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.06(5) (5) Public rights features identified. Public rights features are: NR 1.06(5)(a) (a) Fish and wildlife habitat, including specific sites necessary for breeding, nesting, nursery and feeding. NR 1.06 Note Note: Physical features constituting fish and wildlife habitat include stands of aquatic plants; riffles and pools in streams; undercut banks with overhanging vegetation or that are vegetated above; areas of lake or streambed where fish nests are visible; large woody cover. NR 1.06(5)(b) (b) Physical features of lakes and streams that ensure protection of water quality. NR 1.06 Note Note: Physical features that protect water quality include stands of aquatic plants (that protect against erosion and so minimize sedimentation), natural streambed features such as riffles or boulders (that cause turbulent stream flow and so provide aeration). NR 1.06(5)(c) (c) Reaches of bank, shore or bed that are predominantly natural in appearance (not man-made or artificial) or that screen man-made or artificial features. NR 1.06 Note Note: Reaches include those with stands of vegetation that include intermixed trees, shrubs and grasses; stands of mature pines or other conifer species; bog fringe; bluffs rising from the water's edge; beds of emergent plants such as wild rice, wild celery, reeds, arrowhead. NR 1.06(5)(d) (d) Navigation thoroughfares or areas traditionally used for navigation during recreational boating, angling, hunting or enjoyment of natural scenic beauty. NR 1.06 Note Note: Physical features indicative of navigation thoroughfares include shallow water areas typically used by wading anglers or areas frequently occupied by regularly repeated public uses such as water shows. NR 1.06(6) (6) Basis of department determination. The department shall base its identification of public rights features on factual information obtained from reputable sources, including: NR 1.06(6)(a) (a) Field surveys and inspections, including historical surveys for fish, wildlife, rare species, aquatic plants, geologic features or water quality. NR 1.06(6)(b) (b) Surveys or plans from federal, state or local agencies. NR 1.06.:. Down Down /code/admin_code/nr/001/1 true administrativecode /code/admin_code/nr/001/1/07/3/b Department of Natural Resources (NR) Chs. NR 1-99; Fish, Game and Enforcement, Forestry and Recreation administrativecode/NR 1.07(3)(b) administrativecode/NR 1.07.

https://docs-preview.legis.wisconsin.gov/code/admin_code/nr/001/1/07/3/b

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docs-preview.legis.wisconsin.gov

Note: All pages below are subject to having relevant Roles and Permissions. From the Menu on the left go to Config > Setup > User Name and Formats. From the dropdown menu, select the Name Format to be amended. To amend the Format, clear the items seen in the Name Format Editor field by highlighting and deleting or using your cursor to delete the existing Data Fields. Select an appropriate Name Field from the Name Format Fields dropdown. Build up the Name as required ensuring a space is added between each Field, then click the Save button. An example of the Name Format will appear as each Field is added.

https://docs.bromcom.com/knowledge-base/how-to-manage-user-name-and-formats/

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[array(['https://docs.bromcom.com/wp-content/uploads/2020/07/image-55.png', None], dtype=object) array(['https://docs.bromcom.com/wp-content/uploads/2020/07/image-56.png', None], dtype=object) array(['https://docs.bromcom.com/wp-content/uploads/2020/07/image-57-1024x351.png', None], dtype=object) array(['https://docs.bromcom.com/wp-content/uploads/2020/07/image-58.png', None], dtype=object) array(['https://docs.bromcom.com/wp-content/uploads/2020/07/image-59.png', None], dtype=object) ]

docs.bromcom.com

Reporting No promises made in CFEngine imply automatic aggregation of data to a central location. In CFEngine Enterprise (our commercial version), an optimized aggregation of standardized reports is provided, but the ultimate decision to aggregate must be yours. Monitoring and reporting capabilities in CFEngine depend on your installation: Enterprise Edition Reporting The CFEngine Enterprise edition offers a framework for configuration management that goes beyond building and deploying systems. Features include compliance management, reporting and business integration, and tools for handling the necessary complexity. In a CFEngine Enterprise installation, the CFEngine Server aggregates information about the environment in a centralized database. By default data is collected every 5 minutes from all bootstrapped hosts and includes information about: - logs about promises kept, not kept and repaired - current host contexts and classifications - variables - software information - file changes This data can be mined using SQL queries and then used for inventory management, compliance reporting, system diagnostics, and capacity planning. Access to the data is provided through: Command-Line Reporting Community Edition Basic output to file or logs can be customized on a per-promise basis. Users can design their own log and report formats, but data processing and extraction from CFEngine's embedded databases must be scripted by the user. Note: If you have regular reporting needs, we recommend using our commercially-supported version of CFEngine, Enterprise. It will save considerable time and resources in programming, and you will have access to the latest developments through the software subscription.

https://docs.cfengine.com/docs/3.12/guide-reporting.html

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docs.cfengine.com

GraphExpert Professional 1.6.0 documentation GraphEx GraphExpert Professional. GraphEx GraphEx, With this setting, for example, users that utilize commas for decimals in GraphExpert Professional can choose to read and write files using dots for decimals. And vice versa. Note In the file import dialog (see Importing from file), GraphEx Importing from file. If your file contains the following within the first ten lines of the file: #DataFileProperties: locale=EN or: #DataFileProperties: locale=EURO this signals to the GraphExpert Professional file importing mechanism that the the file uses English or European numerical formatting (as appropriate), and the default there (see Importing from file) GraphExpert Professional contain this line, so that the samples work straightforwardly regardless of the application’s current region settings. This small addition to a text datafile lets the file itself express its own numerical formatting.

https://docs.curveexpert.net/graphexpert/pro/html/localization.html

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docs.curveexpert.net

SentinelOne Endpoint Detection and Response SentinelOne Endpoint Detection and Response (EDR) is agent-based threat detection software that can address malware, exploit, and insider attacks on your network. InsightIDR features a SentinelOne event source that you can configure to parse SentinelOne EDR logs for virus infection documents. You can learn more about SentinelOne EDR on their product website: This SentinelOne event source configuration involves the following steps: - Configure SentinelOne EDR to Send Logs to InsightIDR - Configure the SentinelOne Event Source in InsightIDR Configure SentinelOne EDR to Send Logs to InsightIDR Before you configure the SentinelOne event source in InsightIDR, you need to configure SentineIOne EDR to send its logs to your collector. Consult your SentinelOne product documentation for instructions on how to do this: Configure the SentinelOne Event Source in InsightIDR After you’ve configured SentinelOne to send its logs to your collector, you can configure the event source in InsightIDR. To configure this SentinelOne event source: - From your InsightIDR dashboard, expand your left menu and click the Data Collection tab. - On the “Data Collection Management” screen, expand the Setup Event Source dropdown and click Add Event Source. - In the “Add Event Source” category window, browse to the “Security Data” section and click Virus Scan. The “Add Event Source” panel appears. - Select your configured collector from the dropdown list. This should be the same collector that you configured SentinelOne to target for log ingestion. - Expand the “Event Source” dropdown and select SentinelOne EDR. - If desired, you can give your event source a custom name for reference purposes. - Choose the timezone that matches the location of your event source logs. - If desired, check the provided box to send unfiltered logs. - Select a collection method and specify a port. - If desired, you can choose to encrypt the event source if choosing TCP by downloading the Rapid7 Certificate. - Click Save when finished.

https://docs.rapid7.com/insightidr/sentinelone/

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[array(['/areas/docs/_repos//product-documentation__master/a53365007403634cd965f11c07f241396a4a9e16/insightidr/images/insightidr_eventsourceconfig_sentinelone.png', None], dtype=object) ]

docs.rapid7.com

inst/CITATION Ross, Noam, Evan A. Eskew, and Nicolas Ray. 2019. citesdb: An R package to support analysis of CITES Trade Database shipment-level data. Journal of Open Source Software, 4(37), 1483, @Article{, doi = {10.21105/joss.01483}, url = {}, year = {2019}, month = {May}, publisher = {The Open Journal}, volume = {4}, number = {37}, pages = {1483}, author = {Noam Ross and Evan A. Eskew and Nicolas Ray}, title = {citesdb: An R package to support analysis of CITES Trade Database shipment-level data}, journal = {Journal of Open Source Software}, } UNEP-WCMC (Comps.) 2019. Full CITES Trade Database Download. Version 2019.2. CITES Secretariat, Geneva, Switzerland. Compiled by UNEP-WCMC, Cambridge, UK. Available at:. @Misc{, author = {{UNEP-WCMC}}, title = {Full CITES Trade Database Download. Version 2019.2}, year = {2019}, institution = {CITES Secretariat}, address = {Geneva, Switzerland}, url = {}, } Noam Ross. Author, maintainer. Evan A. Eskew. Author. Nicolas Ray. Contributor. UNEP World Conservation Monitoring Centre. Data contributor. Maintainer of CITES Trade Database Mauricio Vargas. Reviewer. Reviewed package for rOpenSci: Xavier Rotllan-Puig. Reviewer. Reviewed package for rOpenSci: EcoHealth Alliance. Copyright holder, funder.

https://docs.ropensci.org/citesdb/authors.html

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docs.ropensci.org

Use the Environment tab to examine the status of the three badges as they relate to the objects in your environment hierarchy. You can then determine which objects are in a critical state for a particular badge. To view the relationships between your objects to determine whether an ancestor object that has a critical problem might be causing problems with the descendants of the object, use. As you click each of the badges in the Environment tab, you see that several objects are experiencing critical problems with health. Others are reporting critical risk status. Several objects are experiencing stress. You notice that you can reclaim capacity from multiple virtual machines and a host system, but the overall efficiency status for your environment displays no problems. Prerequisites Examine the status of your objects in views and heat maps. See Examine the Environment Details. Examine the status of your objects in views and heat maps. See Examine the Environment Details. Procedure - Click . - Examine the USA-Cluster environment overview to evaluate the badge states of the objects in a hierarchical view. - In the inventory tree, click USA-Cluster, and click the Environment tab. - On the Badge toolbar, click through the three badges - Health, Risk, and Efficiency - and look for red icons to identify critical problems.As you click through the badges, you notice that your vCenter Server and other top-level objects appear to be healthy. However, you see that a host system and several virtual machines are in a critical state for health, risk, and efficiency. - Point to the red icon for the host system to display the IP address. - Enter the IP address in the search text box, and click the link that appears.The host system is highlighted in the inventory tree. You can then look for recommendations or alerts for the host system on the Summary tab. - Examine the environment list and view the badge status for your objects to determine which objects are in a critical state. - Click the Environment tab. - Examine the badge states for the objects in USA-Cluster. - Many of the objects display critical states for risk and health. You notice that multiple virtual machines and a host system named w2-vropsqe2-009 are critically affected. Because the host system is experiencing the most critical problems, and is likely affecting other objects, you must focus on resolving the problems with the host system. - Click the host system named w2-vropsqe2-009, which is in a critical state, to locate it in the inventory tree. - Click w2-vropsqe2-009 in the inventory tree, and click the Summary tab to look for recommendations and alerts to act on. - Examine the relationship map. - Click . - In the inventory tree, click USA-Cluster, and view the map of related objects.In the relationship map, you can see that the USA-Cluster has an ancestor data center, one descendant resource pool, and two descendant host systems. - Click the host system named w2-vropsqe2-009.The types and numbers of descendant objects for this host system appear in the list following. Use the descendant object list identify all the objects related to the host system that might be experiencing problems. What to do next Use the user interface to resolve the problems. Use the user interface to resolve the problems. See Fix the Problem.

https://docs.vmware.com/en/VMware-vRealize-Operations-Cloud/services/user-guide/GUID-996F4F1A-9B5A-47ED-A17D-75829B8079D7.html

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docs.vmware.com

primary key columns. Different tables can use different keys. SSTable data files are immutable once they have been flushed to disk and are only encrypted during the write to disk. To encrypt existing data, use the nodetool upgradesstables with the -aoption to rewrite the tables to disk with encryption.Warning: Primary keys are stored in plain text. Do NOT put sensitive information in partition key or clustering columns. Data that is not encrypted - Table partition key columns - Database files other than the commit log and SSTable data files - DSEFS data files - Spark spill files Requirements To use the DataStax Enterprise (DSE) Transparent Data Encryption (TDE) feature, enable the Java Cryptography Extension (JCE). When using TDE on a secure local file system, encryption keys are stored remotely with KMIP encryption or locally with on-server encryption. TDE limitations and recommendations The following utilities cannot access encrypted data, but will operate on all unencrypted data. Compression and encryption introduce performance overhead. config_encryption_activeis truein DSE and OpsCenter. For LCM limitations, see Configuration encryption. TDE options To get the full capabilities of TDE and to ensure full algorithm support, enable JCE Unlimited.

https://docs.datastax.com/en/security/5.1/security/secEncryptTDE.html

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docs.datastax.com

Introduction The Synthetics section displays the configured synthetic monitors. After creating a synthetic monitor, click the desired monitor to view details such as Overview, Metrics, and Monitors. Dashboard The dashboard displays the total number of configured synthetics, the status of each synthetic configured on the Infrastructure page, Add, Delete button, and a Refresh button. Status of synthetics ImportantAvailability. Overview. Availability Log View root cause To view Root Cause in Availability Log: - From the Availability Log section, click view Root Cause. The Last Collected Root Cause Analysis window appears. - From the Last Collected Root Cause Analysis window, analyze the below details from the configured location. - Analysis started time – The Root Cause Analysis (RCA) collection start time. - Analysis ended time – The RCA collection end time. - Analysis elapsed time – The time taken to perform the RCA. - Initial Validation – Displays the reason for the Down availability status for the selected synthetic monitor. - TraceRoute Test – Displays details that allow you to debug the issue. Last Collected RCA. Traceroute Test The TraceRoute Test section displays the following details: - Route Info – The path taken by packets from the configured location to the host. - Error Log – Displays that Traceroute is a failure. ImportantTraceRoute Test supports only 30 hops maximum and 60-byte packets. Recent log Recent Log displays information about the recently collected metrics. Attributes Attributes display all basic information on the configured synthetic monitor. Attributes: Refers to the URL of the website. - Method: Refers to the REST method of the connected website. - Status Code: Refers to the HTTP response code or Libcurl error code of the Website. - Response Headers: Refers to the HTTP response headers of the website. - Raw Data Response: Refers to the HTTP response provided by the website. - Status: Refers to the success or failure of the test. HTTP. ImportantTo create a device management policy, you can filter synthetic either using synthetic Name or Type when applying Resource filters as Filter Criteria. For example, to apply HTTPS templates, select HTTPS as Synthetic Type for the resource type Synthetics. Filter Criteria - Device Management Policy Use the following synthetic types to filter: - HTTPS - HTTP - TCP - PING - UDP - DNS - SMTP - POP3 - IMAP - SCRIPT - SSL - FTP - SIP - RTT Assigning templates OpsRamp can start monitoring only after assigning the templates to your synthetic monitor. You can assign only one template per synthetic monitor. Monitoring does not work as expected if you assign more than one template. ImportantYou can assign templates that pertain to a specific synthetic monitor. For example, you can assign DNS Templates to DNS synthetic monitors and not to PING synthetic monitors. You can create templates using Setup > Monitoring > Templates. Notes - You can select the following basic details while creating a template: - Collector Type: Synthetics - Applicable For: Synthetics - Type: Select the desired synthetic monitor - To receive alerts for configured metrics from all the configured locations assigned to a template, you must enable the Alert option and configure the Component Threshold for each metric while creating the template. To assign templates: - From Templates, click Assign Templates. Apply Templates screen is displayed. Apply Templates - From Select Templates > Available templates, select the desired templates. Selected templates display the chosen templates. - Click Assign. Enter Configurations section is displayed. Enter Configurations - Provide Value for the Assigned Templates and Configuration Parameters and click Submit. The templates screen displays the selected templates. Configuration Parameter and Value The following table describes the configuration parameters for each monitor: Unassigning templates You can remove an assigned template from the monitor. Use the Unassign Templates option to unassign the templates from the synthetic monitors. OpsRamp removes every graph associated with the templates. Unassign Templates Monitors Monitors allow you to configure alerts and edit threshold values assigned to any metric in the selected synthetic monitor. Monitors Availability rule Availability lets you configure rules to confirm the status of the resources with the availability check depending on the critical alerts received for the metrics. Availability Rule. Parameters. Notes To add Notes: - Click Add displayed on the Notes window. - On the Notes window, provide the required details. - Click Save. You can modify existing notes from the Notes window using Edit Notes. Articles Articles display notes tagged to the configured synthetic monitor. Articles ADD/MODIFY The ADD/MODIFY option allows you to add or remove articles for the selected synthetic monitor. To ADD/MODIFY articles: - Click ADD/MODIFY. The Articles window appears. - Select the check-box of desired articles from the Articles window. - Click Add. The Articles section displays the selected articles. What to do next - See Modify Metric Custom Notifications and Thresholds for information on customizing threshold values. - See Create Templates. - See Credential Set in SCRIPT – HTTP Synthetic Transaction Synthetic Monitor.

https://docs.opsramp.com/solutions/discovery-monitoring/synthetic-monitors/viewing-synthetics-monitor/

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[array(['https://docsmedia.opsramp.com/screenshots/Monitoring/Viewwebservices-overview-7.0.png', 'Overview'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Monitoring/Viewwebservices-availabilitylog-viewrootcause-5.0.png', 'Availability Log'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Monitoring/Viewwebservices-traceroutetest-5.5.0.png', 'Traceroute Test'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Monitoring/Viewwebservices-attributes.png', 'Attributes'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Monitoring/viewwebservices-locationavailability-header-7.0.png', 'HTTP Test'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Synthetics/Viewsynthetics-DMP-8.0.png', 'Filter Criteria - Device Management Policy'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Synthetics/Viewsynthetics-Templates-UnassignTemplates-8.0.png', 'Unassign Templates'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Synthetics/Viewsynthetics-Monitors-8.0.png', 'Monitors'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Synthetics/Viewsynthetics-AvailabilityRule-8.0.png', 'Availability Rule'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Synthetics/Viewsynthetics-Parameters-8.0.png', 'Parameters'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Monitoring/Viewwebservices-Notes.png', 'Notes'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Monitoring/Viewwebservices-articles.png', 'Articles'], dtype=object) ]

docs.opsramp.com

Understand WordPress filesystem permissions Bitnami applies the following default permissions to WordPress files and directories: - Files and directories are owned by user bitnami and group daemon. - Directories are configured with permissions 775 by default. - Files are configured with permissions 664 by default. - The wp-config.php file is configured with permissions 640. If permissions are wrong, use the chmod or chown commands to restore them to their initial state. For example, if TARGET is the WordPress application folder: $ sudo chown -R bitnami:daemon TARGET $ sudo find TARGET -type d -exec chmod 775 {} \; $ sudo find TARGET -type f -exec chmod 664 {} \; $ sudo chmod 640 TARGET/wp-config.php

https://docs.bitnami.com/azure/apps/wordpress-multisite/administration/understand-file-permissions/

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docs.bitnami.com

Create company connect groups in Kaizala Company connect groups are one-way communication channels. They allow an organization to broadcast important announcements, updates, and information to the workforce. An organization can create company connect groups for their employees, partners, and customers. Step 1 – Create a hub-and-spoke group A hub-and-spoke group in Kaizala is a unique group where admins can broadcast messages to all of its members, and members of the group can interact with the admins of the group on a one-to-one basis without their messages being visible to other group members. Note You can only create hub and spoke groups through the Kaizala management portal. On the Kaizala management portal, from the left navigation bar, choose Groups. Select Create Group, and from the drop-down menu, select Broadcast Group. Enter the group name, a short and long description, and a welcome message. Choose between two group types: Managed or Public. - Managed groups allow the group admins to view, manage, and invite subscribers. - Public groups allow subscribers to also invite other subscribers. Step 2 – Add people to the group If you want to add several users without using the comma separated list, use bulk upload. After you create a broadcast group, you can add subscribers (employees, partners, or customers) to it. Once they have been added, the broadcast group will start showing up on their Kaizala app. To add subscribers, select Manage Subscribers, and then select Add Subscribers. On the Add Subscribers page, download the CSV template and follow the format to add your subscribers. Save the file when you're done. Choose Select File to choose the file you just saved, and then click Add. Step 3 – Onboard the content moderation team Identify admins who will manage and moderate group content. Key responsibilities of the group admin are: - User engagement – share company information, articles, and updates. - Content moderation – share and implement guidelines on appropriate usage. - Helping users – show how to perform queries. - User management – remove or add members. Your corporate communications team or senior team members are most likely to fit these roles. Add these users as admins to the group under the Users tab. Tip - You can set up RSS feeds to automatically post organizational content from across channels such as social media, websites and blogs. Follow these steps here. - Consider creating separate groups for company employees, suppliers, and partners. This will allow you to send relevant content to each group depending on the group members. Next> Collect employee feedback

https://docs.microsoft.com/en-us/office365/kaizala/create-company-connect-groups

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docs.microsoft.com

Document type: notice There are 4084 pages with document type 'notice' in the GOV.UK search index. Rendering apps This document type is rendered by: Supertypes Example pages - Notice 144: trade imports by post - how to complete customs documents - Public Sector Decarbonisation Scheme (PSDS) - Notice 143: a guide for international post users - Coronavirus (COVID-19) Pubs Code Declaration No.2 November 2020 - Condition Data Collection 2 (CDC2): provisional school visits - Pubs Code Declaration No.2 November 2020 summary - Notice 5: Transfer of Residence - moving to or returning to the UK from outside the EU - Social Housing Decarbonisation Fund Demonstrator - Who should register for VAT (VAT Notice 700/1) - VAT Notice 733: Flat Rate Scheme for small businesses Source query from Search API

https://docs.publishing.service.gov.uk/document-types/notice.html

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docs.publishing.service.gov.uk

# Installation AdvancedOreGen works best along with a supported SkyBlock plugin. Without a SkyBlock plugin, AdvancedOreGen would use the permission of the closest player mining on the generator. Buy and download the latest version (opens new window) jar-File and place it into your plugin folder, then restart or reload your server. Keep in mind that you will only see the resource when you have created an account on spigotmc.org.

https://docs.spaceio.xyz/plugin/advancedoregen/installation.html

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docs.spaceio.xyz

Before proceeding, make sure you meet the requirements detailed here At a high level, the installation of AI Fabric needs to execute these steps: Network Configuration - The Orchestrator machine (domain and port) needs to be accessible from AI Fabric cluster. - The SQL Server (domain/IP and port) needs to be accessible from AI Fabric cluster. - Robots/Studio that will make use of AI Fabric need connectivity to the AI Fabric Linux machine. For peripheral Document Understanding components (Data Manager and OCR Engines): - Data Manager needs access to AI Fabric on prem :<port_number> or to public SaaS endpoints like in case Prelabelling is needed (prelabeling is optional). - Data Manager needs access to OCR engine :<port_number>. OCR engine might be UiPath Document OCR on premises, Omnipage OCR on premises, Google Cloud Vision OCR, Microsoft Read Azure, Microsoft Read on premises. - Robots need access to OCR :<port_number>. Same OCR options as above, except for Omnipage, which is available in the Robots directly as an Activity Pack. Connectivity Requirements The AI Fabric Online install refers to an on-premises installation that downloads AI Fabric application and all related artifacts (e.g. machine learning models) from the internet. Endpoints the Installer Connects The AI Fabric installer downloads container images and machine learning models to populate your AI Fabric instance with ready-to-use machine learning (this includes Document Understanding models). For this reason, at installation time, the Linux machine needs access to these endpoints over https (port 443): Endpoints GPU Installer Script Connects To These endpoints only need to be allow connections for using a GPU with AI Fabric. All GPU installation is done through our GPU installer script in 4. Run the AI Fabric Infrastructure Installer . Endpoints Connected To at Runtime At runtime, an AI Fabric that was installed via the online installer connects to these endpoints: Updated 28 days ago

https://docs.uipath.com/ai-fabric/docs/ai-fabric-installation-dockeree

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docs.uipath.com

. Global illumination is a group of techniques that model both direct and indirect lighting to provide realistic lighting results. Unity has two global illumination systems, which combine direct and indirect lighting. The Baked Global Illumination system comprises lightmapping, Light Probes, and Reflection Probes.en is deprecated, and the Realtime Global Illumination system will soon be removed from Unity. For more information, see the Unity blog.

https://docs.unity3d.com/ru/2020.1/Manual/LightingInUnity.html

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docs.unity3d.com

vSphere role-based access control (vSphere RBAC) for Azure VMware Solution In Azure VMware Solution, vCenter has a built-in local user called cloudadmin and assigned to the built-in CloudAdmin role. The local cloudadmin user is used to set up users in AD. In general, the CloudAdmin role creates and manages workloads in your private cloud. In Azure VMware Solution, the CloudAdmin role has vCenter privileges that differ from other VMware cloud solutions. Note Azure VMware Solution currently doesn't offer custom roles on vCenter or the Azure VMware Solution portal. In a vCenter and ESXi on-premises deployment, the administrator has access to the vCenter [email protected] account. They can also have additional Active Directory (AD) users/groups assigned. In an Azure VMware Solution deployment, the administrator doesn't have access to the administrator user account. But they can assign AD users and groups to the CloudAdmin role on vCenter. The private cloud user doesn't have access to and can't configure specific management components supported and managed by Microsoft. For example, clusters, hosts, datastores, and distributed virtual switches. Azure VMware Solution CloudAdmin role on vCenter You can view the privileges granted to the Azure VMware Solution CloudAdmin role on your Azure VMware Solution private cloud vCenter. Log into the SDDC vSphere Client and go to Menu > Administration. Under Access Control, select Roles. From the list of roles, select CloudAdmin and then select Privileges. The CloudAdmin role in Azure VMware Solution has the following privileges on vCenter. Refer to the VMware product documentation for a detailed explanation of each privilege. Next steps Refer to the VMware product documentation for a detailed explanation of each privilege.

https://docs.microsoft.com/en-us/azure/azure-vmware/concepts-role-based-access-control

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docs.microsoft.com

sys.dm_hadr_cluster (Transact-SQL) Applies to: SQL Server (all supported versions). Tip Beginning in SQL Server 2014 (12.x),)

https://docs.microsoft.com/en-us/sql/relational-databases/system-dynamic-management-views/sys-dm-hadr-cluster-transact-sql?view=sql-server-ver15

