:[Sublime] a bit recently and one thing I wanted to do was put link:[neo4j cypher] queries into files with arbitrary extensions and have them recognised as cypher files every time I open them.
I'm using the link:[cypher Sublime plugin] to get the syntax highlighting but since I've got my cypher in a .haml file it only remembers that it should have cypher highlighting as long as the file is open.
As soon as I close and then re-open the file it goes back to being highlighted as HAML.
I initially thought that the way around this would be to write a plugin which kept track of files that you'd manually assigned a syntax to but then I came across the https://github.com/facelessuser/ApplySyntax[ApplySyntax] plugin which seems even better.
ApplySyntax allows you to assign syntaxes to files based on regular expression matching on the file name or on the first line of the file.
At the moment, the easiest way to detect that a file is a cypher query is that the first line will begin with 'START' so I wrote the following in my user settings file:
_~/Library/Application Support/Sublime Text 2/Packages/User/ApplySyntax.sublime-settings_
[source,json]
----
{
"reraise_exceptions": false,
"new_file_syntax": false,
"syntaxes": [
{
"name": "Cypher",
"rules": [
{"first_line": "^START"}
]
}
]
}
----
ApplySyntax is a pretty neat plugin, worth having a look if you have this problem to solve! | null | null | [
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] | sublime-overriding-default-file-typeassigning-specific-files-to-a-file-type | https://markhneedham.com/blog/2013/05/05/sublime-overriding-default-file-typeassigning-specific-files-to-a-file-type |
false | 2013-05-18 20:18:50 | A/B Testing: User Experience vs Conversion | [
"absplittesting"
] | [
"Software Development"
] | I've written http://www.markhneedham.com/blog/2013/01/27/ab-testing-thoughts-so-far/[a] http://www.markhneedham.com/blog/2013/04/28/ab-testing-reporting/[couple] of posts over the last few months about my experiences with A/B testing and one conversation we often used to have was around user experience vs conversion rate.
Once you start running an A/B test it encourages you to focus more on the conversion rate of users in different parts of the flow and *your inclination is to make changes that increase that conversion rate*.
Another one of our drivers is to provide the best user experience that we can to our customers and since sometimes this means that the best thing for them is not to switch it seems that these two must be in conflict.
I found it particularly interesting seeing how the conversion rate could be *impacted by the way that information was displayed to a user*.
This was an idea that I first came across when reading about http://kylerush.net/blog/optimization-at-the-obama-campaign-ab-testing/[how the Obama campaign used A/B testing] where they noticed big changes in conversion rates by making small tweaks to sentences and imagery.
Our goal from a user experience perspective was to put all the information in front of the user so that they could make an informed choice about what to do.
Initially we made the negative features of the plans very prominent and had them in a large font which led to a drop in conversion.
We assumed that people were now giving more importance to the negative features than was warranted e.g. some plans had a cancellation fee but it typically only accounted for 5% of the saving they'd make by switching to the plan.
When the product is a bit more complicated we could argue that we improve the user experience by helping the user to make an appropriate choice.
On a website the way that we do this is by how we display information by changing the font size, font weight, positioning and a variety of other things.
It's an interesting balance to find between the two drivers but if we veer towards conversion at all costs then although we'll get a higher conversion rate in the long term we'll have some frustrated customers who won't use our website again.
If we look at it that way then the two drivers don't seem so opposed to each other. | null | null | [
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] | ab-testing-user-experience-vs-conversion | https://markhneedham.com/blog/2013/05/18/ab-testing-user-experience-vs-conversion |
false | 2013-05-27 13:13:49 | A/B Testing: Being pragmatic with statistical significance | [
"absplittesting"
] | [
"Software Development"
] | One of the first things that we did before starting any of the http://www.markhneedham.com/blog/tag/absplittesting/[A/B tests] that I've previously written about was to work out how many users we needed to go through before we could be sure that the results we saw were statistically significant.
We used the http://www.r-tutor.com/elementary-statistics/hypothesis-testing/two-tailed-test-population-proportion[prop.test] function from R to do this and based on our traffic at the time worked out that we'd need to run a test for 6 weeks to achieve statistical significance.
That means that we could manage a maximum of 8 tests a year i.e. 8 changes a year which isn't really feasible for a website which needs to evolve quickly to remain competitive.
We therefore had two ways that we could use A/B tests and make changes more frequently:
. Don't A/B test everything
. Speed up the cycle by:
** iterating within a test i.e. making tweaks/fixing bugs
** ending the test early if control or experiment was consistently better.
== Don't A/B test everything
When we were looking through our product backlog it was interesting to see that there were some features that we agreed would be definite improvements while there were others that we weren't really sure about.
As Nate Silver points out in 'http://www.markhneedham.com/blog/2013/05/14/book-review-the-signal-and-the-noise-nate-silver/[The Signal and the Noise]' whenever we rely on intuition we introduce bias but in this case the people making the call were experts of the domain so it seemed like a reasonable approach.
If we felt that a feature made the user experience more delightful or if it was an integral part of the way we were driving the product then we'd just make the change without running an A/B test.
We still had daily metrics which would highlight if things had been made significantly worse but we didn't track the performance of those features with the same vigour as you would with an A/B test.
== Speed up the cycle
Although it would take 6 weeks to be completely confident in an A/B test we were able to get feedback much more quickly as to which branch (master/experiment) was performing better.
If we noticed a drop off in conversion for a specific browser then we'd http://www.markhneedham.com/blog/2013/04/28/ab-testing-reporting/[make the appropriate bug fixes] while the test was still running.
In an ideal world I suppose we should have started the test from scratch but we didn't do this unless there was something seriously wrong and we had to make major changes.
Instead we started *tracking the daily conversion rate* which allowed us to see whether our bug fixes had made an impact.
Tracking the daily conversion rate also allowed us to see whether one branch was consistently better or not - if we saw that one branch was consistently better over a week or two then we'd be confident that we should end the test.
On the other hand early on in the test cycle we'd see the control converting better on the first day, then the experiment converting better the next day before the control converted better for the next two.
When we observed that behaviour we assumed that random variation was at play and waited a few more days to see if it would settle down before we made a decision.
We were effectively trading off statistical significance against the number of tests that we could run which in this context was a reasonable trade off to make.
== In summary
I'd not done A/B testing before I worked at http://www.uswitch.com/[uSwitch] and I think the approach we took seems reasonable for most web based products.
We want to use data to help us make decisions but we need to leverage our intuition/domain expertise rather than relying completely on the data.
In other words we're more http://blog.iic.uam.es/2010/04/pick-a-number-maybe-data-driven-should-be-data-assisted/[data assisted rather than data driven]. | null | null | [
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] | ab-testing-pragmatica-statistical-significance | https://markhneedham.com/blog/2013/05/27/ab-testing-pragmatica-statistical-significance |
false | 2013-05-27 13:23:36 | Pomodoros: Just start the timer | [
"pomodoro"
] | [
"Software Development"
] | I wrote earlier in the year about http://www.markhneedham.com/blog/2012/01/22/playing-around-with-pomodoros/[my use of pomodoros to track what I'm doing outside of work] and having done this for 6 months I noticed that I'm now procrastinating over picking something off the list to work on.
_(I know...I am awesome!)_
I'm not sure whether this is because I don't have anything really appealing on the list of things to work on or whether having so many things listed (I have 8-10 items) is causing the paralysis.
Either way it's a bit frustrating to achieve nothing in an evening because of something so ridiculous.
To outwit myself I decided to press the 'Start' button on the timer one day without picking something to work on and hoped that this would snap me out of my paralysis.
It's a relatively simple tweak but somehow seeing the time count down has the required effect and I've usually picked what I'm going to do within 30 seconds or so.
So if you are as good at procrastinating as me maybe you can try this too! | null | null | [
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] | pomodoros-just-start-the-timer | https://markhneedham.com/blog/2013/05/27/pomodoros-just-start-the-timer |
false | 2013-05-27 00:11:23 | Polyglot Persistence: Embrace the ETL | [
"software-development"
] | [
"Software Development"
] | Over the past few years I've seen the emergence of http://martinfowler.com/bliki/PolyglotPersistence.html[polyglot persistence] i.e. using different data storage technologies for different data and in most situations we work that out up front.
image::{{<siteurl>}}/uploads/2013/05/etl1.png[Etl1,233]
For example we might use http://www.mongodb.org/[MongoDB] to store data about a customer journey through our website but we might simultaneously write page view data through to something like http://hadoop.apache.org/[Hadoop] or http://aws.amazon.com/redshift/[Redshift]:
This works reasonably well but sometimes it might not be immediately obvious how we want to query our data when we first start collecting it and our storage choice might not be the best for writing these queries.
An interesting thing to think about at this stage is whether it makes sense to add a stage to our data processing pipeline where we write an http://en.wikipedia.org/wiki/Extract,_transform,_load[ETL] job to get it into a more appropriate format:
image::{{<siteurl>}}/uploads/2013/05/etl2.png[Etl2,454]
My initial experience doing this was when I created the http://skillsmatter.com/podcast/home/what-do-you-want-to-know[ThoughtWorks graph] which involved transforming data into a graph so that I could find links between people.
https://twitter.com/A5HOK[Ashok] and I followed a similar approach for a client we went on to work for and it allowed us to find the answers to questions that couldn't be answered when the data was in its original format.
The main down side to this approach is that we now have to keep two data sources in sync but it's interesting to think about whether this trade off is worthwhile if it helps us gain new insights or find the answers to questions more quickly.
I don't have any experience with how this approach plays out over time so I'd be interesting in hearing how people have got on with this approach/if it does or doesn't work. | null | null | [
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] | polyglot-persistence-embrace-the-etl | https://markhneedham.com/blog/2013/05/27/polyglot-persistence-embrace-the-etl |
false | 2013-05-29 11:22:35 | Viewing the contents of an archive | [
"software-development"
] | [
"Software Development"
] | Everyone now and then I want to check the contents of an archive without unpacking it and I tend to use +++<cite>+++http://linux.about.com/od/commands/l/blcmdl1_unzip.htm[unzip]+++</cite>+++ to do so:
[source,bash]
----
$ unzip -l batch-import-jar-with-dependencies.jar | tail -n 10
1645 02-17-13 01:03 org/neo4j/batchimport/StdOutReport.class
3089 02-17-13 01:03 org/neo4j/batchimport/structs/NodeStruct.class
1244 02-17-13 01:03 org/neo4j/batchimport/structs/Property.class
1732 02-17-13 01:03 org/neo4j/batchimport/structs/PropertyHolder.class
1635 02-17-13 01:03 org/neo4j/batchimport/structs/Relationship.class
905 02-17-13 01:03 org/neo4j/batchimport/utils/Chunker.class
1884 02-17-13 01:03 org/neo4j/batchimport/utils/Params.class
4445 02-17-13 01:03 org/neo4j/batchimport/Utils.class
-------- -------
49947859 16447 files
----
It does the job although it does print out some information that we're not really interested in so I was intrigued to see that https://twitter.com/apcj[Alistair] used +++<cite>+++http://linux.about.com/library/cmd/blcmdl1_zipinfo.htm[zipinfo]+++</cite>+++ when he wanted to achieve a similar thing:
[source,bash]
----
$ zipinfo -1 batch-import-jar-with-dependencies.jar | tail -n 10
org/neo4j/batchimport/ParallelImporter.class
org/neo4j/batchimport/Report.class
org/neo4j/batchimport/StdOutReport.class
org/neo4j/batchimport/structs/NodeStruct.class
org/neo4j/batchimport/structs/Property.class
org/neo4j/batchimport/structs/PropertyHolder.class
org/neo4j/batchimport/structs/Relationship.class
org/neo4j/batchimport/utils/Chunker.class
org/neo4j/batchimport/utils/Params.class
org/neo4j/batchimport/Utils.class
----
From a bit of man page reading it sounds like zipinfo is unzip, but with different flags that give an output that's a cross between unzip and ls:
____
The format is a cross between Unix ``ls -l'' and ``unzip -v'' output. See DETAILED DESCRIPTION below. Note that zipinfo is the same program as unzip (under Unix, a link to it); on some systems, however, zipinfo support may have been omitted when unzip was compiled.
____
As long as I remember I'll be using zipinfo from now on! | null | null | [
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] | viewing-the-contents-of-an-archive | https://markhneedham.com/blog/2013/05/29/viewing-the-contents-of-an-archive |
false | 2013-05-17 00:00:16 | neo4j: When the web console returns nothing...use the data browser! | [
"neo4j"
] | [
"neo4j"
] | In my time playing around with http://www.neo4j.org/[neo4j] I've run into a problem a few times where I executed a query using the web console (usually accessible @ http://localhost:7474/webadmin/#/console/) and have got absolutely no response.
I noticed a similar thing today when https://twitter.com/rickardoberg[Rickard] and I were having a look at why a Lucene index query wasn't behaving as we expected.
I setup some data in a neo4j database using https://github.com/maxdemarzi/neography[neography] with the following code:
[source,ruby]
----
require 'neography'
@neo = Neography::Rest.new
@neo.create_node_index("Id_Index", "exact", "lucene")
node1 = @neo.create_node("Hour" => 1, "name" => "Max")
node2 = @neo.create_node("Hour" => 2, "name" => "Mark")
node3 = @neo.create_node("Hour" => 3, "name" => "Rickard")
@neo.add_node_to_index("Id_Index", "Hour", 1, node1)
@neo.add_node_to_index("Id_Index", "Hour", 2, node2)
@neo.add_node_to_index("Id_Index", "Hour", 3, node3)
----
I then ran the following query which I was expecting to return all the nodes:
[source,cypher]
----
start hour=node:Id_Index("Hour:[00 TO 02] or Hour:[03 TO 05]") RETURN hour
----
Instead it returned nothing and I couldn't see anything being logged either.
Rickard pointed out was because the exception is only returned to the API caller and that it would be better to run the query from the Data Browser which is typically accessible from http://localhost:7474/webadmin/#/data/search/
If we run the query from there then we can see what's going wrong:
[source,text]
----
BadInputException
StackTrace:
org.neo4j.server.rest.repr.RepresentationExceptionHandlingIterable.exceptionOnHasNext(RepresentationExceptionHandlingIterable.java:50)
org.neo4j.helpers.collection.ExceptionHandlingIterable$1.hasNext(ExceptionHandlingIterable.java:60)
org.neo4j.helpers.collection.IteratorWrapper.hasNext(IteratorWrapper.java:42)
org.neo4j.server.rest.repr.ListRepresentation.serialize(ListRepresentation.java:58)
org.neo4j.server.rest.repr.Serializer.serialize(Serializer.java:75)
org.neo4j.server.rest.repr.MappingSerializer.putList(MappingSerializer.java:61)
org.neo4j.server.rest.repr.CypherResultRepresentation.serialize(CypherResultRepresentation.java:57)
org.neo4j.server.rest.repr.MappingRepresentation.serialize(MappingRepresentation.java:42)
org.neo4j.server.rest.repr.OutputFormat.assemble(OutputFormat.java:179)
org.neo4j.server.rest.repr.OutputFormat.formatRepresentation(OutputFormat.java:131)
org.neo4j.server.rest.repr.OutputFormat.response(OutputFormat.java:117)
org.neo4j.server.rest.repr.OutputFormat.ok(OutputFormat.java:55)
org.neo4j.server.rest.web.CypherService.cypher(CypherService.java:94)
java.lang.reflect.Method.invoke(Method.java:597)
----
There seemed to be some strangeness going on with how Lucene handles the query when a default search field isn't provided but we noticed that it behaved as expected if we didn't use an OR since Lucene has an implicit OR between statements anyway.
[source,cypher]
----
start hour=node:Id_Index("Hour:[00 TO 02] Hour:[03 TO 05]") RETURN hour
----
Either way, the lesson for me was if the console isn't giving a result run the query in the data browser to work out what's going wrong! | null | null | [
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] | neo4j-when-the-web-console-returns-nothinguse-the-data-browser | https://markhneedham.com/blog/2013/05/17/neo4j-when-the-web-console-returns-nothinguse-the-data-browser |
false | 2013-05-19 22:44:40 | Ruby/Python: Constructing a taxonomy from an array using zip | [
"ruby",
"python"
] | [
"Ruby",
"Python"
] | As I http://www.markhneedham.com/blog/2013/05/19/neo4jcypher-keep-longest-path-when-finding-taxonomy/[mentioned in my previous blog post] I've been hacking on a product taxonomy and I wanted to create a 'CHILD' relationship between a collection of categories.
For example, I had the following array and I wanted to transform it into an array of 'SubCategory, Category' pairs:
[source,python]
----
taxonomy = ["Cat", "SubCat", "SubSubCat"]
# I wanted this to become [("Cat", "SubCat"), ("SubCat", "SubSubCat")
----
In order to do this we need to zip the first 2 items with the last which I found reasonably easy to do using Python:
[source,python]
----
>>> zip(taxonomy[:-1], taxonomy[1:])
[('Cat', 'SubCat'), ('SubCat', 'SubSubCat')]
----
Here we using the http://stackoverflow.com/questions/509211/the-python-slice-notation[python array slicing notation] to get all but the last item of 'taxonomy' and then all but the first item of 'taxonomy' and zip them together.
I wanted to achieve that effect in Ruby though because my import job was written in that!
We can't achieve the open ended slicing as far as I can tell so the following gives us an error:
[source,ruby]
----
> taxonomy[..-1]
SyntaxError: (irb):10: syntax error, unexpected tDOT2, expecting ']'
taxonomy[..-1]
^
from /Users/markhneedham/.rbenv/versions/1.9.3-p327/bin/irb:12:in `<main>'
----
The way negative indexing works is a bit different so to remove the last item of the array we use '-2' rather than '-1':
[source,ruby]
----
> taxonomy[0..-2].zip(taxonomy[1..-1])
=> [["Cat", "SubCat"], ["SubCat", "SubSubCat"]]
---- | null | null | [
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] | rubypython-constructing-a-taxonomy-from-an-array-using-zip | https://markhneedham.com/blog/2013/05/19/rubypython-constructing-a-taxonomy-from-an-array-using-zip |
false | 2013-05-19 22:15:06 | neo4j/cypher: Keep longest path when finding taxonomy | [
"neo4j",
"cypher"
] | [
"neo4j"
] | I've been playing around with modelling a product taxonomy and one thing that I wanted to do was find out the full path where a product sits under the tree.
I created a http://console.neo4j.org/?id=62rmy2[simple data set] to show the problem:
[source,cypher]
----
CREATE (cat { name: "Cat" })
CREATE (subcat1 { name: "SubCat1" })
CREATE (subcat2 { name: "SubCat2" })
CREATE (subsubcat1 { name: "SubSubCat1" })
CREATE (product1 { name: "Product1" })
CREATE (cat)-[:CHILD]-subcat1-[:CHILD]-subsubcat1
CREATE (product1)-[:HAS_CATEGORY]-(subsubcat1)
----
I wanted to write a query which would return 'product1' and the tree 'Cat \-> SubCat1 \-> SubSubCat1' and initially wrote the following query:
[source,cypher]
----
START product=node:node_auto_index(name="Product1")
MATCH product-[:HAS_CATEGORY]-category, taxonomy=category<-[:CHILD*1..]-parent
RETURN product, EXTRACT(n IN NODES(taxonomy): n.name)
----
which returns:
[source,text]
----
==> +--------------------------------------------------------------------+
==> | product | EXTRACT(n IN NODES(taxonomy): n.name) |
==> +--------------------------------------------------------------------+
==> | Node[888]{name:"Product1"} | ["SubSubCat1","SubCat1"] |
==> | Node[888]{name:"Product1"} | ["SubSubCat1","SubCat1","Cat"] |
==> +--------------------------------------------------------------------+
==> 2 rows
----
I didn't want to return the first row since that isn't the full tree and https://twitter.com/andres_taylor[Andres] suggested that looking for nodes which didn't have any incoming children would help me do that:
[source,cypher]
----
START product=node:node_auto_index(name="Product1")
MATCH product-[:HAS_CATEGORY]-category,
taxonomy=category<-[:CHILD*1..]-parent
WHERE NOT parent<-[:CHILD]-()
RETURN product, EXTRACT(n IN NODES(taxonomy): n.name)
----
[source,text]
----
==> +--------------------------------------------------------------------+
==> | product | EXTRACT(n IN NODES(taxonomy): n.name) |
==> +--------------------------------------------------------------------+
==> | Node[888]{name:"Product1"} | ["SubSubCat1","SubCat1","Cat"] |
==> +--------------------------------------------------------------------+
==> 1 row
----
If we want to reverse the taxonomy so it's in the right order we can follow http://stackoverflow.com/questions/13024098/how-to-get-a-null-value-when-using-the-head-function-with-an-empty-list[Wes Freeman's advice from the following Stack Overflow thread]:
[source,cypher]
----
START product=node:node_auto_index(name="Product1")
MATCH product-[:HAS_CATEGORY]-category, taxonomy=category<-[:CHILD*1..]-parent
WHERE NOT parent<-[:CHILD]-()
RETURN product,
REDUCE(acc=[], cat IN EXTRACT(n IN NODES(taxonomy): n.name): cat + acc) AS taxonomy
----
[source,text]
----
==> +-------------------------------------------------------------+
==> | product | taxonomy |
==> +-------------------------------------------------------------+
==> | Node[888]{name:"Product1"} | ["Cat","SubCat1","SubSubCat1"] |
==> +-------------------------------------------------------------+
==> 1 row
---- | null | null | [
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] | neo4jcypher-keep-longest-path-when-finding-taxonomy | https://markhneedham.com/blog/2013/05/19/neo4jcypher-keep-longest-path-when-finding-taxonomy |
false | 2013-05-19 21:44:03 | Unix: Working with parts of large files | [
"unix"
] | [
"Shell Scripting"
] | https://twitter.com/digitalstain[Chris] and I were looking at the neo4j log files of a client earlier in the week and wanted to do some processing of the file so we could ask the client to send us some further information.
The log file was over 10,000 lines long but the bit of the file we were interesting in was only a few hundred lines.
I usually use Vim and the ':set number' when I want to refer to line numbers in a file but Chris showed me that we can achieve the same thing with e.g. 'less -N data/log/neo4j.0.0.log'.
We can then operate on say lines 10-100 by passing the '-n' flag to sed:
____
-n By default, each line of input is echoed to the standard output after all of the commands have been applied to it. The -n option suppresses this behavior.
____
[source,bash]
----
$ sed -n '10,15p' data/log/neo4j.0.0.log
INFO: Enabling HTTPS on port [7473]
May 19, 2013 11:11:52 AM org.neo4j.server.logging.Logger log
INFO: No SSL certificate found, generating a self-signed certificate..
May 19, 2013 11:11:53 AM org.neo4j.server.logging.Logger log
INFO: Mounted discovery module at [/]
May 19, 2013 11:11:53 AM org.neo4j.server.logging.Logger log
----
We then used a combination of grep, awk and sort to work out which log files we needed. | null | null | [
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] | unix-working-with-parts-of-large-files | https://markhneedham.com/blog/2013/05/19/unix-working-with-parts-of-large-files |
false | 2013-05-26 23:29:12 | Polyglot Persistence: The 'boring' relational option | [
"software-development"
] | [
"Software Development"
] | I was chatting with https://twitter.com/gurrie09[Brian Blignaut] last week after the http://nitetech230513.eventbrite.co.uk/?mkt_tok=3RkMMJWWfF9wsRoksq3BZKXonjHpfsX76uUtUaKg38431UFwdcjKPmjr1YAAT8R0aPyQAgobGp5I5FEPTbPYW69ut6ULXg%3D%3D[Equal Experts NoSQL event] and he made an interesting observation that in this age of http://martinfowler.com/bliki/PolyglotPersistence.html[Polyglot Persistence] we often rule out the relational database.
I think it's definitely better that we now have many different options for where we store our data - be it as key/value pairs, documents or as a network/graph.
Having these options forces us to think more about how we're going to read/write data in our application whereas previously our effort was focused around which tables we were going to pull out.
Having said that, I realised I'd fallen into the trap that Brian was referring to when thinking through how we could model the energy plans that users were selecting from a results table.
We wanted to run aggregate queries over the data to work out the most popular plans and suppliers on different days and then across different customer segments.
We represented each user selection as an event, stored as a document in http://www.mongodb.org/[MongoDB] which worked fine to start with but queries started to become much slower as the number of documents approached 500,000 or so.
I started thinking about whether we'd be better off storing the data in a different store which was better optimised for the types of queries that we wanted to run.
My initial thought was to use one of the flashier 'NoSQL' databases but https://twitter.com/a5HOK[Ashok] pointed out that the use case - slicing/dicing data at a scale (< 1 m rows) where everything would fit on disc - was perfect for something like http://www.postgresql.org/[PostgreSQL].
I realised that he was absolutely right and I think it's important to remember that in future - when we talk about *choosing the appropriate data store for our problem that doesn't mean we have to rule out the relational database* even though it's probably more fun to use another store. | null | null | [
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] | polyglot-persistence-the-boring-relational-option | https://markhneedham.com/blog/2013/05/26/polyglot-persistence-the-boring-relational-option |
false | 2013-05-31 00:37:57 | neo4j/cypher: 400 response - Paths can't be created inside of foreach | [
"neo4j",
"cypher"
] | [
"neo4j"
] | In the neo4j 1.9 milestone releases if we wanted to create multiple relationships from a node we could use the following cypher syntax:
[source,ruby]
----
require 'neography'
neo = Neography::Rest.new
neo.execute_query("create (me {name: 'Mark'})")
query = " START n=node:node_auto_index(name={name})"
query << " FOREACH (friend in {friends} : CREATE f=friend, n-[:FRIEND]->f)"
neo.execute_query(query, {"name" => "Mark",
"friends" => [{ "name" => "Will"}, {"name" => "Paul"}]})
----
To check that the 'FRIEND' relationships have been created we'd write the following query:
[source,cypher]
----
START p = node:node_auto_index(name="Mark")
MATCH p-[:FRIEND]-f
RETURN f
----
[source,text]
----
==> +----------------------+
==> | f |
==> +----------------------+
==> | Node[2]{name:"Will"} |
==> | Node[3]{name:"Paul"} |
==> +----------------------+
==> 2 rows
==> 37 ms
----
In between the M04 and RC2 releases there was a change in the cypher parser which meant that this approach doesn't work anymore:
[source,text]
----
/Users/markhneedham/.rbenv/versions/1.9.3-p327/lib/ruby/gems/1.9.1/gems/neography-1.0.10/lib/neography/connection.rb:168:in `handle_4xx_500_response':
Paths can't be created inside of foreach (Neography::SyntaxException)
from /Users/markhneedham/.rbenv/versions/1.9.3-p327/lib/ruby/gems/1.9.1/gems/neography-1.0.10/lib/neography/connection.rb:143:in `return_result'
from /Users/markhneedham/.rbenv/versions/1.9.3-p327/lib/ruby/gems/1.9.1/gems/neography-1.0.10/lib/neography/connection.rb:126:in `evaluate_response'
from /Users/markhneedham/.rbenv/versions/1.9.3-p327/lib/ruby/gems/1.9.1/gems/neography-1.0.10/lib/neography/connection.rb:45:in `post'
from /Users/markhneedham/.rbenv/versions/1.9.3-p327/lib/ruby/gems/1.9.1/gems/neography-1.0.10/lib/neography/rest/cypher.rb:19:in `query'
from /Users/markhneedham/.rbenv/versions/1.9.3-p327/lib/ruby/gems/1.9.1/gems/neography-1.0.10/lib/neography/rest.rb:335:in `execute_query'
from create.rb:12:in `<main>'
----
Instead we can just use +++<cite>+++http://docs.neo4j.org/chunked/stable/query-create.html#create-create-multiple-nodes-from-map[CREATE]+++</cite>+++ to achieve the same thing:
[source,ruby]
----
require 'neography'
neo = Neography::Rest.new
neo.execute_query("create (me {name: 'Mark'})")
query = " START n = node:node_auto_index(name={name})"
query << " CREATE friend = {friends} "
query << " CREATE n-[:FRIEND]->friend"
neo.execute_query(query, {"name" => "Mark",
"friends" => [{ "name" => "Will"}, { "name" => "Paul" }]})
---- | null | null | [
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] | neo4jcypher-400-response-paths-cant-be-created-inside-of-foreach | https://markhneedham.com/blog/2013/05/31/neo4jcypher-400-response-paths-cant-be-created-inside-of-foreach |
false | 2013-05-23 22:43:53 | Feedback: Reacting immediately | [
"feedback"
] | [
"Feedback"
] | I was recently reading http://www.telegraph.co.uk/sport/football/teams/manchester-united/10066147/Sir-Alex-Fergusons-unparalleled-experience-of-success-and-management-must-not-be-wasted-by-football.htm[an article written by Henry Winter] where he mentioned some of the ideas that Sir ALex Ferguson has been covering in some interviews he's been doing at Harvard and one bit stood out for me:
____
In a series of interviews in Harvard, Ferguson debated dealing with "`fragile`" egos in the dressing room, the power of the two simple words "`well done`" in motivating individuals and *the importance of criticising players`' mistakes immediately after the match and then moving on*.
____
I've written previously about http://www.markhneedham.com/blog/category/feedback/[giving feedback] and I think we don't place enough emphasis on how useful it is to give any feedback close to the time of the event.
My former colleague http://twitter.com/ryangreenhall[Ryan Greenhall] was particularly good at noticing situations where timely feedback was required and I hadn't realised how much of an impact doing that had.
As we as the immediate benefit of the event being fresh in people's minds when they're talking about it we get an added benefit as well.
The more time that we leave between an event happening and reflecting on it, the more time there is for us to invent a story about what happened that helps to explain it.
In the story we create in our mind we'll often associate negative traits/motives to the other person and exaggerate the importance of what happened until it doesn't resemble what actually happened any more.
I tended to veer away from addressing things like this immediately because I thought it seemed like overreacting to believe that it was worth having a conversation but it now seems worthwhile.
In the worst case you just discover there was nothing to reflect on and in the best case you resolve any issues at the source. | null | null | [
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] | feedback-reacting-immediately | https://markhneedham.com/blog/2013/05/23/feedback-reacting-immediately |
false | 2013-05-14 00:16:56 | Book Review: The Signal and the Noise - Nate Silver | [
"books",
"book-review",
"statistics"
] | [
"Books"
] | http://en.wikipedia.org/wiki/Nate_Silver[Nate Silver] is famous for having correctly predicted the winner of all 50 states in the 2012 United States elections and https://twitter.com/siddharthdawara[Sid] recommended http://www.amazon.co.uk/The-Signal-Noise-Science-Prediction/dp/0141975652/ref=sr_1_1?ie=UTF8&qid=1368486945&sr=8-1&keywords=nate+silver[his book] so I could learn more about statistics for the http://www.markhneedham.com/blog/2013/04/28/ab-testing-reporting/[A/B tests] that we were running.
I thought the book was a really good introduction to applied statistics and by using real life examples which most people would be able to relate to it makes a potentially dull subject interesting.
Reasonably early on the author points out that there's a difference between making a prediction and making a forecast:
* *Prediction* - a definitive and specific statement about when and where something will happen e.g. a major earthquake will hit Kyoto, Japan, on June 28.
* *Forecast* - a probabilistic statement over a longer time scale e.g. there is a 60% chance of an earthquake in Southern California over the next 30 years. </ul> The book mainly focuses on the latter. </p>
+
We then move onto quite an interesting section about *over fitting which is where we mistake noise for signal in our data*.
+
I first came across this term when https://twitter.com/jennifersmithco[Jen] and I were working through one of the http://www.markhneedham.com/blog/tag/kaggle/[Kaggle] problems and were using a http://www.markhneedham.com/blog/2012/10/27/kaggle-digit-recognizer-mahout-random-forest-attempt/[random forest] of deliberately over fitted Decision Trees to do digit recognition.
+
It's not a problem when we combine lots of decision trees together and use a majority wins algorithm to make our prediction but if we use just one of them its predictions on any new data will be completely wrong.
+
Later on in the book he points out that a lot of conspiracy theories come *when we look at data retrospectively* and can easily detect signal from noise in data when at the time it was much more difficult.