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docs.microsoft.com

Catalogs¶ Description A brief introduction to ZCatalogs, the Catalog Tool and what they're used for. - Why ZCatalogs? - Quick start - Other catalogs - Manually indexing object to a catalog - Manually uncatalog object to a catalog - Rebuilding a catalog - Retrieving unique values from a catalog - Minimal code for creating a new catalog - Register a new catalog via portal_setup - Map an catalog for an new type - Additional info Why ZCatalogs?¶ Plone is built on the CMF, which. So. If you want to perform advanced searches, AdvancedQuery, which is included with Plone since the 3.0 release, is what you're looking for. See Boolean queries (AdvancedQuery) for a brief introduction. 'just in time' as your code requests each result, and second, because retrieving a catalog brain doesn't wake up the objects themselves, avoiding a huge performance hit. To see the ZCatalogs in action, fire up your favourite browser and open the ZMI. You'll see an object in the root of your Plone site named portal_catalog. This is the Catalog Tool, a Plone tool (like the Membership Tool or the Quickinstaller Tool) based on ZCatalogs, because, as it was said earlier, indexes are meant to search by them, and metadata to retrieve certain attributes from the content object without waking it up. Back to the management view of the Catalog Tool, if you click the Indexes or the Metadata tab you'll see the full list of currently available indexes and metadata fields, respectively, its types and more. There you can also add and remove indexes and metadata fields. If you're working on a test environment, you can use this manager view to play with the catalog, but beware indexes and metadata are usually added through GenericSetup and not using the ZMI. object.reindexObject() is defined in CMFCatalogAware and will update the object data to portal_catalog. If your code uses additional catalogs, you need to manually update cataloged values after the object has been modified. Example: # Update email_catalog which mantains loggable email addresses email_catalog = self.portal.email_catalog email_catalog.reindexObject(myuserobject) 'price'. portal_catalog = self.portal.portal_catalog portal_catalog.Indexes['price'].uniqueValues() the result would be a listing of all the prices stored in the 'price') Register a new catalog via portal_setup¶ In toolset.xml add this lines <?xml version="1.0"?> <tool-setup> <required tool_id="my_catalog" class="catalog.MyCatalog"/> </tool-setup> archetype_tool catalog map¶ archetype_tool maintains map between content types and catalogs which are interested int them. When object is modified through Archetypes mechanisms, Archetypes post change notification to all catalogs enlisted. See Catalogs tab on archetype_tool in Zope Management Interface. Map an catalog for an new type¶ code at = getToolByName(context,'archetype_tool') at.setCatalogsByType('MetaType', ['portal_catalog','mycatalog',])

https://docs.plone.org/4/en/develop/plone/searching_and_indexing/catalog.html

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docs.plone.org

Last updated: 21 Oct 2020 The component system Components are packages of template, style, behaviour and documentation. Components live in your application unless needed by multiple applications, then they are shared using the govuk_publishing_components gem. Component guides Components in applications are documented in component guides using the govuk_publishing_components gem. It mounts a component guide at the path /component-guide. Find components in these guides: - govuk_publishing_components] For example, a lead paragraph component would be included in a template like this: <%= render 'components/lead-paragraph', text: "A description is one or two leading sentences" %>

https://docs.publishing.service.gov.uk/manual/components.html

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docs.publishing.service.gov.uk

Configure linting This explains how to configure linting for a GOV.UK application. It is written with the expectation that you are configuring a conventional GOV.UK Rails application although the approaches can be applied to non-Rails applications by minor adjustments to the steps. Linting Ruby We use rubocop-govuk to lint Ruby projects. This is installed by adding gem "rubocop" to your Gemfile and then creating a .rubocop.yml file in the root of your project: inherit_gem: rubocop-govuk: - config/default.yml - config/rails.yml - config/rake.yml - config/rspec.yml inherit_mode: merge: - Exclude After running bundle install you can test the linting by running bundle exec rubocop. Linting JavaScript and SCSS We use StandardJS for JavaScript linting and Stylelint for SCSS linting, using the stylelint-config-gds configuration. To enable these in a Rails application you will first need to install Yarn. Then you should create a package.json file in your project root. You can use the following template: { "name": "My application", "description": "A brief description of the application's purpose", "private": true, "author": "Government Digital Service", "license": "MIT", "scripts": { "lint": "yarn run lint:js && yarn run lint:scss", "lint:js": "standard 'app/assets/javascripts/**/*.js' 'spec/javascripts/**/*.js'", "lint:scss": "stylelint app/assets/stylesheets/" }, "stylelint": { "extends": "stylelint-config-gds/scss" } } The dependencies can then be installed: yarn add --dev standard stylelint stylelint-config-gds You can now test the linting by running yarn run lint. To finish up you should add node_modules and yarn-error.log to your .gitignore file. Configuring Rails To configure this linting in Rails you should create a rake task for this in lib/tasks/lint.rake: desc "Lint files" task "lint" do sh "bundle exec rubocop" sh "yarn run lint" # lint JS and SCSS end You should then configure the default rake task for the application to include linting. For example to run linting and RSpec as the default task add the following code to your Rakefile: # undo any existing default tasks added by depenencies so we have control Rake::Task[:default].clear if Rake::Task.task_defined?(:default) task default: %i[lint spec] You can confirm this works by running bundle exec rake and seeing your linting run followed by specs.

https://docs.publishing.service.gov.uk/manual/configure-linting.html

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docs.publishing.service.gov.uk

Versions Compared Old Version 4 changes.mady.by.user Julie Sullivan Saved on New Version Current changes.mady.by.user Julie Sullivan Saved on Key - This line was added. - This line was removed. - Formatting was changed. General What data is available in CellBase? How can I search for data? What's a "client"? What's the difference between "clients", the RESTful API, and the command line? Which one should I use? Do I need to specify which database to search? Do I need my own database? I want a list of variants associated with my gene of interest. How can I do that? How can I visualise the data? Can I use any visual tool? Yes, you can use Genome Maps to browse variants. None of these questions helped me. Help! PyCellbase How can I use PyCellbase? How can I install? Does pycellbase work on Python 2? PyCellBase requires Python 3.x, although most of the code is fully compatible with Python 2.7. I'm getting an error message. What do I do? Table of Contents:

http://docs.opencb.org/pages/diffpagesbyversion.action?pageId=15598722&selectedPageVersions=5&selectedPageVersions=4

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docs.opencb.org

7.9 datacell This package gives a simple embedded dataflow language based around the notion of “cells”, similar to the concept of a cell in a Spreadsheet. A simple example: Examples: Defines a new cell name, who’s value will be computed by body .... If the body uses any other cells, the then the value of name will be recomputed if the value of those cells change. A cells value is computed on demand, which is to say that body is only evaluated when the value of named is needed. The value of a cell is almost memoized, meaning body is only evaluated if the current value is not known or a cell used by the body has changed. Change the value in cell, which must be an identifier defined by define-cell. The rules for when body is evaluated are the same for definef-cell.

https://docs.racket-lang.org/datacell/index.html

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docs.racket-lang.org

Containers CI/CD Plugin Overview InsightVM features a container assessment plugin that you can utilize via a Continuous Integration, Continuous Delivery (CI/CD) tool. Use this plugin to analyze a container image for vulnerability assessment on the Insight platform. The plugin has the following capabilities: - Configure custom rules for compliant container images - Trigger build actions based on compliance to your rules - Generate an assessment report Container assessment plugin results are available both through your CI/CD tool and the Builds tab of InsightVM’s “Containers” screen. Whitelist Platform Traffic The plugin uses the following hostnames to communicate with the Insight platform: In order for the plugin to transmit data, you must configure your network to allow traffic to the corresponding hostname of your designated InsightVM region. NOTE The region you chose for your plugin MUST match the region that has been selected previously in InsightVM. The plugin cannot communicate through a different region. Install and Configure the Jenkins Plugin Complete the following steps to configure your Jenkins plugin: Generate the Rapid7 API Key To use the Jenkins plugin, you need the Rapid7 API key to access the Rapid7 platform. In order to access the Rapid7 platform, you will need a Rapid7 Insight platform account, which is different from your InsightVM Rapid7 Security Console account. Here's how to get a Rapid7 Insight platform account: - If you are an InsightIDR, InsightAppSec, or InsightOps customer, you already have a Rapid7 Insight platform account. You can link your existing InsightVM Security Console to the Rapid7 Insight platform account you created with your other Rapid7 product by following the instructions here: - If you are already using the cloud capabilities of InsightVM and don’t have a Rapid7 Insight platform account, contact the Rapid7 Technical Support team to request one. Follow these steps to generate the Rapid7 API key: - Go to - Log in to your Rapid7 Insight platform account. - Go to API Keys. - Under Organization Key, click on New Organization Key. - In the “Organization” dropdown menu, select your InsightVM organization name. - Enter a name for your key in the field. - Click Generate. - Copy and save the provided API Key. NOTE This is the only time you will be able to see the API key, so store it in a safe place. If you misplace your API key, you can always generate a new one. - Click Done. - Your new API key will display in the “Organization Key” table. Install the Jenkins Plugin There are two ways to install the Jenkins plugin. Both ways require Jenkins administrative privileges. Jenkins version 2.89.3 is the minimum supported version. NOTE For the latest version information and dependency requirements, see. Install the Jenkins Plugin Through the Jenkins Update Center We recommend this method to install the Jenkins plugin, because it’s the simplest and most common way to install plugins. You must be a Jenkins administrator to navigate through this path: Manage Jenkins > Manage Plugins - In the "Filter" box, search for "InsightVM." - Under the Under the Available tab, select the checkbox for the InsightVM Container Image Scanner. - Click the desired install button. Install the Jenkins Plugin Manually Follow these steps for manual installation: - Download the plugin from Jenkins website. Verify your download with SHA1. - Log into Jenkins as an admin user. - Click Manage Jenkins in your navigation menu. - On the “Manage Jenkins” page, click Manage Plugins. - Click the Advanced tab. - Under the “Upload Plugin” section, click Choose file to browse to the Rapid7 Jenkins plugin. - Click Upload. - Select the “Restart Jenkins” option. Configure the Jenkins Plugin Use the API key and follow these steps: - After Jenkins restarts, return to the “Manage Jenkins” page. - Click Configure System. - Scroll to the “Rapid7 InsightVM Container Assessment” section. - In the “InsightVM Region” field, select the region that InsightVM uses to access the platform. - In the “Insight Platform API Key” field, click Add. In the dropdown menu, select “Jenkins” to configure the Insight platform API key that you generated earlier. - In the window that appears, complete these fields: - In the “Domain” field, select "Global credentials (unrestricted)." - In the “Kind” field, select "Secret text." - In the "Scope" field, select “Global (Jenkins, nodes, items, all child items, etc).” - In the “Secret” field, enter your API key. - Leave the “ID” field blank. - Enter a description for your reference. - Click Add. - Select your newly configured credential from the dropdown menu. NOTE Click Test Connection to verify that the region and token are valid. - Click Save to complete your plugin configuration. Job Setup You must configure your build jobs to use the plugin after installation. Complete the steps for your chosen CI/CD tool: Jenkins Build Job Setup The plugin supports the following Jenkins build methods: Freestyle Build “Freestyle” is the classic job builder. Build steps can be added or removed via the user interface: - In a new or existing job, click Add build step. - Select Assess Container Image with Rapid7 InsightVM. This will add a build step with a blank configuration. - Configure the items under “Options” as desired. - Click Add under the respective “Rules” section to configure the conditions that will trigger a build action. Two rule types are available: - “Threshold Rules” - Sets a numeric limit. Values that exceed this limit will trigger the build action. - “Name Rules” - Matches text using the “contains” operator. A match will trigger the build action. NOTE The order of your configured rules will not matter when the job is run. Any individual rule can trigger the build action specified. - Click Save when finished. Pipeline Build The “Pipeline” method involves generating build step scripts from the plugin and adding them to the existing Pipeline script: - In a new or existing job, browse to the “Pipeline” section. - Click Pipeline Syntax below the “Script” field. - Open the dropdown next to “Sample Step” and select "assessContainerImage: Assess Container Image with Rapid7 InsightVM". - Configure your build options and rules in the same manner as before. - Click Generate Pipeline Script when finished. - Add your new step script to the existing Pipeline script. Pipeline Script 1// Assess the image2assessContainerImage failOnPluginError: true,3imageId: "${my_image.id}",4thresholdRules: [5exploitableVulnerabilities(action: 'Mark Unstable', threshold: '1')6],7nameRules: [8vulnerablePackageName(action: 'Fail', contains: 'nginx')9]10 - See the following example for correct location and syntax: Pipeline Script Example 1node {2// Define a variable to use across stages3def my_image4stage('Build') {5// Get Dockerfile and code from Git (or other source control)6checkout scm7// Build Docker image and set image reference8my_image = docker.build("test-app:${env.BUILD_ID}")9echo "Built image ${my_image.id}"10}11stage('Test') {12// Assess the image13assessContainerImage failOnPluginError: true,14imageId: "${my_image.id}",15thresholdRules: [16exploitableVulnerabilities(action: 'Mark Unstable', threshold: '1')17],18nameRules: [19vulnerablePackageName(action: 'Fail', contains: 'nginx')20]21}22stage('Deploy') {23echo "Deploying image ${my_image.id} to somewhere..."24// Push image and deploy a container25}26}27 - Click Save when finished. NOTE Threshold rules must be unique per type. For example, you cannot have two rules for Critical Vulnerabilities. Only one instance of the rule will be applied. View Assessment Results After running a build, you can view assessment results in the following ways: Assessment Results in Jenkins Jenkins will generate an assessment report once the build finishes. - Open the individual build that ran the plugin. - Click Rapid7 Assessment on your navigation menu. Assessment Results in InsightVM The results of your build jobs are viewable on the Builds tab of the “Containers” screen in InsightVM. See Containers Build Interface: to learn more about this feature.

https://docs.rapid7.com/insightvm/containers-cicd-plugin/

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[array(['/areas/docs/_repos//product-documentation__master/f9d3e5e101c2b1c3b68bc988ad2b8944b48ae896/insightvm/images/api_interface.png', None], dtype=object) array(['/areas/docs/_repos//product-documentation__master/f9d3e5e101c2b1c3b68bc988ad2b8944b48ae896/insightvm/images/copy_api_obscured.png', None], dtype=object) array(['/areas/docs/_repos//product-documentation__master/f9d3e5e101c2b1c3b68bc988ad2b8944b48ae896/insightvm/images/IVM_CS.png', None], dtype=object) array(['/areas/docs/_repos//product-documentation__master/f9d3e5e101c2b1c3b68bc988ad2b8944b48ae896/insightvm/images/restart_Jenkins_annotated.png', None], dtype=object) array(['/areas/docs/_repos//product-documentation__master/f9d3e5e101c2b1c3b68bc988ad2b8944b48ae896/insightvm/images/jenkins_token_obscued.png', None], dtype=object) array(['/areas/docs/_repos//product-documentation__master/f9d3e5e101c2b1c3b68bc988ad2b8944b48ae896/insightvm/images/global_credentials.png', None], dtype=object) array(['/areas/docs/_repos//product-documentation__master/f9d3e5e101c2b1c3b68bc988ad2b8944b48ae896/insightvm/images/add_build_step copy.png', None], dtype=object) ]

docs.rapid7.com

Legacy Imperva integration End-of-Life announcement As of September 4, 2019, Rapid7 will start the End-of-Life (EOL) process for the legacy Imperva integration for InsightVM. The Imperva integration will no longer be publicly available for download on the Rapid7 website. To pursue integration opportunities between Imperva and Rapid7, please contact your Customer Success Manager (CSM). You can also view our other next-generation firewall (NGFW) and web application firewall (WAF) integration options shown on our Technology Partners page. This EOL announcement only pertains to future deployments or feature requests. Customers that currently have the Imperva integration configured will not see changes in functionality, but Rapid7 encourages migration to alternative integration options. Schedule of Events FAQs What do I need to do if I still want to integrate with Imperva? For more information and other integration options, please reach out to your CSM to receive a quote. If I use the Imperva integration now, how will this impact me? Any customers already utilizing this integration will not experience an interruption in service for a period of 12 months from the date of this announcement. You can contact your CSM for options after the last date of support. Who can I contact if I have more questions that are not addressed in this announcement? Contact your CSM.

https://docs.rapid7.com/insightvm/legacy-imperva-integration-end-of-life-announcement/

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docs.rapid7.com

Mapping is one of the most powerful features in Mason. You may use arrays and object keys to modify your UI dynamically using data from your server. Before mapping, make sure you've set up your Datasources. Select any element in the Builder you'd like to map data to, and click the Mapping tab in the right sidebar. All mappable attributes will appear there. If there are no attributes, the element currently does not support mapping. Mapping objects is the simplest use case. For each attribute, select a key path in the object using the dropdown menu to use that key's value as the attribute value. Arrays, or object collections - an object with identical length keys and values that are objects - may be used as repeaters to duplicate parts of your UI once for each entry in the collection. Select the element you'd like to repeat, open the Mapping tab, and set the Repeat value to the path in your data you'd like to use to repeat. Only arrays and object collections will appear there. Once you have selected a repeater, the contents of the collection will be available for mapping to the descendants of the repeater. If your datasource's response is a collection, you must use the top-level collection to repeat an element in your UI before you can use the datasource contents. Arrays of primitives may also be used to repeat an element, and that element and all of its children may use the primitive value to map to attributes.

https://docs.trymason.com/development/mapping-data

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docs.trymason.com

. Multiple: TABLE define a storage tier with the paths and file paths that define the priority order. - paths is the section of file paths that define the data directories for this tier of the disk configuration. Typically list the fastest storage media first.': 'TimeWind 'tiering_strategy': 'TimeWindowStorageStrategy' uses TimeWindowStorageStrategy (TWSS) to determine which tier to move the data to. TWSS is a DSE Tiered Storage strategy that uses TimeWindowCompactionStrategy (TWCS). - TimeWindowStorageStrategy (TWSS),.

https://docs.datastax.com/en/dse/6.0/dse-admin/datastax_enterprise/tieredStorage/tieredStorageConfiguring.html

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docs.datastax.com

Advanced configuration - Using an external PostgreSQL - Using an external Redis - Using an external Mattermost - Using an external Gitaly - Using an external object storage - Using your own NGINX Ingress Controller - After install, managing Persistent Volumes - Using Red Hat UBI-based images - Making use of GitLab Geo

https://docs.gitlab.com/12.10/charts/advanced/

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docs.gitlab.com

- General troubleshooting - What does coordinatormean? - Where are logs stored when run as a service? - Run in --debugmode - OffPeakTimezone - Why can’t I run more than one instance of Runner? - - macOS troubleshooting Troubleshoot GitLab Runner This section can assist when troubleshooting GitLab Runner. General troubleshooting The following relate to general Runner troubleshooting. the GitLab Runner is run as service on Windows it logs to System’s Event Log. Run in --debug mode Is it possible to run GitLab Runner in debug/verbose mode. From a terminal, run: gitlab-runner --debug run I’m seeing x509: certificate signed by unknown authority Please see the self-signed certificates. config config file can cause unexpected and hard-to-debug behavior. In GitLab Runner 12.2, only a single instance of Runner can use a specific config.toml file at one time.

https://docs.gitlab.com/12.10/runner/faq/README.html

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docs.gitlab.com

<center> # OddSlingers: Terms of Service <img src="" style="width: 20%; margin-right: 10px;"> <img src="" style="width: 25%; border-radius: 14px; box-shadow: 4px 4px 4px rgba(0,0,0,0.04);"> <br/> *Effective date: February 16, 2019* <hr/> </center> [TOC] By accessing OddSlingers.com (the "Site") you are indicating your agreement to be bound by these Terms. If you do not agree to be bound by these Terms, disconnect from the Site immediately. The Site is provided by Nycrean Technologies Inc, a company registered in Delaware, USA. If you have any questions, please contact us at: - 1.0 DESCRIPTION OF THE SERVICE + 1.1 OddSlingers (also referred to in these terms as "we" or "us") will provide you with access to the Site. Access to the Site is free. Some other services provided by OddSlingers may not be free. If this is the case, OddSlingers will inform you before you pay for the other services that they are not free and make sure that it is clear what the charges are. + 1.2 In these Terms, Service refers to any services provided by us, including the provision of access to the Site. - 2.0 ACCESS TO OUR SERVICE + 2.1 We make no promise that our Service will be uninterrupted or error free. + 2.2 Your access to the Service may be occasionally restricted to allow for repairs, maintenance or the introduction of new facilities or services. OddSling play games on the website, but you may observe without registering. + 2.5 OddSlingers.0 LINKS TO OTHER WEB SITES AND SERVICES +.0 CONTENT OF THE SITE + 4.1 The content made available in the Site is provided "as is" without warranties of any kind. Because of the nature of the information displayed on the Site, OddSlingers cannot verify whether it is accurate or up to date. If OddSlingers becomes aware that the content is inaccurate or out of date it will endeavour to remove it from the Site or take other appropriate action as soon as is reasonably possible, but it accepts no legal obligation to do so. + 4.2 OddSlingers does not warrant that the content on the Site will be free of errors or viruses or that it will not be defamatory or offensive. + 4.3 OddSling OddSlingers. - 5. USE OF PLAY MONEY CHIPS + OddSlingers at any time. + 5.3 You may not sell your Play money chips to any person, company or entity. + 5.4 These terms may be ammended in the future such that Play Money may also be purchased using real world currency and/or by successfully completing special offers or promotions. OddSlingers may charge fees for the license to use its Play Money or may award and/or distribute its Play Money at no charge, at OddSlingers's sole discretion. + 5.5 Purchases of any virtual currency, including, but not limited to Play Money, sold on OddSlingers, are purchases of a limited, non-transferable, revocable license. The license may be terminated immediately if your account is terminated for any reason, in OddSlingers's sole and absolute discretion, or if OddSlingers discontinues providing and/or dramatically alters the services on the Site. + 5.6 Play Money can be awarded based on successful completion of special offers or promotions at OddSlingers's sole discretion. These awards are dependent on successful completion of said offers, as defined by the company or affiliate providing the offer or promotion. + 5.7 You agree that any awards or granting of Play Money, or any virtual currency, on OddSlingers received from completing a special offer or promotion are dependent upon successful completion of said offer or promotion. + 5.8 You agree that OddSlingers has the right to manage, control, modify and/or eliminate Play Money as it sees fit at its sole discretion. Further, you agree that OddSlingers will have no liability to you based on OddSlingers.com's exercise of these rights. + 5.9 You agree that Play Money transfers between OddSlingers accounts may be blocked by OddSlingers without notice if suspicious activity is detected. + 5.10 You agree that any play deemed unethical by OddSlingers in its sole discretion (including, without limitation, team play, soft play, sale of chips between players or chip dumping) may result in the applicable player(s) accounts being terminated and further access to OddSlingers being suspended permanently with no obligation to the applicable players. - 6. COPYRIGHT / TRADE MARKS + 6.1 All rights in the HTML, designs, programming and information posted by us on the Site are owned by OddSlingers. LIABILITY + 7.1 We will not be liable for your loss of profits, wasted expenditure, corruption or destruction of data or any other loss which does not directly result from something we have done wrong, even if we are aware of the possibility of such damage arising. + 7.2 Oddslingers maintains no liability to users (including where we have been negligent) given we are a free site with no obligations of monetary value to users (from purchases or otherwise). + 7.3 OddSlingers expressly disclaims any and all warranties, including without limitation, any warranties as to the availability, accuracy, or content of information, products, or services, or any terms, conditions or warranties of quality, merchantability or fitness of the content on the Site for a particular purpose. - 8. DATA PROTECTION + 8.1 We will respect your personal data and will comply with all applicable United States data protection legislation currently in force. + 8.2 We will use your personal information, which you provide to us and the records of your visits to the Site to constantly monitor and improve our Service and for marketing purposes in accordance with our Privacy Policy. - 9. GENERAL + United States law. The Courts of the U.S. shall have exclusive jurisdiction over any disputes arising out of these Terms. + 9.3 OddSlingers reserves the right to modify these Terms and Conditions at any time by the posting of modified Terms and Conditions on this website. ---/>

https://docs.oddslingers.com/s/terms-of-service

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docs.oddslingers.com

Introduction A sequence flow connects two elements in a process and varies according to requirements. The following flows are supported: - Conditional flow - Default flow - Sequence flow Sequence flow In a sequence flow, after the execution of an element in the process, all the outgoing sequences are followed. The following screenshot shows a sequence flow in a process: Sample Sequence Flow Conditional flow A conditional flow is for executing a process under certain conditions. The behavior is to evaluate the conditions on the outgoing sequence flows. When a condition evaluates to true, that outgoing sequence flow is selected. The following screenshot shows a conditional flow in a process: Sample Conditional Flow Default flow The default flow refers to the flow that has to be followed when none of the conditions are met. The following screenshot shows a conditional flow in a process: Sample Default Flow To change the flow: - Click the flow. - Click the settings icon. - Select the required flow.

https://docs.opsramp.com/solutions/remediation-automation/process-definitions/sequence-flow-reference/

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[array(['https://docsmedia.opsramp.com/screenshots/Automation/example-sequence-flow-diagram.png', 'Sample Sequence Flow'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Automation/example-conditional-flow-diagram.png', 'Sample Conditional Flow'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Automation/sample-default-flow.png', 'Sample Default Flow'], dtype=object) array(['https://docsmedia.opsramp.com/screenshots/Automation/changing-a-flow.png', 'Changing a Flow'], dtype=object) ]

docs.opsramp.com

Overview Gait is a common and early indicator for many neuromuscular diseases and a great means for orthotics' specialists to quantify lower limb related movements. Zeblok's proprietary Smart shoes provides access to 4 pressure zones and 3-axis 3D motion data real-time. You can visualize the data stream online on our secure cloud dashboard as well as download raw data for further analysis. We also provide data analytics results using our Bio-Informatics cloud which perform data crunching and gives a report within seconds. The smart shoes directly connect to your WiFi with simple setup process. With the help of Zeblok smart shoes, a clinician or researcher can simply ask the patient to wear the shoes and start walking while our smart electronics and sensors capture the walking data real-time and keeps a record of the session. The unique part of our technology is that unlike the Gait mats that are confined to a specific location or bulky electronics that go on top of your shoes, we have a seamless product that has the electronics embedded inside the shoes and doesn't require the user to worry about them. So, just wear them like any other pair of shoes and start collecting your data. Features Tech Specs

https://docs.zeblok.com/scientific/WIFI-smart-shoes.php

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[array(['https://docs.zeblok.com/admin/uploads/Shoes_Product.png', None], dtype=object) array(['https://docs.zeblok.com/admin/uploads/Shoes_Features.png', 'Shoes Features'], dtype=object) ]

docs.zeblok.com

Steps On How To Request For A W-2 From A Former Employer Changing jobs more often can help one to brand themselves and improve their internal consulting skills. When switching to a new job, there are crucial documents one needs to have such as the W-2. The reason why should have a W-2 is that employers must file the document for every employee. The W-2 provides information about the amount you were earning at the previous job and. This article here, will guide you on how to request a W-2 from a previous employer. The first step to ask for a W-2 is checking with payroll. You can get a W-2 by simply sending an e-mail or calling the administrator for payroll. It is recommended to make sure the administrator has the appropriate email address. It is your obligation to make sure they have mailed the W-2 just in case you will. When switching to a new job, there are crucial documents one needs to have such as the W-2. You required to have a W-2 because the very employer needs to file the document for every employee. The W-2 reports the income amount you were earning at your previous job and the withheld taxes of Medicare, state, security, social, and federal. Supplementary to that, the document offers information on further contributions relating to your healthcare and retirement you made in the course of that year. When switching to a new job, it is chiefly important to request a W-2 in time. In the scenario that, your previous employer is not responding to your e-mails and calls, you should call the IRS. The process can be simplified for the IRS, by giving your identification number and the former company’s employer. The IRS will make an initiative to send a notice to your former employer. This step is critical because you will receive the W-2 document in time. You should put into consideration filing your taxes if you do not receive the W-2 after following these tips.

http://docs-prints.com/2020/10/03/my-most-valuable-advice-12/

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docs-prints.com

CurveExpert Professional 2.7.3 documentation After performing some analysis, a typical task is to write data, in some form, to disk for later retrieval. CurveExpert Professional supports several ways to extract data and graphs to a file or the clipboard. If you want to save all of your data, results, graphs, and notes or function pickers. To save your dataset into a text file, choose File-. The same graph can be copied to the clipboard by right clicking and selecting Copy.

https://docs.curveexpert.net/curveexpert/pro/html/writingdata.html

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docs.curveexpert.net

Self-signed certificates or custom Certification Authorities Introduced in GitLab Runner 0.7.0 GitLab Runner allows you to configure certificates that are used to verify TLS peers when connecting to the GitLab server. This solves the x509: certificate signed by unknown authority problem when registering a runner. Supported options for self-signed certificates GitLab Runner supports the following options: Default: GitLab Runner reads the system certificate store and verifies the GitLab server against the certificate authorities (CA) stored in the system. GitLab Runner reads the PEM certificate (DER format is not supported) from a predefined file: /etc/gitlab-runner/certs/hostname.crton *nix systems when GitLab Runner is executed as root. ~/.gitlab-runner/certs/hostname.crton *nix systems when GitLab Runner is executed as non-root. ./certs/hostname.crton other systems. If running Runner as a Windows service, this will not work. Use the last option instead. If your server address is:, create the certificate file at: /etc/gitlab-runner/certs/my.gitlab.server.com.crt.Note: You may need to concatenate the intermediate and server certificate for the chain to be properly identified.. Git cloning The runner injects missing certificates to build the CA chain in build containers. This allows git clone and artifacts to work with servers that do not use publicly trusted certificates. This approach is secure, but makes the runner a single point of

https://docs.gitlab.com/12.10/runner/configuration/tls-self-signed.html

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docs.gitlab.com

A Datasource is any URL that provides data to a feature at runtime. In order to be used as a datasource, a URL must: be publicly accessible using javascript in the browser (have appropriate cross-origin headers) respond with a JSON payload respond to a GET request Datasources are shared across all features in your project, and added using the Project panel under the Datasources tab. This reduces redundancy as multiple features that need to use the same data can share it, and allows events in one feature (say, a successful resource creation) to modify data used in another feature. Give it a name, provide a URL, any query or header parameters, and hit Fetch. You have now added a datasource to your Project. The response is not stored by Mason, but used during the build process to determine the structure of the expected response and configure mapping rules for your data and UI. The response structure must be consistent. Datasources are created in your project, but fetched by your features when they mount. This is because you may not have all the dynamic data relevant to the datasource until a specific feature mounts. In order to tell a feature to fetch a datasource when it mounts, check the box next to the datasource name in the Configure section of the builder under the Fetch Datasources header. Ensure the feature that fetches the datasource has the appropriate url parameters and callbacks, if required. If you are using tokens or unique identifiers in your datasource, you may mark them as private using the key button in the Builder. Any header or query parameters marked as private will not be supplied to your Datasource at runtime, and must be provided by you using a callback (see below). All parameters not marked as private will be supplied to your features at runtime, and will be visible by anyone with access to your application. You may inject dynamic header or query parameters, like authorization tokens, at runtime by using the willFetchData callback. Your function will receive the datasource to be fetched as an argument, which you may modify and return. See below for an example. import React from 'react';import { Canvas } from '@mason-api/react-sdk';class MyFeed extends React.Component {render() {const { search, token, user } = this.props;return <Canvasid="YOUR_COMPONENT_ID"willFetchData={(datasource, featureId) => {if (datasource.id === 'YOUR_DATASOURCE_ID') {return {...datasource,headers: { 'Authorization': token },queries: { 'search': search },};}return datasource;}}/>;}} Your function will receive two arguments: datasource, an object with the following structure {url: '',headers: {'Content-Type': 'application/json'},queries: {foo: 'bar'},name: 'DATASOURCE_NAME',id: 'DATASOURCE_ID' and featureId, the 12-byte unique identifier of your feature (which you can find in the Export instructions in the Builder). You may modify any part of the datasource including the URL. However, URL modifications are most easily accomplished using the urlParams property. You must return the datasource, if you have no modifications return the datasource unmodified. As an alternative to providing callbacks using props, particularly if you are not using React, you may use the Mason.callback function to register your willSendData callback. Here is an example: import Mason from '@mason-api/react-sdk';Mason.callback('willSendData', (datasource, featureId) => {if (datasource.id === 'YOUR_DATASOURCE_ID') {return {...datasource,headers: { 'Authorization': token },queries: { 'search': search },};}return datasource;}, 'YOUR_FEATURE_ID'); The third argument to the callback function is an optional feature id. Even though datasources are shared across all features in a project, fetch events are triggered by feature's mounting (more on this below). If you want Mason to invoke a callback only when a specific feature is fetching a datasource, you may provide its id as the third argument. In some cases you may want to use a form submission response to update a datasource and trigger a UI update. To accomplish this, use the Success event menu in the Form tab of the Configure section of the Builder. You may merge or replace a datasource with the response from your form submission. You may also trigger a refetch of the entire datasource. Replace simply overwrites the entire datasource. When merging, the behavior is as follows: if the Datasource is an object, the response will be shallowly merged if the Datasource is an array, and the response is not an array, the response will be pushed onto the end of the array if the Datasource is an array, and the response is an array, the response's entries will be pushed onto the end of the array

https://docs.trymason.com/development/fetching-data

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docs.trymason.com

The following server roles and features are installed by the InstallRolesAndFeatures.ps1 PowerShell script. Expand Web Server (IIS) > Web Server > Common HTTP Features. The list contains the following items: - Default Document - HTTP Errors - Static Content Expand Web Server (IIS) > Web Server > Security. The list contains the following items: - Request Filtering - URL Authorization - Windows Authentication Expand Web Server (IIS) > Web Server > Application Development. The list contains the following items: - ASP.NET45 - .NET Extensibility 4.5 - Application Initialization - ISAPI Extensions - ISAPI Filter - WebSockets Note: Windows Server 2019 comes with ASP.NET47 by default. The .NET Extensibility 4.7 feature is also selected by default. - Expand Web Server (IIS) > Web Server > Management Tools. The list displays the following items: - IIS Management Console After completing the installation, open a browser and go to. If you do not know your computer name, open Command Prompt and type hostname, or open System and look for Computer Name. The result of opening the address should be the default page of IIS. If the page is not displayed as in the image above, you need to ensure that IIS server is running and port 80 is open. By default, IIS listens for connections on port 80, for any IP bound to the server. That happens even if there are no host headers or bindings set for a specific IP. That can prevent you from running multiple web servers on port 80. To set IIS to listen on specific IPs, follow the instructions below. Note: Minimum Windows Server version required: 2012/IIS 8. - Open an Elevated Command Prompt and type netsh. - Type http. - Enter the show iplistencommand to display the current list of IPs to listen to. If no IPs are displayed, IIS listens to all IPs by default. - Use the add iplisten ipaddress=0.0.0.0command to set IIS to listen to a specific IP. Make sure 0.0.0.0 is replaced by the correct IP. Run the command again for any additional addresses. - Type exitif you want to exit. - (Optionally) If you need to delete any IP from this list, use the following command: delete iplisten ipaddress=0.0.0.0. - Restart IIS to apply the changes, using the iisresetcommand. File Extensions Access to the following file extensions is required by Orchestrator: Imporatant! When using Orchestratortype Storage Buckets, any file extensions may be used beyond the enumerated list above. Updated 3 months ago

https://docs.uipath.com/installation-and-upgrade/docs/server-roles-and-features

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[array(['https://files.readme.io/5003929-image_38.png', 'image_38.png'], dtype=object) array(['https://files.readme.io/5003929-image_38.png', 'Click to close...'], dtype=object) array(['https://files.readme.io/001a2fa-image_39.png', 'image_39.png'], dtype=object) array(['https://files.readme.io/001a2fa-image_39.png', 'Click to close...'], dtype=object) array(['https://files.readme.io/d489157-image_40.png', 'image_40.png'], dtype=object) array(['https://files.readme.io/d489157-image_40.png', 'Click to close...'], dtype=object) array(['https://files.readme.io/7eb0fa7-image_41.png', 'image_41.png'], dtype=object) array(['https://files.readme.io/7eb0fa7-image_41.png', 'Click to close...'], dtype=object) ]

docs.uipath.com

Verification Planning and Requirements¶ A key activity of any verification effort is to capture a Verification Plan (aka Test Plan or just testplan). This document is not that. The purpose of a verification plan is to identify what features need to be verified; the success criteria of the feature and the coverage metrics for testing the feature. At the time of this writing the verification plan for the CV32E40P is under active development. It is located in the core-v-verif GitHub repository at. The Verification Strategy (this document) exists to support the Verification Plan. A trivial example illustrates this point: the CV32E40P verification plan requires that all RV32I instructions be generated and their results checked. Obviously, the testbench needs to have these capabilities and its the purpose of the Verification Strategy document to explain how that is done. Further, an AC will be required to implement the testbench code that supports generation of RV32I instructions and checking of results, and this document defines how testbench and testcase development is done for the OpenHW projects. The subsections below summarize the specific features of the CV32E40* verification environment as identified in the Verification Plan. It will be updated as the verification plan is completed. Base Instruction Set¶ - Capability to generate all legal RV32I instructions using all operands. - Ability to check status of GPRs after instruction execution. - Ability to check side-effects, most notably underflow/overflow after instruction execution.

https://core-v-docs-verif-strat.readthedocs.io/en/latest/planning_requirements.html

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core-v-docs-verif-strat.readthedocs.io

Table of Contents Product Index.

http://docs.daz3d.com/doku.php/public/read_me/index/32961/start

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Table of Contents Product Index with “cross” or “pentagram” options) and fully rigged, circle style sunglasses..

http://docs.daz3d.com/doku.php/public/read_me/index/63615/start

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5 projects in different stage of development or production. Every project will have 7 minutes to pitch including the trailer and it will be followed by a short Q&A. SERBIAN DOCS IN PROGRESS 2019 Boogie and Demons / Bugi i demoni Logline Documentary about Vladimir Milivojević Boogie, a famous street photographer, (), specifically about his work in Belgrade, a series of photographs created by a Wet plate-Collodion process, named “Demons”. Synopsis Familiar with the most unusual deviations of human nature, Boogie is moving away from the street photography and in his Belgrade studio a work on a series of portraits in the technique of the collodion process, called Demons. This procedure is exclusively related to Belgrade. Unlike other cities where dark content is found in everyday life of people from margins in Belgrade, this content is found among acquaintances and friends, and due to long exposures characteristic of the Collodion wet process. With this exotic technique of the long overdue photographic process, Boogie manages to record something deeply, demonically and in the nature of the “ordinary” people portrayed. Boogie speaks of this process as an alchemical process that he says is able to record something “on the outside”. Director’s Biography Ivan Šijak is professor at Faculty of Dramatic Arts in Belgrade. At Belgrade Rectorate of Arts leading professor, course on Digital image. Selected filmography: Director/ Cinematographer/VFX Supervisor: Blue Gypsy Emir Kusturica, Rasta Internacional. Promete moi Emir Kusturica, Rasta Internacional. 12 Nikita Mihalkov, 3T. Burnt by the Sun 2,3 Nikita Mihalkov, 3T. Director, Opera The Man Who Mistook his Wife for a Hat, BITEF 2001, music: Michael Nyman, New Moment. Director Ivan Šijak Producer Milan Kilibarda Co-producer Jasmina Petković Expected duration 80’ Shooting format Digital Premiere end of 2019 Language English Budget 107 000 EUR Financing in place 47 000 EUR Production country Serbia Production company Mikijeva radionica Partners attached Film Center Serbia Contact Milan Kilibarda/Producer, + 381 65 322 3222, [email protected] Christina / Kristina Logline A transgender sex worker lives with a cat and collects antiques. Her life becomes stressful after the arrival of an inspector who is chasing her and a stranger she falls in love with, but who disappears. Synopsis After many years of serious battle, Christina`s sex has finally been adjusted to her gender identity. Today, she lives with her cat in a luxury home and invests her money into antiques and paintings. This film arises from the need for radical reassessment of the term love, similar to Plato’s reassessment in Symposium. Christina is an ideal protagonist because she “sells love” and buys antiques which she loves. She adores her family which consists of her transgender friends. She longs for true love from a man and searches for God’s unconditional love. And just when she thinks that she will finally find true love, her lover suddenly disappears. After an exciting journey, she finds him in a rural area, surrounded by family. Seeing him, she decides that it is best for her to back down, so she could protect him from herself. Director’s Biography Nikola Spasić is a film director, editor and producer based in Novi Sad. In January 2017, his debut documentary film Why Dragan Gathered his Band premiered at the MiradasDoc Festival in Spain and has been shown at more than 40 festivals around the world. At one of the biggest Serbian festivals, Cinema City, it won the best film award. So far, the film has been shown on various TV stations including Al Jazeera Balkans, RTRS, HRT, RTS, etc. Director Nikola Spasić Producer Nikola Spasić, Milanka Gvoić Co-producer David Evans Expected duration 52’; 80’ Shooting format Digital Expected delivery February 2021 Language Serbian Budget 309.500 € Financing in place 15.500 € Production country Serbia, UK Production company Rezon, Shoot from the Hip Partners attached Serbian Office for Human and Minority Rights Contact Nikola Spasić, Director/Producer, +381 62 199 17 05, [email protected] Do Not Come Home / Nemoj da se vraćaš Logline University-educated truck drivers from eastern Europe roam the United States questioning their life choices. Was it worth leaving everything behind, just for the money? Synopsis The characters of our film are neither particularly old nor quite young. They all have university degrees, but they do not perform the jobs they are qualified to perform. Despite loving their homeland, they don’t live there anymore. They are driving trucks across the USA. Driving through the wild and picturesque scenery across America, our characters communicate with their loved ones in the homeland. The truck cabin is the place where all their moments of happiness and sorrow are concentrated. Do not come home is a multilayered psychological, philosophical, political and social story interwined with intimacy and personal struggles of those who left and those who stayed. “Do not come home will take audience on an emotional journey colored by stunning sceneries of North America flavored by a dose of Balkan sense of humor. Directors’s Biographies Miloš Ljubomirović finished Master studies from Faculty of Dramatic Arts (Film and TV production program). Won 7 national and international awards for his short films, 4 of them with his master’s thesis that he both produced and directed – short film Shadows. Faculty of Dramatic Arts awarded that film with Dejan Kosanović Award for best film in Master studies. IDFAcademy and Sarajevo Talent Campus alumni. Danilo Lazović (1985) is a producer, director, media and culture theorist. He has participated and initiated a variety of projects in various social and media fields. Graduated from the Academy of Arts and has a BA in – Production in Culture and Media, and Masters degree from the Faculty of Dramatic Arts. Directors Miloš Ljubomirović, Danilo Lazović Producer Miloš Ljubomirović, Danilo Lazović, Ivica Vidanović Co-producer Jure Pavlović, Dagmar Sedláčková Expected duration 90’, TV Hour Shooting format Digital, 4K Expected Delivery November 2020 Language Serbian, English Budget € 312,975 Financing in place € 91,374 Production country Serbia, Croatia, Czech Republic Production company Cinnamon Films, Serbia Co-production companies Sekvenca, Croatia, MasterFilm, Czech Republic Partners attached Film Center Serbia Contact Miloš Ljubomirović, Director/Producer, +381 64 6150 953, [email protected] Dream Collector // Dreams of Vladan Radovanović / Sakupljač snova // Snovi Vladana Radovanovića Logline This is a documentary fairy tale about the two creators – Dreamer and Dream Transcriber – inhabiting the same physical membrane. We immerse into their stunning collection of dreams assembled throughout almost 70 years. Synopsis Vladan Radovanović is an elderly man living in a small, yet scenic apartment filled with books, instruments and various art objects, together with his wife and a parrot. He sleeps, and after waking up, he often writes down the content of his previous dream. Later on he draws it, too, trying to capture it as accurately as possible. The apartment becomes the portal to Vladan’s dream world; the present melts into one with his memories, thoughts and creations. Gradually, we discover a unique, versatile artist: composer, painter, writer, theorist, art-syntetist, pioneer in electronic music and in several other fields of contemporary art. We follow Radovanović’s key life episodes, recollected from the “night diaries” that he has been keeping from 1953 onwards. At the age of 86 he still dreams, and the need to create is as strong as ever. Director’s Biography Sonja Đekić was born in 1980 in Belgrade, where she works and lives today. She holds a MA degree in Film and TV directing from the Faculty of Drama Arts. Sonja has been passionately working on her documentary projects in many capacities (Joe Goes to Serbia, 2008, KOSMA, 2013, Speleonaut/Under The Stone Sky, 2018), while involved in several film festivals (Martovski, Grafest, Magnificent 7). Sonja recently founded the production company KEVA. Director Sonja Đekić Producer Sonja Đekić Expected duration 70’ Shooting format 4K Premiere winter 2020 Language Serbian with English subtitles Budget 203,000 € Financing in place 21,000 € Production country Serbia Production company KEVA Contact Sonja Đekić, Director/Producer, +381 63 108 0605, [email protected] The Box / Kutija Logline Political ready-made comedy with real consequences exploring the basic nature of politics by looking into one of the strangest periods in Serbian. Synopsis The Box is a documentary film about one of the strangest times in modern Serbian history. After the 45 years of the communist, single party governance in Serbia, in July 1990 political parties gained legal status. Due to the decades-old single-party system, opposition naïvely and comically pioneers its pluralistic beginnings. By using imprecise and subjective memories of protagonists and a bizarre and humorous prism of the 1990. election media campaign landscape, public gatherings, so as interviews with the presidential candidates and party members, specially prepared and conducted for the film, The Box aims to engage the viewer to assess the current political momentum , the ongoing process of democratization and to investigate the basic nature of politics, and question the basic notions of what actually is “political”. Director Luka Papić Producer Srđa Vučo Expected duration 85 ’ Shooting format HD Expected Delivery Early 2020 Language Serbian Budget 113.940,00 EUR Financing in place 22.000,00 EUR (19%) Production country Serbia Production company Cinnamon Films Contact Luka Papić / Director, +381 64 400 7054, [email protected]

https://beldocs.rs/serbian-docs-in-progress/

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beldocs.rs

Run Functional Tests task. This task is deprecated in Azure Pipelines and TFS 2018 and later. Use version 2.x or higher of the Visual Studio Test task together with jobs to run unit and functional tests on the universal agent. For more details, see Testing with unified agents and jobs. TFS 2017 and earlier Use this task to. YAML snippet # Run functional tests # Deprecated: This task and it’s companion task (Visual Studio Test Agent Deployment) are deprecated. Use the 'Visual Studio Test' task instead. The VSTest task can run unit as well as functional tests. Run tests on one or more agents using the multi-agent job setting. Use the 'Visual Studio Test Platform' task to run tests without needing Visual Studio on the agent. VSTest task also brings new capabilities such as automatically rerunning failed tests. - task: RunVisualStudioTestsusingTestAgent@1 inputs: testMachineGroup: dropLocation: #testSelection: 'testAssembly' # Options: testAssembly, testPlan #testPlan: # Required when testSelection == TestPlan #testSuite: # Required when testSelection == TestPlan #testConfiguration: # Required when testSelection == TestPlan #sourcefilters: '**\*test*.dll' # Required when testSelection == TestAssembly #testFilterCriteria: # Optional #runSettingsFile: # Optional #overrideRunParams: # Optional #codeCoverageEnabled: false # Optional #customSlicingEnabled: false # Optional #testRunTitle: # Optional #platform: # Optional #configuration: # Optional #testConfigurations: # Optional #autMachineGroup: # Optional Arguments The task supports a maximum of 32 machines/agents. Azure Pipelines Build agents - Hosted and on-premises agents. - The build agent must be able to communicate with all test machines. If the test machines are on-premises behind a firewall, the hosted build agents Build-Deploy-Test (BDT) tasks are supported in both build and release pipelines. Machine group configuration - Only Windows machines are supported when using BDT tasks inside a Machine Group. Using Linux, mac - Run continuous tests with your builds - Testing in Continuous Integration and Continuous Deployment Workflows Related tasks - Deploy Azure Resource Group - Azure File Copy - Windows Machine File Copy - PowerShell on Target Machines - Visual Studio Test Agent Deployment Open source This task is open source on GitHub. Feedback and contributions are welcome. Customize Code Coverage Analysis. ::: moniker-end Help and support - See our troubleshooting page - Get advice on Stack Overflow, and get support via our Support page

https://docs.microsoft.com/en-us/azure/devops/pipelines/tasks/test/run-functional-tests?view=azure-devops

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docs.microsoft.com

Failure Modes in Machine Learning November 2019 Introduction & Background. Intentional failures wherein the failure is caused by an active adversary attempting to subvert the system to attain her goals – either to misclassify the result, infer private training data, or to steal the underlying algorithm. Unintentional failures wherein the failure is because an ML system produces a formally correct but completely unsafe outcome. We would like to point out that there are other taxonomies and frameworks that individually highlight intentional failure modes[1],[2] and unintentional failure modes[3],[4]. Our classification brings the two separate failure modes together in one place and addresses the following needs: The need to equip software developers, security incident responders, lawyers, and policy makers with a common vernacular to talk about this problem. After developing the initial version of the taxonomy last year, we worked with security and ML teams across Microsoft, 23 external partners, standards organization, and governments to understand how stakeholders would use our framework. Based on this usability study and stakeholder feedback, we iterated on the framework. Results: When presented with an ML failure mode, we frequently observed that software developers and lawyers mentally mapped the ML failure modes to traditional software attacks like data exfiltration. So, throughout the paper, we attempt to highlight how machine learning failure modes are meaningfully different from traditional software failures from a technology and policy perspective. The need for a common platform for engineers to build on top of and to integrate into their existing software development and security practices. Broadly, we wanted the taxonomy to be more than an educational tool – we want it to effectuate tangible engineering outcomes. Results: Using this taxonomy as a lens, Microsoft modified its Security Development Lifecycle process for its entire organization. Specifically, data scientists and security engineers at Microsoft now share the common language of this taxonomy, allowing them to more effectively threat model their ML systems before deploying to production; Security Incident Responders also have a bug bar to triage these net-new threats specific to ML, the standard process for vulnerabilities triage and response used by the Microsoft Security Response Center and all Microsoft product teams. The need for a common vocabulary to describe these attacks amongst policymakers and lawyers. We believe that this for describing different ML failure modes and analysis of how their harms might be regulated is a meaningful first step towards informed policy. Results: This taxonomy is written for a wide interdisciplinary audience – so, policymakers who are looking at the issues from a general ML/AI perspective, as well as specific domains such as misinformation/healthcare should find the failure mode catalogue useful. We also highlight any applicable legal interventions to address the failure modes. See also Microsoft's Threat Modeling AI/ML Systems and Dependencies and SDL Bug Bar Pivots for Machine Learning Vulnerabilities. How to use this document At the outset, we acknowledge that this is a living document which will evolve over time with the threat landscape. We also do not prescribe technological mitigations to these failure modes here, as defenses are scenario-specific and tie in with the threat model and system architecture under consideration. Options presented for threat mitigation are based on current research with the expectation that those defenses will evolve over time as well.. Document Structure In both the Intentional Failure Modes and Unintentional Failure Modes sections, we provide a brief definition of the attack, and an illustrative example from literature. In the Intentional Failure Modes section, we provide the additional fields: What does the attack attempt to compromise in the ML system – Confidentiality, Integrity or Availability? We define Confidentiality as assuring that the components of the ML system (data, algorithm, model) are accessible only by authorized parties; Integrity is defined as assuring that the ML system can be modified only by authorized parties; Availability is defined as an assurance that the ML system is accessible to authorized parties. Together, Confidentiality, Integrity and Availability is called the CIA triad. For each intentional failure mode, we attempt to identify which of the CIA triad is compromised. How much knowledge is required to mount this attack – blackbox or whitebox? In Blackbox style attacks., the attacker does NOT have direct access to the training data, no knowledge of the ML algorithm used and no access to the source code of the model. The attacker only queries the model and observes the response. In a whitebox style attack the attacker has knowledge of either ML algorithm or access to the model source code. Commentary on if the attacker is violating traditional technological notion of access/authorization. Intentionally-Motivated Failures Summary Unintended Failures Summary Details on Intentionally-Motivated Failures Details on Unintended Failures Acknowledgements We would like to thank Andrew Marshall, Magnus Nystrom, John Walton, John Lambert, Sharon Xia, Andi Comissoneru, Emre Kiciman, Jugal Parikh, Sharon Gillet, members of Microsoft’s AI and Ethics in Engineering and Research (AETHER) committee’s Security workstream, Amar Ashar, Samuel Klein, Jonathan Zittrain, members of AI Safety Security Working Group at Berkman Klein for providing helpful feedback. We would also like to thank reviewers from 23 external partners, standards organization, and government organizations for shaping the taxonomy. Bibliography [1] Li, Guofu, et al. "Security Matters: A Survey on Adversarial Machine Learning." arXiv preprint arXiv:1810.07339 (2018). [2] Chakraborty, Anirban, et al. "Adversarial attacks and defences: A survey." arXiv preprint arXiv:1810.00069 (2018). [3] Ortega, Pedro, and Vishal Maini. "Building safe artificial intelligence: specification, robustness, and assurance." DeepMind Safety Research Blog (2018). [4] Amodei, Dario, et al. "Concrete problems in AI safety." arXiv preprint arXiv:1606.06565 (2016). [5] Shankar Siva Kumar, Ram, et al. "Law and Adversarial Machine Learning." arXiv preprint arXiv:1810.10731 (2018). [6] Calo, Ryan, et al. "Is Tricking a Robot Hacking?." University of Washington School of Law Research Paper 2018-05 (2018). [7] Paschali, Magdalini, et al. "Generalizability vs. Robustness: Adversarial Examples for Medical Imaging." arXiv preprint arXiv:1804.00504 (2018). [8] Ebrahimi, Javid, Daniel Lowd, and Dejing Dou. "On Adversarial Examples for Character-Level Neural Machine Translation." arXiv preprint arXiv:1806.09030 (2018) [9] Carlini, Nicholas, and David Wagner. "Audio adversarial examples: Targeted attacks on speech-to-text." arXiv preprint arXiv:1801.01944 (2018). [10] Jagielski, Matthew, et al. "Manipulating machine learning: Poisoning attacks and countermeasures for regression learning." arXiv preprint arXiv:1804.00308 (2018) [11] [] [12] Fredrikson M, Jha S, Ristenpart T. 2015. Model inversion attacks that exploit confidence information and basic countermeasures [13] Shokri R, Stronati M, Song C, Shmatikov V. 2017. Membership inference attacks against machine learning models. In Proc. of the 2017 IEEE Symp. on Security and Privacy (SP), San Jose, CA, 22–24 May 2017, pp. 3–18. New York, NY: IEEE. [14] Tramèr, Florian, et al. "Stealing Machine Learning Models via Prediction APIs." USENIX Security Symposium. 2016. [15] Elsayed, Gamaleldin F., Ian Goodfellow, and Jascha Sohl-Dickstein. "Adversarial Reprogramming of Neural Networks." arXiv preprint arXiv:1806.11146 (2018). [16] Athalye, Anish, and Ilya Sutskever. "Synthesizing robust adversarial examples." arXiv preprint arXiv:1707.07397(2017) [17] Sharif, Mahmood, et al. "Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition." arXiv preprint arXiv:1801.00349 (2017). [19] Xiao, Qixue, et al. "Security Risks in Deep Learning Implementations." arXiv preprint arXiv:1711.11008 (2017). [20] Gu, Tianyu, Brendan Dolan-Gavitt, and Siddharth Garg. "Badnets: Identifying vulnerabilities in the machine learning model supply chain." arXiv preprint arXiv:1708.06733 (2017) [21] [] [22] [] [23] Amodei, Dario, et al. "Concrete problems in AI safety." arXiv preprint arXiv:1606.06565 (2016). [24] Leike, Jan, et al. "AI safety gridworlds." arXiv preprint arXiv:1711.09883 (2017). [25] Gilmer, Justin, et al. "Motivating the rules of the game for adversarial example research." arXiv preprint arXiv:1807.06732 (2018). [26] Hendrycks, Dan, and Thomas Dietterich. "Benchmarking neural network robustness to common corruptions and perturbations." arXiv preprint arXiv:1903.12261 (2019).