+
He also points out that sometimes there isn't actually any signal, it's all noise, and we can fall into the trap of looking for something that isn't there. I think this 'noise' is what we'd refer to as random variation in the context of an http://www.markhneedham.com/blog/2013/01/27/ab-testing-thoughts-so-far/[A/B test].
+
Silver also encourages us to make sure that we understand the theory behind any inference we make:
+
____
Statistical inferences are much stronger when backed up by theory or at least some deeper thinking about their root causes.
____
+
When we were running A/B tests Sid encouraged people to *think whether a theory about why conversion had changed made logical sense* before assuming it was true which I think covers similar ground.
+
A big chunk of the book covers http://en.wikipedia.org/wiki/Bayes'_theorem[Bayes' theorem] and how often when we're making forecasts we have prior beliefs which it forces us to make explicit.
+
For example there is a section which talks about the probability a lady is being cheated on given that she's found some underwear that she doesn't recognise in her house.
+
In order to work out the probability she's being cheated on we need to know the probability that she was being cheated on before she found the underwear. Silver suggests that since 4% of married partners cheat on their spouses that would be a good number to use.
+
He then goes on to show multiple other problems throughout the book that we can apply Bayes' theorem to.
+
Some other interesting things I picked up are that *if we're good at forecasting then being given more information should make our forecast better* and that *when we don't have any special information we're better off following the opinion of the crowd*.
+
image::{{<siteurl>}}/uploads/2013/05/IMG_20130514_011256.jpg[IMG 20130514 011256,400]
+
Silver also showed a clever trick for inferring data points on a data set which follows a power law i.e. the long tail distribution where there are very few massive events but lots of really small ones.
+
We have a power law distribution when modelling the number of terrorists attacks vs number of fatalities but if we *change both scales to be logarithmic* we can come up with a probability of how likely more deadly attacks are.
+
There is then some discussion of how we can make changes in the way that we treat terrorism to try and impact the shape of the chart e.g. in Israel Silver suggests that they really want to avoid a very deadly attack but at the expense of there being more smaller attacks.
+
A lot of the book is spent discussing weather/earthquake forecasting which is very interesting to read about but I couldn't quite see a link back to the software context.
+
Overall though I found it an interesting read although there are probably a few places that you can skim over the detail and still get the gist of what he's saying. | null | null | [
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] | book-review-the-signal-and-the-noise-nate-silver | https://markhneedham.com/blog/2013/05/14/book-review-the-signal-and-the-noise-nate-silver |
false | 2013-05-25 12:21:55 | neo4j/cypher: Properties or relationships? It's easy to switch | [
"neo4j",
"cypher"
] | [
"neo4j"
] | I've written previously about how I've http://www.markhneedham.com/blog/2013/03/06/neo4j-make-properties-relationships/[converted properties on nodes into relationships] and over the past week there was an https://groups.google.com/forum/?fromgroups#!topic/neo4j/Uk5KV9kfFzk[interesting discussion on the neo4j mailing list about where each is appropriate].
https://twitter.com/jimwebber[Jim] gives quite a neat summary of the difference between the two on the thread:
____
Properties are the data that an entity like a node [or relationship] holds. Relationships simply form the semantic glue (type, direction, cardinality) between nodes.
____
To add to that, if you find yourself using a property to help narrow down a traversal then it's worth checking whether you'd be better off modelling it as a relationship.
The neat thing is that it's reasonably easy to change your mind at a later stage and convert a property into a relationship or vice versa.
For example let's say we're modelling some products for a shop. We might start with the following cypher code to create them:
[source,cypher]
----
CREATE (frenchConnection { name: "French Connection" })
CREATE (dress1 { name: "Halter Dress", colour: "Blue"})-[:BRAND]-(frenchConnection)
CREATE (dress2 { name: "Another Dress", colour: "Yellow"})-[:BRAND]-(frenchConnection)
CREATE (dress3 { name: "Different Dress", colour: "Blue"})-[:BRAND]-(frenchConnection)
RETURN dress1, dress2, dress3
----
[source,text]
----
==> +--------------------------------------------------------------------------------------------------------------------------------------------+
==> | dress1 | dress2 | dress3 |
==> +--------------------------------------------------------------------------------------------------------------------------------------------+
==> | Node[2]{name:"Halter Dress",colour:"Blue"} | Node[3]{name:"Another Dress",colour:"Yellow"} | Node[4]{name:"Different Dress",colour:"Blue"} |
==> +--------------------------------------------------------------------------------------------------------------------------------------------+
==> 1 row
==> Nodes created: 4
==> Relationships created: 3
==> Properties set: 7
==> 179 ms
----
If we want to find all the French connection clothing which is blue we could write the following query:
[source,cypher]
----
START brand = node:node_auto_index(name="French Connection")
MATCH brand<-[:BRAND]-product
WHERE product.colour = "Blue"
RETURN brand, product
----
[source,text]
----
==> +-----------------------------------------------------------------------------------+
==> | brand | product |
==> +-----------------------------------------------------------------------------------+
==> | Node[1]{name:"French Connection"} | Node[2]{name:"Halter Dress",colour:"Blue"} |
==> | Node[1]{name:"French Connection"} | Node[3]{name:"Another Dress",colour:"Yellow"} |
==> | Node[1]{name:"French Connection"} | Node[4]{name:"Different Dress",colour:"Blue"} |
==> +-----------------------------------------------------------------------------------+
==> 3 rows
==> 69 ms
----
Since our query doesn't return many records it's not a problem that we have to look up the 'colour' property for every product but with a bigger data set the query would slow down considerably. We might therefore decide to create a 'COLOUR' relationship.
One way to do that would be by running the same cypher query as before except we also create a relationship to a colour node:
[source,cypher]
----
CREATE (blue { name: "Blue"})
----
[source,cypher]
----
START brand = node:node_auto_index(name="French Connection"),
blue = node:node_auto_index(name="Blue")
MATCH brand<-[:BRAND]-product
WHERE product.colour = "Blue"
CREATE UNIQUE product-[:COLOUR]->blue
RETURN brand, product
----
Our query to find the blue French Connection products would now look like this:
[source,cypher]
----
START brand = node:node_auto_index(name="French Connection"),
blue = node:node_auto_index(name="Blue")
MATCH brand<-[:BRAND]-product-[:COLOUR]->blue
RETURN brand, product
----
[source,text]
----
==> +-----------------------------------------------------------------------------------+
==> | brand | product |
==> +-----------------------------------------------------------------------------------+
==> | Node[1]{name:"French Connection"} | Node[2]{name:"Halter Dress",colour:"Blue"} |
==> | Node[1]{name:"French Connection"} | Node[4]{name:"Different Dress",colour:"Blue"} |
==> +-----------------------------------------------------------------------------------+
==> 2 rows
==> 3 ms
----
We can do it the other way around as well i.e. we can search for relationships and then set properties on nodes.
For example let's say we want to mark the blue French connection clothing as being on sale but we weren't interested in searching specifically for on sale items:
[source,cypher]
----
START brand = node:node_auto_index(name="French Connection"),
blue = node:node_auto_index(name="Blue")
MATCH brand<-[:BRAND]-product-[:COLOUR]->blue
SET product.state = "OnSale"
RETURN brand, product
----
If we now repeat our previous query we'll see that property on the products:
[source,cypher]
----
START brand = node:node_auto_index(name="French Connection"),
blue = node:node_auto_index(name="Blue")
MATCH brand<-[:BRAND]-product-[:COLOUR]->blue
RETURN brand, product
----
[source,text]
----
==> +--------------------------------------------------------------------------------------------------+
==> | brand | product |
==> +--------------------------------------------------------------------------------------------------+
==> | Node[1]{name:"French Connection"} | Node[2]{name:"Halter Dress",colour:"Blue",state:"OnSale"} |
==> | Node[1]{name:"French Connection"} | Node[4]{name:"Different Dress",colour:"Blue",state:"OnSale"} |
==> +--------------------------------------------------------------------------------------------------+
==> 2 rows
==> 2 ms
----
All the queries used in this post are on available as a https://gist.github.com/mneedham/5648898[gist] if you're interesting in playing around with it further. You can read more about http://docs.neo4j.org/chunked/stable/query-write.html[cypher write syntax on the documentation pages]. | null | null | [
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] | neo4jcypher-properties-or-relationships-its-easy-to-switch | https://markhneedham.com/blog/2013/05/25/neo4jcypher-properties-or-relationships-its-easy-to-switch |
false | 2013-02-03 10:40:48 | R: Mapping over a list of lists | [
"r-2"
] | [
"R"
] | As part of the coursera https://class.coursera.org/dataanalysis-001/class/index[Data Analysis] course I had the following code to download and then read in a file:
[source,r]
----
> file <- "https://dl.dropbox.com/u/7710864/data/csv_hid/ss06hid.csv"
> download.file(file, destfile="americancommunity.csv", method="curl")
> acomm <- read.csv("americancommunity.csv")
----
We then had to filter the data based on the values in a couple of columns and work out how many rows were returned in each case:
[source,r]
----
> one <- acomm[acomm$RMS == 4 & !is.na(acomm$RMS)
& acomm$BDS == 3 & !is.na(acomm$BDS), c("RMS")]
> one
[1] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
...
[137] 4 4 4 4 4 4 4 4 4 4 4 4
> two <- acomm[acomm$RMS == 5 & !is.na(acomm$RMS)
& acomm$BDS == 2 & !is.na(acomm$BDS), c("RMS")]
> two
[1] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
...
[375] 5 5 5 5 5 5 5 5 5 5 5 5
> three <- acomm[acomm$RMS == 7 & !is.na(acomm$RMS)
& acomm$BDS == 2 & !is.na(acomm$BDS), c("RMS")]
> three
[1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
[36] 7 7 7 7 7 7 7 7 7 7 7 7 7 7
----
So I needed to know how many values were in the variables +++<cite>+++one+++</cite>+++, +++<cite>+++two+++</cite>+++ and +++<cite>+++three+++</cite>+++.
I thought I could probably put those lists into another list and then use +++<cite>+++http://www.math.montana.edu/Rweb/Rhelp/apply.html[apply]+++</cite>+++ or one of its variants to get the length of each one.
I usually use the +++<cite>+++http://www.math.montana.edu/Rweb/Rhelp/c.html[c]+++</cite>+++ function to help me create lists but it's not helpful in this case as it creates one massive vector with all the values concatenated together:
Calling apply doesn't have the intended outcome: ~~~r > lapply(c(one, two, three), length) \... [[582]] [1] 1 [[583]] [1] 1 ~~~
Instead what we need is the +++<cite>+++http://www.math.montana.edu/Rweb/Rhelp/list.html[list]+++</cite>+++ function: ~~~r > lapply(list(one, two, three), length) [[1]] [1] 148 [[2]] [1] 386 [[3]] [1] 49 ~~~
Et voila!
The https://github.com/mneedham/dataanalysis2/blob/master/week2.R[code is on github] as usual. | null | null | [
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] | r-mapping-over-a-list-of-lists | https://markhneedham.com/blog/2013/02/03/r-mapping-over-a-list-of-lists |
false | 2013-02-18 22:16:39 | Micro Services Style Data Work Flow | [
"data-science-2"
] | [
"Data Science"
] | Having worked on a few data related applications over the last ten months or so https://twitter.com/a5hok[Ashok] and I were recently discussing some of the things that we've learnt
One of the things he pointed out is that it's very helpful to *separate the different stages of a data work flow* into their own applications/scripts.
I decided to try out this idea with some football data that I'm currently trying to model and I ended up with the following stages:
image::{{<siteurl>}}/uploads/2013/02/data-workflow.png[Data workflow,600]
The stages do the following:
* *Find* - Finds web pages which have the data we need and writes those URLs of those to a text file.
* *Download* - Reads in the URLs and downloads the contents to the file system.
* *Extract* - Reads in the web pages from the file system and using CSS selectors extracts appropriate data and saves JSON files to disk.
* *Import* - Reads in the JSON files and creates nodes/relationships in neo4j.
It's reasonably similar to http://www.infoq.com/presentations/Micro-Services[micro services] except instead of using HTTP as the protocol between each part we use text files as the interface between different scripts.
In fact it's more like a variation of Unix pipelining as described in http://www.amazon.co.uk/Unix-Programming-Addison-Wesley-Professional-Computing/dp/0131429019/ref=sr_1_1?ie=UTF8&qid=1361141666&sr=8-1[The Art of Unix Programming] except we store the results of each stage of the pipeline instead of piping them directly into the next one.
If following the Unix way isn't enough of a reason to split up the problem like this there are a couple of other reasons why this approach is useful:
* *We end up tweaking some parts more than others* therefore it's good if we don't have to run all the steps each time we make a change e.g. I find that I spend much more time in the extract & import stages than in the other two stages. Once I've got the script for getting all the data written it doesn't seem to change that substantially.
* *We can choose the appropriate technology to do each of the jobs*. In this case I find that any data processing is much easier to do in Ruby but the data import is significantly quicker if you use the Java API.
* *We can easily make changes to the work flow* if we find a better way of doing things.
That third advantage became clear to me on Saturday when I realised that waiting 3 minutes for the import stage to run each time was becoming quite frustrating.
All node/relationship creation was happening via the REST interface from a Ruby script since that was the easiest way to get started.
I was planning to plugin some Java code http://www.markhneedham.com/blog/2012/09/23/neo4j-the-batch-inserter-and-the-sunk-cost-fallacy/[using the batch importer] to speed things up until Ashok pointed me to a https://github.com/jexp/batch-import[CSV driven batch importer] which seemed like it might be even better.
That batch importer takes CSV files of nodes and edges as its input so I needed to add another stage to the work flow if I wanted to use it:
image::{{<siteurl>}}/uploads/2013/02/data-workflow-2.png[Data workflow 2,600]
I spent a few hours working on the 'Extract to CSV' stage and then replaced the initial 'Import' script with a call to the batch importer.
It now takes 1.3 seconds to go through the last two stages instead of 3 minutes for the old import stage.
Since all I added was another script that took a text file as input and created text files as output it was really easy to make this change to the work flow.
I'm not sure how well this scales if you're dealing with massive amounts of data but you can always split the data up into multiple files if the size becomes unmanageable. | null | null | [
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] | micro-services-style-data-work-flow | https://markhneedham.com/blog/2013/02/18/micro-services-style-data-work-flow |
false | 2013-02-27 23:12:49 | Reading Code: Assume it doesn't work | [
"reading-code"
] | [
"Reading Code"
] | http://www.linkedin.com/profile/view?id=13286237&authType=NAME_SEARCH&authToken=Pfg7&locale=en_US&srchid=f17a4889-19b4-4c0a-b61e-7f84ce7b2cce-0&srchindex=1&srchtotal=1842&goback=%2Efps_PBCK_jae+lee_*1_*1_*1_*1_*1_*1_*2_*1_Y_*1_*1_*1_false_1_R_*1_*51_*1_*51_true_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2_*2&pvs=ps&trk=pp_profile_name_link[Jae] and I have spent a reasonable chunk of the past few weeks pairing on code that neither of us are familiar with and at times we've found it quite difficult to work out exactly what it's supposed to be doing.
My default stance in this situation is to assume that the code is probably correct and then try and work out how that's the case.
After I'd vocalised this a few times, Jae pointed out that we couldn't be sure that the code worked and it didn't make sense to start with that as an assumption.
He pointed out that we were often going down paths of the code that were executed infrequently and since there were no tests around the code we couldn't be sure what it did.
A more useful approach was to *assume that it doesn't work and then reason about it from scratch* to determine whether or not that assumption is correct.
I quite like making this assumption because it makes you concentrate much more closely when you're reading the code rather than skimming over it and assuming that it does what it's supposed to do.
Reading the code has become a mini debugging session where we try to discover how some unfamiliar code actually works.
We've found a couple of bits of code that weren't actually working by following this mindset but it is mentally quite tiring and obviously slower than other styles of reading code! | null | null | [
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] | reading-code-assume-it-doesnt-work | https://markhneedham.com/blog/2013/02/27/reading-code-assume-it-doesnt-work |
false | 2013-02-16 12:17:49 | Regular Expressions: Non greedy matching | [
"regular-expressions"
] | [
"Software Development"
] | I was playing around with some football data earlier in the week and I wanted to try and extract just the name 'Rooney' from the following bit of text:
[source,text]
----
Rooney 8′, 27′
----
My initial regular expression was the following which annoyingly captures the time of the first goal:
[source,ruby]
----
> "Rooney 8′, 27′".match(/(.*)\s\d(.*)/)[1]
=> "Rooney 8,"
----
It works fine if the player has only scored one goal...
[source,ruby]
----
> "Rooney 8′".match(/(.*)\s\d(.*)/)[1]
=> "Rooney"
----
\...but since the second part of the regex ("\s\d(.*)") appears twice only the last instance of it is matched and the rest of the text gets captured by the first part of the regex.
One way around this is to make the first part of the regex http://robertpyke.blogspot.co.uk/2009/02/ruby-regex-non-greedy-operator-using_05.html[non greedy/lazy] so that it will make as little as possible rather than as much as possible.
We can do that by using the lazy version of '*' which is '*?':
[source,ruby]
----
> "Rooney 8′, 27′".match(/(.*?)\s\d(.*)/)[1]
=> "Rooney"
----
Of course you could also argue that I'm being very lazy by using ".*" in the first place and you probably have a point! The following more explicit regular expression achieves the same thing:
[source,ruby]
----
> "Rooney 8′, 27′".match(/([A-Za-z\s-]+)\s\d(.*)/)[1]
=> "Rooney"
----
As a side note I find that when I'm playing around with regular expressions it really makes sense to have a bunch of test cases that I can run after each change to make sure I haven't inadvertently broken everything.
http://www.regexper.com/[regexper.com] is also really helpful for visualising what the regular expression is actually doing! | null | null | [
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] | regular-expressions-non-greedy-matching | https://markhneedham.com/blog/2013/02/16/regular-expressions-non-greedy-matching |
false | 2013-02-28 00:00:03 | Micro Services: Where does the complexity go? | [
"micro-services-2"
] | [
"Micro Services"
] | For the past year every system that I've worked on has been designed around a micro services architecture and while there are http://www.infoq.com/presentations/Micro-Services[benefits with this approach] there is an inherent complexity in software which has to go somewhere!
I thought it'd be interesting to run through some of the new complexities that I've noticed in what may well be an acknowledgement of the difficulty of designing distributed systems.
== Interactions between components
One of the advantages of having lots of small applications is that each one is conceptually easier to understand and we only need to keep the mental model of how that one application works when we're working on it.
With the big monolithic application all the code is together which makes it more difficult to make that separation and completely different concepts can end up getting mashed together.
On the other hand there is now complexity in the interactions between the applications which will often communicate with each other over HTTP using JSON/XML messages.
We now need to be careful how you go about changing these messages because we can easily break a client of out service if we change the name of a field for example.
That can be solved by running two versions of the service but that means we have the burden of maintaining two versions of the service!
== Understanding how everything fits together
Despite the fact that you can choose to focus on one component at a time there are occasions where you need to know how everything works together.
For example we recently wanted to make changes to the way data is displayed on our results page.
This involved making changes to a backend CMS, making sure those propagated to a database which another service used, making sure the service's client could handle the new fields we added and handling the new fields on the web front end.
In a big monolithic system we might use an IDE to help us navigate around the code and see how everything fitted together whereas when we have separate applications the only tool we have is text search!
== Deployment
Although in some ways deployment is easier because we don't have to deploy everything when we make a small change, in some ways it's more complicated because we have more moving parts.
We need to deploy components in the right order otherwise we'll end up deploying a component which relies on getting a certain value from another component which it doesn't get because the other component's out of date.
Another thing to look out for is that we don't make our services too fine grained otherwise we'll end up being overwhelmed by how many applications we actually have to deploy.
== Micro services are still cool!
Despite these disadvantages it wouldn't have been possible to have our teams setup the way they are without splitting capabilities in this way.
We have 10 developers in 3 teams working across ~25 different applications. There's some overlap between the applications but each team owns about 1/3 of the repositories and they tend to dominate the commits on there.
Another neat thing about the micro services approach is that it's very easy to see which things need to happen in real time on the web application and which can be processed offline at a later stage.
I'm sure there are many more things to watch out for when choosing to design a distributed system but these are the things I've picked up in my short amount of time working in this area! | null | null | [
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] | micro-services-where-does-the-complexity-go | https://markhneedham.com/blog/2013/02/28/micro-services-where-does-the-complexity-go |
false | 2013-02-28 22:23:27 | Vertical/Horizontal Slicing | [
"software-development"
] | [
"Software Development"
] | A few years ago I wrote a bunch of posts http://www.markhneedham.com/blog/2010/03/02/riskiest-thing-first-vs-outside-in-development/[exploring] http://www.markhneedham.com/blog/2010/04/18/coding-another-outside-in-example/[my experiences] http://www.markhneedham.com/blog/2009/12/19/coding-an-outside-in-observation/[of outside in development] eventually coming to the conclusion that it seemed to make sense to drive out functionality from the UI and work back from there.
i.e. we take a vertical slice of functionality and then drive it end to end.
On the team I'm working on there's been success using an approach where the functionality is still split vertically but we work across a horizontal layer for all the cards before moving onto the next layer.
The advantage of this approach is that *we can do all the work in one layer without context switching* and then move onto the next layer.
In our case this meant adding a bunch of text and check boxes to a backend CMS and then making sure that the values entered there bubbled their way up to a service used by the front end.
This was counter to my previous experience so I was curious why we weren't seeing the problems that I'd seen before where we'd model things incorrectly in the backend and only realise when we tried to call them from the front end.
http://www.linkedin.com/search/fpsearch?fname=Jae&lname=Lee&keepFacets=Y&facet_G=gb%3A0&pplSearchOrigin=TSEO_SN&trk=TSEO_SN[Jae] pointed out that you don't necessarily run into this problem *if you can hold the model of how the whole system fits together in your head*.
Since we have a few people who are able to do that and have knowledge of how the data flows through the different applications that is indeed the case!
I'm sure there are some other cases where this approach makes more sense so if you find success writing code in this style do let me know! | null | null | [
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] | verticalhorizontal-slicing | https://markhneedham.com/blog/2013/02/28/verticalhorizontal-slicing |
false | 2013-02-28 23:36:13 | neo4j: Loading data - REST API vs Batch Import | [
"neo4j"
] | [
"neo4j"
] | A couple of weeks ago when I first started playing around with my football data set I was loading all the data into neo4j using the REST API via https://github.com/maxdemarzi/neography[neography] which was taking around 4 minutes to load.
The data set consisted of just over 250 matches which translated into 8,000 nodes & 30,000 relationships so it's very small by all means.
https://twitter.com/a5hok[Ashok] and I were discussing how that could be quicker and the first thing we tried was to store inserted nodes in an in memory hash map and look them up from there rather than doing an index lookup each time.
These were the timings for different numbers of matches when I did that:
[source,text]
----
--------------------------------------------------------------------
| Matches | Cache-Hits | Cache-Misses | Lucene | In memory |
--------------------------------------------------------------------
| 25 | 501 | 325 | 26.692s | 22.877s |
| 50 | 1275 | 373 | 50.491s | 38.304s |
| 263 | 8016 | 480 | 4m 11.031s | 2m 49.951s |
--------------------------------------------------------------------
----
For the full data set it was about 30% faster which was a nice improvement but still left me waiting around for a bit longer than I wanted to!
I've http://www.markhneedham.com/blog/2012/09/23/neo4j-the-batch-inserter-and-the-sunk-cost-fallacy/[previously used the batch inserter] and I was planning to use that again to get a significant improvement in loading time until Ashok pointed out Michael Hunger's https://github.com/jexp/batch-import[batch-import] which seemed worth a try.
I had to add an extra step to the http://www.markhneedham.com/blog/2013/02/18/micro-services-style-data-work-flow/[pipeline] to put all the nodes and relationships into CSV files and then pass those files to the batch-import JAR.
There was a massive improvement in the load time using it. These were the timings:
[source,text]
----
-------------------------------------------------------------------
| Matches | Lucene | In memory | Batch Import |
-------------------------------------------------------------------
| 25 | 26.692s | 22.877s | 0.378s + 0.921s = 1.299s |
| 50 | 50.491s | 38.304s | 0.392s + 1.025s = 1.417s |
| 263 | 4m 11.031s | 2m 49.951s | 0.524s + 1.239s = 1.763s |
-------------------------------------------------------------------
----
_(the two numbers represent the time taken to generate the CSV files and then the time to import them)_
From my brief skimming of the code it seems to take in the files and then route them through the batch importer API so I imagine similar results would be had by calling that directly.
I know this is not a very fair comparison given that you probably shouldn't be using the REST API to insert data but since I've done it a couple of times I thought it'd be interesting to measure anyway! | null | null | [
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] | neo4j-loading-data-rest-api-vs-batch-import | https://markhneedham.com/blog/2013/02/28/neo4j-loading-data-rest-api-vs-batch-import |
false | 2013-02-28 21:57:11 | Compatible Opinions & Confirmation Bias | [
"software-development"
] | [
"Software Development"
] | In 2011 https://twitter.com/thejayfields[Jay Fields] wrote a blog post in which he suggested that it's better to build teams in which people have http://blog.jayfields.com/2011/01/compatible-opinions-on-software.html[a similar opinion on the way software should be built at a high level] rather than having people whose opinions are in conflict.
He referred to this as having 'compatible opinions on software' and since I read the post I've become much more aware of this myself on the teams that I've worked on.
_(I'm sure noticing this is some sort of cognitive bias but I'm http://en.wikipedia.org/wiki/List_of_biases_in_judgment_and_decision_making[not sure which one]!)_
Some examples that Jay gives of incompatible opinions are preference of a powerful language vs powerful IDE or monolithic application vs micro services.
I've noticed some other opinions which seem incompatible as well but what I find most intriguing is that people (including me!) use *extreme confirmation bias to try and show that their opinion is better*.
For example on one team I worked on there was a difference in opinion on whether we should use an ORM to take care of our mapping or if we should write custom SQL to do that job.
We chose to do the latter on that project but whenever we encountered situations where you had to write a lot of SQL it was pointed out how much easier life would be if we had just let the ORM do its job.
On the other hand we had none of the performance problems that you can often run into when you abstract away the database from your application and this fact was frequently pointed out as well!
I think it's probably fair to say that people have some problems they are more interested in solving and others which they don't really care for.
For example someone who's really into Clojure is going to be completely disinterested in trying to solve a problem involving collections using Java when they know that they can solve it much more easily with a few functions.
Equally a Java programmer may lose patience at how long it takes to work out how to use a low level Java API using Clojure when they would have been able to work it out much more quickly using auto complete in a Java IDE.
My general take away is that if I notice myself or others using confirmation bias to prove a point then assuming we both have experience of both approaches it may well be the case that our opinions aren't compatible.
We can then either choose to work on different teams if that's an option or if not then at least acknowledge what's going on with our opinions!
_Brief footnote so I don't get flamed!_
I'm sure there are ways of exploring Java APIs easily using Clojure but hopefully the main point still comes across! | null | null | [
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] | compatible-opinions-confirmation-bias | https://markhneedham.com/blog/2013/02/28/compatible-opinions-confirmation-bias |
false | 2013-02-17 21:05:27 | neo4j/cypher: SQL style GROUP BY functionality | [
"neo4j",
"cypher"
] | [
"neo4j"
] | As http://www.markhneedham.com/blog/2013/02/16/regular-expressions-non-greedy-matching/[I mentioned in a previous post] I've been playing around with some football related data over the last few days and one query I ran (using http://docs.neo4j.org/chunked/milestone/cypher-query-lang.html[cypher]) was to find all the players who've been sent off this season in the Premiership.
The model in the graph around sending offs looks like this:
image::{{<siteurl>}}/uploads/2013/02/sending_offs.png[Sending offs,600]
My initial query looked like this:
[source,text]
----
START player = node:players('name:*')
MATCH player-[:sent_off_in]-game-[:in_month]-month
RETURN player.name, month.name
----
First we get the names of all the players which are stored in an index and then we follow relationships to the games they were sent off in and then find which months those games were played in.
That query returns:
[source,text]
----
+----------------------------+
| player.name | month.name |
+----------------------------+
| "Jenkinson" | "February" |
| "Chico" | "September" |
| "Odemwingie" | "September" |
| "Agger" | "August" |
| "Cole" | "December" |
| "Whitehead" | "August" |
...
+----------------------------+
----
I thought it'd be interesting to see how many sending offs there were in each month which we'd achieve in SQL by making use of a GROUP BY.
cypher has a bunch of http://docs.neo4j.org/chunked/stable/query-aggregation.html[aggregation functions] which allow us to achieve the same outcome.
In our case we want to use the +++<cite>+++COUNT+++</cite>+++ function and we want our grouping key to be the month of the year so we need to include that as part of our RETURN statement as well:
[source,text]
----
START player = node:players('name:*')
MATCH player-[:sent_off_in]-game-[:in_month]-month
RETURN COUNT(player.name) AS numberOfReds, month.name
ORDER BY numberOfReds DESC
----
which returns:
[source,text]
----
+----------------------------+
| numberOfReds | month.name |
+----------------------------+
| 7 | "October" |
| 6 | "December" |
| 4 | "September" |
| 4 | "November" |
| 3 | "August" |
| 2 | "January" |
| 2 | "February" |
+----------------------------+
----
As far as I can tell anything which isn't an aggregate function is used as part of the grouping key which means we could include more than one field in our grouping key.
This isn't particularly relevant for us for this particular query but would become useful if we add the teams that the players play for.
I extended the graph to included a player's statistics for each game which also includes a relationship indicating which team they played for in a specific game.
The model now looks like this:
image::{{<siteurl>}}/uploads/2013/02/stats.png[Stats,600]
It does now look quite a bit more complicated but this was the best way I could think of modelling player specific details for a match.
I couldn't see another way of modelling the fact that a player played for a certain team in a match which I want to use for some other queries but if you can see a simpler way please let me know.
To get a list of the red cards and the name of the team the offender played for we can write the following query:
[source,text]
----
START player = node:players('name:*')
MATCH player-[:sent_off_in]-game-[:in_month]-month,
game-[:in_match]-stats-[:stats]-player,
stats-[:played_for]-team
RETURN player.name, month.name, team.name
ORDER BY month.name
----
The original query traversed a path from a player to games they were sent off in and then from the games to the month the game was played in.
We've now added a traversal from the game to the game stats for that player and we also traverse from the game stats to the team node that the player played for in that game.
When we run this we get the following results:
[source,text]
----
+--------------------------------------------+
| player.name | month.name | team.name |
+--------------------------------------------+
| "Agger" | "August" | "Liverpool" |
| "Whitehead" | "August" | "Stoke" |
...
| "Shotton" | "December" | "Stoke" |
| "Nzonzi" | "December" | "Stoke" |
| "Jenkinson" | "February" | "Arsenal" |
...
| "Ivanovic" | "October" | "Chelsea" |
| "Torres" | "October" | "Chelsea" |
+--------------------------------------------+
----
So we can see that Stoke got 2 players sent off in December and Chelsea got 2 sent off in October.
We can write the following query to return a result set which uses team and month as the grouping key i.e. we count how many paths there are which have the same team and month:
[source,text]
----
START player = node:players('name:*')
MATCH player-[:sent_off_in]-game-[:in_month]-month,
game-[:in_match]-stats-[:stats]-player,
stats-[:played_for]-team
RETURN month.name, team.name, COUNT(player.name) AS numberOfReds
ORDER BY numberOfReds DESC
----
When we run that query we see the following results:
[source,text]
----
+--------------------------------------------+
| month.name | team.name | numberOfReds |
+--------------------------------------------+
| "December" | "Stoke" | 2 |
| "October" | "Chelsea" | 2 |
...
| "August" | "Stoke" | 1 |
| "November" | "Tottenham" | 1 |
| "December" | "Everton" | 1 |
+--------------------------------------------+
----
This is all explained in more detail in the http://docs.neo4j.org/chunked/stable/query-aggregation.html[documentation] but I thought it'd be interesting to write about it from the perspective of someone more used to writing SQL and trying to work out how to achieve the same thing in cypher. | null | null | [
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] | neo4jcypher-sql-style-group-by-functionality | https://markhneedham.com/blog/2013/02/17/neo4jcypher-sql-style-group-by-functionality |
false | 2013-02-17 20:02:31 | Data Science: Don't filter data prematurely | [
"neo4j",
"cypher"
] | [
"Data Science"
] | Last year I wrote a post describing how I'd http://www.markhneedham.com/blog/2012/09/30/data-science-scrapping-the-data-together/[gone about getting data for my ThoughtWorks graph] and one mistake about my approach in retrospect is that I filtered the data too early.
My workflow looked like this:
* Scrape internal application using web driver and save useful data to JSON files
* Parse JSON files and load nodes/relationships into neo4j
The problem with the first step is that I was trying to determine up front what data was useful and as a result I ended up running the scrapping application multiple times when I realised I didn't have all the data I wanted.
Since it took a couple of hours to run each time it was tremendously frustrating but it took me a while to realise how flawed my approach was.
For some reason I kept tweaking the scrapper just to get a little bit more data each time!
It wasn't until https://twitter.com/a5hok[Ashok] and I were doing some similar work and had to extract data from an existing database that I realised the filtering didn't need to be done so early in the process.
We weren't sure exactly what data we needed but on this occasion we got everything around the area we were working in and looked at how we could actually use it at a later stage.
Given that it's relatively cheap to store the data I think this approach makes sense more often than not - *we can always delete the data if we realise it's not useful to us at a later stage*.
It especially makes sense if it's difficult to get more data either because it's time consuming or we need someone else to give us access to it and they are time constrained.
If I could rework that work flow it'd now be split into three steps:
* Scrape internal application using web driver and save pages as HTML documents
* Parse HTML documents and save useful data to JSON files
* Parse JSON files and load nodes/relationships into neo4j
I think my experiences tie in reasonably closely with those I heard about at http://www.markhneedham.com/blog/2012/10/02/strata-conf-london-day-1-wrap-up/[Strata Conf London] but of course I may well be wrong so if anyone has other points of view I'd love to hear them. | null | null | [
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] | data-science-dont-filter-data-prematurely | https://markhneedham.com/blog/2013/02/17/data-science-dont-filter-data-prematurely |
false | 2013-02-10 13:29:32 | R: Building up a data frame row by row | [
"r-2"
] | [
"R"
] | http://twitter.com/jennifersmithco[Jen] and I recently started working on the http://www.kaggle.com/c/titanic-gettingStarted[Kaggle Titanic problem] and we thought it'd probably be useful to start with some exploratory data analysis to get a feel for the data set.
For this problem you are given a selection of different features describing the passengers on board the Titanic and you have to predict whether or not they would have survived or died based on those features.
I thought an interesting first thing to look at would be the survival rate for passengers of different socio economic status as you would imagine that people of a higher status were more likely to have survived.
The data looks like this:
[source,r]
----
> head(titanic)
survived pclass name sex age sibsp parch ticket fare cabin embarked
1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.2500 S
2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 71.2833 C85 C
3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.9250 S
4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1000 C123 S
5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.0500 S
6 0 3 Moran, Mr. James male NA 0 0 330877 8.4583 Q
----
I started by writing the following function to work out how many people in a particular class survived:
[source,r]
----
survived <- function(class) {
peopleInThatClass <- titanic[titanic$pclass == class,]
survived <- peopleInThatClass[peopleInThatClass$survived == 1,]
nrow(survived)
}
----
I could then work out how many people in each class survived by using +++<cite>+++lapply+++</cite>+++:
[source,r]
----
> lapply(c(1,2,3), survived)
[[1]]
[1] 136
[[2]]
[1] 87
[[3]]
[1] 119
----
This works but the output isn't very nice to read and I wanted to put that data side by side with other information about eeach class for which I figured I'd need to construct a data frame.
I came across a solution which works quite well about http://stackoverflow.com/questions/3642535/creating-an-r-dataframe-row-by-row[half way down a StackOverflow question] and reworked my code to make use of that:
[source,r]
----
survivalSummary <- function(classes) {
summary<-NULL
for(class in classes) {
numberSurvived <- survived(class)
summary <- rbind(summary,data.frame(class=class, survived=numberSurvived))
}
summary[with(summary, order(class)),]
}
----
[source,r]
----
> survivalSummary(c(1,2,3))
class survived
1 1 136
2 2 87
3 3 119
----
We make use of the +++<cite>+++http://stat.ethz.ch/R-manual/R-patched/library/base/html/cbind.html[rbind]+++</cite>+++ function which creates a new data frame with a row appended. We then have to reassign the new data frame to the +++<cite>+++summary+++</cite>+++ variable.
If we want to add other information about the class onto each row such as the total number of passengers and the survival rate it's very easy:
[source,r]
----
survivalSummary <- function(classes) {
summary<-NULL
for(class in classes) {
numberSurvived <- survived(class)
total <- passengers(class)
percentageSurvived <- numberSurvived / total
summary <- rbind(summary,data.frame(class=class, survived=numberSurvived, total=total, percentSurvived=percentageSurvived))
}
summary[with(summary, order(class)),]
}
----
[source,r]
----
> survivalSummary(unique(titanic$pclass))
class survived total percentSurvived
2 1 136 216 0.6296296
3 2 87 184 0.4728261
1 3 119 491 0.2423625
----
We're also now using +++<cite>+++unique+++</cite>+++ to get the different classes rather than hard coding them. | null | null | [
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] | r-building-up-a-data-frame-row-by-row | https://markhneedham.com/blog/2013/02/10/r-building-up-a-data-frame-row-by-row |
false | 2013-02-10 15:42:07 | Feature Extraction/Selection - What I've learnt so far | [
"machine-learning-2"
] | [
"Machine Learning"
] | A couple of weeks ago I wrote about http://www.markhneedham.com/blog/2013/01/31/kaggle-digit-recognizer-a-feature-extraction-fail/[some feature extraction work that I'd done] on the http://www.kaggle.com/c/digit-recognizer[Kaggle Digit Recognizer] data set and having realised that I had no idea what I was doing I thought I should probably learn a bit more.
I came across Dunja Mladenic's 'http://videolectures.net/slsfs05_mladenic_drfsm[Dimensionality Reduction by Feature Selection in Machine Learning]' presentation in which she sweeps across the landscape of feature selection and explains how everything fits together.
The talk starts off by going through some reasons that we'd want to use dimensionality reduce/feature selection:
* Improve the prediction performance
* Improve learning efficiency
* Provide faster predictors possibly requesting less information on the original data
* Reduce complexity of the learned results, enable better understanding of the underlying process
Mladenic suggests that there are a few ways we can go about reducing the dimensionality of data:
* Selecting a subset of the original features
* Constructing features to replace the original features
* Using background knowledge to construct new features to be used in addition to the original features
The talk focuses on the first of these and a lot of it focuses on how we can go about using feature selection as a pre-processing step on our data sets.
The approach seems to involve either starting with all the features and removing them one at a time and seeing how the outcome is affected or starting with none of the features and adding them one at a time.
However, about half way through the talk Mladenic points out that *some algorithms actually have feature selection built into them* so there's no need to have the pre-processing step.
I think this is the case with http://en.wikipedia.org/wiki/Random_forest[random forests of decision trees] because the decision trees are constructed by taking into account which features give the greatest http://en.wikipedia.org/wiki/Information_gain_in_decision_trees[information gain] so low impact features are less likely to be used.
I previously wrote http://www.markhneedham.com/blog/2013/01/08/kaggle-digit-recognizer-finding-pixels-with-no-variance-using-r/[a blog post describing how I removed all the features with zero variance] from the data set and after submitting a random forest trained on the new data set we saw no change in accuracy which proved the point.
I also came across an interesting paper by Isabelle Guyon & Andre Elisseeff titled 'http://clopinet.com/isabelle/Papers/guyon03a.pdf[An Introduction to Variable and Feature Selection]' which has a flow chart-ish set of questions to help you work out where to start.
One of the things I picked up from reading this paper is that if you have domain knowledge then *you might be able to construct a better set of features by making use of this knowledge*.
Another suggestion is to *come up with a variable ranking for each feature* i.e. how much that feature contributes to the outcome/prediction. This is something also suggested in the https://class.coursera.org/dataanalysis-001/class/index[Coursera Data Analysis course] and in R we can use the +++<cite>+++http://web.njit.edu/all_topics/Prog_Lang_Docs/html/library/base/html/glm.html[glm]+++</cite>+++ function to help work this out.
The authors also point out that we should separate the problem of model selection (i.e. working out which features to use) from the problem of testing our classifier.
To test the classifier we'd most likely keep a test set aside but we shouldn't use this data for testing feature selection, rather we should use the training data. http://en.wikipedia.org/wiki/Cross-validation_(statistics)[Cross validation] probably works best here.
There's obviously more covered in the presentation & paper than what I've covered here but I've found that in general the material I've come across tends to drift towards being quite abstract/theoretical and therefore quite difficult for me to follow.
If anyone has come across any articles/books which explain how to go about feature selection using an example I'd love to read it/them! | null | null | [
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] | feature-extractionselection-what-ive-learnt-so-far | https://markhneedham.com/blog/2013/02/10/feature-extractionselection-what-ive-learnt-so-far |
false | 2013-02-19 00:03:18 | neo4j/cypher: Using a WHERE clause to filter paths | [
"neo4j",
"cypher"
] | [
"neo4j"
] | One of the http://docs.neo4j.org/chunked/milestone/cypher-query-lang.html[cypher] queries that I wanted to write recently was one to find all the players that have started matches for Arsenal this season and the number of matches that they've played in.
The data model that I'm querying looks like this:
image::{{<siteurl>}}/uploads/2013/02/games.png[Games,600]
I started off with the following query which traverses from Arsenal to all the games that they've taken part in and finds all the players who've played in those games:
[source,text]
----
START team = node:teams('name:"Arsenal"')
MATCH team-[:home_team|away_team]-game-[:played_in]-player
RETURN player.name, COUNT(player.name) as games
ORDER BY games desc
----
It returns the following result set:
[source,text]
----
------------------------------+
| player.name | games |
+------------------------------+
| "Cazorla" | 25 |
| "Arteta" | 22 |
| "Mertesacker" | 22 |
| "Vermaelen" | 22 |
| "Podolski" | 21 |
| "Gibbs" | 18 |
| "Szczesny" | 17 |
…
| "Tiote" | 1 |
| "Diame" | 1 |
| "Ridgewell" | 1 |
| "Lampard" | 1 |
| "Bramble" | 1 |
| "Simpson" | 1 |
+------------------------------+
258 rows
----
which is partially right but also includes a bunch of players who played for the opposition rather than for Arsenal.
The reason for this is that we're following the 'played_in' relationship from the games and that relationship doesn't distinguish between players playing for a specific team in the match.
I initially tried to reduce the number of rows returned by adding to the MATCH clause like this:
[source,text]
----
START team = node:teams('name:"Arsenal"')
MATCH team-[:home_team|away_team]-game-[:played_in]-player,
player-[:played]-()-[:for]-team
RETURN player.name, COUNT(player.name) as games
ORDER BY games desc
----
This did work to an extent - it now returned only Arsenal players but the count of games was completely wrong:
[source,text]
----
+------------------------------+
| player.name | games |
+------------------------------+
| "Cazorla" | 625 |
| "Mertesacker" | 484 |
| "Arteta" | 484 |
| "Vermaelen" | 484 |
| "Podolski" | 441 |
| "Giroud" | 400 |
| "Gibbs" | 324 |
...
+------------------------------+
21 rows
----
The reason this doesn't work is that for every +++<cite>+++Team \-> Game \-> Player+++</cite>+++ traversal in the first MATCH clauses we are doing a +++<cite>+++Player\->Stats\->Team+++</cite>+++ traversal.
Taking Cazorla as an example: the first traversal matches 25 paths and then the second traversal also matches 25 paths.
The reason we end up with 625 matching paths for him is that we're doing that second traversal 25 times, once for each of the matching paths from the first traversal.
What we actually need to do is make that second MATCH clause a WHERE clause which I hadn't realised was possible until a few days ago.
In all the previous queries I've written with cypher I only ever used the WHERE clause to filter based on node or relationship properties.
If we make this change we end up with the following query
[source,text]
----
START team = node:teams('name:"Arsenal"')
MATCH team-[:home_team|away_team]-game-[:played_in]-player
WHERE player-[:played]-()-[:for]-team
RETURN player.name, COUNT(player.name) as games
ORDER BY games desc
----
This returns the following result set:
[source,text]
----
+------------------------------+
| player.name | games |
+------------------------------+
| "Cazorla" | 25 |
| "Arteta" | 22 |
| "Mertesacker" | 22 |
| "Vermaelen" | 22 |
| "Podolski" | 21 |
| "Gibbs" | 18 |
| "Szczesny" | 17 |
| "Sagna" | 16 |
| "Wilshere" | 16 |
...
+------------------------------+
21 rows
----
Which is exactly what we want!
The use of WHERE clauses in this way is explained in more detail http://docs.neo4j.org/chunked/milestone/query-where.html#where-filter-on-patterns[towards the end of the documentation]. | null | null | [
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] | neo4jcypher-using-a-where-clause-to-filter-paths | https://markhneedham.com/blog/2013/02/19/neo4jcypher-using-a-where-clause-to-filter-paths |
false | 2013-02-07 01:26:12 | R: Modelling a conversion rate with a binomial distribution | [
"r-2"
] | [
"R"
] | As part of some work https://twitter.com/siddharthdawara[Sid] and I were doing last week we wanted to simulate the conversion rate for an A/B testing we were planning.
We started with the following function which returns the simulated conversion rate for a given conversion rate of 12%:
[source,r]
----
generateConversionRates <- function(sampleSize) {
sample_a <- rbinom(seq(0, sampleSize), 1, 0.12)
conversion_a <- length(sample_a[sample_a == 1]) / sampleSize
sample_b <- rbinom(seq(0, sampleSize), 1, 0.12)
conversion_b <- length(sample_b[sample_b == 1]) / sampleSize
c(conversion_a, conversion_b)
}
----
If we call it:
[source,r]
----
> generateConversionRates(10000)
[1] 0.1230 0.1207
----
We have a 12.3% conversion rate on A and a 12.07% conversion rate on B based on 10,000 sample values.
We then wrote the following function to come up with 1000 versions of those conversion rates:
[source,r]
----
generateSample <- function(sampleSize) {
lapply(seq(1, 1000), function(x) generateConversionRates(sampleSize))
}
----
We can call that like this:
[source,r]
----
> getSample(10000)
[[998]]
[1] 0.1179 0.1216
[[999]]
[1] 0.1246 0.1211
[[1000]]
[1] 0.1248 0.1234
----
We were then using these conversion rates to try and work out how many samples we needed to include in an A/B test to have reasonable confidence that it represented the population.
We actually ended up abandoning that exercise but I thought I'd record the code because I thought it was pretty interesting. | null | null | [
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] | r-modelling-a-conversion-rate-with-a-binomial-distribution | https://markhneedham.com/blog/2013/02/07/r-modelling-a-conversion-rate-with-a-binomial-distribution |
false | 2013-02-24 19:19:59 | neo4j/cypher: Combining COUNT and COLLECT in one query | [
"neo4j",
"cypher"
] | [
"neo4j"
] | In my http://www.markhneedham.com/blog/2013/02/19/neo4jcypher-using-a-where-clause-to-filter-paths/[continued] http://www.markhneedham.com/blog/2013/02/17/neo4jcypher-sql-style-group-by-functionality/[playing around] with football data I wanted to write a cypher query against neo4j which would show me which teams had missed the most penalties this season and who missed them.
I started off with a query that returned all the penalties that have been missed this season and the games those misses happened in:
[source,text]
----
START player = node:players('name:*')
MATCH player-[:missed_penalty_in]-game,
player-[:played|subbed_on]-stats-[:in]-game,
stats-[:for]-team,
game-[:home_team]-home,
game-[:away_team]-away
RETURN player.name, team.name, home.name, away.name
----
[source,text]
----
+-------------------------------------------------------------------------------------------------+
| player.name | team.name | home.name | away.name |
+-------------------------------------------------------------------------------------------------+
| "Papiss Demba Cisse" | "Newcastle United" | "Newcastle United" | "Norwich City" |
| "Wayne Rooney" | "Manchester United" | "Manchester United" | "Arsenal" |
| "Mikel Arteta" | "Arsenal" | "Arsenal" | "Fulham" |
| "David Silva" | "Manchester City" | "Manchester City" | "Southampton" |
| "Frank Lampard" | "Chelsea" | "Manchester City" | "Chelsea" |
| "Adel Taarabt" | "Queens Park Rangers" | "Queens Park Rangers" | "Norwich City" |
| "Javier Hernández" | "Manchester United" | "Manchester United" | "Wigan Athletic" |
| "Robin Van Persie" | "Manchester United" | "Southampton" | "Manchester United" |
| "Jonathan Walters" | "Stoke City" | "Fulham" | "Stoke City" |
| "Shane Long" | "West Bromwich Albion" | "West Bromwich Albion" | "Liverpool" |
| "Steven Gerrard" | "Liverpool" | "Liverpool" | "West Bromwich Albion" |
| "Lucas Piazon" | "Chelsea" | "Chelsea" | "Aston Villa" |
+-------------------------------------------------------------------------------------------------+
12 rows
----
(there should actually be another penalty miss for Jonathan Walters against Chelsea but for some reason the data source has missed it off!
I then grouped the penalty misses by team so that I'd have one row for each team and a collection showing the people who'd missed.
We can use the +++<cite>+++http://docs.neo4j.org/chunked/milestone/query-aggregation.html#aggregation-collect[COLLECT]+++</cite>+++ function to do the latter:
[source,text]
----
START player = node:players('name:*')
MATCH player-[:missed_penalty_in]-game,
player-[:played|subbed_on]-stats-[:in]-game,
stats-[:for]-team
RETURN DISTINCT team.name, COLLECT(player.name) AS players
----
[source,text]
----
+---------------------------------------------------------------------------------+
| team.name | players |
+---------------------------------------------------------------------------------+
| "Newcastle United" | ["Papiss Demba Cisse"] |
| "Manchester United" | ["Wayne Rooney","Javier Hernández","Robin Van Persie"] |
| "Stoke City" | ["Jonathan Walters"] |
| "West Bromwich Albion" | ["Shane Long"] |
| "Chelsea" | ["Frank Lampard","Lucas Piazon"] |
| "Arsenal" | ["Mikel Arteta"] |
| "Manchester City" | ["David Silva"] |
| "Liverpool" | ["Steven Gerrard"] |
| "Queens Park Rangers" | ["Adel Taarabt"] |
+---------------------------------------------------------------------------------+
9 rows
----
I wanted to order the teams by the number of penalties they'd missed so Manchester United would be first in the table in this case and initially tried to order the results by a count of players:
[source,text]
----
START player = node:players('name:*')
MATCH player-[:missed_penalty_in]-game,
player-[:played|subbed_on]-stats-[:in]-game,
stats-[:for]-team
RETURN DISTINCT team.name, COLLECT(player.name) AS players
ORDER BY COUNT(player.name)
----
which doesn't actually compile:
[source,text]
----
SyntaxException: Aggregation expressions must be listed in the RETURN clause to be used in ORDER BY
----
I tried a few other variations such as the following:
[source,text]
----
START player = node:players('name:*')
MATCH player-[:missed_penalty_in]-game,
player-[:played|subbed_on]-stats-[:in]-game,
stats-[:for]-team
RETURN DISTINCT team.name, COUNT(player.name) AS numberOfPlayers,
COLLECT(player.name) AS players
ORDER BY numberOfPlayers DESC
----
which again doesn't compile:
[source,text]
----
SyntaxException: Aggregation expressions must be listed in the RETURN clause to be used in ORDER BY
----
I eventually found http://stackoverflow.com/questions/12269009/returning-two-aggregates-in-a-single-cypher-query?rq=1[a post by Andres] where he explains that you need to split the query into two and make use of +++<cite>+++http://docs.neo4j.org/chunked/milestone/query-with.html[WITH]+++</cite>+++ if you want to make use of two aggregation expressions.
I ended up with the following query which does the job:
[source,text]
----
START player = node:players('name:*')
MATCH player-[:missed_penalty_in]-game,
player-[:played|subbed_on]-stats-[:in]-game,
stats-[:for]-team
WITH DISTINCT team, COLLECT(player.name) AS players
MATCH player-[:missed_penalty_in]-game,
player-[:played|subbed_on]-stats-[:in]-game,
stats-[:for]-team
WITH DISTINCT team, COUNT(player) AS numberOfPlayers, players
RETURN team.name, players
ORDER BY numberOfPlayers DESC
----
[source,text]
----
+---------------------------------------------------------------------------------+
| team.name | players |
+---------------------------------------------------------------------------------+
| "Manchester United" | ["Wayne Rooney","Javier Hernández","Robin Van Persie"] |
| "Chelsea" | ["Frank Lampard","Lucas Piazon"] |
| "Liverpool" | ["Steven Gerrard"] |
| "Manchester City" | ["David Silva"] |
| "Newcastle United" | ["Papiss Demba Cisse"] |
| "Queens Park Rangers" | ["Adel Taarabt"] |
| "Stoke City" | ["Jonathan Walters"] |
| "Arsenal" | ["Mikel Arteta"] |
| "West Bromwich Albion" | ["Shane Long"] |
+---------------------------------------------------------------------------------+
9 rows
---- | null | null | [
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] | neo4jcypher-combining-count-and-collect-in-one-query | https://markhneedham.com/blog/2013/02/24/neo4jcypher-combining-count-and-collect-in-one-query |
false | 2013-02-23 15:04:58 | Ruby: Stripping out a non breaking space character ( ) | [
"ruby",
"character-encoding"
] | [
"Ruby"
] | A couple of days ago I was playing with some code to scrape data from a web page and I wanted to skip a row in a table if the row didn't contain any text.
I initially had the following code to do that:
[source,ruby]
----
rows.each do |row|
next if row.strip.empty?
# other scraping code
end
----
Unfortunately that approach broke down fairly quickly because empty rows contained a http://www.yellowpipe.com/yis/tools/ASCII-HTML-Characters/index.php[non breaking space] i.e. ' '.
If we try called +++<cite>+++strip+++</cite>+++ on a string containing that character we can see that it doesn't get stripped:
[source,ruby]
----
# it's hex representation is A0
> "\u00A0".strip
=> " "
> "\u00A0".strip.empty?
=> false
----
I wanted to see whether I could use +++<cite>+++gsub+++</cite>+++ to solve the problem so I tried the following code which didn't help either:
[source,ruby]
----
> "\u00A0".gsub(/\s*/, "")
=> " "
> "\u00A0".gsub(/\s*/, "").empty?
=> false
----
A bit of googling led me to this http://stackoverflow.com/questions/3913900/ruby-1-9-strip-not-removing-whitespace[Stack Overflow post] which suggests using the http://en.wikipedia.org/wiki/Regular_expression#POSIX_character_classes[POSIX space character class] to match the non breaking space rather than '\s' because that will match more of the different space characters.
e.g.
[source,ruby]
----
> "\u00A0".gsub(/[[:space:]]+/, "")
=> ""
> "\u00A0".gsub(/[[:space:]]+/, "").empty?
=> true
----
So that we don't end up indiscriminately removing all spaces to avoid problems like this where we mash the two names together\...
[source,ruby]
----
> "Mark Needham".gsub(/[[:space:]]+/, "")
=> "MarkNeedham"
----
\...the poster suggested the following regex which does the job:
[source,ruby]
----
> "\u00A0".gsub(/\A[[:space:]]+|[[:space:]]+\z/, '')
=> ""
> ("Mark" + "\u00A0" + "Needham").gsub(/\A[[:space:]]+|[[:space:]]+\z/, '')
=> "Mark Needham"
----
* \A matches the beginning of the string
* \z matches the end of the string
So what this bit of code does is match all the spaces that appear at the beginning or end of the string and then replaces them with ''. | null | null | [
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] | ruby-stripping-out-a-non-breaking-space-character-nbsp | https://markhneedham.com/blog/2013/02/23/ruby-stripping-out-a-non-breaking-space-character-nbsp |
false | 2013-02-15 07:36:06 | Onboarding: Sketch the landscape | [
"onboarding"
] | [
"Software Development"
] | For four months during 2012 I was working on the http://digital.cabinetoffice.gov.uk/2012/09/28/meet-the-hosting-and-infrastructure-team/[GDS infrastructure team] and one of the first tasks that https://twitter.com/garethr[Gareth] suggested I do was update a diagram showing how all the different applications and databases worked together.
I thought this was quite a strange thing to ask the 'new guy' to do since I obviously knew nothing at all about how anything worked but he told me that was partly why he wanted me to do it.
I had to go through the puppet and application repositories to work out what monitoring we already had in place, which applications used MongoDB, which used MySQL, which called each other and so on.
If anything didn't make sense I could go and ask someone in one of the application teams and then add my new understanding to the diagram.
As http://2012.33degree.org/talk/show/67[micro services] driven architectures become more and more popular it strikes me that this probably wouldn't be a bad starting point for anyone new to a system.
When you're new you don't yet have the familiarisation that makes things 'obvious' so you're less likely to skip over things and more likely to be inquisitive about why things are the way they are.
As you come across things that don't make sense others who have worked there for longer can play http://www.markhneedham.com/blog/2009/04/25/pimp-my-architecture-dan-north/[the role of the shaman] who explains the history and context behind the decisions which have led to the current state.
Although diagrams can quickly go out of date I still think one of the whole landscape is useful as it gives everyone a common point of reference which can help reduce misunderstandings.
http://www.thekua.com/atwork/[Pat Kua] has http://www.infoq.com/articles/pat-kua-onboarding-new[a bunch of articles covering various different onboarding activities] which are probably worth a read if this is your type of thing. | null | null | [
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] | onboarding-sketch-the-landscape | https://markhneedham.com/blog/2013/02/15/onboarding-sketch-the-landscape |
false | 2013-02-25 22:56:11 | Reading outside your area of interest | [
"learning"
] | [
"Learning"
] | A reasonable amount of the information that I consume comes either via scanning https://twitter.com/[twitter] or from my http://getprismatic.com/news/home[prismatic] feed but I noticed that I'm quite biased to reading things in similar subject areas.
I tend to end up reading about data mining/science, functional programming and startups and while the articles are mostly interesting it does eventually start to feel like you're in an echo chamber.</li>
I have a subscription to the http://www.acm.org/[ACM] mainly because I enjoy reading the 'Communications of the ACM' magazine which gets sent out every month and until recently I only read articles which I thought would be interesting.
This unfortunately meant that I was adding to the problem I mentioned earlier whereby everything I read is about similar topics.
I decided to try and change that by reading the magazine from cover to cover and although I haven't finished yet it's been an interesting experience and I've read about things that I wouldn't have thought to read about including:
* Do more computer science papers get rejected than those in other subjects?
* How do we allow people to vote in areas where there's been a natural disaster?
* Why are there currently so many post-docs in computer science?
* How should we go about analysing performance problems?
This seems to be a reasonably good way of diversifying what I read/learn because an editor has decided what I should read rather than me choosing or an algorithm choosing based on what I've previously read.
Having said that, I'd be intrigued to know what approaches/strategies others have for getting knowledge of a broader range of topics. | null | null | [
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] | reading-outside-your-area-of-interest | https://markhneedham.com/blog/2013/02/25/reading-outside-your-area-of-interest |
false | 2013-02-25 23:58:51 | Micro Services: Readme files | [
"micro-services-2"
] | [
"Micro Services"
] | By my latest count I have around 15 different micro services/applications checked out on my machine which comprise the system that I'm currently working on.
Most of these are Ruby related so it's easy to figure out how to start up a local copy because it's either +++<cite>+++bundle exec rails server+++</cite>+++ if it's a rails application or +++<cite>+++bundle exec backup+++</cite>+++ if it's a sinatra/rack application.
The clojure applications follow a similar convention and we use rake to run any offline tasks.
Despite these conventions there is still a reasonable amount of knowledge around how to actually get these applications to work that remains in people's heads but doesn't necessarily have to.
Although talking to each other is generally a good thing, an exercise that we tried at http://digital.cabinetoffice.gov.uk/category/gds/[GDS] was to try and document in the README file what someone new to an application would need to know to get it working.
They then tried to get it up and running while only referring to the instructions but noted down anything that didn't make sense or any steps which seemed unnecessarily manual and could be automated.
This was almost certainly more frustrating than having someone show you what to do but it scales a bit better and reduces what I think isn't a high value conversation.
I recently came across a blog post Tom Preston Werner wrote a couple of years ago titled 'http://tom.preston-werner.com/2010/08/23/readme-driven-development.html[Readme Driven Development]' in which he goes further in suggesting that we drive out our applications from the README.
____
This document should stand on its own as a testament to your creativity and expressiveness. The Readme should be the single most important document in your codebase; writing it first is the proper thing to do.
____
I don't know if we need to go this far but as we more towards a future where we'll be working with lots of different applications it would be nice not to have to remember everything about them in our head! | null | null | [
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false | 2013-02-25 23:33:34 | Pomodoros and the To-Do list | [
"getting-things-done"
] | [
"Software Development"
] | http://www.annashipman.co.uk/[Anna] and I were recently discussing the way that we get things done outside of work and since December I've been fairly religiously working through various 'to-do' lists with a pomodoro timer.
So far I've done 308 30 minute pomodoros in about 8 weeks which is just under 20 hours a week which is not bad but still leaves time for a ridiculous amount of procrastination.
These are some of the things that I've noticed from only doing things when it's explicitly on a timer:
== I waste a lot of time
I haven't been keeping track of how many pomodoros I've been doing each day but the http://www.timetrackerproapp.com/easypomodoro/[Easy Pomodoro] app which I've been using to track my time allows me to see how many pomodoros I've done on a task so I have a rough idea.
On some days I've managed to go from 8pm to 1am and only done 3 pomodoros which leaves an impressive 3 1/2 hours of Facebook, twitter, gmail time.
I did try the https://chrome.google.com/webstore/detail/stayfocusd/laankejkbhbdhmipfmgcngdelahlfoji?hl=en[StayFocusd] application for a few weeks but it becomes really annoying when you do legitimately want to chill out!
== Things need to be deleted from the 'to-do' list
The first to-do list I created had around 10 things on it initially and I added every new thing I wanted to do to it until I had around 25 things on the list and yet had only completed 3 of the original 10 things.
My rough rule of thumb is that if I re-write the 'to-do' list twice and the same item is still there then it gets deleted because I clearly don't have the motivation to do it!
I thought I'd find it frustrating to delete things from the list but it's actually more relaxing to know there's a shorter list of things I'm actually interested in doing.
== Once I get into something I spend ages doing it
I've been tracking how many pomodoros I spent on individual tasks and when sorted I've spent 12 pomodoros on average on the tasks in positions 2-7.
The item in position 1 is me http://www.markhneedham.com/blog/2013/02/24/neo4jcypher-combining-count-and-collect-in-one-query/[playing] http://www.markhneedham.com/blog/2013/02/19/neo4jcypher-using-a-where-clause-to-filter-paths/[around] http://www.markhneedham.com/blog/2013/02/17/neo4jcypher-sql-style-group-by-functionality/[with football data] which I've written about previously and has taken up 36 pomodoros or 18 hours of time in the last couple of weeks.
I generally try to spend my spare time learning new things but in this case most of it has been spent cleaning up/manipulating data into a format that I can do something useful with.
== It's now clear when I'm giving up without really trying
One of the habits I've been trying to shake is that I often give up learning something if I'm not able to grasp the basics reasonably quickly.
Timing things on a pomodoro has made it visible how quickly I tend to reach that state and when I see that I've only been trying to understand the topic for 1 hour it does make me realise how ridiculous I'm being.
It does seem to motivate me to try for at least a few hours and varying the approach before deciding that a topic maybe isn't for me at least for now!
I http://www.markhneedham.com/blog/2012/01/22/playing-around-with-pomodoros/[last wrote about pomodoros about a year ago] and it's interesting to see that I've covered almost completely different things than I did last time!
I'm not sure exactly what that means but I'd recommend them as a tool for fighting procrastination at the very least! | null | null | [
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] | pomodoros-and-the-to-do-list | https://markhneedham.com/blog/2013/02/25/pomodoros-and-the-to-do-list |
false | 2013-11-03 11:14:48 | Python: matplotlib - Import error ft2font Symbol not found: _FT_Attach_File (Mac OS X 10.8.3/Mountain Lion) | [
"python"
] | [
"Python"
] | As I mentioned at the end of http://www.markhneedham.com/blog/2013/10/30/kaggle-titanic-python-pandas-attempt/[my last post about the Titanic Kaggle problem] our next step was to do some proper machine learning™ using http://scikit-learn.org/stable/index.html[scikit-learn] so I started by looking at the http://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html#example-ensemble-plot-adaboost-regression-py[Decision Tree example].