https://docs.microsoft.com/en-us/security/engineering/failure-modes-in-machine-learning

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docs.microsoft.com

Linino One ;). Linino One has on-board debug probe and IS READY for debugging. You don’t need to use/buy external debug probe.

https://docs.platformio.org/en/stable/boards/atmelavr/one.html

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docs.platformio.org

Technique editor Introduction First, what is a Technique? A technique is a description in code form of what the agent has to do on the node. This code is actually composed of a series of Generic method calls. These different Generic method calls are conditional. What is a Generic method? A generic method is a description of an elementary state independent of the operating system (ex: a package is installed, a file contains such line, etc…). Generic methods are independent of the operating system (It has to work on any operating system). Generic methods calls are conditioned by condition expressions, which are boolean expression combining basic conditions with classic boolean operators (ex : operating system is Debian, such generic method produced a modification, did not produce any modification, produced an error, etc…) Technique Editor Utility (Configuration policy → Techniques), this tool has an easy-to-use interface, which doesn’t require any programming skills but nevertheless allows to create complex Techniques. Interface. You can add parameters to a technique to make it reusable. Go to Parameters and add a name and a description. You can now use it in generic method instead of static value. You can also add resources to a technique. Go to Resources and Manage resources. can be found in the reference documentation) _11<< The Generic method details are divided into 3 blocks : Conditions Conditions allow user to restrict the execution of the method. Parameters Parameters are in mono or multi line text format. They can contains variables which will be extended at the time of the execution. Result conditions One result condition Those conditions can be used in another Generic methods conditions. ie, you can execute a command if a previous one failed or was repaired. Create your first Technique Now we are going to see how to create a simple technique to configure a ntp server, step by step. General information Let’s start from the beginning. Click on the "New Technique" button and start filling in the General information fields (only name is required). In our case: Name: Configure NTP Description: Install, configure and ensure the ntpd is running. Uses a template file to configuration. Add and configure generic methods Now, we have to find and add the generic methods which correspond to the actions we want to execute. In our case, we want to add the following methods: Package present (You can find it in the Package category) This method only take one parameter, the name of the package to install. So here, fill in the package_name field with the value ntp. File content conditions defined following the execution of Package install method is Repaired (package_install_ntp_repaired). So here, fill in the Other conditions field in the Conditions panel with the value package_install_ntp_repaired. Service enabled at boot (You can find it in the Service category) This method only take one parameter, the name of the service we want to check. Again, here, fill in the service_name field with the value ntp. You can also use parameters et resources to replace "File content" method by "File from local source with check" : ← Configuration concepts Variables →

https://docs.rudder.io/reference/6.1/usage/technique_editor.html

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docs.rudder.io

Logging By default, the Release server writes information, such as: warnings, errors, and log messages to your terminal output and to XL_RELEASE_SERVER_HOME/log/xl-release.log. In addition, Release writes access logging to XL_RELEASE_SERVER_HOME/log/access.log. Changing the logging behavior in Release You can customize the logging behavior in Release. Example: To write log output to a file or to log output from a specific source. Release uses Logback as logging technology. The Logback configuration is stored in XL_RELEASE_SERVER_HOME/conf/logback.xml. For more information about the logback.xml file, see Logback documentation. INFO level --> <logger name="com.xebialabs" level="info"/> <!-- Set logging of class HttpClient to DEBUG level --> <logger name="HttpClient" level="debug"/> <!-- Set the logging of all other classes to INFO --> <root level="info"> <!-- Write logging to STDOUT and FILE appenders --> <appender-ref <appender-ref </root> </configuration> Automatically reload the configuration file upon modification Logback can be configured to scan for changes in its configuration file and reconfigure itself accordingly. To enable this feature: - Go to and open XL_RELEASE_SERVER_HOME/conf/logback.xmlin a text editor. - Set the scanattribute of the <configuration>element to true, and optionally, set the scanPeriodattribute to a period of time. Note: By default, the configuration file will be scanned every 60 seconds. Example: <configuration scan="true" scanPeriod="30 seconds" > ... </configuration> For more information, see Logback - auto scan. The audit log Important: The audit log is not available between versions 7.5.0 - 8.5.0 of Release. Release keeps an audit log of each human-initiated event on the server, which complements the auditing provided by the release activity logs (which track activity for each release at a more domain-specific level of granularity). Some of the events that are logged in the audit trail are: - The system is started or stopped - An application is imported - A CI is created, updated, moved, or deleted - A security role is created, updated, or deleted - A task_RELEASE_SERVER_HOME/log/audit.log and is rolled over daily. Enable audit logging You can enable low-level audit logging by changing the log level of the audit logger in XL_RELEASE_SERVER_HOME/conf/logback.xml: <logger name="audit" level="off" additivity="false"> <appender-ref </logger> By default, the log stream is stored in XL_RELEASE_SERVER_HOME/log/audit.log. You can change this location, the file rolling policy, and so on by changing the configuration of the AUDIT appender. You can also pipe the log stream to additional sinks (such as syslog) by configuring additional appenders. Refer to the Logback documentation for details. This is an example of the audit stream in Release pre-7.5.0 with the level of the audit logger set to info: 2014-11-22 11:24:18.764 [audit.system] system - Started 2014-11-22 11:25:18.125 [audit.repository] admin - Created CIs [Configuration/Custom/Configuration1099418] 2014-11-22 11:25:18.274 [audit.repository] admin - CI [Configuration/Custom/Configuration1099418]: <jenkins.Server <title>My Jenkins</title> <url></url> <username>foo</username> <password>{b64}C7JZetqurQo2B8x2V8qUhg==</password> </jenkins.Server> This is an example in Release 8.6.0 and later: 2019-03-07 14:23:28.412 [audit.repository] admin - Created CI git.Repository[Configuration/Custom/Configuration0f924b19069545c9a6d14d9bfccdc5ac] 2019-03-07 14:23:28.415 [audit.repository] admin - CI [Configuration/Custom/Configuration0f924b19069545c9a6d14d9bfccdc5ac]: <git.Repository <title>repo</title> <url>repoUrl</url> <username>tamerlan</username> <password>********</password> <domain>placeholder</domain> </git.Repository> The access log You can use the access log to view all HTTP requests received by Release. Access logging provides the following details: URL, the time taken to process, username, the IP address where the request came from, and the response status code. You can use this logging data to analyze the usage of Release and to troubleshoot. The access log is written to XL_RELEASE_SERVER_HOME/log/access.log.

https://docs.xebialabs.com/v.9.7/release/concept/logging-in-xl-release/

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docs.xebialabs.com

DataKeeper attempts to utilize all of the available network bandwidth. If DataKeeper is sharing the available bandwidth with other applications, you may wish to limit the amount of bandwidth DataKeeper is allowed to use. DataKeeper includes a feature called Bandwidth Throttle that will do this. The feature is enabled via a registry setting. Feedback Thanks for your feedback. Post your comment on this topic.

http://docs.us.sios.com/dkse/8.6.3/en/topic/bandwidth-throttle

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docs.us.sios.com

two kinds of supported transformations: - coalesce the files so that each file is at least a certain size and there will be a maximum of certain number of files. - convert CSV files to Parquet files In Alluxio version 2.3 a maximum of 100 files..file.count.max, 100).option(hive.file.size.min, 2147483648)

https://docs.alluxio.io/os/user/stable/en/core-services/Transformation.html

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docs.alluxio.io

Overview Microsoft recommends that you use one of two basic approaches when you upgrade from SharePoint 2007 to SharePoint 2010: - In-place upgrade. An in-place upgrade is used to upgrade all Microsoft SharePoint sites on the same hardware. See How to Migrate a Bamboo Web Part from SharePoint 2007 to SharePoint 2010 using the In-Place Upgrade Method for details. - Database attach upgrade. A database attach upgrade enables you to move your content to a new farm or new hardware. See How to Migrate a Bamboo Web Part from SharePoint 2007 to SharePoint 2010 using the Database Attach Upgrade Method for details. You can also combine these two types of upgrade in hybrid approaches that reduce downtime during an upgrade. For more information about these approaches, see Determine upgrade approach (SharePoint Server 2010) from Microsoft TechNet. General Migration Restrictions for Bamboo Products - Regardless of the upgrade method you choose, you MUST change existing SharePoint sites to use the new user SharePoint 2010 experience. If you don’t, some features of your Bamboo products will not work as expected. - You need to make sure that you are running the latest available Bamboo product release in your SP2007 environment before migrating. Review information specific to the product(s) you are migrating to learn the minimum product release to migrate. See Bamboo product releases that are supported for migration from SharePoint 2007 to SharePoint 2010 for details. - Bamboo Solutions does not support the migration of Bamboo products deployed on WSSv2/SharePoint 2003 environments to SharePoint 2010 farms. - Bamboo Solutions does not support the downgrade of SharePoint editions when migrating from SharePoint 2007 to SharePoint 2010. For example, Bamboo products are not supported when migrating from MOSS 2007 to SharePoint Foundation 2010. Migration Restrictions for Specific Bamboo Products Before following the instructions for the method you choose, review information specific to the product(s) you are migrating. The In-Place Upgrade method is sometimes easier, but it takes longer. Some Bamboo products do not support the In-Place upgrade method and we recommend a hybrid In-Place Upgrade method as an alternative.

https://docs.bamboosolutions.com/document/before_you_migrate_from_sharepoint_2007_to_sharepoint_2010_read_this/

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docs.bamboosolutions.com

The lab has been privileged to work with the following researchers all around the world. Their affiliation might not be up to date. Rami Albatal (HeyStacks, Ireland) Avishek Anand ( L3S Research Center, Germany) Avi Arampatzis (University of Thrace, Greece) Ioannis Arapakis (Eurecat, Spain) Jaime Arguello (University of North Carolina at Chapel Hill, USA) Leif Azzopardi (Univeristy of Strathclyde, UK) Micheline Beaulieu (University of Sheffield, UK) Roi Blanco (Amazon, Spain) Ivan Cantador (Autonomous University of Madrid, Spain) Lawrence Cavedon (RMIT University, Australia) Long Chen (University of Glasgow, UK) Paul Clough (University of Sheffield, UK) Nicola Ferro (University of Padua, Italy) Gaihua Fu (University of Leeds, UK) Fredric C. Gey (University of California, Berkeley, USA) Cathal Gurrin (Dublin City University, Ireland) Jacek Gwizdka (University of Texas, USA) Matthias Hagen (Bauhaus-Universität Weimar, Germany) Martin Halvey (University of Strathclyde, UK) Jannica Heinström (Oslo Metropolitan University, Norway) Frank Hopfgartner (University of Sheffield, UK) Adam Jatowt (Kyoto University, Japan) Chris Jones (Cardiff University, UK) Joemon Jose (University of Glasgow, UK) Noriko Kando (National Institution of Informatics, Japan) Makoto P Kato (University of Tsukuba, Japan) Tsuneaki Kato (The University of Tokyo, Japan) Hao-Ren Ke (Taiwan Normal University, China) Julia Kiseleva (Microsoft, USA) Kazuaki Kishida (Keio University, Japan) Mounia Lalmas (Yahoo!, UK) Ray R. Larson (University of California, Berkeley, USA) Luis A Leiva (Aalto University, Finland) Dirk Lewandowski (Hamburg University of Applied Sciences, Germany) Pablo Bermejo Lopez (University of Castilla-La Mancha, Spain) Mitsunori Matsushita (Kansai University, Japan) Tetsuya Maeshiro (University of Tsukuba, Japan) Andres Masegosa (The Norwegian University of Science and Technology, Norway) Mamiko Matsubayashi (University of Tsukuba, Japan) Yashar Moshfeghi (University of Glasgow, UK) Shin-ichi Nakayama (University of Tsukuba, Japan) Heather L O'Brien (University of British Columbia, Canada) Antonio Penta (Pompeu Fabra University, Spain) Vivien Petras (Humboldt University of Berlin, Germany) Ross Purves (University of Zurich, Switzerland) Filip Radlinski (Google, UK) Fuji Ren (University of Tokushima, Japan) Reede Ren (University of Surry, UK) Mark Sanderson (RMIT University, Australia) Tetsuya Sakai (Waseda University, Japan) Nicu Sebe (University of Trento, Italy) Chirag Shah (University of Washington, USA) Milad Shokouhi (Microsoft, USA) Tetsuya Shirai (University of Tsukuba, Japan) Vivek K Singh (Rutgers University, USA) Damiano Spina (RMIT University, Australia) Benno Stein (Bauhaus-Universität Weimar, Germany) Paul Thomas (Microsoft Research, Australia) Johanne R Trippas (University of Melbourne, Australia) Jana Urban (Google, Switzerland) Marc van Kreveld (Utrecht University, The Netherlands) C. J. "Keith" van Rijsbergen (University of Glasgow, UK) Wim Vanderbauwhede (University of Glasgow, UK) Robert Villa (University of Edinburgh, UK) Shuhei Yamamoto (NTT Service Evolution Laboratories, Japan) Takehiro Yamamoto (Hyogo Prefecture University, Japan) Xiao Yang (European Bioinformatics Institute, UK) Masatoshi Yoshikawa (Kyoto University, Japan) Fajie Yuan (Tencent, China)

https://docs.joholab.com/lab/v/en/collaboration

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docs.joholab.com

Wizards (and we're not talking Harry Potter) I

https://docs.microsoft.com/en-us/archive/blogs/infopath/wizards-and-were-not-talking-harry-potter

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docs.microsoft.com

[−][src]Crate loom Loom is a tool for testing concurrent programs. Background Testing concurrent programs is challenging. The Rust memory model is relaxed and permits a large number of possible behaviors. Loom provides a way to deterministically explore the various possible execution permutations. Consider a simple example: use std::sync::Arc; use std::sync::atomic::AtomicUsize; use std::sync::atomic::Ordering::SeqCst; use std::thread; #[test] fn test_concurrent_logic() { let v1 = Arc::new(AtomicUsize::new(0)); let v2 = v1.clone(); thread::spawn(move || { v1.store(1, SeqCst); }); assert_eq!(0, v2.load(SeqCst)); } This program is obviously incorrect, yet the test can easily pass. The problem is compounded when Rust's relaxed memory model is considered. Historically, the strategy for testing concurrent code has been to run tests in loops and hope that an execution fails. Doing this is not reliable, and, in the event an iteration should fail, debugging the cause is exceedingly difficult. Solution Loom fixes the problem by controlling the scheduling of each thread. Loom also simulates the Rust memory model such that it attempts all possible valid behaviors. For example, an atomic load may return an old value instead of the newest. The above example can be rewritten as: use loom::sync::atomic::AtomicUsize; use loom::thread; use std::sync::Arc; use std::sync::atomic::Ordering::SeqCst; #[test] fn test_concurrent_logic() { loom::model(|| { let v1 = Arc::new(AtomicUsize::new(0)); let v2 = v1.clone(); thread::spawn(move || { v1.store(1, SeqCst); }); assert_eq!(0, v2.load(SeqCst)); }); } Loom will run the closure many times, each time with a different thread scheduling The test is guaranteed to fail. Writing tests Test cases using loom must be fully determinstic. All sources of non-determism must be via loom types. This allows loom to validate the test case and control the scheduling. Tests must use the loom synchronization types, such as Atomic*, Mutex, RwLock, Condvar, thread::spawn, etc. When writing a concurrent program, the std types should be used when running the program and the loom types when running the test. One way to do this is via cfg flags. It is important to not include other sources of non-determism in tests, such as random number generators or system calls. Yielding Some concurrent algorithms assume a fair scheduler. For example, a spin lock assumes that, at some point, another thread will make enough progress for the lock to become available. This presents a challenge for loom as the scheduler is not fair. In such cases, loops must include calls to yield_now. This tells loom that another thread needs to be scheduled in order for the current one to make progress. Dealing with combinatorial explosion The number of possible threads scheduling has factorial growth as the program complexity increases. Loom deals with this by reducing the state space. Equivalent executions are elimited. For example, if two threads read from the same atomic variable, loom does not attempt another execution given that the order in which two threads read from the same atomic cannot impact the execution. Additional reading For more usage details, see the README

https://docs.rs/loom/0.3.5/loom/

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docs.rs

output Values: json (default), jsontop, text Description: Specifies transcript delivery format. The following outputs are supported: Note Earlier releases included an outer list (enclosing square brackets) in the JSON output, which has since been removed. The structure of the inner JSON dictionary has not changed, and is now returned directly without the outer list. To produce JSON output in the list format that was previously used by the API, refer to the output parameters below.

https://docs.vocitec.com/en/output-58460.html

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docs.vocitec.com

Start Release To start the Release server, open a command prompt or terminal, point to the XL_RELEASE_SERVER_HOME/bin directory, and execute the appropriate command: Start Release in the background Important: To run Release in the background, it must be configured to start without user interaction. The server should not require a password for the encryption key that is used to protect passwords in the repository. Alternatively, you can store the password in the XL_RELEASE_SERVER_HOME/conf/xl-release-server.conf file, by adding following: repository.keystore.password=MY_PASSWORD. Release will encrypt the password when you start the server. To start the Release server as a background process: - Open a command prompt or terminal and point to the XL_RELEASE_SERVER_HOME/bindirectory. - Execute the appropriate command: Important: If you close the command prompt in Microsoft Windows, Release will stop. Server options Specify the options you want to use when starting the Release server in the XL_RELEASE_SERVER_OPTS environment variable. The following server options are available: To view the available server options when starting the Release server, add the -h flag to the start command.

https://docs.xebialabs.com/v.9.7/release/how-to/start-xl-release/

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docs.xebialabs.com

MegaPi Pro Overview MegaPi Pro is an ATmega2560-based micro control board. It is fully compatible with Arduino programming. It provides powerful programming functions and a maximum output power of 120 W. With four port jacks, one 4-way DC motor expansion interface, one RJ25 expansion board interface, and one smart servo interface, MegaPi Pro is highly expansible. Its strong expansion ability enables it to meet the requirements of education, competition, entertainment, etc. MegaPi Pro can be easily installed on Raspberry Pi, and connected through serial ports. With the corresponding programs, Raspberry Pi can be used to control electronic modules, such as motors and sensors. Technical specifications - Microcontroller: ATMEGA2560-16AU - Input voltage: DC 6–12 V - Operating voltage: DC 5 V - Serial port: 3 - I²C interface: 1 - SPI interface: 1 - Analog input port: 16 - DC Current per I/O Pin: 20 mA - Flash memory: 256 KB - SRAM: 8 KB - EEPROM: 4 KB - Clock speed: 16 MHz - Dimensions: 87 mm x 63 mm (width × height) Features - Four motor driver interfaces for adding encoder motor driver and stepper motor driver, and thus drving DC motors, encoder motors, and stepper motors - One wireless communication interface for adding modules, such as Bluetooth module or 2.4G module - One smart servo interface that enables the expansion of four DC motors - One RJ25 expansion board interface that enables the expansion of eight RJ25 interfaces - Three M4 installation holes that are compatible with Raspberry Pi - Overcurrent protection - Fully compatible with Arduino - Uses RJ25 interfaces for communication - Supports Arduino programming, equipped with the professional Makeblock library functions to facilitate programming - Supports graphical programming on mBlock and can be used by users of all ages Interface function - Red pin: firmware burning - Red socket: power output or motor output - Yellow pin, black pin, black socket: input/output - White pin: smart management system interface - Green interface: motor interface - Yellow interface: four-way motor drive power interface - White interface: smart servo interface

http://docs.makeblock.com/diy-platform/en/electronic-modules/main-control-boards/megapi-pro.html

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[array(['../../../en/electronic-modules/main-control-boards/images/megapi-pro-1.png', None], dtype=object) array(['../../../en/electronic-modules/main-control-boards/images/megapi-pro-2.png', None], dtype=object) ]

docs.makeblock.com

) Alternatives This section enumerates other Emacs packages that provide a Clojure programming environment for Emacs..

https://docs.cider.mx/cider/0.24/additional_packages.html

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docs.cider.mx

Set up two-step verification If your Administrator has enabled two-step verification, you can add an extra layer of protection beyond your username and password. Complete the following steps to enable the feature. Go to User menu > Your account > Two-step verification. Use the toggle to enable two-step verification. Use the radio buttons to select your preferred notification method. You can access verification codes through your Contrast-associated email address or the Google Authenticator mobile application, which is available on the following devices: Android iPhone Blackberry If you run into issues using either method, use the backup codes provided. Verification codes If you choose to receive your verification codes by email, Contrast sends you a verification code to enter on the configuration screen. If you select Google Authenticator, Contrast provides a QR code with further instructions. You can scan the QR code, enter the code manually or use the provided dropdown to select the device type. Use the Google Authenticator application to obtain a verification code and validate your device. Before completing two-step verification setup, you can download a set of backup codes in the form of a .txt file, which allows you to login if you encounter an error or get locked out of your account. You must download and save these codes in a secure location. Reconfigure notification methods If you want to change the way you receive verification codes, you can reconfigure notification settings in the Two-Step Verification tab. Once you change your selection, Contrast automatically issues a new set of backup codes. It is not necessary to save your changes. To learn more about Administrator settings, see Two-step verification. For some helpful tips on verification codes, go to the troubleshooting article.

https://docs.contrastsecurity.com/en/user-two-step-verification.html

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docs.contrastsecurity.com

The forma.lms project is led and mantained by the forma.association Scope The forma.association was founded on January 2017, with the purpose of: - PROTECTING the product and the brand, being a guarantee of the project continuity for the community of adopters - DIRECTING the software development, the corrective and evolutive maintenance of forma.lms, and all the activities aimed at the product growth and improvement - PROMOTING forma.lms through marketing activities, the participation or organization of exhibitions, webinars, or similar events. - COORDINATING and animating: Standard Fees Association memberships are valid for 1 year, from January 1 to December 31 Special Discounts Special Discounts will be offered during special initiatives (i.e. crowdfunding) or for outstanding contributions, at discretion of the association board

https://docs.formalms.org/association.html

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[array(['/jfiles/images/layout/association_400px.png', 'association 400px'], dtype=object) ]

docs.formalms.org

Service-Oriented Modeling for Connected Systems – Part 1 Due my intense travel I completely forgot to blog that the first part of Arvindra and my paper on “Service-Oriented Modeling” got published on the Architecture Journal… In this paper we introduce a three part model that helps you to map business capabilities to service oriented implementation artifacts by using a so called service model. The more I work with this model the more I realize how important this separation of concerns really is. Especially defining the conceptional service model allows you to decouple contracts from technology restrictions. If you’re interested in that topic and plan to attend TechEd Israel or TechEd US, my session “Architecting for a Service-Oriented World” might be of interest for you.

https://docs.microsoft.com/en-us/archive/blogs/beatsch/service-oriented-modeling-for-connected-systems-part-1

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docs.microsoft.com

Access cloud data in a notebook. Doing interesting work in a Jupyter notebook requires data. Data, indeed, is the lifeblood of notebooks. You can certainly import data files into a project, even using commands like curl from within a notebook to download a file directly. It's likely, however, that you need to work with much more extensive data that's available from non-file sources such as REST APIs, relational databases, and cloud storage such as Azure tables. This article briefly outlines these different options. Because data access is best seen in action, you can find runnable code in the Azure Notebooks Samples - Access your data. REST APIs Generally speaking, the vast amount of data available from the Internet is accessed not through files, but through REST APIs. Fortunately, because a notebook cell can contain whatever code you like, you can use code to send requests and receive JSON data. You can then convert that JSON into whatever format you want to use, such as a pandas dataframe. To access data using a REST API, use the same code in a notebook's code cells that you use in any other application. The general structure using the requests library is as follows: import pandas import requests # New York City taxi data for 2014 data_url = '' # General data request; include other API keys and credentials as needed in the data argument response = requests.get(data_url, data={"limit": "20"}) if response.status_code == 200: dataframe_rest2 = pandas.DataFrame.from_records(response.json()) print(dataframe_rest2) Azure SQL Database and SQL Managed Instance You can access databases in SQL Database or SQL Managed Instance with the assistance of the pyodbc or pymssql libraries. Use Python to query an Azure SQL database gives you instructions on creating a database in SQL Database containing AdventureWorks data, and shows how to query that data. The same code is shown in the sample notebook for this article. Azure Storage Azure Storage provides several different types of non-relational storage, depending on the type of data you have and how you need to access it: - Table Storage: provides low-cost, high-volume storage for tabular data, such as collected sensor logs, diagnostic logs, and so on. - Blob storage: provides file-like storage for any type of data. The sample notebook demonstrates working with both tables and blobs, including how to use a shared access signature to allow read-only access to blobs. Azure Cosmos DB Azure Cosmos DB provides a fully indexed NoSQL store for JSON documents). The following articles provide a number of different ways to work with Cosmos DB from Python: - Build a SQL API app with Python - Build a Flask app with the Azure Cosmos DB's API for MongoDB - Create a graph database using Python and the Gremlin API - Build a Cassandra app with Python and Azure Cosmos DB - Build a Table API app with Python and Azure Cosmos DB When working with Cosmos DB, you can use the azure-cosmosdb-table library. Other Azure databases Azure provides a number of other database types that you can use. The articles below provide guidance for accessing those databases from Python: - Azure Database for PostgreSQL: Use Python to connect and query data - Quickstart: Use Azure Redis Cache with Python - Azure Database for MySQL: Use Python to connect and query data - Azure Data Factory

https://docs.microsoft.com/en-us/azure/notebooks/access-data-resources-jupyter-notebooks