Unfortunately I ended up on the mother of all yak shaving missions while trying to execute the code which draws a chart using http://matplotlib.org/[matplotlib].
I ran the following line from the tutorial:
[source,python]
----
import pylab as pl
----
which lead to this exception:
[source,text]
----
ImportError: dlopen(/Library/Python/2.7/site-packages/matplotlib/ft2font.so, 2): Symbol not found: _FT_Attach_File
----
I found http://stackoverflow.com/questions/7503058/import-error-ft2font-from-matplotlib-python-macosx[this post on Stack Overflow] which had a variety of suggestions one of which was to attempt to install matplotlib from a https://downloads.sourceforge.net/project/matplotlib/matplotlib/matplotlib-1.2.1/matplotlib-1.2.1-py2.7-python.org-macosx10.6.dmg[dmg] on http://matplotlib.org/downloads.html[the website].
I tried that and got this error when trying to do so:
[source,text]
----
matplotlib 1.2.1 can't be installed on this disk. matplotlib requires System Python 2.7 to install.
----
I do have Python 2.7 installed so the error surprised me. In any case, I eventually came across http://the.taoofmac.com/space/blog/2011/07/24/2222[a post on Tao of Mac which explained how to install matplotlib from source]:
[source,bash]
----
sudo brew install pkgconfig
cd /tmp
git clone git://github.com/matplotlib/matplotlib.git
cd matplotlib/
python setup.py build
sudo python setup.py install
----
That installed successfully so I went back to the example which now failed on an earlier import statement:
[source,python]
----
>>> from sklearn.tree import DecisionTreeRegressor
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
ImportError: No module named sklearn.tree
----
I tried to reinstall scikit-learn:
[source,bash]
----
$ sudo easy_install scikit-learn
----
That installed successfully so I tried to import the DecisionTreeRegressor again:
[source,python]
----
>>> from sklearn.tree import DecisionTreeRegressor
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/__init__.py", line 32, in <module>
from .base import clone
File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/base.py", line 10, in <module>
from scipy import sparse
ImportError: No module named scipy
----
I tried again and got this error message instead:
[source,python]
----
>>> from sklearn.tree import DecisionTreeRegressor
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/__init__.py", line 31, in <module>
from . import __check_build
ImportError: cannot import name __check_build
----
I tried to reinstall scipy to see if that would help:
[source,bash]
----
$ sudo easy_install scipy
----
but that led to the following error:
[source,bash]
----
no previously-included directories found matching 'scipy/special/tests/data/boost'
no previously-included directories found matching 'scipy/special/tests/data/gsl'
no previously-included directories found matching 'doc/build'
no previously-included directories found matching 'doc/source/generated'
no previously-included directories found matching '*/__pycache__'
warning: no previously-included files matching '*~' found anywhere in distribution
warning: no previously-included files matching '*.bak' found anywhere in distribution
warning: no previously-included files matching '*.swp' found anywhere in distribution
warning: no previously-included files matching '*.pyo' found anywhere in distribution
Could not locate executable f95
Could not locate executable f90
Could not locate executable f77
Could not locate executable xlf90
Could not locate executable xlf
Could not locate executable ifort
Could not locate executable ifc
Could not locate executable g77
Could not locate executable gfortran
Could not locate executable g95
Could not locate executable pgf90
Could not locate executable pgf77
don't know how to compile Fortran code on platform 'posix'
error: Setup script exited with error: library dfftpack has Fortran sources but no Fortran compiler found
----
I came across http://stackoverflow.com/questions/11442970/numpy-and-scipy-for-preinstalled-python-2-6-7-on-mac-os-lion[this StackOverflow post which suggested installing the Fortran compiler using brew]:
[source,bash]
----
$ brew install gfortran
----
but that didn't work too well either:
[source,bash]
----
==> Installing gfortran dependency: isl
==> Downloading http://ftp.de.debian.org/debian/pool/main/i/isl/isl_0.11.2.orig.tar.bz2
curl: (22) The requested URL returned error: 404
Trying a mirror...
==> Downloading ftp://ftp.linux.student.kuleuven.be/pub/people/skimo/isl/isl-0.11.2.tar.bz2
curl: (28) connect() timed out!
Trying a mirror...
==> Downloading http://www.kotnet.org/~skimo/isl/isl-0.11.2.tar.bz2
curl: (22) The requested URL returned error: 404
----
Instead I downloaded http://sourceforge.net/projects/hpc/files/hpc/g95/gfortran-4.9-bin.tar.gz/download?use_mirror=optimate&download=[a tar.gz package] from http://hpc.sourceforge.net/[HPC] and installed it manually:
[source,bash]
----
$ gunzip gfortran-4.8-bin.tar.gz
$ sudo tar -xvf gfortran-4.8-bin.tar -C /
----
After that had finished I tried reinstalling scipy using pip this time (based on a suggestion in http://stackoverflow.com/questions/11442970/numpy-and-scipy-for-preinstalled-python-2-6-7-on-mac-os-lion[this post]):
[source,bash]
----
$ sudo pip install scipy
----
And now I can successfully run the tutorial!
---
At some stage in the process I also upgraded numpy but I'm not sure if that's necessary to get things working. Just in case:
[source,bash]
----
$ sudo pip install numpy --upgrade
---- | null | null | [
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] | python-matplotlib-import-error-ft2font-symbol-not-found-_ft_attach_file-mac-os-x-10-8-3mountain-lion | https://markhneedham.com/blog/2013/11/03/python-matplotlib-import-error-ft2font-symbol-not-found-_ft_attach_file-mac-os-x-10-8-3mountain-lion |
false | 2013-11-27 23:09:31 | Neo4j: The 'thinking in graphs' curve | [
"neo4j"
] | [
"neo4j"
] | In a couple of Neo4j talks I've done recently I've been asked how long it takes to get used to modelling data in graphs and whether I felt it's simpler than alternative approaches.
My experience of 'thinking in graphs'™ closely mirrors what I believe is a fairly common curve when learning technologies which change the way you think:+++<div align="center">++++++</div>+++
image::{{<siteurl>}}/uploads/2013/11/2013-11-27_23-06-37.png[2013 11 27 23 06 37,600]
There is an initial stage where *it seems really hard because it's different to what we're used to* and at this stage we might want to go back to what we're used to.
If we persevere, however, we will get over that hump and after that *it will be difficult to imagine another way of modelling data* - at least in domains where the connections between different bits of data are important.
Once we're over the hump data modelling should seem like fun and we'll start looking to see whether we can use graphs to solve other problems we've got.
I wasn't sure whether modelling in graphs is simpler than alternative approaches so as a thought experiment I decided to see what part of my football graph would look like if it was modelled in a relational database.
This is the graph model:
image::{{<siteurl>}}/uploads/2013/11/2013-11-26_19-35-28.png[2013 11 26 19 35 28,600]
If we went for a normalised relational design it might look like this:
image::{{<siteurl>}}/uploads/2013/11/rel-football.png[Rel football,520]
I think this is at least equal in complexity to the graph model and it wouldn't be much fun writing queries against it as the joins pile up!
Of course this comparison is on a data set which is very graphy in nature and therefore well suited for Neo4j. There are certainly other domains where a relational approach would be much more effective. | null | null | [
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] | neo4j-the-thinking-in-graphs-curve | https://markhneedham.com/blog/2013/11/27/neo4j-the-thinking-in-graphs-curve |
false | 2013-11-29 19:50:53 | Neo4j: What is a node? | [
"neo4j"
] | [
"neo4j"
] | One of the first things I needed to learn when I started using Neo4j was how to model my domain using nodes and relationships and it wasn't initially obvious to me what things should be nodes.
Luckily https://twitter.com/iansrobinson[Ian Robinson] showed me a mini-algorithm which I found helpful for getting started. The steps are as follows:
. Write out the questions you want to ask
. Highlight/underline the nouns
. Those are your nodes!
This is reasonably similar to the way that we work out what our objects should be when we're doing OO modelling and I thought I'd give it a try on some of the data sets that I've worked with recently:
* Female *friends* of friends that somebody could go out with
* *Goals* scored by *Arsenal* *players* in a particular *season*
* *Colleagues* who have similar *skills* to me
* *Episodes* of a *TV program* that a particular *actor* appeared in
* *Customers* who would be affected if a piece of *equipment* went in for repair
If you're like me and aren't that great at English grammar we can always cheat and get http://nltk.org/book/ch05.html[NLTK] to help us out:
[source,python]
----
>>> nltk.pos_tag(nltk.word_tokenize("Female friends of friends that somebody could go out with"))
[('Female', 'NNP'), ('friends', 'NNS'), ('of', 'IN'), ('friends', 'NNS'), ('that', 'WDT'), ('somebody', 'NN'), ('could', 'MD'), ('go', 'VB'), ('out', 'RP'), ('with', 'IN')]
----
That tells us the likely tag for each part of speech in the sentence and we can filter the resulting list so we only see nouns like this:
[source,python]
----
>>> nouns = ['NNS', 'NN', 'NP', 'NNP']
>>> [(word, grammar) for (word, grammar) in nltk.pos_tag(nltk.word_tokenize("Female friends of friends that somebody could go out with")) if grammar in nouns]
[('Female', 'NNP'), ('friends', 'NNS'), ('friends', 'NNS'), ('somebody', 'NN')]
----
We can ignore the 'Female' in this sentence (I think it's been picked up as a proper noun because of the capitalisation) which leaves us with 'friends' and 'somebody' In both cases these nouns represent the concept of a person so we'd want to create nodes representing people in this domain.
Let's see how NLTK gets on with our second question:
[source,python]
----
>>> sentence = "Goals scored by Arsenal players in a particular season"
>>> [(word, grammar) for (word, grammar) in nltk.pos_tag(nltk.word_tokenize(sentence)) if grammar in ['NNS', 'NN', 'NP']]
[('Goals', 'NNS'), ('Arsenal', 'NNP'), ('players', 'NNS'), ('season', 'NN')]
----
In this case we'd have goals, teams (e.g. Arsenal), players and seasons as our nodes.
Although this is a very rough algorithm for working out what things should be nodes in a graph I think it's a good way to get started.
After that the other queries we want to write may lead us to change the model to solve our problem even better. | null | null | [
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] | neo4j-what-is-a-node | https://markhneedham.com/blog/2013/11/29/neo4j-what-is-a-node |
false | 2013-11-29 00:51:25 | Neo4j: Modelling 'series' of events | [
"neo4j"
] | [
"neo4j"
] | One of the things I've never worked out how to model in my football graph is series of matches so that I could answer questions like the following:
____
How many goals has Robin Van Persie scored in his last 10 matches in the Barclays Premier League?
____
A brute force approach would be to get all the matches featuring Robin Van Persie in a certain competition, order them by date and take the top ten which would work but doesn't feel very graph.
I wanted to have a linked list of Robin Van Persie's Premier League matches in the graph so that I could very quickly traverse the graph and find the answer to my question.
One example of using a linked list could be for modelling television episodes where we capture the most recent episode and then have relationship between consecutive episodes:
image::{{<siteurl>}}/uploads/2013/11/2013-11-28_00-08-07.png[2013 11 28 00 08 07,600]
This approach works well when a node is only part of one series and if we wanted to create a linked list of the consecutive matches that Robin Van Persie has played in we might model it using this approach:
image::{{<siteurl>}}/uploads/2013/11/2013-11-28_00-22-57.png[2013 11 28 00 22 57,600]
It becomes a bit more interesting if we want to model multiple series of matches e.g. modelling consecutive Premier League matches as well as consecutive Champions League matches.
One way to do this would be to continue to use the 'next' relationship and have multiple linked lists of matches in different competitions
image::{{<siteurl>}}/uploads/2013/11/2013-11-28_00-50-31.png[2013 11 28 00 50 31,600]
A query to find Robin Van Persie's previous 2 Champions League matches from a particular match might read like this:
[source,cypher]
----
MATCH (g:Game)<-[:in]-(playerInMatch)<-[:played]-(player)
WHERE g.name = "Match 4" AND player.name = "Robin Van Persie"
WITH playerInMatch
MATCH (playerInMatch)<-[:NEXT*1..2]-(previousPlayerInMatch)-[:in]->(previousGame)
RETURN previousGame, previousPlayerInMatch
----
This works fine as long as each match is only part of one series but becomes problematic if we want to include a match in multiple series
e.g. the series of all Robin Van Persie matches as well as the series of Robin Van Persie matches played in the Champions League.
One way to do this would be to keep our 'next' relationships as they are but add a property to the relationship to indicate which 'series' we were interested in following:
image::{{<siteurl>}}/uploads/2013/11/2013-11-28_01-06-30.png[2013 11 28 01 06 30,600]
The query to find previous Champions League matches would now read like this:
[source,cypher]
----
MATCH (g:Game)<-[:in]-(playerInMatch)<-[:played]-(player)
WHERE g.name = "Match 4" AND player.name = "Robin Van Persie"
WITH playerInMatch
MATCH (playerInMatch)<-[n:NEXT*1..2]-(previousPlayerInMatch)-[:in]->(previousGame)
WHERE n.series = "CL"
RETURN previousGame, previousPlayerInMatch
----
Whenever I find myself modelling something through properties it's often the case that I've missed a domain concept. In this example the overloading of the _next_ relationship indicates a missing concept.
An alternative would be to rename the _next_ relationships for the Champions League and Premier League matches to _next_champions_league_ and _next_premier_league_ respectively although that could get verbose very quickly.
While working through this with https://twitter.com/apcj[Alistair] he suggested that we make the _series_ concept even more explicit by pulling out a corresponding _playerInMatchSeries_ node to go along side the _playerInMatch_ (PIM) node.
image::{{<siteurl>}}/uploads/2013/11/2013-11-28_09-10-02.png[2013 11 28 09 10 02,600]
If we wanted a series which wasn't for a specific competition we'd exclude the _in_competition_ relationship. If we expand that idea out we might end up with the following graph:
image::{{<siteurl>}}/uploads/2013/11/2013-11-28_01-20-30.png[2013 11 28 01 20 30,600]
I spiked out this idea on a small data set featuring Robin Van Persie's last 10-15 matches in various competitions:
[source,cypher]
----
CREATE (robin:Player {name: "Robin Van Persie"})
CREATE (manUtd:Team {name: "Man Utd"})
CREATE (arsenal:Team {name: "Arsenal"})
CREATE (realSociedad:Team {name: "Real Sociedad"})
CREATE (fulham:Team {name: "Fulham"})
CREATE (stoke:Team {name: "Stoke"})
CREATE (southampton:Team {name: "Southampton"})
CREATE (netherlands:Team {name: "Netherlands"})
CREATE (turkey:Team {name: "Turkey"})
CREATE (hungary:Team {name: "Hungary"})
CREATE (sunderland:Team {name: "Sunderland"})
CREATE (shakhtar:Team {name: "Shakhtar Donetsk"})
CREATE (westBrom:Team {name: "West Brom"})
CREATE (leverkusen:Team {name: "Bayer Leverkusen"})
CREATE (estonia:Team {name: "Estonia"})
CREATE (andorra:Team {name: "Andorra"})
CREATE (premierLeague:Competition {name: "Barclays Premier League"})
CREATE (championsLeague:Competition {name: "Champions League"})
CREATE (worldCupQualifiers:Competition {name: "World Cup Qualifiers"})
CREATE (manUtdVsArsenal:Game {name: "Man Utd vs Arsenal", date: 1384041600, home_goals: 1, away_goals: 0})
CREATE (robinManUtdVsArsenal {name: "RVP in Man Utd vs Arsenal", goals: 1})
CREATE UNIQUE (manUtdVsArsenal)-[:in_competition]->(premierLeague)
CREATE UNIQUE (robin)-[:played]-(robinManUtdVsArsenal)-[:in]->(manUtdVsArsenal)
CREATE UNIQUE (robinManUtdVsArsenal)-[:for]->(manUtd)
CREATE UNIQUE (arsenal)<-[:away_team]-(manUtdVsArsenal)-[:home_team]->(manUtd)
CREATE (realSociedadVsManUtd:Game {name: "Real Sociedad vs Man Utd", date: 1383609600, home_goals: 0, away_goals: 0})
CREATE (robinRealSociedadVsManUtd {name: "RVP in Real Sociedad vs Man Utd", goals: 0})
CREATE UNIQUE (realSociedadVsManUtd)-[:in_competition]->(championsLeague)
CREATE UNIQUE (robin)-[:played]-(robinRealSociedadVsManUtd)-[:in]->(realSociedadVsManUtd)
CREATE UNIQUE (robinRealSociedadVsManUtd)-[:for]->(manUtd)
CREATE UNIQUE (manUtd)<-[:away_team]-(realSociedadVsManUtd)-[:home_team]->(realSociedad)
CREATE (fulhamVsManUtd:Game {name: "Fulham vs Man Utd", date: 1383350400, home_goals: 1, away_goals: 3})
CREATE (robinFulhamVsManUtd {name: "RVP in Fulham vs Man Utd", goals: 1})
CREATE UNIQUE (fulhamVsManUtd)-[:in_competition]->(premierLeague)
CREATE UNIQUE (robin)-[:played]-(robinFulhamVsManUtd)-[:in]->(fulhamVsManUtd)
CREATE UNIQUE (fulhamVsManUtd)-[:for]->(manUtd)
CREATE UNIQUE (manUtd)<-[:away_team]-(fulhamVsManUtd)-[:home_team]->(fulham)
CREATE (manUtdVsStoke:Game {name: "Man Utd vs Stoke", date: 1382745600, home_goals: 3, away_goals: 2})
CREATE (robinManUtdVsStoke {name: "RVP in Man Utd vs Stoke", goals: 1})
CREATE UNIQUE (manUtdVsStoke)-[:in_competition]->(premierLeague)
CREATE UNIQUE (robin)-[:played]-(robinManUtdVsStoke)-[:in]->(manUtdVsStoke)
CREATE UNIQUE (manUtdVsStoke)-[:for]->(manUtd)
CREATE UNIQUE (stoke)<-[:away_team]-(manUtdVsStoke)-[:home_team]->(manUtd)
CREATE (manUtdVsSouthampton:Game {name: "Man Utd vs Southampton", date: 1382140800, home_goals: 1, away_goals: 1})
CREATE (robinManUtdVsSouthampton {name: "RVP in Man Utd vs Southampton", goals: 1})
CREATE UNIQUE (manUtdVsSouthampton)-[:in_competition]->(premierLeague)
CREATE UNIQUE (robin)-[:played]-(robinManUtdVsSouthampton)-[:in]->(manUtdVsSouthampton)
CREATE UNIQUE (manUtdVsSouthampton)-[:for]->(manUtd)
CREATE UNIQUE (southampton)<-[:away_team]-(manUtdVsSouthampton)-[:home_team]->(manUtd)
CREATE (turkeyVsNetherlands:Game {name: "Turkey vs Netherlands", date: 1381795200, home_goals: 0, away_goals: 2})
CREATE (robinTurkeyVsNetherlands {name: "RVP in Turkey vs Netherlands", goals: 0})
CREATE UNIQUE (turkeyVsNetherlands)-[:in_competition]->(worldCupQualifiers)
CREATE UNIQUE (robin)-[:played]-(robinTurkeyVsNetherlands)-[:in]->(turkeyVsNetherlands)
CREATE UNIQUE (turkeyVsNetherlands)-[:for]->(netherlands)
CREATE UNIQUE (netherlands)<-[:away_team]-(turkeyVsNetherlands)-[:home_team]->(turkey)
CREATE (netherlandsVsHungary:Game {name: "Netherlands vs Hungary", date: 1381449600, home_goals: 8, away_goals: 1})
CREATE (robinNetherlandsVsHungary {name: "RVP in Netherlands vs Hungary", goals: 3})
CREATE UNIQUE (netherlandsVsHungary)-[:in_competition]->(worldCupQualifiers)
CREATE UNIQUE (robin)-[:played]-(robinNetherlandsVsHungary)-[:in]->(netherlandsVsHungary)
CREATE UNIQUE (netherlandsVsHungary)-[:for]->(netherlands)
CREATE UNIQUE (hungary)<-[:away_team]-(netherlandsVsHungary)-[:home_team]->(netherlands)
CREATE (sunderlandVsManUtd:Game {name: "Sunderland vs Man Utd", date: 1380931200, home_goals: 1, away_goals: 2})
CREATE (robinSunderlandVsManUtd {name: "RVP in Sunderland vs Man Utd", goals: 0})
CREATE UNIQUE (sunderlandVsManUtd)-[:in_competition]->(premierLeague)
CREATE UNIQUE (robin)-[:played]-(robinSunderlandVsManUtd)-[:in]->(sunderlandVsManUtd)
CREATE UNIQUE (sunderlandVsManUtd)-[:for]->(manUtd)
CREATE UNIQUE (manUtd)<-[:away_team]-(sunderlandVsManUtd)-[:home_team]->(sunderland)
CREATE (shakhtarVsManUtd:Game {name: "Shaktar Donetsk vs Man Utd", date: 1380672000, home_goals: 1, away_goals: 1})
CREATE (robinShaktarVsManUtd {name: "RVP in Shaktar Donetsk vs Man Utd", goals: 0})
CREATE UNIQUE (shakhtarVsManUtd)-[:in_competition]->(championsLeague)
CREATE UNIQUE (robin)-[:played]-(robinShaktarVsManUtd)-[:in]->(shakhtarVsManUtd)
CREATE UNIQUE (shakhtarVsManUtd)-[:for]->(manUtd)
CREATE UNIQUE (manUtd)<-[:away_team]-(shakhtarVsManUtd)-[:home_team]->(shakhtar)
CREATE (manUtdVsWestBrom:Game {name: "Man Utd vs West Brom", date: 1380326400, home_goals: 1, away_goals: 2})
CREATE (robinManUtdVsWestBrom {name: "RVP in Man Utd vs West Brom", goals: 0})
CREATE UNIQUE (manUtdVsWestBrom)-[:in_competition]->(premierLeague)
CREATE UNIQUE (robin)-[:played]-(robinManUtdVsWestBrom)-[:in]->(manUtdVsWestBrom)
CREATE UNIQUE (manUtdVsWestBrom)-[:for]->(manUtd)
CREATE UNIQUE (westBrom)<-[:away_team]-(manUtdVsWestBrom)-[:home_team]->(manUtd)
CREATE (manUtdVsLeverkusen:Game {name: "Man Utd vs Bayer Leverkusen", date: 1379376000, home_goals: 4, away_goals: 2})
CREATE (robinManUtdVsLeverkusen {name: "RVP in Man Utd vs Bayer Leverkusen", goals: 1})
CREATE UNIQUE (manUtdVsLeverkusen)-[:in_competition]->(championsLeague)
CREATE UNIQUE (robin)-[:played]-(robinManUtdVsLeverkusen)-[:in]->(manUtdVsLeverkusen)
CREATE UNIQUE (robinManUtdVsLeverkusen)-[:for]->(manUtd)
CREATE UNIQUE (leverkusen)<-[:away_team]-(manUtdVsLeverkusen)-[:home_team]->(manUtd)
CREATE (manUtdVsPalace:Game {name: "Man Utd vs Crystal Palace", date: 1379116800, home_goals: 2, away_goals: 0})
CREATE (robinManUtdVsPalace {name: "RVP in Man Utd vs Crystal Palace", goals: 1})
CREATE UNIQUE (manUtdVsPalace)-[:in_competition]->(premierLeague)
CREATE UNIQUE (robin)-[:played]-(robinManUtdVsPalace)-[:in]->(manUtdVsPalace)
CREATE UNIQUE (manUtdVsPalace)-[:for]->(manUtd)
CREATE UNIQUE (palace)<-[:away_team]-(manUtdVsPalace)-[:home_team]->(manUtd)
CREATE (andorraVsNetherlands:Game {name: "Andorra vs Netherlands", date: 1378771200, home_goals: 0, away_goals: 2})
CREATE (robinAndorraVsNetherlands {name: "RVP in Andorra vs Netherlands", goals: 2})
CREATE UNIQUE (andorraVsNetherlands)-[:in_competition]->(worldCupQualifiers)
CREATE UNIQUE (robin)-[:played]-(robinAndorraVsNetherlands)-[:in]->(andorraVsNetherlands)
CREATE UNIQUE (andorraVsNetherlands)-[:for]->(netherlands)
CREATE UNIQUE (netherlands)<-[:away_team]-(andorraVsNetherlands)-[:home_team]->(andorra)
CREATE (netherlandsVsEstonia:Game {name: "Netherlands vs Estonia", date: 1378425600, home_goals: 2, away_goals: 2})
CREATE (robinNetherlandsVsEstonia {name: "RVP in Netherlands vs Estonia", goals: 1})
CREATE UNIQUE (netherlandsVsEstonia)-[:in_competition]->(worldCupQualifiers)
CREATE UNIQUE (robin)-[:played]-(robinNetherlandsVsEstonia)-[:in]->(netherlandsVsEstonia)
CREATE UNIQUE (netherlandsVsEstonia)-[:for]->(netherlands)
CREATE UNIQUE (estonia)<-[:away_team]-(netherlandsVsEstonia)-[:home_team]->(netherlands)
----
The first step is to create the _playerInMatchSeries_ 'layer' which we do by returning matches by competition and adding the appropriate relationship:
[source,cypher]
----
MATCH (p:Player)-[:played]->(stats)-[:in]->(game)-[:in_competition]->(c)
WHERE p.name = "Robin Van Persie"
WITH game, c, stats, p
ORDER BY game.date
CREATE UNIQUE (stats)-[:in_series]->(series {name: p.name + " in " + c.name + " Series: " + game.name})-[:for_competition]->(c)
----
We then execute a second query to add the series which represents all the matches Robin Van Persie played in regardless of competition:
[source,cypher]
----
MATCH (p:Player)-[:played]->(stats)-[:in]->(game)
WHERE p.name = "Robin Van Persie"
WITH game, stats, p
ORDER BY game.date
CREATE UNIQUE (stats)-[:in_series]->(series {name: p.name + " in All Competitions Series: " + game.name})-[:all_competitions]->()
----
I couldn't think where the +++<cite>+++all_competitions+++</cite>+++ relationship should be connected to so I've gone for a blank node for the moment.
The next step is to add a 'NEXT' relationship between these series nodes so that we'll be able to easily navigate between them:
[source,cypher]
----
MATCH (p:Player)-[:played]->(stats)-[:in]->(game)-[:in_competition]->(c),
(stats)-[:in_series]->(series)-[:for_competition]->(c)
WHERE p.name = "Robin Van Persie"
WITH series, stats, c, game
ORDER BY game.date
WITH c, COLLECT(series) AS items
FOREACH(i in RANGE(0, length(items)-2) |
FOREACH(itemsi in [items[i]] |
FOREACH(itemsi2 in [items[i+1]] |
CREATE UNIQUE (itemsi)-[:NEXT]->(itemsi2))))
----
[source,cypher]
----
MATCH (p:Player)-[:played]->(stats)-[:in]->(game)-[:in_competition]->(c),
(stats)-[:in_series]->(series)-[:all_competitions]->()
WHERE p.name = "Robin Van Persie"
WITH series, stats, c, game
ORDER BY game.date
WITH COLLECT(series) AS items
FOREACH(i in RANGE(0, length(items)-2) |
FOREACH(itemsi in [items[i]] |
FOREACH(itemsi2 in [items[i+1]] |
CREATE UNIQUE (itemsi)-[:NEXT]->(itemsi2))))
----
The syntax is a bit verbose and I covered http://www.markhneedham.com/blog/2013/11/22/neo4j-cypher-creating-relationships-between-nodes-from-adjacent-rows-in-a-query/[why it ends up being like this] in a previous post.
The next step is to check that we can actually query this part of the graph so we'll start by trying to find Robin Van Persie's previous 5 matches before the 'Man Utd vs Arsenal' match:
[source,cypher]
----
MATCH (g:Game)<-[:in]-(stats)<-[:played]-(player)
WHERE g.name = "Man Utd vs Arsenal" AND player.name = "Robin Van Persie"
WITH g, player, stats
MATCH (stats)-[:in_series]->(series)<-[:NEXT*1..5]-(previousSeries)<-[:in_series]-(previousStats),
(series)-[:all_competitions]-(),
(previousStats)-[:in]->(previousGame)
RETURN previousGame.name as match,
previousGame.date as date,
previousGame.home_goals + "-" + previousGame.away_goals as score,
previousStats.goals as goals
----
[source,bash]
----
==> +---------------------------------------------------------+
==> | match | date | score | goals |
==> +---------------------------------------------------------+
==> | "Real Sociedad vs Man Utd" | 1383609600 | "0-0" | 0 |
==> | "Fulham vs Man Utd" | 1383350400 | "1-3" | 1 |
==> | "Man Utd vs Stoke" | 1382745600 | "3-2" | 1 |
==> | "Man Utd vs Southampton" | 1382140800 | "1-1" | 1 |
==> | "Turkey vs Netherlands" | 1381795200 | "0-2" | 0 |
==> +---------------------------------------------------------+
----
We can check that against http://espnfc.com/player/_/id/18858/robin-van-persie?cc=5739[his ESPN profile] to confirm its correct and indeed it is:
image::{{<siteurl>}}/uploads/2013/11/2013-11-29_00-41-54.png[2013 11 29 00 41 54,600]
If we wanted to only find his previous matches in that particular competition we would need to tweak the query to take the _for_competition_ relationship into account:
[source,cypher]
----
MATCH (competition)<-[:in_competition]-(g:Game)<-[:in]-(stats)<-[:played]-(player)
WHERE g.name = "Man Utd vs Arsenal" AND player.name = "Robin Van Persie"
WITH g, player, stats, competition
MATCH (stats)-[:in_series]->(series)<-[:NEXT*1..5]-(previousSeries)<-[:in_series]-(previousStats),
(series)-[:for_competition]-(competition),
(previousStats)-[:in]->(previousGame)
RETURN previousGame.name as match,
previousGame.date as date,
previousGame.home_goals + "-" + previousGame.away_goals as score,
previousStats.goals as goals
----
If we run that we get the following results:
[source,bash]
----
==> +-------------------------------------------------------+
==> | match | date | score | goals |
==> +-------------------------------------------------------+
==> | "Fulham vs Man Utd" | 1383350400 | "1-3" | 1 |
==> | "Man Utd vs Stoke" | 1382745600 | "3-2" | 1 |
==> | "Man Utd vs Southampton" | 1382140800 | "1-1" | 1 |
==> | "Sunderland vs Man Utd" | 1380931200 | "1-2" | 0 |
==> | "Man Utd vs West Brom" | 1380326400 | "1-2" | 0 |
==> +-------------------------------------------------------+
----
A quick glance at his profile suggests all is well. Finally we'll do a sanity check by starting from a different match in a different competition:
[source,cypher]
----
MATCH (competition)<-[:in_competition]-(g:Game)<-[:in]-(stats)<-[:played]-(player)
WHERE g.name = "Turkey vs Netherlands" AND player.name = "Robin Van Persie"
WITH g, player, stats, competition
MATCH (stats)-[:in_series]->(series)<-[:NEXT*1..5]-(previousSeries)<-[:in_series]-(previousStats),
(series)-[:for_competition]-(competition),
(previousStats)-[:in]->(previousGame)
RETURN previousGame.name as match,
previousGame.date as date,
previousGame.home_goals + "-" + previousGame.away_goals as score,
previousStats.goals as goals
----
And if we run that\...
[source,bash]
----
==> +-------------------------------------------------------+
==> | match | date | score | goals |
==> +-------------------------------------------------------+
==> | "Netherlands vs Hungary" | 1381449600 | "8-1" | 3 |
==> | "Andorra vs Netherlands" | 1378771200 | "0-2" | 2 |
==> | "Netherlands vs Estonia" | 1378425600 | "2-2" | 1 |
==> +-------------------------------------------------------+
----
\...the results are as we'd expect.
This post has got ridiculously long so if you've reached this point I'd love to hear your thoughts about this approach - does it make sense or is it way too complicated? Let me know! | null | null | [
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] | neo4j-modelling-series-of-events | https://markhneedham.com/blog/2013/11/29/neo4j-modelling-series-of-events |
false | 2013-11-29 17:08:49 | Neo4j: The case of neo4j-shell and the invisible text ft. Windows and the neo4j-desktop | [
"neo4j"
] | [
"neo4j"
] | I've been playing around with Neo4j on a Windows VM recently and I wanted to launch http://docs.neo4j.org/chunked/stable/shell.html[neo4j-shell] to run a few queries.
The neo4j-shell script isn't shipped with http://blog.neo4j.org/2013/09/installer-check-desktop-launcher-check.html[Neo4j desktop] which I used to install Neo4j on my VM but we can still launch it from the Windows Command Prompt with the following command:</p ~~~bash C:\Users\Mark> cd "C:\Program Files\Neo4j Community" C:\Program Files\Neo4j Community>jre\bin\java -cp bin\neo4j-desktop-2.0.0-RC1.jar org.neo4j.shell.StartClient Welcome to the Neo4j Shell! Enter 'help' for a list of commands NOTE: Remote Neo4j graph database service 'shell' at port 1337 Want bash-like features? throw in jLine (http://jline.sourceforge.net) on the classpath ~~~
Unfortunately if we start typing in a query we don't get any feedback as to what we're typed, although if we type something syntactically valid and suffix it with a semi colon and press enter it will be evaluated!
e.g. this is the output from entering the following query ~~~cypher START n = node(*) RETURN COUNT(n); ~~~
image::{{<siteurl>}}/uploads/2013/11/2013-11-29_16-53-56.png[2013 11 29 16 53 56,600]
The reason for the problem is to do with the '.shell_history' file that the shell tries to write to the directory it's launched from. In this case we don't have permission to write anything to 'C:\Program Files\Neo4j Community' and so we get no feedback.
To workaround this problem we need to start the command prompt as an administrator which we can do from the apps menu by right clicking the 'Command Prompt' icon:
image::{{<siteurl>}}/uploads/2013/11/2013-11-29_17-00-59.png[2013 11 29 17 00 59,244]
Next we click 'Start as Administrator' and click through the next screen:
image::{{<siteurl>}}/uploads/2013/11/2013-11-29_17-01-14.png[2013 11 29 17 01 14,501]
Now if we launch neo4j-shell and type in our query we get the expected feedback:
image::{{<siteurl>}}/uploads/2013/11/2013-11-29_17-04-26.png[2013 11 29 17 04 26,600] | null | null | [
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] | neo4j-the-case-of-windows-neo4j-desktop-and-the-invisible-text | https://markhneedham.com/blog/2013/11/29/neo4j-the-case-of-windows-neo4j-desktop-and-the-invisible-text |
false | 2013-11-16 21:58:08 | Git: Viewing the last commit on all the tags | [
"git",
"neo4j"
] | [
"Version Control"
] | A couple of days ago I was curious when different versions of http://www.neo4j.org/[Neo4j] had been released and although the http://www.neo4j.org/release-notes[release notes page] was helpful I thought I'd find more detailed information if I looked up the git tags.
Assuming that we've already got a clone of the repository on our machine:
[source,bash]
----
$ git clone [email protected]:neo4j/neo4j.git
----
We can pull down the latest tags by calling +++<cite>+++git fetch --tags+++</cite>+++ or +++<cite>+++git fetch -t+++</cite>+++
[source,bash]
----
$ git fetch -t
remote: Counting objects: 542, done.
remote: Compressing objects: 100% (231/231), done.
remote: Total 287 (delta 247), reused 84 (delta 50)
Receiving objects: 100% (287/287), 42.85 KiB, done.
Resolving deltas: 100% (247/247), completed with 191 local objects.
From github.com:neo4j/neo4j
* [new tag] 1.9.2 -> 1.9.2
* [new tag] 1.9.5 -> 1.9.5
* [new tag] 2.0.0-M06 -> 2.0.0-M06
----
We can get a list of all the tags in the repository with the following command:
[source,bash]
----
$ git tag | head -n5
1.3
1.4
1.4.1
1.4.2
1.4.M01
----
Now let's have a look which commit that tag points at:
[source,bash]
----
$ git show 1.3
tag 1.3
Tagger: Neo4j Build Server <[email protected]>
Date: Tue Nov 20 17:03:38 2012 +0000
Tagging for release 1.3
commit ff16757dd53399eccb8f3db40eb48bab065459b0
Author: Neo Technology buildbox <[email protected]>
Date: Tue Apr 12 22:03:33 2011 +0000
----
That command gets us the appropriate information but ideally we want to get the commit hash and the date on a single line which we can do by passing the 'https://www.kernel.org/pub/software/scm/git/docs/git-log.html#_pretty_formats[--format]' flag to +++<cite>+++git log+++</cite>+++:
[source,bash]
----
$ git log --format="%h %ad%n" 1.3
ff16757 Tue Apr 12 22:03:33 2011 +0000
9651aa8 Tue Apr 12 21:58:58 2011 +0000
21c637d Tue Apr 12 12:39:49 2011 +0200
4ed65eb Tue Apr 12 12:39:28 2011 +0200
----
We can pipe that to +++<cite>+++head+++</cite>+++ to get the most recent commit:
[source,bash]
----
$ git log --format="%h %ad%n" 1.3 | head -n1
ff16757 Tue Apr 12 22:03:33 2011 +0000
----
I tried to pipe the output of +++<cite>+++git tag+++</cite>+++ to +++<cite>+++git log+++</cite>+++ using +++<cite>+++xargs+++</cite>+++ but I couldn't get it to work so I resorted to a for loop instead:
[source,bash]
----
$ for tag in `git tag`; do printf "%-20s %-100s \n" $tag "`git log --format="%h %ad%n" $tag | head -n1`"; done | head -n5
1.3 ff16757 Tue Apr 12 22:03:33 2011 +0000
1.4 5c19dc3 Fri Jul 8 16:22:37 2011 +0200
1.4.1 55f4ab2 Tue Aug 2 15:14:11 2011 +0300
1.4.2 cb85742 Tue Sep 27 18:59:13 2011 +0100
1.4.M01 f5aacf4 Fri Apr 29 10:12:52 2011 +0200
----
We could then pipe that output through grep to only show non point releases:
[source,bash]
----
$ for tag in `git tag`; do printf "%-20s %-100s \n" $tag "`git log --format="%h %ad%n" $tag | head -n1`"; done | grep -E "^\d\.\d "
1.3 ff16757 Tue Apr 12 22:03:33 2011 +0000
1.4 5c19dc3 Fri Jul 8 16:22:37 2011 +0200
1.5 0225cb7 Thu Oct 20 03:51:06 2011 +0200
1.6 f6f3cc1 Sun Jan 22 15:02:04 2012 +0100
1.7 cc4ad98 Wed Apr 18 18:32:20 2012 +0200
1.8 084acc9 Tue Sep 25 09:47:04 2012 +0100
1.9 2efc04c Mon May 20 12:08:24 2013 +0100
----
I first played around with Neo4j in September 2011 and I now know that I was using version 1.4 at the time.
We're now at 1.9.5 and the latest beta release is 2.0.0-M06 so there have been quite a few releases in between! | null | null | [
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] | git-viewing-the-last-commit-on-all-the-tags | https://markhneedham.com/blog/2013/11/16/git-viewing-the-last-commit-on-all-the-tags |
false | 2013-11-17 22:58:35 | Java: Schedule a job to run on a time interval | [
"java"
] | [
"Java"
] | Recently I've spent some time building a set of tests around rolling upgrades between http://www.neo4j.org/[Neo4j] versions and as part of that I wanted to log the state of the cluster as the upgrade was happening.
The main thread of the test blocks waiting until the upgrade is done so I wanted to log on another thread every few seconds. Alistair pointed me at the +++<cite>+++http://docs.oracle.com/javase/7/docs/api/java/util/concurrent/ScheduledExecutorService.html[ScheduledExecutorService]+++</cite>+++ which worked quite nicely.
I ended up with a test which looked roughly like this:
[source,java]
----
public class MyUpgradeTest {
@Test
public void shouldUpgradeFromOneVersionToAnother() throws InterruptedException
{
ScheduledExecutorService scheduledExecutorService = Executors.newSingleThreadScheduledExecutor();
scheduledExecutorService.scheduleAtFixedRate( new LogAllTheThings(), 0, 1, TimeUnit.SECONDS );
Thread.sleep(10000);
// do upgrade of cluster
scheduledExecutorService.shutdown();
}
static class LogAllTheThings implements Runnable
{
@Override
public void run()
{
Date time = new Date( System.currentTimeMillis() );
try
{
Map<String, Object> masterProperties = selectedProperties( client(), URI.create( "http://localhost:7474/" ) );
System.out.println( String.format( "%s: %s", time, masterProperties ) );
}
catch ( Exception ignored )
{
ignored.printStackTrace();
}
}
private static Client client()
{
DefaultClientConfig defaultClientConfig = new DefaultClientConfig();
defaultClientConfig.getClasses().add( JacksonJsonProvider.class );
return Client.create( defaultClientConfig );
}
public static Map<String, Object> selectedProperties( Client client, URI uri )
{
Map<String, Object> jmxProperties = new HashMap<String, Object>();
ArrayNode transactionsProperties = jmxBean( client, uri, "org.neo4j/instance%3Dkernel%230%2Cname%3DTransactions" );
addProperty( jmxProperties, transactionsProperties, "LastCommittedTxId" );
ArrayNode kernelProperties = jmxBean( client, uri, "org.neo4j/instance%3Dkernel%230%2Cname%3DKernel" );
addProperty( jmxProperties, kernelProperties, "KernelVersion" );
ArrayNode haProperties = jmxBean( client, uri, "org.neo4j/instance%3Dkernel%230%2Cname%3DHigh+Availability" );
addProperty( jmxProperties, haProperties, "Role" );
addProperty( jmxProperties, haProperties, "InstanceId" );
return jmxProperties;
}
private static void addProperty( Map<String, Object> jmxProperties, ArrayNode properties, String propertyName )
{
jmxProperties.put( propertyName, getProperty( properties, propertyName ) );
}
private static String getProperty( ArrayNode properties, String propertyName )
{
for ( JsonNode property : properties )
{
if ( property.get( "name" ).asText().equals( propertyName ) )
{
return property.get( "value" ).asText();
}
}
throw new RuntimeException( "Could not find requested property: " + propertyName );
}
private static ArrayNode jmxBean( Client client, URI uri, String beanExtension )
{
ClientResponse clientResponse = client
.resource( uri + "db/manage/server/jmx/domain/" + beanExtension )
.accept( MediaType.APPLICATION_JSON )
.get( ClientResponse.class );
JsonNode transactionsBean = clientResponse.getEntity( JsonNode.class );
return (ArrayNode) transactionsBean.get( 0 ).get( "attributes" );
}
}
}
----
+++<cite>+++LogAllTheThings+++</cite>+++ gets called once every second and it logs the KernelVersion, InstanceId, LastCommittedTxId and Role which Neo4j server exposes as JMX properties.
If we run that against a local Neo4j cluster we'd see something like the following:
[source,text]
----
Sun Nov 17 22:31:55 GMT 2013: {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=1, LastCommittedTxId=18, Role=master}
Sun Nov 17 22:31:56 GMT 2013: {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=1, LastCommittedTxId=18, Role=master}
Sun Nov 17 22:31:57 GMT 2013: {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=1, LastCommittedTxId=18, Role=master}
Sun Nov 17 22:31:58 GMT 2013: {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=1, LastCommittedTxId=18, Role=master}
Sun Nov 17 22:31:59 GMT 2013: {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=1, LastCommittedTxId=18, Role=master}
...
removed for brevity
----
The next step was to get the properties of all the members of the cluster at the same time and to do that we can introduce another +++<cite>+++ExecutorService+++</cite>+++ which has a thread pool of 3 so that it will evaluate each machine (at least close to) simultaneously:
[source,java]
----
static class LogAllTheThings implements Runnable
{
private ExecutorService executorService = Executors.newFixedThreadPool( 3 );
@Override
public void run()
{
List<URI> machines = new ArrayList<>( );
machines.add(URI.create( "http://localhost:7474/" ));
machines.add(URI.create( "http://localhost:7484/" ));
machines.add(URI.create( "http://localhost:7494/" ));
Map<URI, Future<Map<String, Object>>> futureJmxProperties = new HashMap<>( );
for ( final URI machine : machines )
{
Future<Map<String, Object>> futureProperties = executorService.submit( new Callable<Map<String, Object>>()
{
@Override
public Map<String, Object> call() throws Exception
{
try
{
return selectedProperties( client(), machine );
}
catch ( Exception ignored )
{
ignored.printStackTrace();
return new HashMap<>();
}
}
} );
futureJmxProperties.put( machine, futureProperties );
}
Date time = new Date( System.currentTimeMillis() );
System.out.println( time );
for ( Map.Entry<URI, Future<Map<String, Object>>> uriFutureEntry : futureJmxProperties.entrySet() )
{
try
{
System.out.println( "==> " + uriFutureEntry.getValue().get() );
}
catch ( Exception ignored )
{
}
}
}
// other methods the same as above
}
----
We submit each job to the +++<cite>+++ExecutorService+++</cite>+++ and receive back a +++<cite>+++Future+++</cite>+++ which we store in a map before retrieving its result later on. If we run that we'll see the following output:
[source,text]
----
Sun Nov 17 22:49:58 GMT 2013
==> {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=1, LastCommittedTxId=18, Role=master}
==> {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=2, LastCommittedTxId=18, Role=slave}
==> {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=3, LastCommittedTxId=18, Role=slave}
Sun Nov 17 22:49:59 GMT 2013
==> {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=1, LastCommittedTxId=18, Role=master}
==> {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=2, LastCommittedTxId=18, Role=slave}
==> {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=3, LastCommittedTxId=18, Role=slave}
Sun Nov 17 22:50:00 GMT 2013
==> {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=1, LastCommittedTxId=18, Role=master}
==> {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=2, LastCommittedTxId=18, Role=slave}
==> {KernelVersion=Neo4j - Graph Database Kernel 2.0.0-M06, InstanceId=3, LastCommittedTxId=18, Role=slave}
...
removed for brevity
----
Overall the approach works quite well although I'm always open to learning of a better way if there is one! | null | null | [
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] | java-schedule-a-job-to-run-on-a-time-interval | https://markhneedham.com/blog/2013/11/17/java-schedule-a-job-to-run-on-a-time-interval |
false | 2013-11-26 00:12:56 | Neo4j: Using aliases to handle messy data | [
"neo4j"
] | [
"neo4j"
] | One of the common problems when building data heavy applications is that names of things in the domain are often named differently depending on which system you get the data from.
This means that we'll typically end up running the data from different sources through a normalisation process to ensure that we have consistent naming in the database:
image::{{<siteurl>}}/uploads/2013/11/data-nromalisation.png[Data nromalisation,444]
I've recently started linking the football stadium a match was played in to the match in my football graph but unfortunately different match compilers use different spellings or even names for the same stadium.
My first instinct was to write a normalisation layer but instead I decided to store the stadium names as they were but separately create an 'alias' relationship back in to the correct spelling.
The model looks like this:
image::{{<siteurl>}}/uploads/2013/11/2013-11-26_00-16-42.png[2013 11 26 00 16 42,600]
I add a 'Stadium' label to all the stadiums which means that if we do a query to find the number of games at each stadium we'll get back games at the misspelled versions too:
[source,cypher]
----
MATCH (stadium:Stadium)<-[:in_stadium]-(game)
RETURN stadium.name, count(game) AS games
ORDER BY games DESC
----
[source,bash]
----
==> +-------------------------------+
==> | stadium.name | games |
==> +-------------------------------+
==> | "Craven Cottage" | 57 |
==> | "Villa Park" | 57 |
==> | "Anfield" | 57 |
==> | "Stamford Bridge" | 57 |
==> | "Britannia Stadium" | 57 |
==> | "Emirates Stadium" | 57 |
==> | "Etihad Stadium" | 57 |
==> | "Stadium of Light" | 57 |
==> | "Old Trafford" | 57 |
==> | "The Hawthorns" | 57 |
==> | "White Hart Lane" | 57 |
==> | "Goodison Park" | 57 |
==> | "DW Stadium" | 39 |
==> | "Molineux Stadium" | 38 |
==> | "Liberty Stadium" | 38 |
==> | "Ewood Park" | 38 |
==> | "Carrow Road" | 38 |
==> | "Reebok Stadium" | 38 |
==> | "Loftus Road Stadium" | 37 |
==> | "St James' Park" | 34 |
==> | "Upton Park" | 33 |
==> | "Bloomfield Road" | 19 |
==> | "Madejski Stadium" | 19 |
==> | "St. James' Park" | 19 |
==> | "St Andrews Stadium" | 19 |
==> | "St. Mary's Stadium" | 19 |
==> | "The DW Stadium" | 18 |
==> | "Boleyn Ground" | 5 |
==> | "St. James Park" | 4 |
==> | "Loftus Road" | 1 |
==> +-------------------------------+
==> 30 rows
----
We only want to show the proper spellings of stadiums which we can do with the following query:
[source,cypher]
----
MATCH (stadium:Stadium)<-[:alias_of*0..1]-()<-[:in_stadium]-(game)
WHERE NOT(stadium-[:alias_of]->())
return stadium.name, count(game) AS games
ORDER BY games DESC
----
Here we get all the stadiums, then get any incoming aliases and find the games played in those stadiums. We then filter out any stadium which has an outgoing 'alias_of' relationship because that indicates that the node isn't the original node for the stadium.
If we run the query we'll get the expected result:
[source,bash]
----
==> +-------------------------------+
==> | stadium.name | games |
==> +-------------------------------+
==> | "St James' Park" | 57 |
==> | "Craven Cottage" | 57 |
==> | "Villa Park" | 57 |
==> | "Anfield" | 57 |
==> | "Stamford Bridge" | 57 |
==> | "Britannia Stadium" | 57 |
==> | "Emirates Stadium" | 57 |
==> | "Etihad Stadium" | 57 |
==> | "Stadium of Light" | 57 |
==> | "Old Trafford" | 57 |
==> | "The Hawthorns" | 57 |
==> | "White Hart Lane" | 57 |
==> | "The DW Stadium" | 57 |
==> | "Goodison Park" | 57 |
==> | "Loftus Road Stadium" | 38 |
==> | "Molineux Stadium" | 38 |
==> | "Upton Park" | 38 |
==> | "Liberty Stadium" | 38 |
==> | "Ewood Park" | 38 |
==> | "Carrow Road" | 38 |
==> | "Reebok Stadium" | 38 |
==> | "Bloomfield Road" | 19 |
==> | "Madejski Stadium" | 19 |
==> | "St Andrews Stadium" | 19 |
==> | "St. Mary's Stadium" | 19 |
==> +-------------------------------+
==> 25 rows
----
The nice thing about this approach is that I only need to collect aliases in one place and I can just import the data as is from the source.
On the other hand it does add some complexity to queries as you need to take aliases into account each time.
I'd love to hear your thoughts on what you think of this approach. | null | null | [
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] | neo4j-using-aliases-to-handle-messy-data | https://markhneedham.com/blog/2013/11/26/neo4j-using-aliases-to-handle-messy-data |
false | 2013-11-09 13:58:54 | Python: Making scikit-learn and pandas play nice | [
"python"
] | [
"Python"
] | In the last post I wrote about http://junctionbox.ca/[Nathan] and my http://www.markhneedham.com/blog/2013/10/30/kaggle-titanic-python-pandas-attempt/[attempts at the http://www.kaggle.com/c/titanic-gettingStarted[Kaggle Titanic Problem\] I mentioned that we our next step was to try out http://scikit-learn.org/stable/tutorial/[scikit-learn\] so I thought I should summarise where we've got up to.</p>
We needed to write a classification algorithm to work out whether a person onboard the Titanic survived and luckily scikit-learn has http://scikit-learn.org/stable/supervised_learning.html#supervised-learning[extensive documentation on each of the algorithms\].
Unfortunately almost all those examples use http://www.numpy.org/[numpy\] data structures and we'd loaded the data using http://pandas.pydata.org/[pandas\] and didn't particularly want to switch back!
Luckily it was really easy to get the data into numpy format by calling 'values' on the pandas data structure, something we learnt from http://stackoverflow.com/questions/17682613/how-to-convert-a-pandas-dataframe-subset-of-columns-and-rows-into-a-numpy-array[a reply on Stack Overflow\].
For example if we were to wire up an http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html[ExtraTreesClassifier\] which worked out survival rate based on the 'Fare' and 'Pclass' attributes we could write the following code: ~~~python import pandas as pd from sklearn.ensemble import ExtraTreesClassifier from sklearn.cross_validation import cross_val_score train_df = pd.read_csv("train.csv") et = ExtraTreesClassifier(n_estimators=100, max_depth=None, min_samples_split=1, random_state=0) columns = ["Fare", "Pclass"\] labels = train_df["Survived"\].values features = train_df[list(columns)\].values et_score = cross_val_score(et, features, labels, n_jobs=-1).mean() print("\{0} \-> ET: \{1})".format(columns, et_score)) ~~~
To start with with read in the CSV file which looks like this: ~~~bash $ head -n5 train.csv PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S ~~~ </p> Next we create our classifier which "_fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting._". i.e. a better version of a http://www.markhneedham.com/blog/2012/10/27/kaggle-digit-recognizer-mahout-random-forest-attempt/[random forest\].</p>
On the next line we describe the features we want the classifier to use, then we convert the labels and features into numpy format so we can pass the to the classifier.
Finally we call the +++<cite>+++http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.cross_val_score.html[cross_val_score\]+++</cite>+++ function which splits our training data set into training and test components and trains the classifier against the former and checks its accuracy using the latter.
If we run this code we'll get roughly the following output: ~~~bash $ python et.py ['Fare', 'Pclass'\] \-> ET: 0.687991021324) ~~~
This is actually a worse accuracy than we'd get by saying that females survived and males didn't.
We can introduce 'Sex' into the classifier by adding it to the list of columns: ~~~python columns = ["Fare", "Pclass", "Sex"\] ~~~
If we re-run the code we'll get the following error: ~~~bash $ python et.py An unexpected error occurred while tokenizing input The following traceback may be corrupted or invalid The error message is: ('EOF in multi-line statement', (514, 0)) \... Traceback (most recent call last): File "et.py", line 14, in +++<module>+++et_score = cross_val_score(et, features, labels, n_jobs=-1).mean() File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/cross_validation.py", line 1152, in cross_val_score for train, test in cv) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/externals/joblib/parallel.py", line 519, in __call__ self.retrieve() File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/externals/joblib/parallel.py", line 450, in retrieve raise exception_type(report) sklearn.externals.joblib.my_exceptions.JoblibValueError/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/externals/joblib/my_exceptions.py:26: DeprecationWarning: BaseException.message has been deprecated as of Python 2.6 self.message, : JoblibValueError ___________________________________________________________________________ Multiprocessing exception: \... ValueError: could not convert string to float: male ___________________________________________________________________________ ~~~
This is a slightly verbose way of telling us that we can't pass non numeric features to the classifier - in this case 'Sex' has the values 'female' and 'male'. We'll need to write a function to replace those values with numeric equivalents. ~~~python train_df["Sex"\] = train_df["Sex"\].apply(lambda sex: 0 if sex == "male" else 1) ~~~
Now if we re-run the classifier we'll get a slightly more accurate prediction: ~~~bash $ python et.py ['Fare', 'Pclass', 'Sex'\] \-> ET: 0.813692480359) ~~~
The next step is use the classifier against the test data set so let's load the data and run the prediction: ~~~python test_df = pd.read_csv("test.csv") et.fit(features, labels) et.predict(test_df[columns\].values) ~~~
Now if we run that: ~~~bash $ python et.py ['Fare', 'Pclass', 'Sex'\] \-> ET: 0.813692480359) Traceback (most recent call last): File "et.py", line 22, in +++<module>+++et.predict(test_df[columns\].values) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/ensemble/forest.py", line 444, in predict proba = self.predict_proba(X) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/ensemble/forest.py", line 479, in predict_proba X = array2d(X, dtype=DTYPE) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/utils/validation.py", line 91, in array2d X_2d = np.asarray(np.atleast_2d(X), dtype=dtype, order=order) File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/core/numeric.py", line 235, in asarray return array(a, dtype, copy=False, order=order) ValueError: could not convert string to float: male ~~~
which is the same problem we had earlier! We need to replace the 'male' and 'female' values in the test set too so we'll pull out a function to do that now. ~~~python def replace_non_numeric(df): df["Sex"\] = df["Sex"\].apply(lambda sex: 0 if sex == "male" else 1) return df ~~~
Now we'll call that function with our training and test data frames: ~~~python train_df = replace_non_numeric(pd.read_csv("train.csv")) \... test_df = replace_non_numeric(pd.read_csv("test.csv")) ~~~
If we run the program again: ~~~bash $ python et.py ['Fare', 'Pclass', 'Sex'\] \-> ET: 0.813692480359) Traceback (most recent call last): File "et.py", line 26, in +++<module>+++et.predict(test_df[columns\].values) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/ensemble/forest.py", line 444, in predict proba = self.predict_proba(X) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/ensemble/forest.py", line 479, in predict_proba X = array2d(X, dtype=DTYPE) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/utils/validation.py", line 93, in array2d _assert_all_finite(X_2d) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/utils/validation.py", line 27, in _assert_all_finite raise ValueError("Array contains NaN or infinity.") ValueError: Array contains NaN or infinity. ~~~
There are missing values in the test set so we'll replace those with average values from our training set using an http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html[Imputer\]: ~~~python from sklearn.preprocessing import Imputer imp = Imputer(missing_values='NaN', strategy='mean', axis=0) imp.fit(features) test_df = replace_non_numeric(pd.read_csv("test.csv")) et.fit(features, labels) print et.predict(imp.transform(test_df[columns\].values)) ~~~
If we run that it completes successfully: ~~~python $ python et.py ['Fare', 'Pclass', 'Sex'\] \-> ET: 0.813692480359) [0 1 0 0 1 0 0 1 1 0 0 0 1 0 1 1 0 0 1 1 0 0 1 0 1 0 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 1 1 0 1 0 1 0 0 1 0 1 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 1 1 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 1 1 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 1 0 0 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 1 1 1 0 0 1 0 0 0\] ~~~
The final step is to add these values to our test data frame and then write that to a file so we can submit it to Kaggle.
The type of those values is 'numpy.ndarray' which we can convert to a pandas Series quite easily: ~~~python predictions = et.predict(imp.transform(test_df[columns\].values)) test_df["Survived"\] = pd.Series(predictions) ~~~
We can then write the 'PassengerId' and 'Survived' columns to a file: ~~~python test_df.to_csv("foo.csv", cols=['PassengerId', 'Survived'\], index=False) ~~~
Then output file looks like this: ~~~bash $ head -n5 foo.csv PassengerId,Survived 892,0 893,1 894,0 ~~~
The https://github.com/mneedham/kaggle-titanic/blob/master/et.py[code we've written is on github\] in case it's useful to anyone.+++</module>++++++</module>++++++</module>+++] | null | null | [
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] | python-making-scikit-learn-and-pandas-play-nice | https://markhneedham.com/blog/2013/11/09/python-making-scikit-learn-and-pandas-play-nice |
false | 2013-11-09 11:01:08 | Python: Scoping variables to use with timeit | [
"python"
] | [
"Python"
] | I've been playing around with Python's http://docs.python.org/2/library/timeit.html[timeit] library to help http://www.markhneedham.com/blog/2013/11/08/neo4j-2-0-0-m06-applying-wes-freemans-cypher-optimisation-tricks/[benchmark some Neo4j cypher queries] but I ran into some problems when trying to give it accessible to variables in my program.
I had the following python script which I would call from the terminal using +++<cite>+++python top-away-scorers.py+++</cite>+++:
[source,python]
----
import query_profiler as qp
attempts = [
{"query": '''MATCH (player:Player)-[:played]->stats-[:in]->game, stats-[:for]->team
WHERE game<-[:away_team]-team
RETURN player.name, SUM(stats.goals) AS goals
ORDER BY goals DESC
LIMIT 10'''}
]
qp.profile(attempts, iterations=5, runs=3)
----
+++<cite>+++query_profiler+++</cite>+++ initially read like this:
[source,python]
----
from py2neo import neo4j
import timeit
graph_db = neo4j.GraphDatabaseService()
def run_query(query, params):
query = neo4j.CypherQuery(graph_db, query)
return query.execute(**params).data
def profile(attempts, iterations=10, runs=3):
print ""
for attempt in attempts:
query = attempt["query"]
potential_params = attempt.get("params")
params = {} if potential_params == None else potential_params
timings = timeit.repeat("run_query(query, params)", setup="from query_profiler import run_query", number=iterations, repeat=runs)
print re.sub('\n[ \t]', '\n', re.sub('[ \t]+', ' ', query))
print timings
----
but when I ran top-away-scorers.py I got the following exception:
[source,bash]
----
$ python top-away-scorers.py
Traceback (most recent call last):
File "top-away-scorers.py", line 11, in <module>
qp.profile(attempts, iterations=5, runs=3)
File "/Users/markhneedham/code/cypher-query-tuning/query_profiler.py", line 19, in profile
timings = timeit.repeat("run_query(query, params)", setup="from query_profiler import run_query", number=iterations, repeat=runs)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/timeit.py", line 233, in repeat
return Timer(stmt, setup, timer).repeat(repeat, number)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/timeit.py", line 221, in repeat
t = self.timeit(number)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/timeit.py", line 194, in timeit
timing = self.inner(it, self.timer)
File "<timeit-src>", line 6, in inner
NameError: global name 'query' is not defined
----
As far as I understand, timeit couldn't understand what 'query' is because we didn't explicitly import it in the setup and it doesn't automatically pick up values from the scope its running in.
I tried adding query and params to the list of imports from query_profiler like so:
[source,python]
----
def profile(attempts, iterations=10, runs=3):
print ""
for attempt in attempts:
query = attempt["query"]
potential_params = attempt.get("params")
params = {} if potential_params == None else potential_params
timings = timeit.repeat("run_query(query, params)", setup="from query_profiler import run_query, query, params", number=iterations, repeat=runs)
print re.sub('\n[ \t]', '\n', re.sub('[ \t]+', ' ', query))
print timings
----
Unfortunately that didn't work:
[source,bash]
----
$ python top-away-scorers.py
Traceback (most recent call last):
File "top-away-scorers.py", line 11, in <module>
qp.profile(attempts, iterations=5, runs=3)
File "/Users/markhneedham/code/cypher-query-tuning/query_profiler.py", line 21, in profile
timings = timeit.repeat("run_query(query, params)", setup="from query_profiler import run_query, query, params", number=iterations, repeat=runs)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/timeit.py", line 233, in repeat
return Timer(stmt, setup, timer).repeat(repeat, number)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/timeit.py", line 221, in repeat
t = self.timeit(number)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/timeit.py", line 194, in timeit
timing = self.inner(it, self.timer)
File "<timeit-src>", line 3, in inner
ImportError: cannot import name query
----
I eventually came across the http://docs.python.org/2/reference/simple_stmts.html#grammar-token-global_stmt[global] keyword which allows me to scope query and params in a way that we can import them from the query_profiler module:
[source,python]
----
def profile(attempts, iterations=10, runs=3):
print ""
for attempt in attempts:
global query
query = attempt["query"]
potential_params = attempt.get("params")
global params
params = {} if potential_params == None else potential_params
timings = timeit.repeat("run_query(query, params)", setup="from query_profiler import run_query, query, params", number=iterations, repeat=runs)
print re.sub('\n[ \t]', '\n', re.sub('[ \t]+', ' ', query))
print timings
----
I'm generally wary of using anything global but in this case it seems necessary...or I've completely misunderstood how you're meant to use timeit.
Any Pythonistas able to shed some light? | null | null | [
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] | python-scoping-variables-to-use-with-timeit | https://markhneedham.com/blog/2013/11/09/python-scoping-variables-to-use-with-timeit |
false | 2013-11-08 09:40:01 | Neo4j 2.0.0-M06: Applying Wes Freeman's Cypher Optimisation tricks | [
"neo4j"
] | [
"neo4j"
] | https://twitter.com/wefreema[Wes] has been teaching me some of his tricks for http://wes.skeweredrook.com/pragmatic-cypher-optimization-2-0-m06/[tuning Neo4j cypher queries] over the last few weeks so I thought I should write up a few examples of the master's advice in action.