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docs.microsoft.com

Phalcon is compiled into a C extension loaded on your web server. Because of that, bugs lead to segmentation faults, causing Phalcon to crash some of your web server processes. For debugging these segmentation faults a stacktrace is required. Creating a stack trace requires a special build of php and some steps need to be done to generate a trace that allows the Phalcon team to debug this behavior. Please follow this guide to understand how to generate the backtrace.

https://docs.phalcon.io/4.0/pt-br/generating-backtrace

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docs.phalcon.io

[−][src]Module sequoia_openpgp:: packet:: key Key-related functionality. Data Types The main data type is the Key enum. This enum abstracts away the differences between the key formats (the deprecated version 3, the current version 4, and the proposed version 5 formats). Nevertheless, some functionality remains format specific. For instance, the Key enum doesn't provide a mechanism to generate keys. This functionality depends on the format. This version of Sequoia only supports version 4 keys ( Key4). However, future versions may include limited support for version 3 keys to allow working with archived messages, and we intend to add support for version 5 keys once the new version of the specification has been finalized. OpenPGP specifies four different types of keys: public keys, secret keys, public subkeys, and secret subkeys. These are all represented by the Key enum and the Key4 struct using marker types. We use marker types rather than an enum, to better exploit the type checking. For instance, type-specific methods like Key::secret are only exposed for those types that actually support them. See the documentation for Key for an explanation of how the markers work. The SecretKeyMaterial data type allows working with secret key material directly. This enum has two variants: Unencrypted, and Encrypted. It is not normally necessary to use this data structure directly. The primary functionality that is of interest to most users is decrypting secret key material. This is usually more conveniently done using Key::decrypt_secret. Key Creation Use Key4::generate_rsa or Key4::generate_ecc to create a new key. Existing key material can be turned into an OpenPGP key using Key4::import_public_cv25519, Key4::import_public_ed25519, Key4::import_public_rsa, Key4::import_secret_cv25519, Key4::import_secret_ed25519, and Key4::import_secret_rsa. Whether you create a new key or import existing key material, you still need to create a binding signature, and, for signing keys, a back signature for the key to be usable. In-Memory Protection of Secret Key Material Whether the secret key material is protected on disk or not, Sequoia encrypts unencrypted secret key material ( Unencrypted) while it is memory. This helps protect against heartbleed-style attacks where a buffer over-read allows an attacker to read from the process's address space. This protection is less important for Rust programs, which are memory safe. However, it is essential when Sequoia is used via its FFI. See crypto::mem::Encrypted for details.

https://docs.sequoia-pgp.org/sequoia_openpgp/packet/key/index.html

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docs.sequoia-pgp.org

Remote completion plugin This topic describes how to install and configure the Release Remote completion plugin. For more information, see Using the Remote completion plugin. Install the Remote completion plugin To install the Remote completion plugin: - In the top navigation bar, click Plugins. - Click Browse. - Locate the Release Remote completion plugin, and click Install. - Restart Release to enable the newly installed plugin. Server configuration SMTP server Release sends remote completion requests to users of the system by email. For more details on how to set up an SMTP server, see Configure SMTP server. Important: The SMTP server page is only available to users with the Admin global permission. To configure the email server that is used to send these requests: - In the top navigation bar, click Settings. - Click SMTP server. - Fill in the required fields. - Click Save. IMAP server Release receives remote completion emails sent by users that want to complete or fail a remote completion task. To configure the email server that is used to receive the remote completion emails: Important: IMAP server settings are only available to users with the Admin global permission. Important: Release supports the use of one 1 IMAP server only. Important: Set up a new email account specifically for receiving remote completion emails. All emails are deleted from the inbox after they are processed by Release, including unrecognized and existing emails. - In the top navigation bar, click Settings. - Click Shared configuration. - Locate IMAP server, and click . Fill in the required fields. The following is a list of fields and descriptions: - IMAP server host: the internet address of the mail server. - IMAP server port: port where the server is listening on. - Use TLS: used to secure the connection. - IMAP from address: the email address of the IMAP server account; requests to remotely complete or fail a task are received from this email account. - IMAP server login ID. - IMAP server login password. - Enable whitelisting: when enable whitelisting is checked, only emails to and from whitelisted domains are processed for remote completion. - Domain whitelist: used for adding whitelisted domains. - Secret for generating email signatures: generate an email signature that verifies the integrity of a received remote completion email. Notice that changing the secret will invalidate all previously send completion request emails. - Click Save. Settings in xl-release.conf Advanced configuration settings can be specified inside the XL_RELEASE_SERVER_HOME/conf/xl-release.conf file. The advanced configuration is used by the email fetcher which processes incoming remote completion emails. xl { remote-completion { sync-interval = 30 seconds startup-delay = 30 seconds } } sync-interval: specifies the interval time for the email fetcher. The default value is 30 seconds. startup-delay: specifies the initial startup delay of the mail fetcher. The default value is 30 seconds. Mailbox auditing Mailbox auditing can be enabled to log mailbox access by mailbox owners, delegates, and administrators. Contact your mailbox provider to set up mailbox auditing. Troubleshooting Release server The Release server provides a mechanism for logging the application. By default, only the basic remote completion process is logged. To enable detailed logging, you can add the following line into the XL_RELEASE_SERVER_HOME/conf/logback.xml file: <logger name="com.xebialabs.xlrelease.plugins.remotecompletion" level="debug" /> Use the log level trace for more detailed logging. JavaMail debugging To turn on session debugging, add the following system property to the $JAVACMD inside the shell script that runs the Release server, located in bin\run.sh: -Dmail.debug=true This property enables printing of debugging information to the console, including a protocol trace. Security recommendations The Release remote completion feature uses emails sent by users to complete or fail any task. These are the risks associated with this feature: Spamming and flooding attacks Release processes each incoming email for the configured mailbox. To avoid receiving thousands of emails that can flood your mailbox, you can enable whitelisting. Only emails sent to and received from whitelisted domains are processed for remote completion. Use content filters, enable DNS-based blacklists (DNSBL), enable Spam URI Real-time Block Lists (SURBL), and maintain the local blacklists of IP addresses of spam senders. Configure the email relay parameter on the email server to prevent open relay. Data leakage Release sends and receives email from a task owner to take action on any task. To prevent any data leakage during this process, you must encrypt IMAP and SMTP protocols with SSL/TLS and set up SMTP authentication to control user access. DoS and DDoS attack To avoid DoS and DDoS attacks, limit the number of connection and authentication errors with your SMTP server. The majority of the abusive email messages carry fake sender addresses. Activate Sender Policy Framework (SPF) to prevent spoofed sources. The SPF check ensures that the sending Message Transfer Agent (MTA) is allowed to send emails on behalf of the senders domain name. You must also activate Reverse DNS to block fake senders. After the Reverse DNS Lookup is activated, your SMTP verifies that the senders IP address matches both the host and domain names that were submitted by the SMTP client in the EHLO/HELO command. Release notes Release Remote Completion plugin 9.5.0 Bug Fixes - [XLINT-808] - Allow email recipient configuration with case-sensitive email addresses - [XLINT-818] - Fix Release crashing on remote completion task - [XLINT-959] - Fix whitelist with case-sensitive email addresses

https://docs.xebialabs.com/v.9.7/release/how-to/configure-the-remote-completion-plugin/

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docs.xebialabs.com

The following configuration options are available for PM Central Resource Reports. Option 1: Generate resource reports using the System Account: By default, all resource reports are security trimmed so users with access to the Resource Center will only see resource information pertaining to projects they have permissions to access. PM Central provides the option of displaying reports generated for the System Account, providing a comprehensive, portfolio-wide, reports to all users with access to the Report Center. The Report Settings feature was added in PM Central 4.3 Option 2. Configure the reports to “Run Now” This option removes the wait time associated with the default report generation method by referencing content from List Rollup rather than the Report Information list. Run Now is a configuration option associated with the following reports on the Portfolio and Department sites: - All Allocations - By Resource - By Project - By Project Department - Allocation by Manager - Resource Availability - By Department - Risk Chart (accessed from the Risks tab) Important: This option can result in page time out errors.

https://docs.bamboosolutions.com/document/configuring_resource_reports/

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docs.bamboosolutions.com

- - - - - - - event age for events You can set the event age option to specify the time interval (in seconds). Citrix ADM monitors the appliances until the set duration and generates an event only if the event age exceeds the set duration. Note: The minimum value for the event age is 60 seconds. If you keep the Event Age field blank, the event rule is applied immediately after the event is occurred. For example, consider that you want to manage various ADC appliances and get notified by email when any of your virtual servers goes down for 60 seconds or longer. You can create an event rule with the necessary filters and set the rule’s event age to 60 seconds. Then, whenever a virtual server remains down for 60 or more seconds, you will receive an email notification with details such as entity name, status change, and time. To set event age in Citrix ADM: In Citrix ADM, navigate to Networks > Events > Rules, and click Add. On the Create Rule page, set the rule parameters. Specify the event age in seconds. Ensure to set all the co-related traps in the Category section and also set the respective severity in the Severity section when you set event age. In the above example, select the entityup, entitydown, and entityofs traps. Set event age for.

https://docs.citrix.com/en-us/citrix-application-delivery-management-software/12-1/networks/events/how-to-set-event-age.html

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docs.citrix.com

. Applying license from file The code below will explain how to apply a product license. // initialize License License lic = new License(); // apply license lic.setLicense("D:\\GroupDocs.Metadata.lic"); Applying license from stream The following example shows how to load a license from a stream. try (InputStream stream = new FileInputStream("D:\\GroupDocs.Metadata.lic")) { License license = new License(); license.setLicense(stream); } Applying Metered license Here are the simple steps to use the Metered class. - Create an instance of Meteredclass. -class (Since version 19.5). - It will return the credit that you have consumed so far. Following is the sample code demonstrating how to use Metered class. // initialize Metered API Metered metered = new Metered(); // set-up credentials metered.setMeteredKey("publicKey", "privateKey");

https://docs.groupdocs.com/metadata/java/evaluation-limitations-and-licensing/

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docs.groupdocs.com

Interfaces To maximize component reuse, it's vital components be designed for reuse. Reusability partly depends on portability, which the Legato framework provides, but it also depends largely on interoperability. Reusable components must be designed for interoperability with as many other things as possible, including things that don't exist - yet! Interface design is the most important factor to make something interoperable. Standardized data types and interaction methods must be used, and the simpler the interfaces, the better. Legato embraces the philosophy that developers should always be allowed to choose the implementation language best suited to the task at hand, regardless of the components used. Some developers are better at programming in certain languages, and some languages are better suited to solving certain problems. That's why Legato provides developers with an easy way to interface components with each other, even if they have been written in different languages. A common example of a programming-language-independent interface is a networking protocol. But networking protocols come with pitfalls and overhead, things like endian issues, race conditions, protocol stack framing overheads and poor processing time. Networking protocols also tend to require a lot of hand coding specific to the protocol implementation. Legato supports networking, if that's what's needed, but it also has tools to implement much lighter-weight, language-independent communication between components that exist on the same host device or even running within the same process. Inter-process Communication Function Call APIs Virtually all programmers are familiar with function calls. While Legato allows normal libraries to use their specific programming-language function call interfaces, Legato also supports language-independent function call interfaces.. See API Files for more information.

https://docs.legato.io/18_09/conceptsInterfaces.html

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docs.legato.io

Free Windows Azure Webinars for ISVs If you are an ISV or a partner you know that you had better be thinking about how your product of service will continue to exist and be successful in the new cloud paradigm. The Windows Azure Team Blog just listed this free three part webinar series for ISVs that covers the benefits, implications of - and best practices for - adopting Windows Azure. They include: - The Business Case for Azure (Part 1) - January 12, 2011 - Understanding Implications of the Cloud (Part 2) - January 19, 2011 - Easing (Leaping) Into the Cloud (Part 3) - January 29, 2011 See this blog post for more information and how to register. Bill Zack

https://docs.microsoft.com/en-us/archive/blogs/ignitionshowcase/free-windows-azure-webinars-for-isvs

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docs.microsoft.com

Privileged, and who receives them. Sender email address and subject line Emails sent from Privileged Identity Management for both Azure AD and Azure resource roles have the following sender email address: - Display name: Microsoft Azure These emails include a PIM prefix in the subject line. Here's an example: - PIM: Alain Charon was permanently assigned the Backup Reader role Notifications for Azure AD roles Privileged Identity Management sends emails when the following events occur for Azure AD roles: - When a privileged role activation is pending approval - When a privileged role activation request is completed - When Azure AD Privileged Identity Management is enabled Who receives these emails for Azure AD roles depends on your role, the event, and the notifications setting: * If the Notifications setting is set to Enable. The following shows an example email that is sent when a user activates an Azure AD role for the fictional Contoso organization. Weekly Privileged Identity Management digest email for Azure AD roles A weekly Privileged Identity Management summary email for Azure AD roles is sent to Privileged Role Administrators, Security Administrators, and Global Administrators that have enabled Privileged Identity Management. This weekly email provides a snapshot of Privileged Identity Management activities for the week as well as privileged role assignments. It is only available for Azure AD organizations on the public cloud. Here's an example email: The email includes four tiles: The Overview of your top roles section lists the top five roles in your organization based on total number of permanent and eligible administrators for each role. The Take action link opens the PIM wizard where you can convert permanent administrators to eligible administrators in batches. When users activates their role and the role setting requires approval, approvers will receive three emails for each approval: - Request to approve or deny the user's activation request (sent by the request approval engine) - The user's request is approved (sent by the request approval engine) - The user's role is activated (sent by Privileged Identity Management) The first two emails sent by the request approval engine can be delayed. Currently, 90% of emails take three to ten minutes, but for 1% customers it can be much longer, up to fifteen minutes. If an approval request is approved in the Azure portal before the first email is sent, the first email will no longer be triggered and other approvers won't be notified by email of the approval request. It might appear as if the they didn't get an email but it's the expected behavior. PIM emails for Azure resource roles Privileged Identity Management sends emails to Owners and User Access Administrators when the following events occur for Azure resource roles: - When a role assignment is pending approval - When a role is assigned - When a role is soon to expire - When a role is eligible to extend - When a role is being renewed by an end user - When a role activation request is completed Privileged Identity Management sends emails to end users when the following events occur for Azure resource roles: - When a role is assigned to the user - When a user's role is expired - When a user's role is extended - When a user's role activation request is completed The following shows an example email that is sent when a user is assigned an Azure resource role for the fictional Contoso organization.

https://docs.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-email-notifications

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[array(['media/pim-email-notifications/email-directory-new.png', 'New Privileged Identity Management email for Azure AD roles'], dtype=object) array(['media/pim-email-notifications/email-directory-weekly.png', 'Weekly Privileged Identity Management digest email for Azure AD roles'], dtype=object) array(['media/pim-email-notifications/email-resources-new.png', 'New Privileged Identity Management email for Azure resource roles'], dtype=object) ]

docs.microsoft.com

What is a content delivery network on Azure? A content delivery network (CDN) is a distributed network of servers that can efficiently deliver web content to users. CDNs store cached content on edge servers in point-of-presence (POP) locations that are close to end users, to minimize latency. Azure Content Delivery Network (CDN) offers developers a global solution for rapidly delivering high-bandwidth content to users by caching their content at strategically placed physical nodes across the world. Azure CDN can also accelerate dynamic content, which cannot be cached, by leveraging various network optimizations using CDN POPs. For example, route optimization to bypass Border Gateway Protocol (BGP).. For a list of current CDN node locations, see Azure CDN POP locations. How it works A user (Alice) requests a file (also called an asset) by using a URL with a special domain name, such as <endpoint name>..

https://docs.microsoft.com/en-us/azure/cdn/cdn-overview?WT.mc_id=partlycloudy-blog-masoucou

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[array(['media/cdn-overview/cdn-overview.png', 'CDN Overview'], dtype=object) ]

docs.microsoft.com

This manual describes the Open Systems Pharmacology Suite. It includes a technical description of each software element with examples and references for further reading. The aim of the manual is to assist users in effectively developing PBPK models. The handbook is divided into the following parts: "Mechanistic Modeling of Pharmacokinetics and Dynamics" provides a brief general introduction to the science of computational systems biology with a strong focus on mechanistic modeling of pharmacokinetics and –dynamics. Go to: Mechanistic Modeling of Pharmacokinetics and Dynamics "Open Systems Pharmacology Suite" provides a brief overview of our software platform, its scope, and puts it into context with the science. Go to: Open Systems Pharmacology Suite A technical description of the different software elements is presented starting with PK-Sim® focusing on physiologically-based pharmacokinetics in "Working with PK-Sim®". Go to: Working with PK-Sim® MoBi® focusing on model customization and extension as well as on pharmacodynamics in "Working with MoBi®". Go to: Working with MoBi® Tools shared between PK-Sim® and MoBi® and some workflow examples are presented in "Shared Tools and Example Workflows". Go to: Shared Tools and Example Workflows The interfaces to the common computing environment R is described in "R Toolbox".

https://docs.open-systems-pharmacology.org/

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docs.open-systems-pharmacology.org

Overview: Some of the things you need to know before you begin installing out Pocket Core CLI and configure a Pocket Node or Pocket Validator Node, you must meet the following prerequisites in order to continue: - Have basic knowledge of Linux/Mac OS - Basic Web architecture - Static IP address or domain name - SSL cert (self-signed not recommended) - Basic network principals - A process manager: - e.g: Systemd - Knowledge on implementing a reverse proxy using(but not limited to) one of the following: - Apache - Envoy - Ngnix - Basic knowledge with File descriptors Hardware Requirements: The base node hardware requirements are: CPU: 2 CPU’s (or vCPU’s) Memory: 4 GB RAM* Disk: Blockchain is expected to grow 154 GB a year given our 15 minutes(4 MB blocks) Note The RAM requirement could vary, depending on network load and relays processed, we will be releasing more details on this process at a later date. Next Step: Learn more about each node in our node breakdown and our node reference guide to understand how the nodes work and interact on the protocol. Updated 8 days ago

https://docs.pokt.network/docs/before-you-dive-in

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docs.pokt.network

Plugin format Rudder has a specific package format for plugins. You can manage Rudder packages with the rudder-pkg command. This is the documentation of how they are created. File description A Rudder package file ends with the .rpkg extension. A Rudder package file is an archive file and can be managed with the 'ar' command. The archive contains: A metadata file in JSON format named medatata A tarball file in txz format name scripts.txz that contains package setup utility scripts One or more tarball files in txz format that contain the package files The metadata file is a JSON file and is named 'metadata': { # the only currently supported type in "plugin" (mandatory) "type": "plugin", # the package name must consist of ascii characters without whitespace (mandatory) "name": "myplugin", # the package version has the form "rudder_major-version_major.version_minor" for a plugin (mandatory) "version": "4.1-1.0", # these are is purely informative (optional) "build-date": "2017-02-22T13:58:23Z", "build-commit": "34aea1077f34e5abdaf88eb3455352aa4559ba8b", # the list of jar files to enable if this is a webapp plugin (optional) "jar-files": [ "test.jar" ], # the list of packages or other plugins that this package depends on (optional) # this is currently only informative "depends": { # dependency on a specific binary that must be in the PATH "binary": [ "zip" ] # dependencies on dpkg based systems "dpkg": [ "apache2" ], "rpm": [ ], # dependency specific to debian-8 "debian-8": [ ], "sles-11": [ ], # rudder dependency, ie this is a Rudder format package "rudder": [ "new-plugin" ] }, # the plugin content (mandatory) "content": { # this will put the content of the extracted files.txz into /opt/rudder/share "files.txz": "/opt/rudder/share", "var_rudder.txz": "/var/rudder" } } To see a package metadata file use: ar p package.rpkg medatada The scripts.txz is a tarball that can contain zero or more executable files named: preinstthat will be run before installing the package files postinstthat will be run after installing the package files prermthat will be run before removing the package files postrmthat will be run after removing the package files preinst and postinst take one parameter that can be 'install' or 'upgrade'. The value 'upgrade' is used when a previous version of the package is already installed. To create the scripts.txz file use: tar cvfJ scripts.txz preinst postinst prerm postrm To create a Rudder package file use the ar command: ar r mypackage-4.1-3.0.rpkg medatada scripts.txz files.txz Note that ar r inserts or replaces files so you can create your package with incremental inserts. To extract files, use ar x instead. ← Variables Security considerations →

https://docs.rudder.io/reference/6.1/reference/plugin_format.html

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docs.rudder.io

. following image shows the results of the search. -. Your dashboard should look like the following image.: - At the top of the dashboard click Edit. - In the VIP Client Purchases: 8.0.0, 8.0.1, 8.0.2, 8.0.3, 8.0.4, 8.0.5 Feedback submitted, thanks!

https://docs.splunk.com/Documentation/Splunk/8.0.4/SearchTutorial/Addreportstodashboard

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[array(['/skins/OxfordComma/images/acrobat-logo.png', None], dtype=object)]

docs.splunk.com

DC Motor-25 6V Description DC motors are the most commonly used motors in Makeblock Platform. With Makeblock DC Motor-25 Brackets, they may be easy to connect to Makeblock structural components. Specification Size Chart (mm) Demo It can be connected with Makeblock Me Orion or Makeblock Me Dual Motor Driver V1 by adding the plug 3.96-2P on the stripped-end to control the motors.

http://docs.makeblock.com/diy-platform/en/electronic-modules/motors/dc-motor-25-6v.html

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[array(['images/dc-motor-25-6v_微信截图_20160128160459.png', '微信截图_20160128160459'], dtype=object) array(['images/dc-motor-25-6v_微信截图_20160128160649.png', '微信截图_20160128160649'], dtype=object) array(['images/dc-motor-25-6v_微信截图_20160128160612.png', '微信截图_20160128160612'], dtype=object) array(['images/dc-motor-25-6v_微信截图_20160128160724.png', '微信截图_20160128160724'], dtype=object) array(['images/dc-motor-25-6v_微信截图_20160128160812.png', '微信截图_20160128160812'], dtype=object) ]

docs.makeblock.com

DKHealthCheck.exe, found in the Note: DKHEALTHCHECK output is captured by DKSupport automatically and does not need to be run separately if you are already running DKSupport. You can run this tool by right clicking the DataKeeper Notification Icon and then clicking on ‘Launch Health Check’ or by following the below procedure. Open a command prompt - Type cd %extmirrbase% - You will now be placed in the DataKeeper directory or c:\Program Files (x86) \SIOS\DataKeeper - From the aforementioned directory type cd DKTools - From within the DKTools directory, execute the following command DKHealthCheck.exe The results of the tool can be copied and pasted from the command prompt and emailed to [email protected]. Alternatively, you may direct the output to a file, by running this command inside of the DKTools directory. - DKHealthCheck.exe > HealthCheck.txt This file can then be attached and sent as part of an email. Note: This command may take some time to execute. Post your comment on this topic.

http://docs.us.sios.com/dkse/8.6.3/en/topic/dkhealthcheck

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docs.us.sios.com

We introduced an experimental feature called Keen Flows. All steps of the Keen Flow launch simultaneously as the flow starts and continue running until the flow’s status changes to sleeping. We introduced the UI’s option for defining the CRON expression to schedule Flow executions. This functionality is available under the Settings tab on the Designer page. We made the Node.js SDK for proper RabbitMQ’s disconnection. In case, one of the RabbitMQ’s instances fails or reports errors, the Node.js process terminates immediately and then restarts by the Platform’s orchestrator. Thus the process can reconnect to the already running RabbitMQ’s instance. The workspace_id and workspace_role were added as optional attributes to the POST /v2/contracts/:id/invites endpoint. In case the workspace_id has already been provided, then the workspace_role will be required. Previously it was possible to delete the Credential for any Component corrupting the integration Flows. Now you can’t delete any of the Credentials, while it is used in at least one Integration Flow. As a owner of the Contract you can now retrieve any details of the Workspace you are member of using the /v2/workspaces/:id API endpoint request. Updated the error messages on the password recovery page. Expression tooltip in the mapper UI is now flashing when hovered with the mouse.

https://docs.elastic.io/releases/2018Q4.html

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docs.elastic.io

The list below contains information relating to the most common Active Directory attributes. Not all attributes are appropriate for use with SecureAuth. More Information related to syntax, ranges, Global catalog replication, etc for these and other AD Attributes can be found at here Friendly Name: This is the name shown in Active Directory Users and Computers. Attribute Name: This is the Active Directory attribute name. Example: This column shows example usage or notes. General Tab Address Tab Group Tab Account Tab Telephones Tab Organization Tab Exchange Tab Exchange Attributes Tab

https://docs.secureauth.com/display/KBA/Active+Directory+Attributes+List

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docs.secureauth.com

LagoInitFile Specification¶ Note: this is work under progress, if you’d like to contribute to the documentation, please feel free to open a PR. In the meanwhile, we recommend looking at LagoInitFile examples available at: Each environment in Lago is created from an init file, the recommended format is YAML, although at the moment of writing JSON is still supported. By default, Lago will look for a file named LagoInitFile in the directory it was triggered. However you can pick a different file by running: $ lago init <FILENAME> Sections¶ The init file is composed out of two major sections: domains, and nets. Each virtual machine you wish to create needs to be under the domains section. nets will define the network topology, and when you add a nic to a domain, it must be defined in the nets section. Example: domains: vm-el73: memory: 2048 service_provider: systemd nics: - net: lago disks: - template_name: el7.3-base type: template name: root dev: vda format: qcow2 artifacts: - /var/log nets: lago: type: nat dhcp: start: 100 end: 254 management: true dns_domain_name: lago.local domains¶ <name>: The name of the virtual machine. - memory(int) - The virtual machine memory in GBs. - vcpu(int) - Number of virtual CPUs. - service_provider(string) - This will instruct which service provider to use when enabling services in the VM by calling lago.plugins.vm.VMPlugin.service(), Possible values: systemd, sysvinit. - cpu_model(string) - CPU Family to emulate for the virtual machine. The list of supported types depends on your hardware and the libvirtd version you use, to list them you can run locally:$ virsh cpu-models x86_64 - cpu_custom(dict) - This allows more fine-grained control of the CPU type, see CPU section for details. - nics(list) - Network interfaces. Each network interface must be defined in the global nets section. By default each nic will be assigned an IP according to the network definition. However, you may also use static IPs here, by writing:nics: - net: net-01 ip: 192.168.220.2 The same network can be declared multiple times for each domain. - disks(list) - - type - Disk type, possible values: - template - A Lago template, this would normally the bootable device. - file - A local disk image. Lago will thinly provision it during init stage, this device will not be bootable. But can obviously be used for additional storage. - template_name(string) - Applies only to disks of type template. This should be one of the available Lago templates, see Templates section for the list. - size(string) - Disk size to thinly provision in GB. This is only supported in filedisks. - format(string) - TO-DO: no docs yet.. - device(string) - Linux device: vda, sdb, etc. Using a device named “sd*” will use virtio-scsi. - build(list) - This section should describe how to build/configure VMs. The build/configure action will happen during init. - virt-customize(dict) - Instructions to pass to virt-customize, where the key is the name of the option and the value is the arguments for that option. This operation is only supported on disks which contains OS. A special instruction is ssh-inject: ''Which will ensure Lago’s generated SSH keys will be injected into the VM. This is useful when you don’t want to run the bootstrap stage. For example:- template_name: el7.3-base build: - virt-customize: ssh-inject: '' touch: [/root/file1, /root/file2] See build section for details. - artifacts(list) - Paths on the VM that Lago should collect when using lago collect from the CLI, or collect_artifacts()from the SDK. - groups(list) - Groups this VM belongs to. This is most usefull when deploying the VM with Ansible. - bootstrap(bool) - Whether to run bootstrap stage on the VM’s template disk, defaults to True. - ssh-user(string) - SSH user to use and configure, defaults to root - vm-provider(string) - VM Provider plugin to use, defaults to local-libvirt. - vm-type(string) - VM Plugin to use. A custom VM Plugin can be passed here, note that it needs to be available in your Python Entry points. See lago-ost-plugin for an example. - metadata(dict) - TO-DO: no docs yet..