I've created a https://github.com/mneedham/cypher-query-tuning[mini benchmarking tool] using Python's http://docs.python.org/2/library/timeit.html[timeit] and http://www.numpy.org/[numpy] to run different queries multiple times and return the mean, min, max and 95th percentile times.
I've made my https://www.dropbox.com/s/y4gp00gfryc9syx/football.zip[football data set available] in case you want to follow along and we'll start with a query to find the https://github.com/mneedham/cypher-query-tuning/blob/master/top-away-scorers.py[top goal scorers away from home].
A quick refresher of the model (created using http://www.apcjones.com/arrows/[Alistair's arrows tool]):
image::{{<siteurl>}}/uploads/2013/11/2013-11-08_07-58-57.png[2013 11 08 07 58 57,600]
And the query in question:
[source,cypher]
----
MATCH (player:Player)-[:played]->stats-[:in]->game, stats-[:for]->team
WHERE game<-[:away_team]-team
RETURN player.name, SUM(stats.goals) AS goals
ORDER BY goals DESC
LIMIT 10
----
To run just that query in a 5 iteration loop 3 times we can wire up the benchmarking tool like this:
[source,python]
----
import query_profiler as qp
attempts = [
{"query": '''MATCH (player:Player)-[:played]->stats-[:in]->game, stats-[:for]->team
WHERE game<-[:away_team]-team
RETURN player.name, SUM(stats.goals) AS goals
ORDER BY goals DESC
LIMIT 10'''}
]
qp.profile(attempts, iterations=5, runs=3)
----
If we run that we'll get the following output:
[source,bash]
----
$ python top-away-scorers.py
MATCH (player:Player)-[:played]->stats-[:in]->game, stats-[:for]->team
WHERE game<-[:away_team]-team
RETURN player.name, SUM(stats.goals) AS goals
ORDER BY goals DESC
LIMIT 10
Min 0.682677030563 Mean 0.689069747925 95% 0.697918820381 Max 0.699331998825
----
Our average time per query run is therefore 0.689069747925 / 5 which is 0.137 seconds or 137 milliseconds so it isn't that slow but there are still some tweaks we can make.
To refresh, Wes' 6 rules for cypher optimisation are:
. Use parameters whenever you can
. Avoid cartesian products when they aren't required to get the data you need.
. Avoid patterns in the WHERE clause
. Start your MATCH patterns at the lowest cardinality identifier you can (ideally 1), and expand outward.
. Separate your MATCH patterns, doing the minimal amount of expansion for each pattern. Add 1 new identifier per pattern.
. Call for Wes!
The first rule we violate is #3 _'Avoid patterns in the WHERE clause'_ which we can replace with a 'WITH MATCH' combination:
[source,cypher]
----
MATCH (player:Player)-[:played]->stats-[:in]->game, stats-[:for]->team
WITH player, stats, team, game
MATCH team-[:away_team]->game
RETURN player.name, SUM(stats.goals) AS goals
ORDER BY goals DESC
LIMIT 10
----
If we run that we get a 20% improvement:
[source,bash]
----
$ python top-away-scorers.py
...
Min 0.549772977829 Mean 0.553097963333 95% 0.555728816986 Max 0.555970907211
----
The next thing to look at is rule #4 _'Start your MATCH patterns at the lowest cardinality identifier you can'_. We can work out the cardinality of an identifier by prefixing the query with 'PROFILE' and analysing the output:
[source,bash]
----
$ PROFILE MATCH (player:Player)-[:played]->stats-[:in]->game, stats-[:for]->team
> WITH player, stats, team, game
> MATCH team-[:away_team]->game
> RETURN player.name, SUM(stats.goals) AS goals
> ORDER BY goals DESC
> LIMIT 10;
ColumnFilter(symKeys=["player.name", " INTERNAL_AGGREGATEab0a95e7-e701-4be3-9266-eb111c17b53c"], returnItemNames=["player.name", "goals"], _rows=10, _db_hits=0)
Top(orderBy=["SortItem(Cached( INTERNAL_AGGREGATEab0a95e7-e701-4be3-9266-eb111c17b53c of type Number),false)"], limit="Literal(10)", _rows=10, _db_hits=0)
EagerAggregation(keys=["Cached(player.name of type Any)"], aggregates=["( INTERNAL_AGGREGATEab0a95e7-e701-4be3-9266-eb111c17b53c,Sum(Product(stats,goals(13),true)))"], _rows=503, _db_hits=5192)
Extract(symKeys=["stats", "player", "team", " UNNAMED112", "game"], exprKeys=["player.name"], _rows=5192, _db_hits=5192)
PatternMatch(g="(team)-[' UNNAMED112']-(game)", _rows=5192, _db_hits=0)
ColumnFilter(symKeys=[" UNNAMED38", "stats", "player", "team", " UNNAMED21", "game", " UNNAMED57"], returnItemNames=["player", "stats", "team", "game"], _rows=10394, _db_hits=0)
PatternMatch(g="(stats)-[' UNNAMED38']-(game),(stats)-[' UNNAMED57']-(team),(player)-[' UNNAMED21']-(stats)", _rows=10394, _db_hits=0)
Filter(pred="hasLabel(player:Player(8))", _rows=523, _db_hits=0)
NodeByLabel(label="Player", identifier="player", _rows=523, _db_hits=0)
----
The main thing to look at is the value of +++<cite>+++_rows+++</cite>+++. Here it starts at 523 (the number of nodes labelled with 'Player') and then explodes out to 10394 after evaluating the first MATCH expression. The second MATCH expression takes us back down to 5192 which gets further reduced to 503 after we group by player.
An alternative to starting our query from players is to start from games instead and a COUNT based query shows us that this will result in our query starting out with slightly less rows:
[source,bash]
----
$ MATCH (game:Game) RETURN COUNT(game);
+-------------+
| COUNT(game) |
+-------------+
| 380 |
+-------------+
1 row
15 ms
----
The rewritten query reads like this:
[source,cypher]
----
MATCH (game:Game)<-[:away_team]-(team)
WITH game, team
MATCH (player:Player)-[:played]->stats-[:in]->game, stats-[:for]->team
RETURN player.name, SUM(stats.goals) AS goals
ORDER BY goals DESC
LIMIT 10
----
If we benchmark that we get a 50% improvement:
[source,bash]
----
$ python top-away-scorers.py
...
Min 0.276885986328 Mean 0.28545498848 95% 0.293333411217 Max 0.294231891632
----
If we profile that query we see the following:
[source,bash]
----
ColumnFilter(symKeys=["player.name", " INTERNAL_AGGREGATEa7d9d5ca-1f5f-4379-8675-9a01ca1f7ff0"], returnItemNames=["player.name", "goals"], _rows=10, _db_hits=0)
Top(orderBy=["SortItem(Cached( INTERNAL_AGGREGATEa7d9d5ca-1f5f-4379-8675-9a01ca1f7ff0 of type Number),false)"], limit="Literal(10)", _rows=10, _db_hits=0)
EagerAggregation(keys=["Cached(player.name of type Any)"], aggregates=["( INTERNAL_AGGREGATEa7d9d5ca-1f5f-4379-8675-9a01ca1f7ff0,Sum(Product(stats,goals(13),true)))"], _rows=503, _db_hits=5192)
Extract(symKeys=["stats", " UNNAMED93", " UNNAMED76", "player", "team", " UNNAMED112", "game"], exprKeys=["player.name"], _rows=5192, _db_hits=5192)
Filter(pred="hasLabel(player:Player(8))", _rows=5192, _db_hits=0)
PatternMatch(g="(player)-[' UNNAMED76']-(stats),(stats)-[' UNNAMED112']-(team),(stats)-[' UNNAMED93']-(game)", _rows=5192, _db_hits=0)
ColumnFilter(symKeys=["game", "team", " UNNAMED17"], returnItemNames=["game", "team"], _rows=380, _db_hits=0)
PatternMatch(g="(team)-[' UNNAMED17']-(game)", _rows=380, _db_hits=0)
Filter(pred="hasLabel(game:Game(7))", _rows=380, _db_hits=0)
NodeByLabel(label="Game", identifier="game", _rows=380, _db_hits=0)
----
The maximum +++<cite>+++db_rows+++</cite>+++ value we see here is 5192 so we didn't have the explosion to 10394 which is probably responsible for the reduced execution time.
Another tweak could be to start with a 'http://www.markhneedham.com/blog/2013/10/22/neo4j-2-0-labels-indexes-and-the-like/[label scan]' of teams rather than games which yields a slight improvement:
[source,python]
----
$ python top-away-scorers.py
...
Min 0.255274057388 Mean 0.268081267675 95% 0.2759329319 Max 0.276294946671
----
We could go even further and get rid of some of the 'stats' nodes which don't have any goals and therefore don't need to be used by our 'SUM' function:
[source,cypher]
----
MATCH (game)<-[:away_team]-(team:Team)
WITH game, team
MATCH (player:Player)-[:played]->stats-[:in]->game, stats-[:for]->team
WHERE stats.goals > 0
RETURN player.name, SUM(stats.goals) AS goals
ORDER BY goals DESC
LIMIT 10
----
[source,python]
----
$ python top-away-scorers.py
...
Min 0.231870174408 Mean 0.236128012339 95% 0.240220928192 Max 0.240711927414
----
If we have a look at the profile of that query\...
[source,bash]
----
ColumnFilter(symKeys=["player.name", " INTERNAL_AGGREGATE95b48f9b-b262-4383-9afd-6eb5a51004b1"], returnItemNames=["player.name", "goals"], _rows=10, _db_hits=0)
Top(orderBy=["SortItem(Cached( INTERNAL_AGGREGATE95b48f9b-b262-4383-9afd-6eb5a51004b1 of type Number),false)"], limit="Literal(10)", _rows=10, _db_hits=0)
EagerAggregation(keys=["Cached(player.name of type Any)"], aggregates=["( INTERNAL_AGGREGATE95b48f9b-b262-4383-9afd-6eb5a51004b1,Sum(Product(stats,goals(13),true)))"], _rows=180, _db_hits=394)
Extract(symKeys=["stats", " UNNAMED93", " UNNAMED76", "player", "team", " UNNAMED112", "game"], exprKeys=["player.name"], _rows=394, _db_hits=394)
Filter(pred="(Product(stats,goals(13),true) > Literal(0) AND hasLabel(player:Player(8)))", _rows=394, _db_hits=394)
PatternMatch(g="(player)-[' UNNAMED76']-(stats),(stats)-[' UNNAMED112']-(team),(stats)-[' UNNAMED93']-(game)", _rows=394, _db_hits=5192)
ColumnFilter(symKeys=["team", "game", " UNNAMED12"], returnItemNames=["game", "team"], _rows=380, _db_hits=0)
PatternMatch(g="(team)-[' UNNAMED12']-(game)", _rows=380, _db_hits=0)
Filter(pred="hasLabel(team:Team(2))", _rows=37, _db_hits=0)
NodeByLabel(label="Team", identifier="team", _rows=37, _db_hits=0)
----
\...we can see that the number of nodes that SUM had to deal with was reduced from 5192 to 394 which explains why using a WHERE to lookup a property is slightly cheaper in this case. If using a WHERE had reduced the number of nodes more marginally it wouldn't be worthwhile.
Overall we've taken the time it takes for 5 runs of the query down from an average of *689 milliseconds* to *236 milliseconds* so thank you Wes! | null | null | [
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] | neo4j-2-0-0-m06-applying-wes-freemans-cypher-optimisation-tricks | https://markhneedham.com/blog/2013/11/08/neo4j-2-0-0-m06-applying-wes-freemans-cypher-optimisation-tricks |
false | 2013-11-01 20:41:18 | Neo4j: A first attempt at retail product substitution | [
"neo4j"
] | [
"neo4j"
] | One of the interesting problems in the world of online shopping from the perspective of the retailer is working out *whether there is a suitable substitute product* if an ordered item isn't currently in stock.
Since this problem brings together three types of data - order history, stock levels and products - it seems like it should be a nice fit for Neo4j so I 'graphed up' a quick example.
I wrote the following cypher to create some products, a person, a few orders and then the availability of those products in an imaginary store.
[source,cypher]
----
CREATE (bakedBeans :Category {name: "Baked Beans"} )
CREATE (fruit :Category {name: "Fruit"} )
CREATE (hbb :Product {name: "Heinz Baked Beans", type: "brand"} )
CREATE (bbb :Product {name: "Branstone Baked Beans", type: "brand"} )
CREATE (sbb :Product {name: "Sainsbury's Baked Beans", type: "own"} )
CREATE (apple :Product {name: "Bag of Apples"} )
CREATE UNIQUE (hbb)-[:HAS_CATEGORY]->(bakedBeans)
CREATE UNIQUE (bbb)-[:HAS_CATEGORY]->(bakedBeans)
CREATE UNIQUE (sbb)-[:HAS_CATEGORY]->(bakedBeans)
CREATE (southwark :Store {name: "Southwark"})
CREATE UNIQUE (southwark)-[:HAS_IN_STOCK {availability: 0}]->(hbb)
CREATE UNIQUE (southwark)-[:HAS_IN_STOCK {availability: 2}]->(bbb)
CREATE UNIQUE (southwark)-[:HAS_IN_STOCK {availability: 10}]->(sbb)
CREATE UNIQUE (southwark)-[:HAS_IN_STOCK {availability: 10}]->(apple)
CREATE (mark :Person {name: "Mark"})
CREATE (order1 :Order {id: 1, date: 1380884632})
CREATE UNIQUE (order1)-[:CONTAINS {count: 1}]->(hbb)
CREATE UNIQUE (order1)-[:CONTAINS {count: 5}]->(apple)
CREATE UNIQUE (mark)-[:PLACED_ORDER]->(order1)
CREATE (order2 :Order {id: 2, date: 1380885051})
CREATE UNIQUE (order2)-[:CONTAINS {count: 1}]->bbb
CREATE UNIQUE (mark)-[:PLACED_ORDER]->(order2)
----
We may then have a new order which we're trying to fulfil and we want to check whether the products are in stock.
First we'll create the order:
[source,cypher]
----
// Create the order
CREATE (order3:Order {id: 3, date: 1380895051})
WITH order3
// Assign the order to Mark
MATCH (p:Person)
WHERE p.name = "Mark"
CREATE UNIQUE (p)-[:PLACED_ORDER]->(order3)
WITH order3
// Populate the order with some products
MATCH (p:Product)
WHERE p.name = "Heinz Baked Beans"
CREATE UNIQUE (order3)-[:CONTAINS {count: 2}]->(p)
WITH order3
MATCH (p:Product)
WHERE p.name = "Bag of Apples"
CREATE UNIQUE (order3)-[:CONTAINS {count: 2}]->(p)
----
Now let's check on the availability of each item in the order in a particular store:
[source,cypher]
----
// Find the products in the order
MATCH (o:Order)-[c:CONTAINS]->(product)
WHERE o.id = 3
WITH product, c.count AS count
// Check which items are out of stock in our store
MATCH (s:Store)-[inStock:HAS_IN_STOCK]->(product)
WHERE s.name = "Southwark"
RETURN product, inStock
==> +--------------------------------------------------------------------------------------------+
==> | product | inStock |
==> +--------------------------------------------------------------------------------------------+
==> | Node[11444]{name:"Heinz Baked Beans",type:"brand"} | :HAS_IN_STOCK[60053]{availability:0} |
==> | Node[11447]{name:"Bag of Apples"} | :HAS_IN_STOCK[60056]{availability:10} |
==> +--------------------------------------------------------------------------------------------+
----
Now if we change that query to only return items which have less items in stock than we attempted to order we'll see that Heinz Baked Beans aren't available:
[source,bash]
----
// Find the products in the order
MATCH (o:Order)-[c:CONTAINS]->(product)
WHERE o.id = 3
WITH product, c.count AS count
// Check which items are out of stock in our store
MATCH (s:Store)-[inStock:HAS_IN_STOCK]->(product)
WHERE s.name = "Southwark" AND count > inStock.availability
RETURN product, inStock
==> +-------------------------------------------------------------------------------------------+
==> | product | inStock |
==> +-------------------------------------------------------------------------------------------+
==> | Node[15281]{name:"Heinz Baked Beans",type:"brand"} | :HAS_IN_STOCK[86079]{availability:0} |
==> +-------------------------------------------------------------------------------------------+
----
{blank}
[source,cypher]
----
MATCH (p:Person)-[:PLACED_ORDER]->order-[c:CONTAINS]->product-[:HAS_CATEGORY]->category
WHERE p.name = "Mark" AND category.name = "Baked Beans" AND order.id <> 3
RETURN product.name, product.type, order.id
==> +---------------------------------------------------+
==> | product.name | product.type | order.id |
==> +---------------------------------------------------+
==> | "Heinz Baked Beans" | "brand" | 1 |
==> | "Branstone Baked Beans" | "brand" | 2 |
==> +---------------------------------------------------+
----
Having identified Mark as a connoisseur of branded baked beans we might then run a query to check if there are any other branded baked beans available in that store:
[source,cypher]
----
MATCH (s:Store)-[inStock:HAS_IN_STOCK]->p-[:HAS_CATEGORY]->c
WHERE s.name = "Southwark"
AND c.name = "Baked Beans"
AND inStock.availability > 0
AND p.type = "brand"
RETURN p.name, inStock.availability
==> +------------------------------------------------+
==> | p.name | inStock.availability |
==> +------------------------------------------------+
==> | "Branstone Baked Beans" | 2 |
==> +------------------------------------------------+
----
This is obviously an extremely naive approach so I went to twitter in the hope that I could find a more sophisticated approach:
[,Mark Needham (@markhneedham) https://twitter.com/markhneedham/statuses/394062759089803264[October 26, 2013]]
____
Are there any general algorithms for product similarity? e.g. based on category/description etc. Or is it very much domain specific?
____+++<script async="" src="//platform.twitter.com/widgets.js" charset="utf-8">++++++</script>+++
Nicole White is currently playing around with http://en.wikipedia.org/wiki/Association_rule_learning[association rule mining] which sounds interesting.
[,Nicole White (@_nicolemargaret) https://twitter.com/_nicolemargaret/statuses/395442129243103232[October 30, 2013]]
____
https://twitter.com/markhneedham[@markhneedham] I'm sure it can be! I'm actually working on association rule mining in a https://twitter.com/search?q=%23neo4j&src=hash[#neo4j] db with purchases transaction data.
____+++<script async="" src="//platform.twitter.com/widgets.js" charset="utf-8">++++++</script>+++
It is described on its Wikipedia page like so:
____
Based on the concept of strong rules, Rakesh Agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.
____
Although it's not exactly the same as what I want to do I need to look into it more to see if some of the ideas can be applied.
I also learnt that the terminology for what I'm looking for is a 'https://www.google.com/?q=similar+items+algorithm#q=similar+items+algorithm[similar items]' algorithm and I think what I'm looking to spike would be a http://en.wikipedia.org/wiki/Recommender_system#Hybrid_Recommender_Systems[hybrid recommender system] which combines content similarity and user's previous purchase history.
I've been looking around to see if there are any open or anonymised retail data sets to play around with but all I've come across is the 'http://fimi.ua.ac.be/data/[Frequent Itemset Mining Dataset Repository]'. Unfortunately when I tried to open the files they seem to just contain random numbers so I must be doing something wrong.
If anyone knows of a retail data set I can play around with please point me in the right direction. | null | null | [
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] | neo4j-a-first-attempt-at-retail-product-substitution | https://markhneedham.com/blog/2013/11/01/neo4j-a-first-attempt-at-retail-product-substitution |
false | 2013-11-06 07:25:24 | Python: Generate all combinations of a list | [
"python"
] | [
"Python"
] | Nathan and I have been playing around with different http://scikit-learn.org/stable/auto_examples/[scikit-learn machine learning classifiers] and we wanted to run different combinations of features through each one and work out which gave the best result.
We started with a list of features:
[source,python]
----
all_columns = ["Fare", "Sex", "Pclass", 'Embarked']
----
http://docs.python.org/2/library/itertools.html#itertools.combinations[itertools#combinations] allows us to create combinations with a length of our choice:
[source,python]
----
>>> import itertools as it
>>> list(it.combinations(all_columns, 3))
[('Fare', 'Sex', 'Pclass'), ('Fare', 'Sex', 'Embarked'), ('Fare', 'Pclass', 'Embarked'), ('Sex', 'Pclass', 'Embarked')]
----
We wanted to create combinations of arbitrary length so we wanted to combine a few invocations of that functions like this:
[source,python]
----
>>> list(it.combinations(all_columns, 2)) + list(it.combinations(all_columns, 3))
[('Fare', 'Sex'), ('Fare', 'Pclass'), ('Fare', 'Embarked'), ('Sex', 'Pclass'), ('Sex', 'Embarked'), ('Pclass', 'Embarked'), ('Fare', 'Sex', 'Pclass'), ('Fare', 'Sex', 'Embarked'), ('Fare', 'Pclass', 'Embarked'), ('Sex', 'Pclass', 'Embarked')]
----
If we generify that code to remove the repetition we end up with the following:
[source,python]
----
all_the_features = []
for r in range(1, len(all_columns) + 1):
all_the_features + list(it.combinations(all_columns, r))
>>> all_the_features
[('Fare',), ('Sex',), ('Pclass',), ('Embarked',), ('Fare', 'Sex'), ('Fare', 'Pclass'), ('Fare', 'Embarked'), ('Sex', 'Pclass'), ('Sex', 'Embarked'), ('Pclass', 'Embarked'), ('Fare', 'Sex', 'Pclass'), ('Fare', 'Sex', 'Embarked'), ('Fare', 'Pclass', 'Embarked'), ('Sex', 'Pclass', 'Embarked'), ('Fare', 'Sex', 'Pclass', 'Embarked')]
----
or if we want to use reduce instead:
[source,python]
----
>>> reduce(lambda acc, x: acc + list(it.combinations(all_columns, x)), range(1, len(all_columns) + 1), [])
[('Fare',), ('Sex',), ('Pclass',), ('Embarked',), ('Fare', 'Sex'), ('Fare', 'Pclass'), ('Fare', 'Embarked'), ('Sex', 'Pclass'), ('Sex', 'Embarked'), ('Pclass', 'Embarked'), ('Fare', 'Sex', 'Pclass'), ('Fare', 'Sex', 'Embarked'), ('Fare', 'Pclass', 'Embarked'), ('Sex', 'Pclass', 'Embarked'), ('Fare', 'Sex', 'Pclass', 'Embarked')]
----
I imagine there is probably a simpler way that I don't know about yet...! | null | null | [
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] | python-generate-all-combinations-of-a-list | https://markhneedham.com/blog/2013/11/06/python-generate-all-combinations-of-a-list |
false | 2013-11-23 10:30:41 | Neo4j 2.0.0-M06 \-> 2.0.0-RC1: Working with path expressions | [
"neo4j",
"cypher"
] | [
"neo4j"
] | We http://blog.neo4j.org/2013/11/neo4j-200-rc1-final-preparations.html[recently released Neo4j 2.0.0-RC1] and since there were some breaking changes from Neo4j 2.0.0-M06 I decided to check if I needed to update any of my football graph queries.
On query which no longer worked as I expected was the following one which calculated the top goal scorers for televised games:
[source,cypher]
----
MATCH (player:Player)-[:played|subbed_on]->stats
WITH stats.goals AS goals, player, stats-[:in]->()-[:on_tv]-() as onTv
RETURN player.name,
SUM(CASE WHEN onTv = FALSE THEN goals ELSE 0 END) as nonTvGoals,
SUM(CASE WHEN onTv = TRUE THEN goals ELSE 0 END) as tvGoals,
SUM(goals) as allGoals
ORDER BY tvGoals DESC
LIMIT 10
----
This is what that section of the graph looks like visually:
image::{{<siteurl>}}/uploads/2013/11/2013-11-23_10-11-24.png[2013 11 23 10 11 24,600]
When I run that query in 2.0.0-M06 I get the following output:
[source,bash]
----
==> +------------------------------------------------------+
==> | player.name | nonTvGoals | tvGoals | allGoals |
==> +------------------------------------------------------+
==> | "Robin Van Persie" | 11 | 15 | 26 |
==> | "Gareth Bale" | 8 | 13 | 21 |
==> | "Luis Suárez" | 12 | 11 | 23 |
==> | "Theo Walcott" | 5 | 9 | 14 |
==> | "Demba Ba" | 7 | 8 | 15 |
==> | "Santi Cazorla" | 5 | 7 | 12 |
==> | "Edin Dzeko" | 7 | 7 | 14 |
==> | "Carlos Tevez" | 5 | 6 | 11 |
==> | "Juan Mata" | 6 | 6 | 12 |
==> | "Steven Gerrard" | 3 | 6 | 9 |
==> +------------------------------------------------------+
----
However, when I tried it in 2.0.0-RC1 the 'nonTvGoals' and 'tvGoals' columns don't show any goals!
[source,bash]
----
==> +--------------------------------------------------------+
==> | player.name | nonTvGoals | tvGoals | allGoals |
==> +--------------------------------------------------------+
==> | "Djibril Cissé" | 0 | 0 | 3 |
==> | "Gabriel Agbonlahor" | 0 | 0 | 9 |
==> | "Shaun Derry" | 0 | 0 | 0 |
==> | "Marouane Fellaini" | 0 | 0 | 11 |
==> | "Jermaine Jenas" | 0 | 0 | 2 |
==> | "Claudio Yacob" | 0 | 0 | 0 |
==> | "Michael Owen" | 0 | 0 | 1 |
==> | "Sean Morrison" | 0 | 0 | 2 |
==> | "Kolo Touré" | 0 | 0 | 0 |
==> | "Antonio Valencia" | 0 | 0 | 1 |
==> +--------------------------------------------------------+
----
I simplified the query a bit and then ran it against both versions to try and work out what was going on:
[source,cypher]
----
MATCH (player:Player)-[:played|subbed_on]->stats
RETURN stats-[:in]->()-[:on_tv]-() as onTv
LIMIT 1
----
_on 2.0.0-M06_
[source,bash]
----
==> +-------+
==> | onTv |
==> +-------+
==> | false |
==> +-------+
----
_on 2.0.0-RC1_
[source,bash]
----
==> +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | onTv |
==> +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | [[Node[9058]{name:"Alou Diarra stats for 345824",node_id:"9058",match_id:"345824",type:"stats",goals:0},:in[47190]{},Node[9032]{name:"Swansea City vs West Ham United",node_id:"9032",friendly_date:"2012-08-25 12:45:00 +0100",time:1245,home_goals:3,match_id:"345824",away_goals:0,attendance:"20424",date:1345898700,type:"match"},:on_tv[47059]{},Node[143]{name:"Sky",node_id:"143"}]] |
==> +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
----
As you can see the path expression which indicates whether a match was televised now returns the matching path if there is one rather than a boolean value indicating if there is a path.
Now that we know that we can tweak the query to check if the _onTV path_ contains any relationships which would indicate that a path does exist:
[source,cypher]
----
MATCH (player:Player)-[:played|subbed_on]->stats
WITH stats.goals AS goals, player, stats-[:in]->()-[:on_tv]-() as onTv
RETURN player.name,
SUM(CASE WHEN LENGTH(onTv) = 0 THEN goals ELSE 0 END) as nonTVGoals,
SUM(CASE WHEN LENGTH(onTv) > 0 THEN goals ELSE 0 END) as tvGoals,
SUM(goals) AS allGoals
ORDER BY tvGoals DESC
LIMIT 10
----
If we run that it works as before:
[source,bash]
----
==> +------------------------------------------------------+
==> | player.name | nonTVGoals | tvGoals | allGoals |
==> +------------------------------------------------------+
==> | "Robin Van Persie" | 11 | 15 | 26 |
==> | "Gareth Bale" | 8 | 13 | 21 |
==> | "Luis Suárez" | 12 | 11 | 23 |
==> | "Theo Walcott" | 5 | 9 | 14 |
==> | "Demba Ba" | 7 | 8 | 15 |
==> | "Santi Cazorla" | 5 | 7 | 12 |
==> | "Edin Dzeko" | 7 | 7 | 14 |
==> | "Carlos Tevez" | 5 | 6 | 11 |
==> | "Juan Mata" | 6 | 6 | 12 |
==> | "Steven Gerrard" | 3 | 6 | 9 |
==> +------------------------------------------------------+
----
I'm still going through the rest of my queries to check they work but if any of your pre 2.0.0-RC1 queries are breaking do post them up on the https://groups.google.com/forum/#!forum/neo4j[mailing list] and someone will help you sort them out. | null | null | [
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] | neo4j-2-0-0-m06-2-0-0-rc1-working-with-path-expressions | https://markhneedham.com/blog/2013/11/23/neo4j-2-0-0-m06-2-0-0-rc1-working-with-path-expressions |
false | 2013-11-23 22:54:58 | Neo4j 2.0.0-M06 \-> 2.0.0-RC1: Optional relationships with OPTIONAL MATCH | [
"neo4j",
"cypher"
] | [
"neo4j"
] | One of the http://blog.neo4j.org/2013/11/neo4j-200-rc1-final-preparations.html[breaking changes in Neo4j 2.0.0-RC1] compared to previous versions is that the +++<cite>+++-[?]\->+++</cite>+++ syntax for matching optional relationships has been retired and replaced with the +++<cite>+++http://docs.neo4j.org/chunked/milestone/query-optional-match.html[OPTIONAL MATCH]+++</cite>+++ construct.
An example where we might want to match an optional relationship could be if we want to find colleagues that we haven't worked with given the following model:
image::{{<siteurl>}}/uploads/2013/11/2013-11-23_21-43-57.png[2013 11 23 21 43 57,336]
Suppose we have the following data set:
[source,cypher]
----
CREATE (steve:Person {name: "Steve"})
CREATE (john:Person {name: "John"})
CREATE (david:Person {name: "David"})
CREATE (paul:Person {name: "Paul"})
CREATE (sam:Person {name: "Sam"})
CREATE (londonOffice:Office {name: "London Office"})
CREATE UNIQUE (steve)-[:WORKS_IN]->(londonOffice)
CREATE UNIQUE (john)-[:WORKS_IN]->(londonOffice)
CREATE UNIQUE (david)-[:WORKS_IN]->(londonOffice)
CREATE UNIQUE (paul)-[:WORKS_IN]->(londonOffice)
CREATE UNIQUE (sam)-[:WORKS_IN]->(londonOffice)
CREATE UNIQUE (steve)-[:COLLEAGUES_WITH]->(john)
CREATE UNIQUE (steve)-[:COLLEAGUES_WITH]->(david)
----
We might write the following query to find people from the same office as Steve but that he hasn't worked with:
[source,cypher]
----
MATCH (person:Person)-[:WORKS_IN]->(office)<-[:WORKS_IN]-(potentialColleague)
WHERE person.name = "Steve" AND office.name = "London Office"
WITH person, potentialColleague
MATCH (potentialColleague)-[c?:COLLEAGUES_WITH]-(person)
WHERE c IS null
RETURN potentialColleague
----
[source,bash]
----
==> +----------------------+
==> | potentialColleague |
==> +----------------------+
==> | Node[4]{name:"Paul"} |
==> | Node[5]{name:"Sam"} |
==> +----------------------+
----
We first find which office Steve works in and find the people who also work in that office. Then we optionally match the 'COLLEAGUES_WITH' relationship and only return people who Steve doesn't have that relationship with.
If we run that query in 2.0.0-RC1 we get this exception:
[source,bash]
----
==> SyntaxException: Question mark is no longer used for optional patterns - use OPTIONAL MATCH instead (line 1, column 199)
==> "MATCH (person:Person)-[:WORKS_IN]->(office)<-[:WORKS_IN]-(potentialColleague) WHERE person.name = "Steve" AND office.name = "London Office" WITH person, potentialColleague MATCH (potentialColleague)-[c?:COLLEAGUES_WITH]-(person) WHERE c IS null RETURN potentialColleague"
==>
----
Based on that advice we might translate our query to read like this:
[source,cypher]
----
MATCH (person:Person)-[:WORKS_IN]->(office)<-[:WORKS_IN]-(potentialColleague)
WHERE person.name = "Steve" AND office.name = "London Office"
WITH person, potentialColleague
OPTIONAL MATCH (potentialColleague)-[c:COLLEAGUES_WITH]-(person)
WHERE c IS null
RETURN potentialColleague
----
If we run that we get back more people than we'd expect:
[source,bash]
----
==> +------------------------+
==> | potentialColleague |
==> +------------------------+
==> | Node[15]{name:"John"} |
==> | Node[14]{name:"David"} |
==> | Node[13]{name:"Paul"} |
==> | Node[12]{name:"Sam"} |
==> +------------------------+
----
The reason this query doesn't work as we'd expect is because the +++<cite>+++WHERE+++</cite>+++ clause immediately following +++<cite>+++OPTIONAL MATCH+++</cite>+++ is part of the pattern rather than being evaluated afterwards as we've become used to.
The +++<cite>+++OPTIONAL MATCH+++</cite>+++ part of the query matches a 'COLLEAGUES_WITH' relationship where the relationship is actually null, something of a contradiction! However, since the match is optional a row is still returned.
If we include 'c' in the +++<cite>+++RETURN+++</cite>+++ part of the query we can see that this is the case:
[source,cypher]
----
MATCH (person:Person)-[:WORKS_IN]->(office)<-[:WORKS_IN]-(potentialColleague)
WHERE person.name = "Steve" AND office.name = "London Office"
WITH person, potentialColleague
OPTIONAL MATCH (potentialColleague)-[c:COLLEAGUES_WITH]-(person)
WHERE c IS null
RETURN potentialColleague, c
----
[source,bash]
----
==> +---------------------------------+
==> | potentialColleague | c |
==> +---------------------------------+
==> | Node[15]{name:"John"} | <null> |
==> | Node[14]{name:"David"} | <null> |
==> | Node[13]{name:"Paul"} | <null> |
==> | Node[12]{name:"Sam"} | <null> |
==> +---------------------------------+
----
If we take out the +++<cite>+++WHERE+++</cite>+++ part of the +++<cite>+++OPTIONAL MATCH+++</cite>+++ the query is a bit closer to what we want:
[source,cypher]
----
MATCH (person:Person)-[:WORKS_IN]->(office)<-[:WORKS_IN]-(potentialColleague)
WHERE person.name = "Steve" AND office.name = "London Office"
WITH person, potentialColleague
OPTIONAL MATCH (potentialColleague)-[c:COLLEAGUES_WITH]-(person)
RETURN potentialColleague, c
----
[source,bash]
----
==> +-----------------------------------------------+
==> | potentialColleague | c |
==> +-----------------------------------------------+
==> | Node[2]{name:"John"} | :COLLEAGUES_WITH[5]{} |
==> | Node[3]{name:"David"} | :COLLEAGUES_WITH[6]{} |
==> | Node[4]{name:"Paul"} | <null> |
==> | Node[5]{name:"Sam"} | <null> |
==> +-----------------------------------------------+
----
If we introduce a +++<cite>+++WITH+++</cite>+++ after the +++<cite>+++OPTIONAL MATCH+++</cite>+++ we can choose to filter out those people that we've already worked with:
[source,cypher]
----
MATCH (person:Person)-[:WORKS_IN]->(office)<-[:WORKS_IN]-(potentialColleague)
WHERE person.name = "Steve" AND office.name = "London Office"
WITH person, potentialColleague
OPTIONAL MATCH (potentialColleague)-[c:COLLEAGUES_WITH]-(person)
WITH potentialColleague, c
WHERE c IS null
RETURN potentialColleague
----
If we evaluate that query it returns the same output as our original query:
[source,bash]
----
==> +----------------------+
==> | potentialColleague |
==> +----------------------+
==> | Node[4]{name:"Paul"} |
==> | Node[5]{name:"Sam"} |
==> +----------------------+
---- | null | null | [
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] | neo4j-2-0-0-m06-2-0-0-rc1-optional-relationships-with-optional-match | https://markhneedham.com/blog/2013/11/23/neo4j-2-0-0-m06-2-0-0-rc1-optional-relationships-with-optional-match |
false | 2013-11-22 22:45:32 | Neo4j: Cypher - Creating relationships between nodes from adjacent rows in a query | [
"neo4j",
"cypher"
] | [
"neo4j"
] | I want to introduce the concept of a season into my graph so I can have import matches for multiple years and then vary the time period which queries take into account.
I started by creating season nodes like this:
[source,cypher]
----
CREATE (:Season {name: "2013/2014", timestamp: 1375315200})
CREATE (:Season {name: "2012/2013", timestamp: 1343779200})
CREATE (:Season {name: "2011/2012", timestamp: 1312156800})
CREATE (:Season {name: "2010/2011", timestamp: 1280620800})
CREATE (:Season {name: "2009/2010", timestamp: 1249084800})
----
I wanted to add a 'NEXT' relationship between the seasons so that I could have an http://blog.neo4j.org/2013/05/reloading-my-beergraph-using-in-graph.html[in graph] season index which would allow me to write queries like the following:
[source,cypher]
----
// return all the matches for 2010/2011, 2011/2012, 2012/2013
MATCH (base:Season)<-[:NEXT*0..2]-(s)
WHERE base.name = "2012/2013"
MATCH s-[:contains]->game
RETURN game
----
I started out by writing a query which returned the seasons ordered by date:
[source,cypher]
----
MATCH (s:Season)
WITH s
ORDER BY s.timestamp
RETURN s
----
[source,bash]
----
==> +------------------------------------------------+
==> | s |
==> +------------------------------------------------+
==> | Node[0]{name:"2009/2010",timestamp:1249084800} |
==> | Node[1]{name:"2010/2011",timestamp:1280620800} |
==> | Node[2]{name:"2011/2012",timestamp:1312156800} |
==> | Node[3]{name:"2012/2013",timestamp:1343779200} |
==> | Node[4]{name:"2013/2014",timestamp:1375315200} |
==> +------------------------------------------------+
----
The next step was to pair up adjacent rows/nodes so that I could create a relationship between them. To do this we first need to return the seasons as a collection which we can do with the following query:
[source,cypher]
----
MATCH (s:Season)
WITH s
ORDER BY s.timestamp
RETURN COLLECT(s) AS seasons
----
[source,bash]
----
==> +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | seasons |
==> +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
==> | [Node[0]{name:"2009/2010",timestamp:1249084800},Node[1]{name:"2010/2011",timestamp:1280620800},Node[2]{name:"2011/2012",timestamp:1312156800},Node[3]{name:"2012/2013",timestamp:1343779200},Node[4]{name:"2013/2014",timestamp:1375315200}] |
==> +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
----
In a functional language we might not use the +++<cite>+++http://stackoverflow.com/questions/1115563/what-is-zip-functional-programming[zip]+++</cite>+++ function create pairs from the collection. Cypher doesn't have this function but we can get close by using the +++<cite>+++http://docs.neo4j.org/chunked/milestone/query-functions-collection.html#functions-range[RANGE]+++</cite>+++ function to select adjacent nodes.
I started off with this query:
[source,cypher]
----
MATCH (s:Season)
WITH s
ORDER BY s.timestamp
WITH COLLECT(s) AS seasons
FOREACH(i in RANGE(0, length(seasons)-2) |
CREATE UNIQUE (seasons[i])-[:NEXT]->(seasons[i+1])))
----
which fails with the following exception:
[source,bash]
----
==> SyntaxException: Invalid input '[': expected an identifier character, node labels, a property map, whitespace, ')' or a relationship pattern (line 1, column 142)
==> "MATCH (s:Season) WITH s ORDER BY s.timestamp WITH COLLECT(s) AS seasons FOREACH(i in RANGE(0, length(seasons)-2) | CREATE UNIQUE (seasons[i])-[:NEXT]->(seasons[i+1])))"
----
You can't use the slice syntax in a place where a node is expected but luckily https://twitter.com/wefreema[Wes] showed me a workaround using multiple +++<cite>+++FOREACH+++</cite>+++ statements which does the job:
[source,cypher]
----
MATCH (s:Season)
WITH s
ORDER BY s.timestamp
WITH COLLECT(s) AS seasons
FOREACH(i in RANGE(0, length(seasons)-2) |
FOREACH(si in [seasons[i]] |
FOREACH(si2 in [seasons[i+1]] |
CREATE UNIQUE (si)-[:NEXT]->(si2))))
----
Here we create one node collections which then allows us to reference each pair of nodes using the identifiers 'si' and 'si2'. We then create a relationship between them.
We can then write the following query to show how the seasons are connected:
[source,cypher]
----
MATCH (s:Season)<-[:NEXT*0..]-(ss)
WHERE s.name = "2013/2014"
RETURN ss
----
image::{{<siteurl>}}/uploads/2013/11/2013-11-22_22-42-10.png[2013 11 22 22 42 10,600] | null | null | [
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] | neo4j-cypher-creating-relationships-between-nodes-from-adjacent-rows-in-a-query | https://markhneedham.com/blog/2013/11/22/neo4j-cypher-creating-relationships-between-nodes-from-adjacent-rows-in-a-query |
false | 2013-10-03 16:16:02 | neo4j/cypher: Converting queries from 1.9 to 2.0 - 'Can't use optional patterns without explicit START clause' | [
"neo4j",
"cypher"
] | [
"neo4j"
] | I've been playing around with the most recent Neo4j 2.0 milestone release - http://blog.neo4j.org/2013/09/neo4j-200m05-released.html[2.0.0-M05] - and one of the first things I did was translate the queries from my football data set which were written against Neo4j 1.9.
The following query calculates the number of goals scored by players in matches that were shown on television, not on television and in total.
[source,cypher]
----
START player=node:players('name:*')
MATCH player-[:played|subbed_on]->stats-[:in]->game-[t?:on_tv]->channel
WITH COLLECT([stats.goals, TYPE(t)]) AS games, player
RETURN player.name,
REDUCE(goals = 0, g' IN FILTER(g IN games WHERE HEAD(TAIL(g)) IS NULL)| goals + HEAD(g')) AS nonTvGoals,
REDUCE(goals = 0, g' IN FILTER(g IN games WHERE HEAD(TAIL(g)) <> NULL)| goals + HEAD(g')) AS tvGoals,
REDUCE(goals = 0, g' in games | goals + HEAD(g')) AS totalGoals
ORDER BY tvGoals DESC
LIMIT 10
----
We use a legacy index to get all of the players, find the games they participated in, check if those games were on television and then group by player to work out whether the goals were scored in televised games.
When we evaluate that query we get the following result:
[source,bash]
----
==> +--------------------------------------------------------+
==> | player.name | nonTvGoals | tvGoals | totalGoals |
==> +--------------------------------------------------------+
==> | "Robin Van Persie" | 11 | 15 | 26 |
==> | "Gareth Bale" | 8 | 13 | 21 |
==> | "Luis Suárez" | 12 | 11 | 23 |
==> | "Theo Walcott" | 5 | 9 | 14 |
==> | "Demba Ba" | 7 | 8 | 15 |
==> | "Edin Dzeko" | 7 | 7 | 14 |
==> | "Santi Cazorla" | 5 | 7 | 12 |
==> | "Juan Mata" | 6 | 6 | 12 |
==> | "Steven Gerrard" | 3 | 6 | 9 |
==> | "Carlos Tevez" | 5 | 6 | 11 |
==> +--------------------------------------------------------+
==> 10 rows
----
The first step was to get rid of the legacy index and replace it with a http://jexp.de/blog/2013/04/cool-first-neo4j-2-0-milestone-now-with-labels-and-real-indexes/[label based one]. I created a label 'Player' for this purpose:
[source,cypher]
----
START player = node:players('name:*') SET player :Player RETURN player
CREATE INDEX on :Player(name)
----
Then I got rid of the legacy index lookup and replaced it with a label based one:
[source,cypher]
----
MATCH (player:Player)-[:played|subbed_on]->stats-[:in]->game-[t?:on_tv]->channel
WITH COLLECT([stats.goals, TYPE(t)]) AS games, player
RETURN player.name,
REDUCE(goals = 0, g' IN FILTER(g IN games WHERE HEAD(TAIL(g)) IS NULL)| goals + HEAD(g')) AS nonTvGoals,
REDUCE(goals = 0, g' IN FILTER(g IN games WHERE HEAD(TAIL(g)) <> NULL)| goals + HEAD(g')) AS tvGoals,
REDUCE(goals = 0, g' in games | goals + HEAD(g')) AS totalGoals
ORDER BY tvGoals DESC
LIMIT 10
----
Unfortunately you can't use optional relationships like this:
[source,cypher]
----
PatternException: Can't use optional patterns without explicit START clause. Optional relationships: `t`
----
A neat workaround which https://twitter.com/andres_taylor[Andres] showed me is to first match all of the players and then make them available using a +++<cite>+++WITH+++</cite>+++ statement:
[source,cypher]
----
MATCH (player:Player)
WITH player
MATCH player-[:played|subbed_on]->stats-[:in]->game-[t?:on_tv]->channel
WITH COLLECT([stats.goals, TYPE(t)]) AS games, player
RETURN player.name,
REDUCE(goals = 0, g' IN FILTER(g IN games WHERE HEAD(TAIL(g)) IS NULL)| goals + HEAD(g')) AS nonTvGoals,
REDUCE(goals = 0, g' IN FILTER(g IN games WHERE HEAD(TAIL(g)) <> NULL)| goals + HEAD(g')) AS tvGoals,
REDUCE(goals = 0, g' in games | goals + HEAD(g')) AS totalGoals
ORDER BY tvGoals DESC
LIMIT 10
----
If we evaluate this query it returns us the same results as before. However, given that we now have http://docs.neo4j.org/chunked/2.0.0-M05/syntax-collections.html[better collection support] it seemed a shame to still use +++<cite>+++HEAD+++</cite>+++ and +++<cite>+++TAIL+++</cite>+++ so I replaced those with the following:
[source,cypher]
----
MATCH (player:Player)
WITH player
MATCH player-[:played|subbed_on]->stats-[:in]->game-[t?:on_tv]->channel
WITH COLLECT([stats.goals, TYPE(t)]) AS games, player
RETURN player.name,
REDUCE(goals = 0, g' IN FILTER(g IN games WHERE g[1] IS NULL)| goals + g'[0]) AS nonTvGoals,
REDUCE(goals = 0, g' IN FILTER(g IN games WHERE g[1] <> NULL)| goals + g'[0]) AS tvGoals,
REDUCE(goals = 0, g' in games | goals + g'[0]) AS totalGoals
ORDER BY tvGoals DESC
LIMIT 10
----
I didn't like those array indexes either and since we can now http://docs.neo4j.org/chunked/2.0.0-M05/syntax-collections.html#_literal_maps[create maps] I thought the query would be clearer if we created one on line 4 of the query rather than an array:
[source,cypher]
----
MATCH (player:Player)
WITH player
MATCH player-[:played|subbed_on]->stats-[:in]->game-[t?:on_tv]->channel
WITH COLLECT({goals: stats.goals, ontv: TYPE(t)}) AS games, player
RETURN player.name,
REDUCE(goals = 0, g' IN FILTER(g IN games WHERE g.ontv IS NULL)| goals + g'.goals) AS nonTvGoals,
REDUCE(goals = 0, g' IN FILTER(g IN games WHERE g.ontv <> NULL)| goals + g'.goals) AS tvGoals,
REDUCE(goals = 0, g' in games | goals + g'.goals) AS totalGoals
ORDER BY tvGoals DESC
LIMIT 10
----
The next tweak would be to remove the +++<cite>+++FILTER+++</cite>+++ and replace that with a list comprehension:
[source,cypher]
----
MATCH (player:Player)
WITH player
MATCH player-[:played|subbed_on]->stats-[:in]->game-[t?:on_tv]->channel
WITH COLLECT({goals: stats.goals, ontv: TYPE(t)}) AS games, player
RETURN player.name,
REDUCE(goals = 0, g' IN [g IN games WHERE g.ontv IS NULL] | goals + g'.goals) AS nonTvGoals,
REDUCE(goals = 0, g' IN [g IN games WHERE g.ontv <> NULL] | goals + g'.goals) AS tvGoals,
REDUCE(goals = 0, g' in games | goals + g'.goals) AS totalGoals
ORDER BY tvGoals DESC
LIMIT 10
----
Although the query ends up being a couple of lines longer than its 1.9 cousin I think there's less noise and the intent is clearer. | null | null | [
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] | neo4jcypher-converting-queries-from-1-9-to-2-0-cant-use-optional-patterns-without-explicit-start-clause | https://markhneedham.com/blog/2013/10/03/neo4jcypher-converting-queries-from-1-9-to-2-0-cant-use-optional-patterns-without-explicit-start-clause |
false | 2013-10-20 21:46:16 | Neo4j: Testing an unmanaged extension using CommunityServerBuilder | [
"neo4j"
] | [
"neo4j"
] | I've been playing around with http://docs.neo4j.org/chunked/milestone/server-unmanaged-extensions.html[Neo4j unmanaged extensions] recently and I wanted to be able to check that it worked properly without having to deploy it to a real Neo4j server.
I'd previously used +++<cite>+++http://grepcode.com/file/repo1.maven.org/maven2/org.neo4j/neo4j-kernel/1.2-1.2/org/neo4j/kernel/ImpermanentGraphDatabase.java[ImpermanentGraphDatabase]+++</cite>+++ when using Neo4j embedded and https://twitter.com/iansrobinson[Ian] pointed me towards +++<cite>+++CommunityServerBuilder+++</cite>+++ which allows us to do a similar thing in Neo4j server world.
I've created an example of a https://github.com/mneedham/dummy-unmanaged-extension/blob/master/src/main/java/org/neo4j/unmanaged/DummyResource.java[dummy unmanaged extension] and https://github.com/mneedham/dummy-unmanaged-extension/blob/master/src/test/java/org/neo4j/unmanaged/DummyResourceTest.java[test] showing this approach but it's reasonably simple.
Given the following unmanaged extension:
[source,java]
----
package org.neo4j.unmanaged;
@Path("/dummy")
public class DummyResource
{
private final ExecutionEngine executionEngine;
private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper();
public DummyResource( @Context GraphDatabaseService database )
{
this.executionEngine = new ExecutionEngine(database);
}
@GET
@Produces(MediaType.APPLICATION_JSON)
@Path("/all-nodes")
public Response uploadNodesFile( ) throws IOException
{
ExecutionResult result = executionEngine.execute("START n = node(*) RETURN n.name ORDER BY n.name");
ObjectNode root = JsonNodeFactory.instance.objectNode();
for (String column : result.columns()) {
ResourceIterator<Object> rows = result.columnAs(column);
ArrayNode resultRows = JsonNodeFactory.instance.arrayNode();
while(rows.hasNext()) {
Object row = rows.next();
if(row != null) {
resultRows.add(row.toString());
}
}
root.put(column, resultRows);
}
return Response.status( 200 )
.entity(OBJECT_MAPPER.writeValueAsString(root))
.type(MediaType.APPLICATION_JSON).build();
}
}
----
We would write the following test which exposes the 'all-nodes' resource at '/unmanaged/dummy/all-nodes':
[source,java]
----
package org.neo4j.unmanaged;
public class DummyResourceTest {
private GraphDatabaseAPI db;
private CommunityNeoServer server;
@Before
public void before() throws IOException {
ServerSocket serverSocket = new ServerSocket(0);
server = CommunityServerBuilder
.server()
.onPort(serverSocket.getLocalPort())
.withThirdPartyJaxRsPackage("org.neo4j.unmanaged", "/unmanaged")
.build();
server.start();
db = server.getDatabase().getGraph();
}
@After
public void after() {
server.stop();
}
@Test
public void shouldReturnAllTheNodes() {
Transaction tx = db.beginTx();
db.createNode().setProperty("name", "Mark");
db.createNode().setProperty("name", "Dave");
tx.success();
tx.close();
JsonNode response = jerseyClient()
.resource(server.baseUri().toString() + "unmanaged/dummy/all-nodes")
.get(ClientResponse.class)
.getEntity(JsonNode.class);
assertEquals("Dave", response.get("n.name").get(0).asText());
assertEquals("Mark", response.get("n.name").get(1).asText());
}
private Client jerseyClient() {
DefaultClientConfig defaultClientConfig = new DefaultClientConfig();
defaultClientConfig.getClasses().add(JacksonJsonProvider.class);
return Client.create(defaultClientConfig);
}
}
----
Here we add a couple of nodes directly against the Java API and then call our unmanaged extension using a Jersey HTTP client. +++<cite>+++CommunityServerBuilder+++</cite>+++ spins up an ephemeral store under the covers and we pick a random free port for Neo4j server by using +++<cite>+++ServerSocket#getLocalPort+++</cite>+++.
We import +++<cite>+++CommunityServerBuilder+++</cite>+++ by including the following dependency:
[source,xml]
----
<dependency>
<groupId>org.neo4j.app</groupId>
<artifactId>neo4j-server</artifactId>
<version>${neo4j.version}</version>
<type>test-jar</type>
</dependency>
----
And that's it! | null | null | [
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] | neo4j-testing-an-unmanaged-extension-using-communitserverbuilder | https://markhneedham.com/blog/2013/10/20/neo4j-testing-an-unmanaged-extension-using-communitserverbuilder |
false | 2013-10-20 11:13:54 | Neo4j: Accessing JMX beans via HTTP | [
"neo4j"
] | [
"neo4j"
] | One of the additional features that Neo4j enterprise provides is access to various http://docs.neo4j.org/chunked/milestone/jmx-remote.html[JMX properties] which describe http://docs.neo4j.org/chunked/stable/jmx-mxbeans.html[various aspects] of the database.
These would typically be accessed by using http://docs.oracle.com/javase/6/docs/technotes/guides/management/jconsole.html[jConsole] or similar but some monitoring tools aren't able to use the JMX hook and a HTTP interface would work better.
Luckily Neo4j server does expose the JMX beans and we can get a list of URIs to query by hitting the following URI:
[source,bash]
----
$ curl -H "Content-Type:application/json" http://localhost:7474/db/manage/server/jmx/
{
"resources" : {
"kernelquery" : "http://localhost:7474/db/manage/server/jmx/kernelquery",
"bean" : "http://localhost:7474/db/manage/server/jmx/domain/{domain}/{objectName}",
"query" : "http://localhost:7474/db/manage/server/jmx/query",
"domains" : "http://localhost:7474/db/manage/server/jmx/domain",
"domain" : "http://localhost:7474/db/manage/server/jmx/domain/{domain}"
}
}
----
If we want to get the output for all JMX beans we can hit the following URI:
[source,bash]
----
$ curl -H "Content-Type:application/json" http://localhost:7474/db/manage/server/jmx/domain/*/*
// cut for verbosity
...
{
"description" : "Estimates of the numbers of different kinds of Neo4j primitives",
"name" : "org.neo4j:instance=kernel#0,name=Primitive count",
"attributes" : [ {
"description" : "An estimation of the number of nodes used in this Neo4j instance",
"name" : "NumberOfNodeIdsInUse",
"value" : 24117,
"isReadable" : "true",
"type" : "long",
"isWriteable" : "false ",
"isIs" : "false "
}, {
"description" : "An estimation of the number of relationships used in this Neo4j instance",
"name" : "NumberOfRelationshipIdsInUse",
"value" : 1,
"isReadable" : "true",
"type" : "long",
"isWriteable" : "false ",
"isIs" : "false "
}, {
"description" : "An estimation of the number of properties used in this Neo4j instance",
"name" : "NumberOfPropertyIdsInUse",
"value" : 19078,
"isReadable" : "true",
"type" : "long",
"isWriteable" : "false ",
"isIs" : "false "
}, {
"description" : "The number of relationship types used in this Neo4j instance",
"name" : "NumberOfRelationshipTypeIdsInUse",
"value" : 0,
"isReadable" : "true",
"type" : "long",
"isWriteable" : "false ",
"isIs" : "false "
} ],
"url" : "org.neo4j/instance%3Dkernel%230%2Cname%3DPrimitive+count"
}
...
----
If we only wanted to return the the numbers of different kinds of Neo4j primitives we could run the following query which uses the name returned by the previous query:
[source,bash]
----
$ curl -H "Content-Type:application/json" -d '["org.neo4j:name=Primitive count,instance=*"]' http://localhost:7474/db/manage/server/jmx/query
[ {
"description" : "Estimates of the numbers of different kinds of Neo4j primitives",
"name" : "org.neo4j:instance=kernel#0,name=Primitive count",
"attributes" : [ {
"description" : "An estimation of the number of nodes used in this Neo4j instance",
"name" : "NumberOfNodeIdsInUse",
"value" : 24117,
"isReadable" : "true",
"type" : "long",
"isWriteable" : "false ",
"isIs" : "false "
}],
...
"url" : "org.neo4j/instance%3Dkernel%230%2Cname%3DPrimitive+count"
} ]
----
We could also use the following URI if we don't want to/can't send a POST body:
[source,bash]
----
$ curl -H "Content-Type:application/json" http://localhost:7474/db/manage/server/jmx/domain/org.neo4j/instance%3Dkernel%230%2Cname%3DPrimitive+count
[ {
"description" : "Estimates of the numbers of different kinds of Neo4j primitives",
"name" : "org.neo4j:instance=kernel#0,name=Primitive count",
"attributes" : [ {
"description" : "An estimation of the number of nodes used in this Neo4j instance",
"name" : "NumberOfNodeIdsInUse",
"value" : 24117,
"isReadable" : "true",
"type" : "long",
"isWriteable" : "false ",
"isIs" : "false "
}],
...
"url" : "org.neo4j/instance%3Dkernel%230%2Cname%3DPrimitive+count"
} ]
---- | null | null | [
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] | neo4j-accessing-jmx-beans-via-http | https://markhneedham.com/blog/2013/10/20/neo4j-accessing-jmx-beans-via-http |
false | 2013-10-18 11:43:59 | Neo4j: Exploring new data sets with help from Neo4j browser | [
"neo4j"
] | [
"neo4j"
] | One of the things that I've found difficult when looking at a new Neo4j database is working out the structure of the data it contains.
I'm used to relational databases where you can easily get a list of the table and the foreign keys that allow you to join them to each other.
image::{{<siteurl>}}/uploads/2013/10/2013-10-17_21-27-36.png[2013 10 17 21 27 36,226]
This has traditionally been difficult when using Neo4j but with the http://blog.neo4j.org/2013/10/neo4j-200-m06-introducing-neo4js-browser.html[release of the Neo4j browser] we can now easily get this type of overview by clicking on the Neo4j icon at the top left of the browser.
We'll see something similar to the image on the left which shows the structure of my football graph and we can now discover parts of the graph by clicking on the various labels, properties or relationships.
For example, if we click on the +++<cite>+++Month+++</cite>+++ label the following query will be executed:
image::{{<siteurl>}}/uploads/2013/10/2013-10-18_11-59-49.png[2013 10 18 11 59 49,400]
That query actually returns all the months in the database since there are only 12 in total, but for other queries we'd get back a subset of the nodes/relationships with that structure.
To explore the structure around the months we might explore which relationships it has by running a query like this:
image::{{<siteurl>}}/uploads/2013/10/2013-10-18_12-18-54.png[2013 10 18 12 18 54,600]
From running this query we learn that there is a +++<cite>+++in_month+++</cite>+++ relationship connecting matches to the month that they were played in.
Another useful exploration mechanism is the +++<cite>+++http://docs.neo4j.org/chunked/milestone/query-match.html#match-all-shortest-paths[allShortestPaths]+++</cite>+++ function which will show us the path between two nodes if one exists. For example the following query would show us the path between Wayne Rooney and March:
image::{{<siteurl>}}/uploads/2013/10/2013-10-18_12-27-12.png[2013 10 18 12 27 12,600]
The text is a bit small but the relationship between Rooney and a match is named +++<cite>+++played_in+++</cite>+++ so if we want to find which matches Rooney played in in March we could easily write that query.
Another neat feature of the browser is that we can save our queries as we go by clicking on the save button and optionally naming the query in a comment:
image::{{<siteurl>}}/uploads/2013/10/2013-10-18_12-37-31.png[2013 10 18 12 37 31,600]
We can then access those queries and re-run them if we want via the star tab on the left hand side:
image::{{<siteurl>}}/uploads/2013/10/2013-10-18_12-34-06.png[2013 10 18 12 34 06,600]
You can get a hold of the browser by downloading milestone 2.0.0-M06 about half way down the http://www.neo4j.org/download[download page] or if you want to hack on the browser it's on https://github.com/neo4j/neo4j-browser[github]. | null | null | [
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] | neo4j-exploring-new-data-sets-with-help-from-neo4j-browser | https://markhneedham.com/blog/2013/10/18/neo4j-exploring-new-data-sets-with-help-from-neo4j-browser |
false | 2013-10-27 00:33:54 | Neo4j: Cypher - Profiling ORDER BY LIMIT vs LIMIT | [
"neo4j"
] | [
"neo4j"
] | Something I've seen people get confused by when writing queries using Neo4j's cypher query language is the sometimes significant difference in query execution time when using 'LIMIT' on its own compared to using it in combination with 'ORDER BY'.
The confusion is centred around the fact that at first glance it seems like the only thing different between these queries is the sorting of the rows but there's actually more to it.
For example given a small graph with ~ 11,000 nodes\...
[source,bash]
----
$ MATCH n RETURN COUNT(n);
==> +----------+
==> | COUNT(n) |
==> +----------+
==> | 11442 |
==> +----------+
==> 1 row
==> 1384 ms
----
\...we can return 5 random nodes really quickly with the following query:
[source,bash]
----
$ MATCH n RETURN n LIMIT 5;
==> +-------------------------------------------+
==> | n |
==> +-------------------------------------------+
==> | Node[0]{} |
==> | Node[1]{name:"Africa",node_id:"1"} |
==> | Node[2]{name:"Asia",node_id:"2"} |
==> | Node[3]{name:"Europe",node_id:"3"} |
==> | Node[4]{name:"North America",node_id:"4"} |
==> +-------------------------------------------+
==> 5 rows
==> 38 ms
----
However, if we order those nodes by ID first then it'll take a bit longer:
[source,bash]
----
$ MATCH n RETURN n ORDER BY ID(n) LIMIT 5;
==> +-------------------------------------------+
==> | n |
==> +-------------------------------------------+
==> | Node[0]{} |
==> | Node[1]{name:"Africa",node_id:"1"} |
==> | Node[2]{name:"Asia",node_id:"2"} |
==> | Node[3]{name:"Europe",node_id:"3"} |
==> | Node[4]{name:"North America",node_id:"4"} |
==> +-------------------------------------------+
==> 5 rows
==> 157 ms
----
If we have a look at the profile of the second query we can see why this is:
[source,bash]
----
$ PROFILE MATCH n RETURN n ORDER BY ID(n) LIMIT 5;
==> ColumnFilter(symKeys=["n", " UNNAMEDS1215244997"], returnItemNames=["n"], _rows=5, _db_hits=0)
==> Top(orderBy=["SortItem(Cached( UNNAMEDS1215244997 of type Long),true)"], limit="Literal(5)", _rows=5, _db_hits=0)
==> Extract(symKeys=["n"], exprKeys=[" UNNAMEDS1215244997"], _rows=11442, _db_hits=0)
==> AllNodes(identifier="n", _rows=11442, _db_hits=11442)
----
+++<cite>+++Top+++</cite>+++ refers to +++<cite>+++https://github.com/neo4j/neo4j/blob/master/community/cypher/src/main/scala/org/neo4j/cypher/internal/compiler/v2_0/pipes/TopPipe.scala[TopPipe]+++</cite>+++ and if we look at the +++<cite>+++https://github.com/neo4j/neo4j/blob/master/community/cypher/src/main/scala/org/neo4j/cypher/internal/compiler/v2_0/pipes/TopPipe.scala#L34[internalCreateResults]+++</cite>+++ function we can see that the entire collection gets evaluated so that we can sort it correctly on line 63.