https://lago.readthedocs.io/en/0.42/LagoInitFile.html

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Paste Deployment¶ Contents - Paste Deployment - Introduction - Status - Installation - From the User Perspective - Basic Usage config:URIs egg:URIs - Defining Factories - Outstanding Issues Documents: Introduction¶. The result is something a system administrator can install and manage without knowing any Python, or the details of the WSGI application or its container. Paste Deployment currently does not require other parts of Paste, and is distributed as a separate package. To see updates that have been made to Paste Deploy see the news file. Paste Deploy is released under the MIT license. Status¶ Paste Deploy has passed version 1.0. Paste Script is an actively maintained project. As of 1.0, we’ll make a strong effort to maintain backward compatibility (this actually started happening long before 1.0, but now it is explicit). This will include deprecation warnings when necessary. Major changes will take place under new functions or with new entry points. Note that the most key aspect of Paste Deploy is the entry points it defines (such as paste.app_factory). Paste Deploy is not the only consumer of these entry points, and many extensions can best take place by utilizing the entry points instead of using Paste Deploy directly. The entry points will not change; if changes are necessary, new entry points will be defined. Installation¶ First make sure you have either setuptools or its modern replacement distribute installed. For Python 3.x you need distribute as setuptools does not work on it. Then you can install Paste Deployment using pip by running: $ sudo pip install PasteDeploy If you want to track development, do: $ hg clone $ cd pastedeploy $ sudo python setup.py develop This will install the package globally, but will load the files in the PasteDeploy==dev. For downloads and other information see the Cheese Shop PasteDeploy page. A complimentary package is Paste Script. To install that, use pip install PasteScript (or pip install PasteScript==dev). From the User Perspective¶ In the following sections, the Python API for using Paste Deploy is given. This isn’t what users will be using (but it is useful for Python developers and useful for setting up tests fixtures). The primary interaction with Paste Deploy is through its configuration files. The primary thing you want to do with a configuration file is serve it. To learn about serving configuration files, see the ``paster serve` command <>`_. The Config File¶ A config file has different sections. The only sections Paste Deploy cares about have prefixes, like app:main or filter:errors – the part after the : is the “name” of the section, and the part before gives the “type”. Other sections are ignored. The format is a simple INI format: name = value. You can extend the value by indenting subsequent lines. # is a comment. Typically you have one or two sections, named “main”: an application section ( [app:main]) and a server section ( [server:main]). [composite:...] signifies something that dispatches to multiple applications (example below). Here’s a typical configuration file that also shows off mounting multiple applications using paste.urlmap: [composite:main] use = egg:Paste#urlmap / = home /blog = blog /wiki = wiki /cms = config:cms.ini [app:home] use = egg:Paste#static document_root = %(here)s/htdocs [filter-app:blog] use = egg:Authentication#auth next = blogapp roles = admin htpasswd = /home/me/users.htpasswd [app:blogapp] use = egg:BlogApp database = sqlite:/home/me/blog.db [app:wiki] use = call:mywiki.main:application database = sqlite:/home/me/wiki.db I’ll explain each section in detail now: [composite:main] use = egg:Paste#urlmap / = home /blog = blog /cms = config:cms.ini That this is a composite section means it dispatches the request to other applications. use = egg:Paste#urlmap means to use the composite application named urlmap from the Paste package. urlmap is a particularly common composite application – it uses a path prefix to map your request to another application. These are the applications like “home”, “blog”, “wiki” and “config:cms.ini”. The last one just refers to another file cms.ini in the same directory. Next up: [app:home] use = egg:Paste#static document_root = %(here)s/htdocs egg:Paste#static is another simple application, in this case it just serves up non-dynamic files. It takes one bit of configuration: document_root. You can use variable substitution, which will pull variables from the section [DEFAULT] (case sensitive!) with markers like %(var_name)s. The special variable %(here)s is the directory containing the configuration file; you should use that in lieu of relative filenames (which depend on the current directory, which can change depending how the server is run). Then: [filter-app:blog] use = egg:Authentication#auth next = blogapp roles = admin htpasswd = /home/me/users.htpasswd [app:blogapp] use = egg:BlogApp database = sqlite:/home/me/blog.db The [filter-app:blog] section means that you want an application with a filter applied. The application being filtered is indicated with next (which refers to the next section). The egg:Authentication#auth filter doesn’t actually exist, but one could imagine it logs people in and checks permissions. That last section is just a reference to an application that you probably installed with pip install BlogApp, and one bit of configuration you passed to it ( database). Lastly: [app:wiki] use = call:mywiki.main:application database = sqlite:/home/me/wiki.db This section is similar to the previous one, with one important difference. Instead of an entry point in an egg, it refers directly to the application variable in the mywiki.main module. The reference consist of two parts, separated by a colon. The left part is the full name of the module and the right part is the path to the variable, as a Python expression relative to the containing module. So, that’s most of the features you’ll use. Basic Usage¶ The basic way you’ll use Paste Deployment is to load WSGI applications. Many Python frameworks now support WSGI, so applications written for these frameworks should be usable. The primary function is paste.deploy.loadapp. This loads an application given a URI. You can use it like: from paste.deploy import loadapp wsgi_app = loadapp('config:/path/to/config.ini') There’s two URI formats currently supported: config: and egg:. config: URIs¶ URIs that being with config: refer to configuration files. These filenames can be relative if you pass the relative_to keyword argument to loadapp(). Note Filenames are never considered relative to the current working directory, as that is a unpredictable location. Generally when a URI has a context it will be seen as relative to that context; for example, if you have a config: URI inside another configuration file, the path is considered relative to the directory that contains that configuration file. Config Format¶ Configuration files are in the INI format. This is a simple format that looks like: [section_name] key = value another key = a long value that extends over multiple lines All values are strings (no quoting is necessary). The keys and section names are case-sensitive, and may contain punctuation and spaces (though both keys and values are stripped of leading and trailing whitespace). Lines can be continued with leading whitespace. Lines beginning with # (preferred) or ; are considered Applications¶ You can define multiple applications in a single file; each application goes in its own section. Even if you have just one application, you must put it in a section. Each section name defining an application should be prefixed with app:. The “main” section (when just defining one application) would go in [app:main] or just [app]. There’s two ways to indicate the Python code for the application. The first is to refer to another URI or name: [app:myapp] use = config:another_config_file.ini#app_name # or any URI: [app:myotherapp] use = egg:MyApp # or a callable from a module: [app:mythirdapp] use = call:my.project:myapplication # or even another section: [app:mylastapp] use = myotherapp It would seem at first that this was pointless; just a way to point to another location. However, in addition to loading the application from that location, you can also add or change the configuration. The other way to define an application is to point exactly to some Python code: [app:myapp] paste.app_factory = myapp.modulename:app_factory You must give an explicit protocol (in this case paste.app_factory), and the value is something to import. In this case the module myapp.modulename is loaded, and the app_factory object retrieved from it. See Defining Factories for more about the protocols. Configuration¶ Configuration is done through keys besides use (or the protocol names). Any other keys found in the section will be passed as keyword arguments to the factory. This might look like: [app:blog] use = egg:MyBlog database = mysql://localhost/blogdb blogname = This Is My Blog! You can override these in other sections, like: [app:otherblog] use = blog blogname = The other face of my blog This way some settings could be defined in a generic configuration file (if you have use = config:other_config_file) or you can publish multiple (more specialized) applications just by adding a section. Global Configuration¶ Often many applications share the same configuration. While you can do that a bit by using other config sections and overriding values, often you want that done for a bunch of disparate configuration values. And typically applications can’t take “extra” configuration parameters; with global configuration you do something equivalent to “if this application wants to know the admin email, this is it”. Applications are passed the global configuration separately, so they must specifically pull values out of it; typically the global configuration serves as the basis for defaults when no local configuration is passed in. Global configuration to apply to every application defined in a file should go in a special section named [DEFAULT]. You can override global configuration locally like: [DEFAULT] admin_email = [email protected] [app:main] use = ... set admin_email = [email protected] That is, by using set in front of the key. Composite Applications¶ “Composite” applications are things that act like applications, but are made up of other applications. One example would be a URL mapper, where you mount applications at different URL paths. This might look like: [composite:main] use = egg:Paste#urlmap / = mainapp /files = staticapp [app:mainapp] use = egg:MyApp [app:staticapp] use = egg:Paste#static document_root = /path/to/docroot The composite application “main” is just like any other application from the outside (you load it with loadapp for instance), but it has access to other applications defined in the configuration file. Other Objects¶ In addition to sections with app:, you can define filters and servers in a configuration file, with server: and filter: prefixes. You load these with loadserver and loadfilter. The configuration works just the same; you just get back different kinds of objects. Filter Composition¶ There are several ways to apply filters to applications. It mostly depends on how many filters, and in what order you want to apply them. The first way is to use the filter-with setting, like: [app:main] use = egg:MyEgg filter-with = printdebug [filter:printdebug] use = egg:Paste#printdebug # and you could have another filter-with here, and so on... Also, two special section types exist to apply filters to your applications: [filter-app:...] and [pipeline:...]. Both of these sections define applications, and so can be used wherever an application is needed. filter-app defines a filter (just like you would in a [filter:...] section), and then a special key next which points to the application to apply the filter to. pipeline: is used when you need apply a number of filters. It takes one configuration key pipeline (plus any global configuration overrides you want). pipeline is a list of filters ended by an application, like: [pipeline:main] pipeline = filter1 egg:FilterEgg#filter2 filter3 app [filter:filter1] ... Getting Configuration¶ If you want to get the configuration without creating the application, you can use the appconfig(uri) function, which is just like the loadapp() function except it returns the configuration that would be used, as a dictionary. Both global and local configuration is combined into a single dictionary, but you can look at just one or the other with the attributes .local_conf and .global_conf. egg: URIs¶ Python Eggs are a distribution and installation format produced by setuptools and distribute that adds metadata to a normal Python package (among other things). You don’t need to understand a whole lot about Eggs to use them. If you have a distutils setup.py script, just change: from distutils.core import setup to: from setuptools import setup Now when you install the package it will be installed as an egg. The first important part about an Egg is that it has a specification. This is formed from the name of your distribution (the name keyword argument to setup()), and you can specify a specific version. So you can have an egg named MyApp, or MyApp==0.1 to specify a specific version. The second is entry points. These are references to Python objects in your packages that are named and have a specific protocol. “Protocol” here is just a way of saying that we will call them with certain arguments, and expect a specific return value. We’ll talk more about the protocols later. The important part here is how we define entry points. You’ll add an argument to setup() like: setup( name='MyApp', ... entry_points={ 'paste.app_factory': [ 'main=myapp.mymodule:app_factory', 'ob2=myapp.mymodule:ob_factory'], }, ) This defines two applications named main and ob2. You can then refer to these by egg:MyApp#main (or just egg:MyApp, since main is the default) and egg:MyApp#ob2. The values are instructions for importing the objects. main is located in the myapp.mymodule module, in an object named app_factory. There’s no way to add configuration to objects imported as Eggs. Defining Factories¶ This lets you point to factories (that obey the specific protocols we mentioned). But that’s not much use unless you can create factories for your applications. There’s a few protocols: paste.app_factory, paste.composite_factory, paste.filter_factory, and lastly paste.server_factory. Each of these expects a callable (like a function, method, or class). paste.app_factory¶ The application is the most common. You define one like: def app_factory(global_config, **local_conf): return wsgi_app The global_config is a dictionary, and local configuration is passed as keyword arguments. The function returns a WSGI application. paste.composite_factory¶ Composites are just slightly more complex: def composite_factory(loader, global_config, **local_conf): return wsgi_app The loader argument is an object that has a couple interesting methods. get_app(name_or_uri, global_conf=None) return a WSGI application with the given name. get_filter and get_server work the same way. A more interesting example might be a composite factory that does something. For instance, consider a “pipeline” application: def pipeline_factory(loader, global_config, pipeline): # space-separated list of filter and app names: pipeline = pipeline.split() filters = [loader.get_filter(n) for n in pipeline[:-1]] app = loader.get_app(pipeline[-1]) filters.reverse() # apply in reverse order! for filter in filters: app = filter(app) return app Then we use it like: [composite:main] use = <pipeline_factory_uri> pipeline = egg:Paste#printdebug session myapp [filter:session] use = egg:Paste#session store = memory [app:myapp] use = egg:MyApp paste.filter_factory¶ Filter factories are just like app factories (same signature), except they return filters. Filters are callables that take a WSGI application as the only argument, and return a “filtered” version of that application. Here’s an example of a filter that checks that the REMOTE_USER CGI variable is set, creating a really simple authentication filter: def auth_filter_factory(global_conf, req_usernames): # space-separated list of usernames: req_usernames = req_usernames.split() def filter(app): return AuthFilter(app, req_usernames) return filter class AuthFilter(object): def __init__(self, app, req_usernames): self.app = app self.req_usernames = req_usernames def __call__(self, environ, start_response): if environ.get('REMOTE_USER') in self.req_usernames: return self.app(environ, start_response) start_response( '403 Forbidden', [('Content-type', 'text/html')]) return ['You are forbidden to view this resource'] paste.filter_app_factory¶ This is very similar to paste.filter_factory, except that it also takes a wsgi_app argument, and returns a WSGI application. So if you changed the above example to: class AuthFilter(object): def __init__(self, app, global_conf, req_usernames): .... Then AuthFilter would serve as a filter_app_factory ( req_usernames is a required local configuration key in this case). paste.server_factory¶ This takes the same signature as applications and filters, but returns a server. A server is a callable that takes a single argument, a WSGI application. It then serves the application. An example might look like: def server_factory(global_conf, host, port): port = int(port) def serve(app): s = Server(app, host=host, port=port) s.serve_forever() return serve The implementation of Server is left to the user. paste.server_runner¶ Like paste.server_factory, except wsgi_app is passed as the first argument, and the server should run immediately. Outstanding Issues¶ Should there be a “default” protocol for each type of object? Since there’s currently only one protocol, it seems like it makes sense (in the future there could be multiple). Except that paste.app_factoryand paste.composite_factoryoverlap considerably. ConfigParser’s INI parsing is kind of annoying. I’d like it both more constrained and less constrained. Some parts are sloppy (like the way it interprets [DEFAULT]). config:URLs should be potentially relative to other locations, e.g., config:$docroot/.... Maybe using variables from global_conf? Should other variables have access to global_conf? Should objects be Python-syntax, instead of always strings? Lots of code isn’t usable with Python strings without a thin wrapper to translate objects into their proper types. Some short-form for a filter/app, where the filter refers to the “next app”. Maybe like: [app-filter:app_name] use = egg:... next = next_app [app:next_app] ...

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If you must run CHKDSK on a volume that is being mirrored by SIOS DataKeeper, it is recommended that you first pause the mirror. After running CHKDSK, continue the mirror. A partial resync occurs (updating those writes generated by the CHKDSK) and mirroring will continue. Failure to first pause the mirror may result in the mirror automatically entering the Paused state and performing a Resync while CHKDSK is in operation. While this will not cause any obvious problems, it will slow the CHKDSK down and result in unnecessary state changes in SIOS DataKeeper. SIOS DataKeeper automatically ensures that volumes participating in a mirror, as either source or target, are not automatically checked at system startup. This ensures that the data on the mirrored volumes remains consistent.). Post your comment on this topic.

http://docs.us.sios.com/dkse/8.6.3/en/topic/chkdsk-considerations

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TOPICS× Features of AEM Forms workspace not available in Flex workspace AEM Forms workspace innovates beyond Flex-based workspace, to offer features, and capabilities that help improve business integration and user productivity. Following is a quick overview of these capabilities. For more details, see the related articles listed at the end of this article. Support for a summary pane for tasks When you open a task, before the form opens, a pane allows you to show information about the task, using an external URL. Using Task Summary Pane additional and relevant information for a task can be displayed to add more value for the end user of AEM Forms workspace. See Display Summary Page for the implementation details. Support for Manager View This capability allows managers to access or act on tasks of their reports. Managers can also drill down, in the organization hierarchy, to tasks of their indirect reports. See Managing tasks in an organizational hierarchy using Manager View for more details. Support for user avatars Images, or avatars, for logged in user can now be displayed in the upper-right corner of the AEM Forms workspace. Also, in the Manager View, user avatars can be displayed to show the images of the managers and their reports. See Displaying the user avatar for more details. Support for integrating third-party applications The capability to integrate with third-party applications can be used to bring your workflows entirely to AEM Forms workspace. For example, you can render Correspondence Management letter templates as tasks within the AEM Forms workspace window itself. Thus, you can complete the task without leaving AEM Forms workspace. See Integrating Correspondence Management in AEM Forms workspace for detailed instructions. Support for custom task rendering based on end user's device AEM Forms workspace provides support for HTML rendition of XDP forms. This support, when used in a render process that routes to different renditions of XDP based on the device or user-agent, allows users to view an XDP form as HTML on the mobile devices and as PDF on a desktop. This helps in providing seamless coverage of Process Management to users who work in varied environments on different devices.

https://docs.adobe.com/content/help/en/experience-manager-64/forms/use-aem-forms-workspace/features-html-workspace-available-flex.html

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dse.yaml configuration file The DataStax Enterprise configuration file for security, DSE Search, DataStax Graph, and DSE Analytics. logback.xmlThe location of the logback.xml file depends on the type of installation: dse.yamlThe location of the dse.yaml file depends on the type of installation: cassandra.yamlThe location of the cassandra.yaml file depends on the type of installation: The cassandra.yaml file is the primary configuration file for the DataStax Enterprise database. Syntax node_health_options: refresh_rate_ms: 60000 uptime_ramp_up_period_seconds: 10800 dropped_mutation_window_minutes: 30 Security and authentication Authentication optionsDSE Authenticator supports multiple schemes for authentication at the same time in a DataStax Enterprise cluster. Additional authenticator configuration is required in cassandra.yaml. # authentication_options: # enabled: false # default_scheme: internal # other_schemes: # scheme_permissions: false # allow_digest_with_kerberos: true # plain_text_without_ssl: warn # transitional_mode: disabled - authentication_options - Configures DseAuthenticator to authenticate users when the authenticator option in cassandra.yaml is set to com.datastax.bdp.cassandra.auth.DseAuthenticator. Authenticators other than DseAuthenticator are not supported. - enabled - Enables user authentication. - true - The DseAuthenticator authenticates users. - false - The DseAuthenticator does not authenticate users and allows all connections. Default: false - default_scheme - The first scheme to validate a user against when the driver does not request a specific scheme. - internal - Plain text authentication using the internal password authentication. - ldap - Plain text authentication using pass-through LDAP authentication. - kerberos - GSSAPI authentication using the Kerberos authenticator. Default: internal - other_schemes - List of schemes that are checked if validation against the first scheme fails and no scheme was specified by the driver. - ldap - Plain text authentication using pass-through LDAP authentication. - kerberos - GSSAPI authentication using the Kerberos authenticator. Default: none - scheme_permissions - Determines if roles need to have permission granted to them to use specific authentication schemes. These permissions can be granted only when the DseAuthorizer is used. - true - Use multiple schemes for authentication. To be assigned, every role requires permissions to a scheme. - false - Do not use multiple schemes for authentication. Prevents unintentional role assignment that might occur if user or group names overlap in the authentication service. Default: false - allow_digest_with_kerberos - Controls whether DIGEST-MD5 authentication is allowed with Kerberos. Kerberos uses DIGEST-MD5 to pass credentials between nodes and jobs. The DIGEST-MD5 mechanism is not associated directly with an authentication scheme. - true - Allow DIGEST-MD5 authentication with Kerberos. In analytics clusters, set to trueto use Hadoop internode authentication with Hadoop and Spark jobs. - false - Do not allow DIGEST-MD5 authentication with Kerberos. Default: true - plain_text_without_ssl - Controls how the DseAuthenticator responds to plain text authentication requests over unencrypted client connections. - block - Block the request with an authentication error. - warn - Log a warning but allow the request. - allow - Allow the request without any warning. Default: warn - transitional_mode - Sets transitional mode for temporary use during authentication setup in an established environment.Transitional mode allows access to the database using the anonymousrole, which has all permissions except AUTHORIZE. Important: Credentials are required for all connections after authentication is enabled; use a blank username and password to login with anonymous role in transitional mode. - disabled - Disable transitional mode. All connections must provide valid credentials and map to a login-enabled role. - permissive - Only super users are authenticated and logged in. All other authentication attempts are logged in as the anonymous user. - normal - Allow all connections that provide credentials. Maps all authenticated users to their role, and maps all other connections to anonymous. - strict - Allow only authenticated connections that map to a login-enabled role OR connections that provide a blank username and password as anonymous. Default: disabled Role management options #role_management_options: # mode: internal - role_management_options - Configures the DSE Role Manager. To enable role manager, set: Tip: See Setting up logins and users.When scheme_permissions is enabled, all roles must have permission to execute on the authentication scheme. See Binding a role to an authentication scheme. - authorization_options enabled to true - role_manager in cassandra.yaml to com.datastax.bdp.cassandra.auth.DseRoleManager - mode - Manages granting and revoking of roles. - internal - Manage granting and revoking of roles internally using the GRANT ROLE and REVOKE ROLE CQL statements. See Managing database access. Internal role management allows nesting roles for permission management. - ldap - Manage granting and revoking of roles using an external LDAP server configured using the ldap_options. To configure an LDAP scheme, complete the steps in Defining an LDAP scheme. Nesting roles for permission management is disabled. Default: internal - stats - Set to true, to enable logging of DSE role creation and modification events in the dse_security.role_statssystem table. All nodes must have the stats option enabled, and must be restarted for the functionality to take effect. - To query role events: SELECT * FROM dse_security.role_stats; role | created | password_changed -------+---------------------------------+--------------------------------- user1 | 2020-04-13 00:44:09.221000+0000 | null user2 | 2020-04-12 23:49:21.457000+0000 | 2020-04-12 23:49:21.457000+0000 (2 rows) - Default: commented out ( false) #authorization_options: # enabled: false # transitional_mode: disabled # allow_row_level_security: false - Configures the DSE Authorizer to authorize users when the authorization option in cassandra.yaml is set to com.datastax.bdp.cassandra.auth.DseAuthorizer. - enabled - Enables the DSE Authorizer for role-based access control (RBAC). - true - Enable the DSE Authorizer for RBAC. - false - Do not use the DSE Authorizer. Default: false - transitional_mode - Allows the DSE Authorizer to operate in a temporary mode during authorization setup in a cluster. - disabled - Transitional mode is disabled. - normal - Permissions can be passed to resources, but are not enforced. - strict - Permissions can be passed to resources, and are enforced on authenticated users. Permissions are not enforced against anonymous users. Default: disabled - allow_row_level_security - Enables row-level access control (RLAC) permissions. Use the same setting on all nodes. See Setting up Row Level Access Control (RLAC). - true - Use row-level security. - false - Do not use row-level security. Default: false Kerberos options kerberos_options: keytab: resources/dse/conf/dse.keytab service_principal: dse/_HOST@REALM http_principal: HTTP/_HOST@REALM qop: auth - kerberos_options - Configures security for a DataStax Enterprise cluster using Kerberos. - keytab - The filepath of dse.keytab. Default: resources/dse/conf/dse.keytab - service_principal - The service_principal that the DataStax Enterprise process runs under must use the form dse_user/_HOST@REALM, where: - dse_user is the username of the user that starts the DataStax Enterprise process. - _HOST is converted to a reverse DNS lookup of the broadcast address. - REALM is the name of your Kerberos realm. In the Kerberos principal, REALM must be uppercase. Default: dse/_HOST@REALM - http_principal - Used by the Tomcat application container to run DSE Search. The Tomcat web server uses the GSSAPI mechanism (SPNEGO) to negotiate the GSSAPI security mechanism (Kerberos). REALM is the name of your Kerberos realm. In the Kerberos principal, REALM must be uppercase. Default: HTTP/_HOST@REALM - qop - A comma-delimited list of Quality of Protection (QOP) values that clients and servers can use for each connection. The client can have multiple QOP values, while the server can have only a single QOP value. - auth - Authentication only. - auth-int - Authentication plus integrity protection for all transmitted data. - auth-conf - Authentication plus integrity protection and encryption of all transmitted data. Encryption using auth-confis separate and independent of whether encryption is done using SSL. If both auth-conf and SSL are enabled, the transmitted data is encrypted twice. DataStax recommends choosing only one method and using it for encryption and authentication. Default: auth LDAP options ldap_options.server_portparameter is used by default. This way, there is no change in configuration for existing users who have LDAP configured. - A connection pool is created of each server separately. Once the connection is attempted, the best pool is chosen using a heuristic. DSE uses a circuit breaker to temporarily disable those servers that frequently fail to connect. Also, DSE tries to choose the pool that has the greatest number of idle connections. - Failover parameters are configured through system properties. - A new method was added in DSE 6.8.2 to the LDAP MBean to reset LDAP connectors - that is, close all connection pools and recreate them. # ldap_options: # server_host: # server_port: 389 # hostname_verification: false # search_dn: # search_password: # use_ssl: false # use_tls: false # truststore_path: # truststore_password: # truststore_type: jks # user_search_base: # user_search_filter: (uid={0}) # user_memberof_attribute: memberof # group_search_type: directory_search # group_search_base: # group_search_filter: (uniquemember={0}) # group_name_attribute: cn # credentials_validity_in_ms: 0 # search_validity_in_seconds: 0 # connection_pool: # max_active: 8 # max_idle: 8 ldap_options: server_host: win2012ad_server.mycompany.lan server_port: 389 search_dn: cn=lookup_user,cn=users,dc=win2012domain,dc=mycompany,dc=lan search_password: lookup_user_password use_ssl: false use_tls: false truststore_path: truststore_password: truststore_type: jks #group_search_type: directory_search group_search_type: memberof_search #group_search_base: #group_search_filter: group_name_attribute: cn user_search_base: cn=users,dc=win2012domain,dc=mycompany,dc=lan user_search_filter: (sAMAccountName={0}) user_memberof_attribute: memberOf connection_pool: max_active: 8 max_idle: 8 - ldap_options - Configures LDAP security when the authenticator option in cassandra.yaml is set to com.datastax.bdp.cassandra.auth.DseAuthenticator. - server_host - A comma separated list of LDAP server hosts.Important: Do not use LDAP on the same host (localhost) in production environments. Using LDAP on the same host (localhost) is appropriate only in single node test or development environments. Default: none - server_port - The port on which the LDAP server listens. - 389 - The default port for unencrypted connections. - 636 - Used for encrypted connections. Default SSL or TLS port for LDAP. Default: 389 - hostname_verification - Enable hostname verification. The following conditions must be met: - Either use_sslor use_tlsmust be set to true. - A valid truststore with the correct path specified in truststore_pathmust exist. The truststore must have a certificate entry, trustedCertEntry, including a SAN DNSNameentry that matches the hostname of the LDAP server. Default: false - search_dn - Distinguished name (DN) of an account with read access to the user_search_baseand group_search_base. For example: Warning: Do not create/use an LDAP account or group calledWhen not set, the LDAP server uses an anonymous bind for search. - OpenLDAP: uid=lookup,ou=users,dc=springsource,dc=com - Microsoft Active Directory (AD): cn=lookup, cn=users, dc=springsource, dc=com cassandra. The DSE database comes with a default cassandralogin role that has access to all database objects and uses the consistency level QUOROM. Default: commented out - search_password - The password of the search_dnaccount. Default: commented out - use_ssl - Enables an SSL-encrypted connection to the LDAP server.Tip: See Defining an LDAP scheme. - true - Use an SSL-encrypted connection. - false - Do not enable SSL connections to the LDAP server. Default: false - use_tls - Enables TLS connections to the LDAP server. - true - Enable TLS connections to the LDAP server. - false - Do not enable TLS connections to the LDAP server Default: false - truststore_path - The filepath to the SSL certificates truststore. Default: commented out - truststore_password - The password to access the truststore. Default: commented out - truststore_type - Valid types are JKS, JCEKS, or PKCS12. Default: jks - user_search_base - Distinguished name (DN) of the object to start the recursive search for user entries for authentication and role management memberof searches. - For your LDAP domain, set the ouand dcelements. Typically set to ou=users,dc=domain,dc=top_level_domain. For example, ou=users,dc=example,dc=com. - For your Active Directory, set the dcelement for a different search base. Typically set to CN=search,CN=Users,DC=ActDir_domname,DC=internal. For example, CN=search,CN=Users,DC=example-sales,DC=internal. Default: none - user_search_filter - Identifies the user that the search filter uses for looking up usernames. - uid={0} - When using LDAP. - samAccountName={0} - When using AD (Microsoft Active Directory). For example, (sAMAccountName={0}). Default: uid={0} - user_memberof_attribute - Contains a list of group names. Role manager assigns DSE roles that exactly match any group name in the list. Required when managing roles using group_search_type: memberof_searchwith LDAP (role_manager.mode:ldap). The directory server must have memberof support, which is a default user attribute in Microsoft Active Directory (AD). Default: memberof - group_search_type - Defines how group membership is determined for a user. Required when managing roles with LDAP (role_manager.mode: ldap). - directory_search - Filters the results with a subtree search of group_search_base to find groups that contain the username in the attribute defined in the group_search_filter. - memberof_search - Recursively searches for user entries using the user_search_baseand user_search_filter. Gets groups from the user attribute defined in user_memberof_attribute. The directory server must have memberof support. Default: directory_search - group_search_base - The unique distinguished name (DN) of the group record from which to start the group membership search. Default: commented out - group_search_filter - Set to any valid LDAP filter. Default: uniquemember={0} - group_name_attribute - The attribute in the group record that contains the LDAP group name. Role names are case-sensitive and must match exactly on DSE for assignment. Unmatched groups are ignored. Default: cn - credentials_validity_in_ms - A credentials cache improves performance by reducing the number of requests that are sent to the internal or LDAP server. See Defining an LDAP scheme. Note: Starting in DSE 6.8.2, the upper limit for - 0 - Disable credentials cache. - duration period - The duration period in milliseconds of the credentials cache. ldap_options.credentials_validity_in_msincreased to 864,000,000 ms, which is 10 days. Default: 0 - search_validity_in_seconds - Configures a search cache to improve performance by reducing the number of requests that are sent to the internal or LDAP server. Note: Starting in DSE 6.8.2, the upper limit for - 0 - Disables search credentials cache. - positive number - The duration period in seconds for the search cache. ldap_options.search_validity_in_secondsincreased to 864,000 seconds, which is 10 days. Default: 0 - connection_pool - Configures the connection pool for making LDAP requests. - max_active - The maximum number of active connections to the LDAP server. Default: 8 - max_idle - The maximum number of idle connections in the pool awaiting requests. Default: 8 Encrypt sensitive system resources Options to encrypt sensitive system resources using a local encryption key or a remote KMIP key. system_info_encryption: enabled: false cipher_algorithm: AES secret_key_strength: 128 chunk_length_kb: 64 key_provider: KmipKeyProviderFactory kmip_host: kmip_host_name - system_info_encryption - Sets the encryption settings for system resources that might contain sensitive information, including the system.batchlogand system.paxostables, hint files, and the database commit log. - enabled - Enables encryption of system resources. See Encrypting system resources. Note: TheDefault: false - true - Enable encryption of system resources. - false - Does not encryption of system resources. system_tracekeyspace is not encrypted by enabling the system_information_encryptionsection. In environments that also have tracing enabled, manually configure encryption with compression on the system_tracekeyspace. See Transparent data encryption. - cipher_algorithm - The name of the JCE cipher algorithm used to encrypt system resources. Default: AES - secret_key_strength - Length of key to use for the system resources. See Table 1.Note: DSE uses a matching local key or requests the key type from the KMIP server. For KMIP, if an existing key does not match, the KMIP server automatically generates a new key.Default: 128 - chunk_length_kb - Optional. Size of SSTable chunks when data from the system.batchlog or system.paxos are written to disk.Note: To encrypt existing data, runDefault: 64 nodetool upgradesstables -a system batchlog paxoson all nodes in the cluster. - key_provider - KMIP key provider to enable encrypting sensitive system data with a KMIP key. Comment out if using a local encryption key. Default: KmipKeyProviderFactory - kmip_host - The KMIP key server host. Set to the kmip_group_name that defines the KMIP host in kmip_hosts section. DSE requests a key from the KMIP host and uses the key generated by the KMIP provider. Default: kmip_host_name Encrypted configuration properties system_key_directory: /etc/dse/conf config_encryption_active: false config_encryption_key_name: (key_filename | KMIP_key_URL ) - system_key_directory - Path to the directory where local encryption key files are stored, also called system keys. Distributes the system keys to all nodes in the cluster. Ensure the DSE account is the folder owner and has read/write/execute (700) permissions.See Setting up local encryption keys.Note: This directory is not used for KMIP keys. Default: /etc/dse/conf - config_encryption_active - Enables encryption on sensitive data stored in tables and in configuration files. - true - Enable encryption of configuration property values using the specified config_encryption_key_name. When set to true, the configuration values must be encrypted or commented out. See Encrypting configuration file properties.Restriction: Lifecycle Manager (LCM) is not compatible when config_encryption_activeis truein DSE and OpsCenter. For LCM limitations, see Encrypted DSE configuration values. - false - Do not enable encryption of configuration property values. Default: false - config_encryption_key_name - The local encryption key filename or KMIP key URL to use for configuration file property value decryption.Note: Use dsetool encryptconfigvalue to generate encrypted values for the configuration file properties.Default: system_keyNote: The default name is not configurable. KMIP encryption options kmip_hosts: your key_cache_millis: 300000 timeout: 1000 - kmip_hosts - Configures connections for key servers that support the KMIP protocol. - kmip_groupname - A user-defined name for a group of options to configure a KMIP server or servers, key settings, and certificates. For each KMIP key server or group of KMIP key servers, you must configure options for a kmip_groupname section. Using separate key server configuration settings allows use of different key servers to encrypt table data and eliminates the need to enter key server configuration information in Data Definition Language (DDL) statements and other configurations. DDL statements are database schema change commands like CREATE TABLE. Multiple KMIP hosts are supported. - Default: commented out - hosts - A comma-separated list of KMIP hosts (host[:port]) using the FQDN (Fully Qualified Domain Name). Add KMIP hosts in the intended failover sequence because DSE queries the host in the listed order. For example, if the host list contains kmip1.yourdomain.com, kmip2.yourdomain.com, DSE tries kmip1.yourdomain.comand then kmip2.yourdomain.com. - keystore_path - The path to a Java keystore created from the KMIP agent PEM files. Default: /etc/dse/conf/KMIP_keystore.jks - keystore_type - Valid types are JKS, JCEKS, PKCS11, and PKCS12. For file-based keystores, use PKCS12. Default: JKS - keystore_password - Password used to protect the private key of the key pair. Default: none - truststore_path - The path to a Java truststore that was created using the KMIP root certificate. Default: /etc/dse/conf/KMIP_truststore - truststore_password - Password required to access the keystore. Default: none - key_cache_millis - Milliseconds to locally cache the encryption keys that are read from the KMIP hosts. The longer the encryption keys are cached, the fewer requests to the KMIP key server are made and the longer it takes for changes, like revocation, to propagate to the DSE node. DataStax Enterprise uses concurrent encryption, so multiple threads fetch the secret key from the KMIP key server at the same time. DataStax recommends using the default value. Default: 300000 - timeout - Socket timeout in milliseconds. Default: 1000 DSE Search index encryption # solr_encryption_options: # decryption_cache_offheap_allocation: true # decryption_cache_size_in_mb: 256 - solr_encryption_options - Tunes encryption of search indexes. - decryption_cache_offheap_allocation - Allocates shared DSE Search decryption cache off JVM heap. - true - Allocate shared DSE Search decryption cache off JVM heap. - false - Do not allocate shared DSE Search decryption cache off JVM heap. Default: true - decryption_cache_size_in_mb - The maximum size of the shared DSE Search decryption cache in megabytes (MB). Default: 256 DSE In-Memory options To use DSE In-Memory, specify how much system memory to use for all in-memory tables by fraction or size. # max_memory_to_lock_fraction: 0.20 # max_memory_to_lock_mb: 10240 - max_memory_to_lock_fraction - A fraction of the system memory. For example, 0.20 allows use up to 20% of system memory. This setting is ignored if max_memory_to_lock_mbis set to a non-zero value. Default: 0.20 - max_memory_to_lock_mb - Maximum amount of memory in megabytes (MB) for DSE In-Memory tables. - not set - Use the fraction specified with max_memory_to_lock_fraction. - number greater than 0 - Maximum amount of memory in megabytes (MB). Default: 10240 Node health options node_health_options: refresh_rate_ms: 60000 uptime_ramp_up_period_seconds: 10800 dropped_mutation_window_minutes: 30 - node_health_options - Node health options are always enabled. Node health is a score-based representation of how healthy a node is to handle search queries. See Collecting node health and indexing status scores. - refresh_rate_ms - How frequently statistics update., increase the uptime period to the expected repair time. Default: 10800 (3 hours) - dropped_mutation_window_minutes - The historic time window over which the rate of dropped mutations affects the node health score. Default: 30 Health-based routing enable_health_based_routing: true - enable_health_based_routing - Enables node health as a consideration for replication selection for distributed DSE Search queries. Health-based routing enables a trade-off between index consistency and query throughput. - true - Consider node health when multiple candidates exist for a particular token range. - false - Ignore node health for replication selection. log entries related to lease holders. - true - Enable log entries related to lease holders to help monitor performance of the lease subsystem. - false - No not enable log entries. Default: false - ttl_seconds - Time interval in milliseconds to persist the log of lease holder changes. Default: 604800 DSE Search Scheduler settings for DSE Search indexesTo ensure that records with time-to-live (TTL) are purged from search indexes when they expire, the search indexes are periodically checked for expired documents. ttl_index_rebuild_options: fixed_rate_period: 300 initial_delay: 20 max_docs_per_batch: 4096 thread_pool_size: 1 - ttl_index_rebuild_options - Configures the schedulers in charge of querying for expired records, removing expired records, and the execution of the checks. - fix_rate_period - Time interval in seconds expired documents are deleted from the index during each check. To avoid memory pressure, their unique keys are retrieved and then deletes are issued in batches. Default: 4096 - thread_pool_size - The maximum number of search indexes (cores) that can execute TTL cleanup concurrently. Manages system resource consumption and prevents many search cores from executing simultaneous TTL deletes. Default: 1 Reindexing of bootstrapped data async_bootstrap_reindex: false - async_bootstrap_reindex - For DSE Search, configure whether to asynchronously reindex bootstrapped data. - true - The node joins the ring immediately after bootstrap and reindexing occurs asynchronously. Do not wait for post-bootstrap reindexing so that the node is not marked down. The dsetool ring command can be used to check the status of the reindexing. - false - The node joins the ring after reindexing the bootstrapped data. Default: false CQL Solr paging cql_solr_query_paging: off - cql_solr_query_paging - driver - Respects driver paging settings. Uses Solr pagination (cursors) only when the driver uses pagination. Enabled automatically for DSE SearchAnalytics workloads. - off - Paging is off. Ignore driver paging settings for CQL. Default: off Solr CQL query option cql_solr_query_row_timeout: 10000 - cql_solr_query_row_timeout - The maximum time in milliseconds to wait for all rows to be read from the database during CQL Solr queries. Default: 10000 (10 seconds) DSE Search resource upload limit solr_resource_upload_limit_mb: 10 - solr_resource_upload_limit_mb - Configures Shard transport shard_transport_options: netty_client_request_timeout: 60000 - shard_transport_options - Fault tolerance option for internode) DSE Search indexing # back_pressure_threshold_per_core: 1024 # flush_max_time_per_core: 5 # load_max_time_per_core: 5 # enable_index_disk_failure_policy: false # solr_data_dir: /MyDir # solr_field_cache_enabled: false # ram_buffer_heap_space_in_mb: 1024 # ram_buffer_offheap_space_in_mb: 1024 - back_pressure_threshold_per_core - The maximum number of queued partitions during search index rebuilding and reindexing. This maximum number safeguards against excessive heap use by the indexing queue. If set lower than the number of threads per core (TPC), not all TPC threads can be actively indexing. Default: 1024 - flush_max_time_per_core - The maximum time, in minutes, to wait for the flushing of asynchronous index updates that occurs at DSE Search commit time or at flush time.CAUTION: Expert knowledge is required to change this value.Always set the wait time high enough to ensure flushing completes successfully to fully sync DSE Search indexes with the database data. If the wait time is exceeded, index updates are only partially committed and the commit log is not truncated which can undermine data durability.Note: When a timeout occurs, this node is typically overloaded and cannot flush in a timely manner. Live indexing increases the time to flush asynchronous index updates. Default: 5 - load_max_time_per_core - The maximum time, in minutes, to wait for each DSE Search index to load on startup or create/reload operations. This advanced option should be changed only if exceptions happen during search index loading. Default: 5 - enable_index_disk_failure_policy - Whether to apply the configured disk failure policy if IOExceptions occur during index update operations. - true - Apply the configured Cassandra disk failure policy to index write failures - false - Do not apply the disk failure policy Default: false - solr_data_dir - The directory to store index data. See Managing the location of DSE Search data. By default, each DSE Search index is saved in solr_data_dir/keyspace_name.table_name or as specified by the dse.solr.data.dirsystem property. Default: A solr.data directory in the cassandra data directory, like /var/lib/cassandra/solr.data - solr_field_cache_enabled - The Apache Lucene® field cache is deprecated. Instead, for fields that are sorted, faceted, or grouped by, set docValues="true"on the field in the search index schema. Then reload the search index and reindex. Default: false - ram_buffer_heap_space_in_mb - Global Lucene RAM buffer usage threshold for heap to force segment flush. Setting too low can cause a state of constant flushing during periods of ongoing write activity. For near-real-time (NRT) indexing, forced segment flushes also de-schedule pending auto-soft commits to avoid potentially flushing too many small segments. Default: 1024 - ram_buffer_offheap_space_in_mb - Global Lucene RAM buffer usage threshold for offheap to force segment flush. Setting too low can cause a state of constant flushing during periods of ongoing write activity. For NRT, forced segment flushes also de-schedule pending auto-soft commits to avoid potentially flushing too many small segments. When not set, the default is 1024. Default: 1024 Performance Service - configDseYaml.html#configDseYaml__global-perf-optionsGlobal Performance Service - configDseYaml.html#configDseYaml__cql-perform-opsPerformance Service - configDseYaml.html#configDseYaml__solr-cql-queryDSE Search Performance Service - configDseYaml.html#configDseYaml__sparkPerformanceSpark Performance Service Global Performance Service performance_max_threads+ performance_queue_capacity. When a task is dropped, collected statistics might not be current. # performance_core_threads: 4 # performance_max_threads: 32 # performance_queue_capacity: 32000 - performance_core_threads - Number of background threads used by the performance service under normal conditions. Default: 4 - performance_max_threads - Maximum number of background threads used by the performance service. Default: 32 - performance_queue_capacity - Allowed number of queued tasks in the backlog when the number of performance_max_threadsare busy. Default: 32000 Performance Service Configures the collection of performance metrics on transactional nodes. Performance metrics are stored in the dse_perf keyspace and can be queried using any CQL-based utility, such as cqlsh or any application using a CQL driver. To temporarily make changes for diagnostics and testing, use the dsetool perf subcommands. graph_events: ttl_seconds: 600 - graph_events - Graph event information. - ttl_seconds - Number of seconds a record survives before it is expired. Default: 600 # cql_slow_log_options: # enabled: true # threshold: 200.0 # minimum_samples: 100 # ttl_seconds: 259200 # skip_writing_to_db: true # num_slowest_queries: 5 - cql_slow_log_options - Configures reporting distributed sub-queries for search (query executions on individual shards) that take longer than a specified period of time. - enabled - true - Enables log entries for slow queries. - false - Does not enable log entries. Default: true - threshold - The threshold in milliseconds or as a percentile. - A value greater than 1 is expressed in time and will log queries that take longer than the specified number of milliseconds. For example, 200.0 sets the threshold at 0.2 seconds. - A value of 0 to 1 is expressed as a percentile and will log queries that exceed this percentile. For example, .95 collects information on 5% of the slowest queries. Default: 200.0 - minimum_samples - The initial number of queries before activating the percentile filter. Default: commented out ( 100) - ttl_seconds - Number of seconds a slow log record survives before it is expired. Default: 259200 - skip_writing_to_db - Keeps slow queries only in-memory and does not write data to database. - true - Keep slow queries only in-memory. Skip writing to database. - false - Write slow query information in the node_slow_logtable. The threshold must be >= 2000 ms to prevent a high load on the database. Default: commented out ( true) - num_slowest_queries - The number of slow queries to keep in-memory. Default: commented out ( 5) cql_system_info_options: enabled: false refresh_rate_ms: 10000 - cql_system_info_options - Configures collection of system-wide performance information about a cluster. - enabled - Enables collection of system-wide performance information about a cluster. - true - Collect metrics. - false - Do not collect metrics. Default: false - refresh_rate_ms - The length of the sampling period in milliseconds; the frequency to update the performance statistics. Default: 10000 (10 seconds) resource_level_latency_tracking_options: enabled: false refresh_rate_ms: 10000 - resource_level_latency_tracking_options - Configures collection of object I/O performance statistics.Tip: See Collecting system level diagnostics. - enabled - Enables collection of object input output performance statistics. - true - Collect metrics. - false - Do not collect metrics. Default: false - refresh_rate_ms - The length of the sampling period in milliseconds; the frequency to update the performance statistics. Default: 10000 (10 seconds) db_summary_stats_options: enabled: false refresh_rate_ms: 10000 - db_summary_stats_options - Configures collection of summary statistics at the database level.Tip: See Collecting database summary diagnostics. - enabled - Enables collection of database summary performance information. - true - Collect metrics. - false - Do not collect metrics. Default: false - refresh_rate_ms - The length of the sampling period in milliseconds; the frequency to update the performance statistics. Default: 10000 (10 seconds) cluster_summary_stats_options: enabled: false refresh_rate_ms: 10000 - cluster_summary_stats_options - Configures collection of statistics at a cluster-wide level.Tip: See Collecting cluster summary diagnostics. - enabled - Enables collection of statistics at a cluster-wide level. - true - Collect metrics. - false - Do not collect metrics. Default: false - refresh_rate_ms - The length of the sampling period in milliseconds; the frequency to update the performance statistics. Default: 10000 (10 seconds) - spark_cluster_info_options - Configures collection of data associated with Spark cluster and Spark applications. spark_cluster_info_options: enabled: false refresh_rate_ms: 10000 - enabled - Enables collection of Spark performance statistics. - true - Collect metrics. - false - Do not collect metrics. Default: false - refresh_rate_ms - The length of the sampling period in milliseconds; the frequency to update the performance statistics. Default: 10000 (10 seconds) histogram_data_options: enabled: false refresh_rate_ms: 10000 retention_count: 3 - histogram_data_options - Histogram data for the dropped mutation metrics are stored in the dropped_messages table in the dse_perf keyspace.Tip: See Collecting histogram diagnostics. - enabled - true - Collect metrics. - false - Do not collect metrics. Default: false - refresh_rate_ms - The length of the sampling period in milliseconds; the frequency to update the performance statistics. Default: 10000 (10 seconds) - retention_count - Default: 3 user_level_latency_tracking_options: enabled: false refresh_rate_ms: 10000 top_stats_limit: 100 quantiles: false - user_level_latency_tracking_options - User-resource latency tracking settings.Tip: See Collecting user activity diagnostics. - enabled - true - Collect metrics. - false - Do not collect metrics. Default: false - refresh_rate_ms - The length of the sampling period in milliseconds; the frequency to update the performance statistics. Default: 10000 (10 seconds) - top_stats_limit - The maximum number of individual metrics. Default: 100 - quantiles Default: false DSE Search Performance Service solr_slow_sub_query_log_options: enabled: false ttl_seconds: 604800 async_writers: 1 threshold_ms: 3000 - solr_slow_sub_query_log_options - See Collecting slow search queries. - enabled - true - Collect metrics. - false - Do not collect metrics. Default: false - ttl_seconds - The number of seconds a record survives before it is expired. Default: 604800(about 10 minutes) - async_writers - The number of server threads dedicated to writing in the log. More than one server thread might degrade performance. Default: 1 - threshold_ms Default: 3000 solr_update_handler_metrics_options: enabled: false ttl_seconds: 604800 refresh_rate_ms: 60000 - solr_update_handler_metrics_options - Options to collect search index direct update handler statistics over time.Tip: See Collecting handler statistics. solr_request_handler_metrics_options: enabled: false ttl_seconds: 604800 refresh_rate_ms: 60000 - solr_request_handler_metrics_options - Options to collect search index request handler statistics over time.Tip: See Collecting handler statistics. solr_index_stats_options: enabled: false ttl_seconds: 604800 refresh_rate_ms: 60000 - solr_index_stats_options - Options to record search index statistics over time.Tip: See Collecting index statistics. solr_cache_stats_options: enabled: false ttl_seconds: 604800 refresh_rate_ms: 60000 - solr_cache_stats_options - See Collecting cache statistics. solr_latency_snapshot_options: enabled: false ttl_seconds: 604800 refresh_rate_ms: 60000 - solr_latency_snapshot_options - See Collecting Apache Solr performance statistics. Spark Performance Service spark_application_info_options: enabled: false refresh_rate_ms: 10000 driver: sink: false connectorSource: false jvmSource: false stateSource: false executor: sink: false connectorSource: false jvmSource: false - spark_application_info_options - Collection of Spark application metrics. - enabled - true - Collect metrics. - false - Do not collect metrics. Default: false - refresh_rate_ms - The length of the sampling period in milliseconds; the frequency to update the performance statistics. Default: 10000 (10 seconds) - driver - Collection that configures collection of metrics at the Spark Driver. - connectorSource - Enables collecting Spark Cassandra Connector metrics at the Spark Driver. - true - Collect metrics. - false - Do not collect metrics. Default: false - jvmSource - Enables collection of JVM heap and garbage collection (GC) metrics from the Spark Driver. - true - Collect metrics. - false - Do not collect metrics. Default: false - stateSource - Enables collection of application state metrics at the Spark Driver. - true - Collect metrics. - false - Do not collect metrics. Default: false - executor - Configures collection of metrics at Spark executors. - sink - Enables collecting metrics collected at Spark executors. - true - Collect metrics. - false - Do not collect metrics. Default: false - connectorSource - Enables collection of Spark Cassandra Connector metrics at Spark executors. - true - Collect metrics. - false - Do not collect metrics. Default: false - jvmSource - Enables collection of JVM heap and GC metrics at Spark executors. - true - Collect metrics. - false - Do not collect metrics. Default: false DSE Analytics Spark resource options spark_shared_secret_bit_length: 256 spark_security_enabled: false spark_security_encryption_enabled: false spark_daemon_readiness_assertion_interval: 1000 resource_manager_options: worker_options: cores_total: 0.7 memory_total: 0.6 workpools: - name: alwayson_sql cores: 0.25 memory: 0.25 - The length of a shared secret used to authenticate Spark components and encrypt the connections between them. This value is not the strength of the cipher for encrypting connections. Default: 256 - spark_security_enabled When DSE authentication is enabled with authentication_options, Spark security is enabled regardless of this setting. Default: false - spark_security_encryption_enabled - When DSE authentication is enabled with authentication_options, Spark security encryption is enabled regardless of this setting.Tip: Configure encryption between the Spark processes and DSE with client-to-node encryption in cassandra.yaml. Default: false - spark_daemon_readiness_assertion_interval - Time interval in milliseconds between subsequent retries by the Spark plugin for Spark Master and Worker readiness to start. Default: 1000 - resource_manager_options - Controls the physical resources used by Spark applications on this node. Optionally add named workpools with specific dedicated resources. See Core management. - worker_options - Configures the amount of system resources that are made available to the Spark Worker. - cores_total - The number of total system cores available to Spark.Note: The SPARK_WORKER_TOTAL_CORESenvironment variables takes precedence over this setting. The lowest value that you can assign to Spark Worker cores is 1 core. If the results are lower, no exception is thrown and the values are automatically limited.Note: Setting cores_totalor a workpool's coresto 1.0 is a decimal value, meaning 100% of the available cores will be reserved. Setting cores_totalor coresto 1 (no decimal point) is an explicit value, and one core will be reserved. - Default: 0.7 - memory_total - The amount of total system memory available to Spark. Note: The - absolute value - Use standard suffixes like M for megabyte and G for gigabyte. For example, 12G. - decimal value - Maximum fraction of system memory to give all executors for all applications running on a particular node. For example, 0.8.When the value is expressed as a decimal, the available resources are calculated in the following way: The lowest values that you can assign to Spark Worker memory is 64 MB. If the results are lower, no exception is thrown and the values are automatically limited. Spark Worker memory = memory_total x (total system memory - memory assigned to DataStax Enterprise) SPARK_WORKER_TOTAL_MEMORYenvironment variables takes precedence over this setting. Default: 0.6 - workpools - A collection of named workpools that can use a portion of the total resources defined under worker_options. A default workpool namedThe total amount of resources defined in the defaultis used if no workpools are defined in this section. If workpools are defined, the resources allocated to the workpools are taken from the total amount, with the remaining resources available to the defaultworkpool. workpoolssection must not exceed the resources available to Spark in worker_options. - name - The name of the workpool. A workpool named alwayson_sqlis created by default for AlwaysOn SQL. By default, the alwayson_sqlworkpool is configured to use 25% of the resources available to Spark. Default: alwayson_sql - cores - The number of system cores to use in this workpool expressed as an absolute value or a decimal value. This option follows the same rules as cores_total. - memory - The amount of memory to use in this workpool expressed as either an absolute value or a decimal value. This option follows the same rules as memory_total. Spark encryption options spark_ui_options: encryption: inherit encryption_options: enabled: false keystore: resources/dse/conf/.ui-keystore keystore_password: cassandra require_client_auth: false truststore: .truststore truststore_password: cassandra # Advanced settings # protocol: TLS # algorithm: SunX509 # keystore_type: JKS # truststore_type: JKS # cipher_suites: ] - spark_ui_options - Configures encryption for Spark Master and Spark Worker UIs. These options apply only to Spark daemon UIs, and do not apply to user applications even when the user applications are run in cluster mode.Tip: To set permissions on roles to allow Spark applications to be started, stopped, managed, and viewed, see Using authorization with Spark - encryption - The source for SSL settings. - inherit - Inherit the SSL settings from the client_encryption_options in cassandra.yaml. - custom - Use the following encryption_options in dse.yaml. - encryption_options - When encryption: custom, configures encryption for HTTPS of Spark Master and Worker UI. - enabled - Enables Spark encryption for Spark client-to-Spark cluster and Spark internode communication. Default: false - keystore - The keystore for Spark encryption keys. The relative filepath is the base Spark configuration directory that is defined by the SPARK_CONF_DIRenvironment variable. The default Spark configuration directory is resources/spark/conf. Default: resources/dse/conf/.ui-keystore - keystore_password - The password to access the keystore. Default: cassandra - require_client_auth - Enables custom truststore for client authentication. - true - Require custom truststore for client authentication. - false - Do not require custom truststore. Default: false - truststore - The filepath to the truststore for Spark encryption keys if require_client_auth: true. The relative filepath is the base Spark configuration directory that is defined by theDefault: resources/dse/conf/.ui-truststore SPARK_CONF_DIRenvironment variable. The default Spark configuration directory is resources/spark/conf. - truststore_password - The password to access the truststore. Default: cassandra - protocol - The Transport Layer Security (TLS) authentication protocol. The TLS protocol must be supported by JVM and Spark. TLS 1.2 is the most common JVM default. Default: JVM default - algorithm - The key manager algorithm. Default: SunX509 - keystore_type - Valid types are JKS, JCEKS, PKCS11, and PKCS12. For file-based keystores, use PKCS12. Default: JKS - truststore_type - Valid types are JKS, JCEKS, and PKCS12. Default: commented out ( JKS) - cipher_suites - A comma-separated list of cipher suites for Spark encryption. Enclose the list in square brackets. - Starting Spark drivers and executors spark_process_runner: runner_type: default run_as_runner_options: user_slots: - slot1 - slot2 - spark_process_runner: - Configures how Spark driver and executor processes are created and managed. See Running Spark processes as separate users. - runner_type - default - Use the default runner type. - run_as - Spark applications run as a different OS user than the DSE service user. - run_as_runner_options - When runner_type: run_as, Spark applications run as a different OS user than the DSE service user. - user_slots - The list slot users to separate Spark processes users from the DSE service user. Default: slot1, slot2 AlwaysOn SQL Properties to enable and configure AlwaysOn SQL on analytics nodes. # AlwaysOn SQL options # alwayson_sql_options: # enabled: false # thrift_port: 10000 # web_ui_port: 9077 # reserve_port_wait_time_ms: 100 # alwayson_sql_status_check_wait_time_ms: 500 # workpool: alwayson_sql # log_dsefs_dir: /spark/log/alwayson_sql # auth_user: alwayson_sql # runner_max_errors: 10 # heartbeat_update_interval_seconds: 30 - alwayson_sql_options - Configures the AlwaysOn SQL server. - enabled - Enables AlwaysOn SQL for this node. - true - Enable AlwaysOn SQL for this node. The node must be an analytics node. Set workpools in Spark resource_manager_options. - false - Do not enable AlwaysOn SQL for this node. Default: false - thrift_port - The Thrift port on which AlwaysOn SQL listens. Default: 10000 - web_ui_port - The port on which the AlwaysOn SQL web UI is available. Default: 9077 - reserve_port_wait_time_ms - The wait time in milliseconds to reserve the thrift_portif it is not available. Default: 100 - alwayson_sql_status_check_wait_time_ms - The time in milliseconds to wait for a health check status of the AlwaysOn SQL server. Default: 500 - workpool - The named workpool used by AlwaysOn SQL. Default: alwayson_sql - log_dsefs_dir - Location in DSEFS of the AlwaysOn SQL log files. Default: /spark/log/alwayson_sql - auth_user - The role to use for internal communication by AlwaysOn SQL if authentication is enabled. Custom roles must be created with login=true. Default: alwayson_sql - runner_max_errors - The maximum number of errors that can occur during AlwaysOn SQL service runner thread runs before stopping the service. A service stop requires a manual restart. Default: 10 - heartbeat_update_interval_seconds - The time interval to update heartbeat of AlwaysOn SQL. If heartbeat is not updated for more than three times the interval, AlwaysOn SQL automatically restarts. Default: 30 DSE File System (DSEFS) # dsefs_options: # enabled: # keyspace_name: dsefs # work_dir: /var/lib/dsefs # public_port: 5598 # private_port: 5599 # data_directories: # - dir: /var/lib/dsefs/data # storage_weight: 1.0 # min_free_space: 268435456 - dsefs_options - Configures DSEFS. See Configuring DSEFS. - enabled - Enables DSEFS. - true - Enables DSEFS on this node, regardless of the workload. - false - Disables DSEFS on this node, regardless of the workload. - blank or commented out (#) - DSEFS starts only if the node is configured to run analytics workloads. Default: -. Default: /var/lib/dsefs - public_port - The public port on which DSEFS listens for clients.Note: DataStax recommends that all nodes in the cluster have the same value. Firewalls must open this port to trusted clients. The service on this port is bound to the native_transport_address. Default: 5598 - private_port - The private port for DSEFS internode communication.CAUTION: that are different from the devices that are used for DataStax Enterprise. Using multiple directories on JBOD improves performance and capacity. Default: /var/lib/dsefs/data - storage_weight - Weighting factor for this location. Determines TB), gigabyte (10 GB), and megabyte (5000 MB). Default: 268435456 # service_startup_timeout_ms: - service_startup_timeout_ms - Wait time in milliseconds before the DSEFS server times out while waiting for services to bootstrap. Default: 60000 - service_close_timeout_ms - Wait time in milliseconds before the DSEFS server times out while waiting for services to close. Default: 60000 - server_close_timeout_ms - Wait time in milliseconds that the DSEFS server waits during shutdown before closing all pending connections. Default: 2147483647 - compression_frame_max_size - The maximum accepted size of a compression frame defined during file upload. Default: 1048576 - query_cache_size - Maximum number of elements in a single DSEFS Server query cache. Default: 2048 - query_cache_expire_after_ms - The time to retain the DSEFS Server query cache element in cache. The cache element expires when this time is exceeded. Default: 2000 - gossip options - Configures DSEFS gossip rounds. - round_delay_ms - The delay in milliseconds between gossip rounds. Default: 2000 - startup_delay_ms - The delay in milliseconds between registering the location and reading back all other locations from the database. Default: 5000 - shutdown_delay_ms - The delay time in milliseconds between announcing shutdown and shutting down the node. Default: 30000 - rest_options - Configures DSEFS rest times. - request_timeout_ms - The time in milliseconds that the client waits for a response that corresponds to a given request. Default: 330000 - connection_open_timeout_ms - - idle_connection_timeout_ms - The time in milliseconds for RestClient to wait before closing an idle connection. If RestClient does not close connection after timeout, the connection is closed after 2 x this wait time. - time - Wait time to close idle connection. - 0 - Disable closing idle connections. Default: 60000 - internode_idle_connection_timeout_ms - Wait time in milliseconds before closing idle internode connection. The internode connections are primarily used to exchange data during replication. Do not set lower than the default value for heavily utilized DSEFS clusters. Default: 0 - core_max_concurrent_connections_per_host - Maximum number of connections to a given host per single CPU core. DSEFS keeps a connection pool for each CPU core. Default: 8 - transaction_options - Configures DSEFS transaction times. - transaction_timeout_ms - Transaction run time in milliseconds before the transaction is considered for timeout and rollback. Default: 3000 - conflict_retry_delay_ms - Wait time in milliseconds before retrying a transaction that was ended due to a conflict. Default: 200 - conflict_retry_count - The number of times to retry a transaction before giving up. Default: 40 - execution_retry_delay_ms - Wait time in milliseconds before retrying a failed transaction payload execution. Default: 1000 - execution_retry_count - The number of payload execution retries before signaling the error to the application. Default: 3 - block_allocator_options - Controls how much additional data can be placed on the local coordinator before the local node overflows to the other nodes. The trade-off is between data locality of writes and balancing the cluster. A local node is preferred for a new block allocation, if: used_size_on_the_local_node < average_used_size_per_node x overflow_factor + overflow_margin - overflow_margin_mb - margin_size - Overflow margin size in megabytes. - 0 - Disable block allocation overflow Default: 1024 - overflow_factor - factor - Overflow factor on an exponential scale. - 1.0 - Disable block allocation overflow Default: 1.05 DSE Metrics Collector # insights_options: # data_dir: /var/lib/cassandra/insights_data # log_dir: /var/log/cassandra/ Uncomment these options only to change the default directories. - insights_options - Options for DSE Metrics Collector. -/ Audit logging for database activities audit_logging_options: enabled: false logger: SLF4JAuditWriter # included_categories: # excluded_categories: # # included_keyspaces: # excluded_keyspaces: # # included_roles: # excluded_roles: - audit_logging_options - Configures database activity logging. - enabled - Enables database activity auditing. - true - Enable database activity auditing. - false - Disable database activity auditing. Default: false - logger - The logger to use for recording events: Tip: Configure logging level, sensitive data masking, and log file name/location in the logback.xml file. - SLF4JAuditWriter - Capture events in a log file. - CassandraAuditWriter - Capture events in the dse_audit.audit_logtable. Default: SLF4JAuditWriter - included_categories - Comma-separated list of event categories that are captured.. When specifying included categories leave excluded_categories blank or commented out. Default: none (include all categories) - excluded_categories - Comma-separated list of categories to ignore, where the categories are:. Default: exclude no categories - included_keyspaces - Comma-separated list of keyspaces for which events are logged. You can also use a regular expression to filter on keyspace name.Warning: DSE supports using either included_keyspacesor excluded_keyspacesbut not both. Default: include all keyspaces - excluded_keyspaces - Comma-separated list of keyspaces to exclude. You can also use a regular expression to filter on keyspace name. Default: exclude no keyspaces - included_roles - Comma-separated list of the roles for which events are logged.Warning: DSE supports using either included_rolesor excluded_rolesbut not both. Default: include all roles - excluded_roles - The roles for which events are not logged. Specify a comma separated list role names. Default: exclude no roles Cassandra audit writer options retention_time: 0 cassandra_audit_writer_options: mode: sync batch_size: 50 flush_time: 250 queue_size: 30000 write_consistency: QUORUM # dropped_event_log: /var/log/cassandra/dropped_audit_events.log # day_partition_millis: 3600000 - retention_time - The number of hours to retain audit events by supporting loggers for the CassandraAuditWriter. - hours - The number of hours to retain audit events. - 0 - Retain events forever. Default: 0 - cassandra_audit_writer_options - Audit writer options. - mode - The mode the writer runs in. -.Important: While async substantially improves performance under load, if there is a failure between when a query is executed, and its audit event is written to the table, the audit table might be missing entries for queries that were executed. Default: sync - - queue_size - The size of the queue feeding the asynchronous audit log writer threads. - Number of events - When there are more events being produced than the writers can write out, the queue fills up, and newer queries are blocked until there is space on the queue. - 0 - The queue size is unbounded, which can lead to resource exhaustion under heavy query load. Default: 30000 - write_consistency - The consistency level that is used to write audit events. Default: QUORUM - dropped_event_log - The directory to store the log file that reports dropped events. Default: /var/log/cassandra/dropped_audit_events.log - day_partition_millis - The time interval in milliseconds between changing nodes to spread audit log information across multiple nodes. For example, to change the target node every 12 hours, specify 43200000 milliseconds. Default: 3600000 (1 hour) DSE Tiered Storage # tiered_storage_options: # strategy1: # tiers: # - paths: # - /mnt1 # - /mnt2 # - paths: [ /mnt3, /mnt4 ] # - paths: [ /mnt5, /mnt6 ] # # local_options: # k1: v1 # k2: v2 # # 'another strategy': # tiers: [ paths: [ /mnt1 ] ] - tiered_storage_options - Configures the smart movement of data across different types of storage media so that data is matched to the most suitable drive type, according to the required performance and cost characteristics. - strategy1 - The first disk configuration strategy. Create a strategy2, strategy3, and so on. In this example, strategy1 is the configurable name of the tiered storage configuration strategy. - tiers - The unnamed tiers in this section configure a storage tier with the paths and filepaths that define the priority order. - local_options - Local configuration options overwrite the tiered storage settings for the table schema in the local dse.yaml file. See Testing DSE Tiered Storage configurations. - - paths - The section of filepaths that define the data directories for this tier of the disk configuration. List the fastest storage media first. These paths are used to store only data that is configured to use tiered storage and are independent of any settings in the cassandra.yaml file. - - /filepath - The filepaths that define the data directories for this tier of the disk configuration. DSE Advanced Replication # advanced_replication_options: # enabled: false # conf_driver_password_encryption_enabled: false # advanced_replication_directory: /var/lib/cassandra/advrep # security_base_path: /base/path/to/advrep/security/files/ - advanced_replication_options - Configure DSE Advanced Replication. - enabled - Enables an edge node to collect data in the replication log. Default: false - conf_driver_password_encryption_enabled - Enables encryption of driver passwords. See Encrypting configuration file properties. Default: false - advanced_replication_directory - The directory for storing advanced replication CDC logs. The replication_logs directory will be created in the specified directory. Default: /var/lib/cassandra/advrep - security_base_path - The base path to prepend to paths in the Advanced Replication configuration locations, including locations to SSL keystore, SSL truststore, and so on. Default: /base/path/to/advrep/security/files/ Internode messaging internode_messaging_options: port: 8609 # frame_length_in_mb: 256 # server_acceptor_threads: 8 # server_worker_threads: 16 # client_max_connections: 100 # client_worker_threads: 16 # handshake_timeout_seconds: 10 # client_request_timeout_seconds: 60 - internode_messaging_options - Configures the internal messaging service used by several components of DataStax Enterprise. All internode messaging requests use this service. - port - The mandatory port for the internode messaging service. Default: 8609 - frame_length_in_mb - Maximum message frame length. Default: 256 - server_acceptor_threads - The number of server acceptor threads. Default: The number of available processors - server_worker_threads - The number of server worker threads. Default: The default is the number of available processors x 8 - client_max_connections - The maximum number of client connections. Default: 100 - client_worker_threads - The number of client worker threads. Default: The default is the number of available processors x 8 - handshake_timeout_seconds - Timeout for communication handshake process. Default: 10 - client_request_timeout_seconds - Timeout for non-query search requests like core creation and distributed deletes. Default: 60 DSE Multi-Instance - server_id - Unique generated ID of the physical server in DSE Multi-Instance /etc/dse-nodeId/dse.yaml files. You can change server_id when the MAC address is not unique, such as a virtualized server where the host’s physical MAC is cloned. Default: the media access control address (MAC address) of the physical server DataStax Graph (DSG) - configDseYaml.html#configDseYaml__graphDSG system-level - configDseYaml.html#configDseYaml__gremlin_serverDSG Gremlin Server options DSG Gremlin Server # gremlin_server: # port: 8182 # threadPoolWorker: 2 # gremlinPool: 0 # scriptEngines: # gremlin-groovy: # config: # sandbox_enabled: false # sandbox_rules: # whitelist_packages: # - package.name # whitelist_types: # - fully.qualified.type.name # whitelist_supers: # - fully.qualified.class.name # blacklist_packages: # - package.name # blacklist_supers: # - fully.qualified.class.name - gremlin_server - The top-level configurations in Gremlin Server. - port - The available communications port for Gremlin Server. Default: 8182 - threadPoolWorker - The number of worker threads that handle non-blocking read and write (requests and responses) on the Gremlin Server channel, including routing requests to the right server operations, handling scheduled jobs on the server, and writing serialized responses back to the client. Default: 2 - gremlinPool - This pool represents the workers available to handle blocking operations in Gremlin Server. - 0 - the value of the JVM property cassandra.available_processors, if that property is set - positive number - The number of Gremlin threads available to execute actual scripts in a ScriptEngine. Default: the value of Runtime.getRuntime().availableProcessors() - scriptEngines - Configures gremlin server scripts. - gremlin-groovy - Configures for gremlin-groovy scripts. - sandbox_enabled - Configures gremlim groovy sandbox. - true - Enable the gremlim groovy sandbox. - false - Disable the gremlin groovy sandbox entirely. Default: true - sandbox_rules - Configures sandbox rules. - whitelist_packages - List of packages, one package per line, to whitelist. - -package.name - The fully qualified package name. - whitelist_types - List of types, one type per line, to whitelist. - -fully.qualified.type.name - The fully qualified type name. - whitelist_supers - List of super classes, one class per line, to whitelist. - -fully.qualified.class.name - The fully qualified class name. - blacklist_packages - List of packages, one package per line, to blacklist. - -package.name - The fully qualified package name. - blacklist_supers - List of super classes, one class per line, to blacklist. Retain the hyphen before the fully qualified class name. - -fully.qualified.class.name - The fully qualified class name. DSG system-level # graph: # analytic_evaluation_timeout_in_minutes: 10080 # realtime_evaluation_timeout_in_seconds: 30 # schema_agreement_timeout_in_ms: 10000 # system_evaluation_timeout_in_seconds: 180 # adjacency_cache_size_in_mb: 128 # index_cache_size_in_mb: 128 # max_query_params: 16 - graph - System-level configuration options and options that are shared between graph instances. Add an option if it is not present in the provided dse.yaml file. Option names and values expressed in ISO 8601 format used in earlier DSE 5.0 releases are still valid. The ISO 8601 format is deprecated. - analytic_evaluation_timeout_in_minutes - Maximum time to wait for an OLAP analytic (Spark) traversal to evaluate. Default: 10080 (168 hours) - realtime_evaluation_timeout_in_seconds - Maximum time to wait for an OLTP real-time traversal to evaluate. Default: 30 - schema_agreement_timeout_in_ms - Maximum time to wait for the database to agree on schema versions before timing out. Default: 10000 - system_evaluation_timeout_in_seconds - Maximum time to wait for a graph system-based request to execute, like creating a new graph. Default: 180 (3 minutes) - adjacency_cache_size_in_mb - The amount of ram to allocate to each graph's adjacency (edge and property) cache. Default: 128 - index_cache_size_in_mb - The amount of ram to allocate to the index cache. Default: 128 - max_query_params - The maximum number of parameters that can be passed on a graph query request for TinkerPop drivers and drivers using the: 16