[source,java]
----
if (input.isEmpty)
Iterator.empty
else {
val first = input.next()
val count = countExpression(first).asInstanceOf[Number].intValue()
val iter = new HeadAndTail(first, input)
iter.foreach {
case ctx =>
if (size < count) {
result += ctx
size += 1
if (largerThanLast(ctx)) {
last = Some(ctx)
}
} else
if (!largerThanLast(ctx)) {
result -= last.get
result += ctx
result = result.sortWith((a, b) => compareBy(a, b, sortDescription)(state))
sorted = true
last = Some(result.last)
}
}
}
----
In this case it means that we load the next node but in a more complex query we'd have to evaluate a more complex pattern for every single match.
On the other hand, if we don't have the 'ORDER BY' we use the +++<cite>+++https://github.com/neo4j/neo4j/blob/master/community/cypher/src/main/scala/org/neo4j/cypher/internal/compiler/v2_0/pipes/SlicePipe.scala#L47[SlicePipe]+++</cite>+++ which does a +++<cite>+++take+++</cite>+++ on the iterator which lazily retrieves the first 5 results in this instance:
[source,bash]
----
$ PROFILE MATCH n RETURN n LIMIT 5;
==> Slice(limit="Literal(5)", _rows=5, _db_hits=0)
==> AllNodes(identifier="n", _rows=5, _db_hits=5)
----
[source,java]
----
(skip, limit) match {
case (Some(x), None) => sourceIter.drop(asInt(x))
case (None, Some(x)) => sourceIter.take(asInt(x))
case (Some(startAt), Some(count)) =>
val start = asInt(startAt)
sourceIter.slice(start, start + asInt(count))
case (None, None) =>
throw new ThisShouldNotHappenError("Andres Taylor", "A slice pipe that doesn't slice should never exist.")
}
----
Now that we've drilled into the queries a bit more we can see that with the 'ORDER BY LIMIT' query many more paths need to be evaluated to make that possible.
A common anti pattern is to return a result set containing 100s of thousands of rows and then using 'ORDER BY LIMIT' to filter that down to 10.
In that case there might be a better way of modelling the data so that we have less rows coming back from our MATCH clause and http://blog.neo4j.org/2013/05/reloading-my-beergraph-using-in-graph.html[In graph indexes] are one such way of doing that.</p> | null | null | [
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] | neo4j-cypher-profiling-order-by-limit-vs-limit | https://markhneedham.com/blog/2013/10/27/neo4j-cypher-profiling-order-by-limit-vs-limit |
false | 2013-10-27 22:53:49 | Thinking Fast and Slow - Daniel Kahneman: Book Review | [
"book-review",
"books",
"kahneman",
"thinking",
"cognitive-bias"
] | [
"Books"
] | I picked up Daniel Kahneman's 'http://www.amazon.co.uk/Thinking-Fast-Slow-Daniel-Kahneman/dp/0141033576/ref=sr_1_1?ie=UTF8&qid=1382906786&sr=8-1&keywords=thinking+fast+and+slow[Thinking Fast and Slow]' after a recommendation by https://twitter.com/michael_jones[Mike Jones] in early 2013 - it's taken me quite a while to get through it.
The book starts by describing our two styles of thinking\...
* *System 1* -- operates automatically and quickly, with little or no effort and no sense of voluntary control.
* *System 2* -- allocates attention to the effortful mental activities that demand it, including complex computations. The operations of System 2 are often associated with the subjective experience of agency, choice, and concentration.
\...and the next 30+ chapters describe situations where System 1 can get us into trouble.
The book focuses on different cognitive biases that humans get fooled by and there was a spell of about 100 pages starting around chapter 4 where it started to feel repetitive and I felt like putting the book down.
That said, I kept going and enjoyed the remaining 2/3 of the book much more.
These were some of my favourite parts:
* http://en.wikipedia.org/wiki/Outcome_bias[Outcome bias] - this bias describes the tendency to *build a story around events after we already know the outcome* and come to the conclusion that the outcome was inevitable. For example, many people claim that they 'knew well before it happened that the 2008 financial crisis was inevitable'. Kahneman points out that we only remember the intuitions which turned out to be true, noone talks about those intuitions which turned out to be false. He also cautions us to consider the role that luck plays in any success.
* Intuitions vs Formulas - Kahneman quotes http://en.wikipedia.org/wiki/Paul_E._Meehl[Paul Meehl] who suggests that *most of the time human decision makers are inferior to a prediction formula* even when they are given the score suggested by the formula. In the world of software this reliance on intuition is often used when interviewing people for jobs and choosing who to hire based on an intuitive judgement. Kahneman suggests making a list of about 6 traits that are pre-requisites for the position and then ranking each candidate against those. The candidate who scores the highest is the best choice.
* Over reliance on representativeness - often *we judge the probability of something based on stereotypes rather than taking the base rate of the population into account*. e.g. if we saw a person reading The New York Times on the subway which of the following is a better bet about this stranger?
** She has a PhD.
** She does not have a college degree.
+
If we judge based on representativeness we'll bet on the PhD because our stereotypes tell us that a PhD student is more likely to be reading the New York Times than a person without a college degree. However, there are many more non graduates on the subway than PhDs so the likelihood is that the person doesn't have a college degree. Kahneman encourages us to take the base rates of the population into account even if we have evidence about the case at hand. He uses the example of predicting how long a project will take and suggests using the data from previous similar projects as a baseline and then adjusting that based on our specific case.
* http://en.wikipedia.org/wiki/Framing_(social_sciences)[Framing] - Kahneman talks about *the impact that the way information is presented to us can have on the way we react to it*. For example, he describes a study in which the merits of surgery for a condition are considered and the descriptions of the surgery are like so:
** The one month survival rate is 90%.
** There is 10% mortality is in the first month.
+
People who were presented with the first option were much more likely to favour surgery than those shown the second option.
* The default option - *people feel much more regret if they deviate from the normal choice* and something goes wrong than if they stick with the default and something equally wrong happens. This theory is well known on forms where authors will make the option they want people to select the default.
There are many more insights and these are just a few that stood out for me.
Overall I enjoyed reading the book and I feel it may the same impact as Malcolm Gladwell's http://www.amazon.co.uk/Blink-The-Power-Thinking-Without/dp/0141014598/ref=sr_1_1?ie=UTF8&qid=1382909781&sr=8-1&keywords=blink[Blink] and http://www.amazon.co.uk/Outliers-Story-Success-Malcolm-Gladwell/dp/0141036257/ref=la_B000APOE98_1_2?s=books&ie=UTF8&qid=1382907541&sr=1-2[Outliers] by making you think about situations differently than you may have done before.
I'd encourage you to read the book if you find human decision making interesting although I'd try and forget everything I've said as you've probably been inadvertently http://en.wikipedia.org/wiki/Priming_(psychology)[primed] by me. | null | null | [
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] | thinking-fast-and-slow-daniel-kahneman-book-review | https://markhneedham.com/blog/2013/10/27/thinking-fast-and-slow-daniel-kahneman-book-review |
false | 2013-10-17 06:47:10 | neo4j: Setting query timeout | [
"neo4j"
] | [
"neo4j"
] | == Updated December 2015
When I initially wrote this post in 2013 this was an experimental feature that worked using the Neo4j 1.9 series but no longer does in more recent Neo4j versions (2.2, 2.3). The http://neo4j.com/docs/stable/tutorials-java-embedded-tx-terminate.html[terminating a running transaction] page in the docs describes the supported way of terminating queries.
- - - - - - - - - - -
When I was first learning cypher, neo4j's query language, I frequently wrote queries which traversed the whole graph multiple times and 'hung' for hours as they were evaluated.
At the time I terminated them by restarting the server but I recently learned from an https://groups.google.com/forum/#!topic/neo4j/5ec8FThLTeo[old post on the mailing list] and from my colleague https://twitter.com/darthvader42[Stefan Armbruster] that you can set a query timeout to avoid this problem.
To do this you need to set the following properties:
+++<em>+++conf/neo4j-server.properties</p>
+++</em>+++ ~~~text # timeout in milliseconds org.neo4j.server.webserver.limit.executiontime=1000 ~~~
+++<em>+++conf/neo4j.properties</p>
+++</em>+++ ~~~text execution_guard_enabled=true ~~~
Now if I run a query which takes longer than 1 second to evaluate it will terminate early with the following exception: ~~~bash $ start n = node(*) return n; =\=> GuardTimeoutException: timeout occured (overtime=1718) ~~~
If you want a stricter maximum execution time on a per query basis you can do so by passing the 'max-execution-time' header on the request. e.g. ~~~bash $ curl -H "Content-Type:application/json" -H "max-execution-time: 500" -X POST -d '{"query" : "start n = node(*) return n"}' http://localhost:7474/db/data/cypher { "message" : "timeout occured (overtime=1)", "exception" : "BadInputException", "fullname" : "org.neo4j.server.rest.repr.BadInputException", "stacktrace" : [ "org.neo4j.server.rest.repr.RepresentationExceptionHandlingIterable.exceptionOnNext(RepresentationExceptionHandlingIterable.java:39)", "org.neo4j.helpers.collection.ExceptionHandlingIterable$1.next(ExceptionHandlingIterable.java:55)", "org.neo4j.helpers.collection.IteratorWrapper.next(IteratorWrapper.java:47)", "org.neo4j.server.rest.repr.ListRepresentation.serialize(ListRepresentation.java:58)", "org.neo4j.server.rest.repr.Serializer.serialize(Serializer.java:75)", "org.neo4j.server.rest.repr.MappingSerializer.putList(MappingSerializer.java:61)", "org.neo4j.server.rest.repr.CypherResultRepresentation.serialize(CypherResultRepresentation.java:83)", "org.neo4j.server.rest.repr.MappingRepresentation.serialize(MappingRepresentation.java:41)", "org.neo4j.server.rest.repr.OutputFormat.assemble(OutputFormat.java:215)", "org.neo4j.server.rest.repr.OutputFormat.formatRepresentation(OutputFormat.java:147)", "org.neo4j.server.rest.repr.OutputFormat.response(OutputFormat.java:130)", "org.neo4j.server.rest.repr.OutputFormat.ok(OutputFormat.java:67)", "org.neo4j.server.rest.web.CypherService.cypher(CypherService.java:101)", "java.lang.reflect.Method.invoke(Method.java:601)", "org.neo4j.server.rest.transactional.TransactionalRequestDispatcher.dispatch(TransactionalRequestDispatcher.java:132)", "org.neo4j.server.rest.security.SecurityFilter.doFilter(SecurityFilter.java:112)", "org.neo4j.server.guard.GuardingRequestFilter.doFilter(GuardingRequestFilter.java:68)", "java.lang.Thread.run(Thread.java:722)" ], "cause" : { "message" : "timeout occured (overtime=1)", "exception" : "GuardTimeoutException", "stacktrace" : [ "org.neo4j.kernel.guard.Guard$Timeout.check(Guard.java:132)", "org.neo4j.kernel.guard.Guard.check(Guard.java:43)", "org.neo4j.kernel.InternalAbstractGraphDatabase$4.getNodeByIdOrNull(InternalAbstractGraphDatabase.java:692)", "org.neo4j.kernel.impl.core.NodeManager$3.fetchNextOrNull(NodeManager.java:339)", "org.neo4j.kernel.impl.core.NodeManager$3.fetchNextOrNull(NodeManager.java:326)", "org.neo4j.helpers.collection.PrefetchingIterator.hasNext(PrefetchingIterator.java:55)", "scala.collection.convert.Wrappers$JIteratorWrapper.hasNext(Wrappers.scala:41)", "scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)", "scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)", "org.neo4j.cypher.internal.ClosingIterator$$anonfun$next$1.apply(ClosingIterator.scala:48)", "org.neo4j.cypher.internal.ClosingIterator$$anonfun$next$1.apply(ClosingIterator.scala:45)", "org.neo4j.cypher.internal.ClosingIterator.failIfThrows(ClosingIterator.scala:91)", "org.neo4j.cypher.internal.ClosingIterator.next(ClosingIterator.scala:45)", "org.neo4j.cypher.PipeExecutionResult.next(PipeExecutionResult.scala:168)", "org.neo4j.cypher.PipeExecutionResult.next(PipeExecutionResult.scala:34)", "scala.collection.Iterator$$anon$11.next(Iterator.scala:328)", "scala.collection.convert.Wrappers$IteratorWrapper.next(Wrappers.scala:30)", "org.neo4j.cypher.PipeExecutionResult$$anon$1.next(PipeExecutionResult.scala:76)", "org.neo4j.helpers.collection.ExceptionHandlingIterable$1.next(ExceptionHandlingIterable.java:53)", "org.neo4j.helpers.collection.IteratorWrapper.next(IteratorWrapper.java:47)", "org.neo4j.server.rest.repr.ListRepresentation.serialize(ListRepresentation.java:58)", "org.neo4j.server.rest.repr.Serializer.serialize(Serializer.java:75)", "org.neo4j.server.rest.repr.MappingSerializer.putList(MappingSerializer.java:61)", "org.neo4j.server.rest.repr.CypherResultRepresentation.serialize(CypherResultRepresentation.java:83)", "org.neo4j.server.rest.repr.MappingRepresentation.serialize(MappingRepresentation.java:41)", "org.neo4j.server.rest.repr.OutputFormat.assemble(OutputFormat.java:215)", "org.neo4j.server.rest.repr.OutputFormat.formatRepresentation(OutputFormat.java:147)", "org.neo4j.server.rest.repr.OutputFormat.response(OutputFormat.java:130)", "org.neo4j.server.rest.repr.OutputFormat.ok(OutputFormat.java:67)", "org.neo4j.server.rest.web.CypherService.cypher(CypherService.java:101)", "java.lang.reflect.Method.invoke(Method.java:601)", "org.neo4j.server.rest.transactional.TransactionalRequestDispatcher.dispatch(TransactionalRequestDispatcher.java:132)", "org.neo4j.server.rest.security.SecurityFilter.doFilter(SecurityFilter.java:112)", "org.neo4j.server.guard.GuardingRequestFilter.doFilter(GuardingRequestFilter.java:68)", "java.lang.Thread.run(Thread.java:722)" ], "fullname" : "org.neo4j.kernel.guard.GuardTimeoutException" } } ~~~
At some stage hopefully we'll have the ability to terminate individual queries from the server but for now this is a decent workaround. | null | null | [
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] | neo4j-setting-query-timeout | https://markhneedham.com/blog/2013/10/17/neo4j-setting-query-timeout |
false | 2013-10-30 07:26:49 | Kaggle Titanic: Python pandas attempt | [
"machine-learning-2",
"kaggle"
] | [
"Machine Learning"
] | http://junctionbox.ca/[Nathan] and I have been looking at http://www.kaggle.com/c/titanic-gettingStarted[Kaggle's Titanic problem] and while working through the http://www.kaggle.com/c/titanic-gettingStarted/details/getting-started-with-python[Python tutorial] Nathan pointed out that we could greatly simplify the code if we used http://pandas.pydata.org/[pandas] instead.
The problem we had with numpy is that you use integers to reference columns. We spent a lot of time being thoroughly confused as to why something wasn't working only to realise we were using the wrong column.
The algorithm scores an accuracy of 77.99% and the way it works is that we build a 'survival table' which works out an average survival rate of people based on 3 features:
* Passenger Class
* Passenger Fare (grouped into those who paid 0-9, 10-19, 20-29, 30+)
* Gender
It looks like this:
image::{{<siteurl>}}/uploads/2013/10/2013-10-30_07-05-03.png[2013 10 30 07 05 03,316]
And the code that creates that is:
[source,python]
----
import pandas as pd
def addrow(df, row):
return df.append(pd.DataFrame(row), ignore_index=True)
def fare_in_bucket(fare, fare_bracket_size, bucket):
return (fare > bucket * fare_bracket_size) & (fare <= ((bucket+1) * fare_bracket_size))
def build_survival_table(training_file):
fare_ceiling = 40
train_df = pd.read_csv(training_file)
train_df[train_df['Fare'] >= 39.0] = 39.0
fare_bracket_size = 10
number_of_price_brackets = fare_ceiling / fare_bracket_size
number_of_classes = 3 #There were 1st, 2nd and 3rd classes on board
survival_table = pd.DataFrame(columns=['Sex', 'Pclass', 'PriceDist', 'Survived', 'NumberOfPeople'])
for pclass in range(1, number_of_classes + 1): # add 1 to handle 0 start
for bucket in range(0, number_of_price_brackets):
for sex in ['female', 'male']:
survival = train_df[(train_df['Sex'] == sex)
& (train_df['Pclass'] == pclass)
& fare_in_bucket(train_df["Fare"], fare_bracket_size, bucket)]
row = [dict(Pclass=pclass, Sex=sex, PriceDist = bucket,
Survived = round(survival['Survived'].mean()),
NumberOfPeople = survival.count()[0]) ]
survival_table = addrow(survival_table, row)
return survival_table.fillna(0)
survival_table = build_survival_table("train.csv")
----
where 'train.csv' is structured like so:
[source,bash]
----
$ head -n5 train.csv
PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
----
After we've built that we iterate through the test data set and look up each person in the table and find their survival rate.
[source,python]
----
def select_bucket(fare):
if (fare >= 0 and fare < 10):
return 0
elif (fare >= 10 and fare < 20):
return 1
elif (fare >= 20 and fare < 30):
return 2
else:
return 3
def calculate_survival(survival_table, row):
survival_row = survival_table[(survival_table["Sex"] == row["Sex"]) & (survival_table["Pclass"] == row["Pclass"]) & (survival_table["PriceDist"] == select_bucket(row["Fare"]))]
return int(survival_row["Survived"].iat[0])
test_df = pd.read_csv('test.csv')
test_df["Survived"] = test_df.apply(lambda row: calculate_survival(survival_table, row), axis=1)
----
I wrote up http://www.markhneedham.com/blog/2013/10/30/pandas-adding-a-column-to-a-dataframe-based-on-another-dataframe/[the difficulties we had working out how to append the 'Survived' column] if you want more detail.
'test.csv' looks like this:
[source,bash]
----
$ head -n5 test.csv
PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q
893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S
894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q
895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S
----
We then write out the survival value for each customer along with their ID:
[source,python]
----
test_df.to_csv("result.csv", cols=['PassengerId', 'Survived'], index=False)
----
[source,bash]
----
$ head -n5 result.csv
PassengerId,Survived
892,0
893,1
894,0
895,0
----
I've pasted the https://gist.github.com/mneedham/7227543[code as a gist] for those who want to see it all as one.
Next step: introduce some real machine learning, probably using http://scikit-learn.org/stable/[scikit-learn] unless there's something else we should be using? | null | null | [
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] | kaggle-titanic-python-pandas-attempt | https://markhneedham.com/blog/2013/10/30/kaggle-titanic-python-pandas-attempt |
false | 2013-10-30 06:12:08 | pandas: Adding a column to a DataFrame (based on another DataFrame) | [
"python",
"pandas"
] | [
"Python"
] | http://junctionbox.ca/[Nathan] and I have been working on the http://www.kaggle.com/c/titanic-gettingStarted[Titanic Kaggle problem] using the http://pandas.pydata.org/[pandas] data analysis library and one thing we wanted to do was add a column to a DataFrame indicating if someone survived.
We had the following (simplified) DataFrame containing some information about customers on board the Titanic:
[source,python]
----
def addrow(df, row):
return df.append(pd.DataFrame(row), ignore_index=True)
customers = pd.DataFrame(columns=['PassengerId','Pclass','Name','Sex','Fare'])
customers = addrow(customers, [dict(PassengerId=892, Pclass=3, Name="Kelly, Mr. James", Sex="male", Fare=7.8292)])
customers = addrow(customers, [dict(PassengerId=893, Pclass=3, Name="Wilkes, Mrs. James (Ellen Needs)", Sex="female", Fare=7)])
>>> customers
Fare Name PassengerId Pclass Sex
0 7.8292 Kelly, Mr. James 892 3 male
1 7.0000 Wilkes, Mrs. James (Ellen Needs) 893 3 female
----
We wanted to add a 'Survived' column to that by doing a lookup in the +++<cite>+++survival_table+++</cite>+++ below to work out the appropriate value:
[source,python]
----
survival_table = pd.DataFrame(columns=['Sex', 'Pclass', 'PriceDist', 'Survived'])
survival_table = addrow(survival_table, [dict(Pclass=1, Sex="female", PriceDist = 0, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=1, Sex="male", PriceDist = 0, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=1, Sex="female", PriceDist = 1, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=1, Sex="male", PriceDist = 1, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=1, Sex="female", PriceDist = 2, Survived = 1)])
survival_table = addrow(survival_table, [dict(Pclass=1, Sex="male", PriceDist = 2, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=1, Sex="female", PriceDist = 3, Survived = 1)])
survival_table = addrow(survival_table, [dict(Pclass=1, Sex="male", PriceDist = 3, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=2, Sex="female", PriceDist = 0, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=2, Sex="male", PriceDist = 0, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=2, Sex="female", PriceDist = 1, Survived = 1)])
survival_table = addrow(survival_table, [dict(Pclass=2, Sex="male", PriceDist = 1, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=2, Sex="female", PriceDist = 2, Survived = 1)])
survival_table = addrow(survival_table, [dict(Pclass=2, Sex="male", PriceDist = 2, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=2, Sex="female", PriceDist = 3, Survived = 1)])
survival_table = addrow(survival_table, [dict(Pclass=2, Sex="male", PriceDist = 3, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=3, Sex="female", PriceDist = 0, Survived = 1)])
survival_table = addrow(survival_table, [dict(Pclass=3, Sex="male", PriceDist = 0, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=3, Sex="female", PriceDist = 1, Survived = 1)])
survival_table = addrow(survival_table, [dict(Pclass=3, Sex="male", PriceDist = 1, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=3, Sex="female", PriceDist = 2, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=3, Sex="male", PriceDist = 2, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=3, Sex="female", PriceDist = 3, Survived = 0)])
survival_table = addrow(survival_table, [dict(Pclass=3, Sex="male", PriceDist = 3, Survived = 0)])
>>> survival_table
Pclass PriceDist Sex Survived
0 1 0 female 0
1 1 0 male 0
2 1 1 female 0
3 1 1 male 0
4 1 2 female 1
5 1 2 male 0
6 1 3 female 1
7 1 3 male 0
8 2 0 female 0
9 2 0 male 0
10 2 1 female 1
11 2 1 male 0
12 2 2 female 1
13 2 2 male 0
14 2 3 female 1
15 2 3 male 0
16 3 0 female 1
17 3 0 male 0
18 3 1 female 1
19 3 1 male 0
20 3 2 female 0
21 3 2 male 0
22 3 3 female 0
23 3 3 male 0
----
To do this we can use the +++<cite>+++http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.apply.html[DataFrame#apply]+++</cite>+++ function which allows us to map over each row.
Our initial attempt read like this:
[source,python]
----
def select_bucket(fare):
if (fare >= 0 and fare < 10):
return 0
elif (fare >= 10 and fare < 20):
return 1
elif (fare >= 20 and fare < 30):
return 2
else:
return 3
def calculate_survival(survival_table, customer):
survival_row = survival_table[(survival_table["Sex"] == customer["Sex"]) & (survival_table["Pclass"] == customer["Pclass"]) & (survival_table["PriceDist"] == select_bucket(customer["Fare"]))]
return survival_row["Survived"]
>>> customers["Survived"] = customers.apply(lambda customer: calculate_survival(survival_table, customer), axis=1)
----
When we ran that we got the following exception:
[source,python]
----
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 2119, in __setitem__
self._set_item(key, value)
File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 2166, in _set_item
NDFrame._set_item(self, key, value)
File "/Library/Python/2.7/site-packages/pandas/core/generic.py", line 679, in _set_item
self._data.set(key, value)
File "/Library/Python/2.7/site-packages/pandas/core/internals.py", line 1781, in set
self.insert(len(self.items), item, value)
File "/Library/Python/2.7/site-packages/pandas/core/internals.py", line 1795, in insert
self._add_new_block(item, value, loc=loc)
File "/Library/Python/2.7/site-packages/pandas/core/internals.py", line 1911, in _add_new_block
self.items, fastpath=True)
File "/Library/Python/2.7/site-packages/pandas/core/internals.py", line 966, in make_block
return klass(values, items, ref_items, ndim=values.ndim, fastpath=fastpath, placement=placement)
File "/Library/Python/2.7/site-packages/pandas/core/internals.py", line 44, in __init__
% (len(items), len(values)))
ValueError: Wrong number of items passed 1, indices imply 2
----
After much googling and confusion as to why we were getting this error I tried printing out the result of calling apply rather than immediately assigning it and realised that the output wasn't what I expected:
[source,python]
----
>>> customers.apply(lambda customer: calculate_survival(survival_table, customer), axis=1)
16 17
0 NaN 0
1 1 NaN
----
I'd expected to get one column showing the survived values but instead we've got a 2x2 DataFrame. Adding some logging to the +++<cite>+++calculate_survival+++</cite>+++ function revealed why:
[source,python]
----
def calculate_survival(survival_table, customer):
survival_row = survival_table[(survival_table["Sex"] == customer["Sex"]) & (survival_table["Pclass"] == customer["Pclass"]) & (survival_table["PriceDist"] == select_bucket(customer["Fare"]))]
print(type(survival_row["Survived"]))
return survival_row["Survived"]
>>> customers.apply(lambda customer: calculate_survival(survival_table, customer), axis=1)
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
16 17
0 NaN 0
1 1 NaN
----
Our function is actually returning a Series object rather than a single value 0 or 1 which I found surprising. We can http://pandas.pydata.org/pandas-docs/stable/indexing.html#fast-scalar-value-getting-and-setting[use the +++<cite>+++iat+++</cite>+++ function] to retrieve a scalar value from a Series:
[source,python]
----
def calculate_survival(survival_table, customer):
survival_row = survival_table[(survival_table["Sex"] == customer["Sex"]) & (survival_table["Pclass"] == customer["Pclass"]) & (survival_table["PriceDist"] == select_bucket(customer["Fare"]))]
return int(survival_row["Survived"].iat[0])
>>> customers.apply(lambda customer: calculate_survival(survival_table, customer), axis=1)
0 0
1 1
dtype: int64
----
Now if we assign the output of that function like before it works as expected:
[source,python]
----
>>> customers["Survived"] = customers.apply(lambda customer: calculate_survival(survival_table, customer), axis=1)
>>> customers
Fare Name PassengerId Pclass Sex Survived
0 7.8292 Kelly, Mr. James 892 3 male 0
1 7.0000 Wilkes, Mrs. James (Ellen Needs) 893 3 female 1
---- | null | null | [
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] | pandas-adding-a-column-to-a-dataframe-based-on-another-dataframe | https://markhneedham.com/blog/2013/10/30/pandas-adding-a-column-to-a-dataframe-based-on-another-dataframe |
false | 2013-10-13 21:59:51 | neo4j/cypher: Getting rid of an optional match | [
"neo4j",
"cypher"
] | [
"neo4j"
] | I was looking back over some of the queries I wrote for my football data set and I came across one I'd written to work out how many goals players scored in matches that were televised.
The data model looks like this:
image::{{<siteurl>}}/uploads/2013/10/2013-10-13_22-31-19.png[2013 10 13 22 31 19,600]
My initial query to work out the top 10 scorers in televised games was as follows:
[source,cypher]
----
MATCH (player:Player)
WITH player
MATCH player-[:played|subbed_on]->stats-[:in]->game-[t?:on_tv]->channel
WITH COLLECT({goals: stats.goals, type: TYPE(t)}) AS games, player
RETURN player.name,
REDUCE(goals = 0, h IN [g IN games WHERE g.type IS NULL] | goals + h.goals) AS nonTvGoals,
REDUCE(goals = 0, h IN [g IN games WHERE g.type <> NULL] | goals + h.goals) AS tvGoals,
REDUCE(goals = 0, h in games | goals + h.goals) AS totalGoals
ORDER BY tvGoals DESC
LIMIT 10
----
If we evaluate the query we see that Robin Van Persie scored most goals in televised games:
[source,bash]
----
==> +--------------------------------------------------------+
==> | player.name | nonTvGoals | tvGoals | totalGoals |
==> +--------------------------------------------------------+
==> | "Robin Van Persie" | 11 | 15 | 26 |
==> | "Gareth Bale" | 8 | 13 | 21 |
==> | "Luis Suárez" | 12 | 11 | 23 |
==> | "Theo Walcott" | 5 | 9 | 14 |
==> | "Demba Ba" | 7 | 8 | 15 |
==> | "Edin Dzeko" | 7 | 7 | 14 |
==> | "Santi Cazorla" | 5 | 7 | 12 |
==> | "Juan Mata" | 6 | 6 | 12 |
==> | "Steven Gerrard" | 3 | 6 | 9 |
==> | "Carlos Tevez" | 5 | 6 | 11 |
==> +--------------------------------------------------------+
==> 10 rows
==> 520 ms
----
The query takes 1/2 second to evaluate even after running it a few times and since using optional patterns often leads to slow queries I thought I should try and remove the optional +++<cite>+++on_tv+++</cite>+++ relationship.
I introduced it so that I could determine which matches were on TV, which then allowed me to aggregate goals scored in other games as well.
https://twitter.com/wefreema[Wes] helped me do this although his first suggestion was to replace the +++<cite>+++REDUCE+++</cite>+++ with a +++<cite>+++SUM+++</cite>+++ to simplify the query. The +++<cite>+++CASE+++</cite>+++ statement makes this easier:
[source,cypher]
----
MATCH (player:Player)
WITH player
MATCH player-[:played|subbed_on]->stats-[:in]->game-[t?:on_tv]->channel
WITH {goals: stats.goals, type: TYPE(t)} AS game, player
RETURN player.name,
SUM(CASE WHEN game.type IS NULL THEN game.goals ELSE 0 END) as nonTvGoals,
SUM(CASE WHEN game.type <> NULL THEN game.goals ELSE 0 END) as tvGoals,
SUM(game.goals) as allGoals
ORDER BY tvGoals DESC
LIMIT 10
----
Our next step is to split up the finding of +++<cite>+++stats+++</cite>+++ nodes (which represent a player's performance in a match) from the finding of the TV channel that the match may have been shown on.
[source,cypher]
----
MATCH (player:Player)-[:played|subbed_on]->stats
WITH player, stats
MATCH stats-[:in]->game-[t?:on_tv]->channel
WITH {goals: stats.goals, type: TYPE(t)} AS game, player
RETURN player.name,
SUM(CASE WHEN game.type IS NULL THEN game.goals ELSE 0 END) as nonTvGoals,
SUM(CASE WHEN game.type <> NULL THEN game.goals ELSE 0 END) as tvGoals,
SUM(game.goals) as allGoals
ORDER BY tvGoals DESC
LIMIT 10
----
Finally we can make the +++<cite>+++on_tv+++</cite>+++ relationship explicit and change the way we sum the goals slightly:
[source,cypher]
----
MATCH (player:Player)-[:played|subbed_on]->stats
WITH stats.goals AS goals, player, stats-[:in]->()-[:on_tv]-() as onTv
RETURN player.name,
SUM(CASE WHEN onTv = FALSE THEN goals ELSE 0 END) as nonTvGoals,
SUM(CASE WHEN onTv = TRUE THEN goals ELSE 0 END) as tvGoals,
SUM(goals) as allGoals
ORDER BY tvGoals DESC
LIMIT 10
----
If we run that a few times the query execution time has now halved:
[source,bash]
----
==> +------------------------------------------------------+
==> | player.name | nonTvGoals | tvGoals | allGoals |
==> +------------------------------------------------------+
==> | "Robin Van Persie" | 11 | 15 | 26 |
==> | "Gareth Bale" | 8 | 13 | 21 |
==> | "Luis Suárez" | 12 | 11 | 23 |
==> | "Theo Walcott" | 5 | 9 | 14 |
==> | "Demba Ba" | 7 | 8 | 15 |
==> | "Santi Cazorla" | 5 | 7 | 12 |
==> | "Edin Dzeko" | 7 | 7 | 14 |
==> | "Carlos Tevez" | 5 | 6 | 11 |
==> | "Juan Mata" | 6 | 6 | 12 |
==> | "Steven Gerrard" | 3 | 6 | 9 |
==> +------------------------------------------------------+
==> 10 rows
==> 219 ms
----
Thanks Wes for showing me how to do this! | null | null | [
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] | neo4jcypher-getting-rid-of-an-optional-match | https://markhneedham.com/blog/2013/10/13/neo4jcypher-getting-rid-of-an-optional-match |