https://docs.datastax.com/en/dse/6.8/dse-admin/datastax_enterprise/config/configDseYaml.html

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Joystick CHOP Summary[edit] The Joystick CHOP outputs values for all 6 possible axes on any game controller (joysticks, game controllers, driving wheels, etc.), as well as up to 32 button, 2 sliders and 4 POV Hats. It handles game controllers connected to the gameport or USB ports, including the 3D Connexion mouse. You can have several devices attached, and any number of Joystick CHOPs in a project per device. Before you use the game controller on your computer, calibrate them using Start -> Settings -> Control Panel -> Gaming Options -> Properties. The main two outputs, the X-axis and Y-axis are output through channels called xaxis and yaxis. The other four axes are output through channels with similar names. The range of the values for each channel is 0 to 1. For any axis, a value 0.5 is considered "centered". A value of 0 is given if the axis doesn't exist. For any button, a value of 0 means the button is up or doesn't exist. A value of 1 means the button is pressed. POV Hats behave like an X and Y axis. A POV axis only has 3 values though, 0, 0.5 and 1. Contents Parameters - Control Page Joystick Source source - This menu will list all the game controllers currently attached to the computer. The selected game controller is the one the CHOP reads data from. If the synth is saved with one joystick name, and the synth is moved to a machine with another joystick type, the CHOP will adopt the first game controller it find to replace the missing device. Axis Range axisrange - ⊞ - - [-1, 1] negoneone- - [0, 1] zeroone- X Axis xaxis - The name of the channel that records the X-axis position of the game controller. Y Axis yaxis - The name of the channel that records the Y-axis position of the game controller. Invert Y Axis yaxisinvert - Z Axis zaxis - The name of the channel that records the Z-axis position of the game controller. X Rotation xrot - The names of the channels that record the X-rotation axis position of the game controller. Y Rotation yrot - The names of the channels that record the Y-rotation axis position of the game controller. Invert Y Rotation yrotinvert - Z Rotation zrot - The names of the channels that record the Z-rotation axis position of the game controller. Slider 1 slider0 - The name of the channel that records the position of the first slider on the game controller. Slider 2 slider1 - The name of the channel that records the position of the second slider on the game controller. Button Array buttonarray - The names of the channels for the buttons on the game controller. This CHOP can handle up to 32 buttons. POV Hat Array povarrray - The names of the channels for the POV Hats. This CHOP can handle up to 4 POV Hats. The channels a POV hat is split up into are POVHatName_X and POVHatName_Y. POV Hat State Array povstatearray - Connected connected - Axis Dead Zone axisdeadzone - This value defines how much of the area in the center of the joystick is considered 'dead zone'. When a joystick axis is in this dead zone it is considered to be centered. This value applies to all normal axes and rotation axes. This value is a percentage that defaults to 7%. Parameters - Channel Page Sample Rate rate - Extend Left left - ⊞ - - Hold hold- - Slope slope- - Cycle cycle- - Mirror mirror- - Default Value default- Extend Right right - ⊞ - - Hold hold- - Slope slope- - Cycle cycle- - Mirror mirror- - Default Value default- Default Value def custom interactive control panel built within TouchDesigner. Panels are created using Panel Components whose look is created entirely with TOPs.'.

https://docs.derivative.ca/Joystick_CHOP

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Use the following options for your WHMCS: - Module User Type : If you are an admin or reseller (Currently only Admin supported, Non of beta versions will have this function) - Default Node : The node the virtual server will be built on if no overide is given - Master Server : The master server this package is assigned to - Default Plan : Then plan on the master the package is assigned to - Virtualisation Type : Choose either OpenVZ, Xen-PV, Xen-HVM or KVM - Default Operating System : The operating system that is used if not defined in configurable options. - Username Prefix : This is the unique prefix that defines the username for each client. Set this the same in all plans. i.e: vmuser - IP Addresses : The amount of ip addresses for this package - Node Group : The node group this product should be built on (This will override the default node) Click Save Changes. Select the Custom Fields tab.

https://docs.solusvm.com/display/DOCS/Options+Explained

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Objects when the player gets or loses focus. OnApplicationFocus is called when the application loses or gains focus. Alt-tabbing or Cmd-tabbing can take focus away from the Unity application to another desktop application. This causes the GameObjects to receive an OnApplicationFocus call with the argument set to false. When the user switches back to the Unity application, the GameObjects receive an OnApplicationFocus call with the argument set to true. OnApplicationFocus; } }

https://docs.unity3d.com/2020.2/Documentation/ScriptReference/MonoBehaviour.OnApplicationFocus.html

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docs.unity3d.com

Use the Python SDK to extend the Release plugin for Deploy You can extend the functionality of the official Release plugin for Deploy by using xldeploy-py, the Python SDK for Deploy. The SDK allows interaction with Deploy over the REST services and supports the following: - Deployfile - Deployment - Metadata - Package - Repository - Tasks Each of the above services has specific methods that correspond to the REST services offered by Deploy. Note: Not all methods and services available in the REST API are currently supported by the SDK. The SDK is customizable depending on the requirements of the clients using it. Custom tasks In Release, you can create custom tasks. A custom task contains an XML section that becomes part of the synthetic.xml in the ext folder of the Release server, and a Python file stored to a location adjacent to synthetic.xml. For more information, see How to Create Custom Task Types. To extend the Release plugin for Deploy, you can create custom tasks that can retrieve items from Deploy or perform actions in Deploy using the SDK. These custom tasks can extend the xldeploy.XldTask to reuse properties required for any task related to Deploy. Example of defining a custom task Create a custom task to check if a CI exists on the Deploy Server. The new type to be included in synthetic.xml contains one new scriptLocation parameter representing the location of the python script within the ext directory. The other parameters are inherited from xldeploy.XldTask. Modify the synthetic.xml <type type="xld.CheckCIExist" extends="xldeploy.XldTask" label="XL-Deploy: Check CI exists" description="Custom Task to check if a CI exists"> <property name="scriptLocation" default="CheckExists.py" hidden="true"/> <property name="ci_path" category="input" label="CI Path" required="true"/> </type> The CheckExists.py Python script referred to in the sample XML above can perform the required actions using the Python SDK. The official plugin already contains methods to create the xldeploy-py client. You must pass the server property of the task as an argument to method get_api_client(). The client returned can be used to call methods on any of the services mentioned above. The CheckExists.py python script from xlrxldeploy import * client = get_api_client(task.getPythonScript().getProperty("server")) path="some/path/to/a/ci" print (client.repository.exists(path))

https://docs.xebialabs.com/v.9.7/release/how-to/extend-official-xlr-xld-plugin-using-python-sdk/

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docs.xebialabs.com

Cisco ISE appliance The Cisco ISE platform is a comprehensive, next-generation, contextually-based access control solution. Cisco ISE offers authenticated network access, profiling, posture, guest management, and security group access services along with monitoring, reporting, and troubleshooting capabilities on a single physical or virtual appliance. More informations on Starting ISE will start an installation of ISE onto a blank 200GB Drive. This will take time. The intial username is setup. This appliance requires KVM. You may try it on a system without KVM, but it will run really slow, if at all. RAM: 4096 MB You need KVM enable on your machine or in the GNS3 VM. Documentation for using the appliance is available on

http://docs.gns3.com/appliances/cisco-ise.html

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docs.gns3.com

Manual repair: Anti-entropy repair Describe how manual repair works. Anti-entropy node repairs are important for every Cassandra cluster. Frequent data deletions and downed nodes are common causes of data inconsistency. Use anti-entropy repair for routine maintenance and when a cluster needs fixing by running the nodetool repair command. How does anti-entropy repair work? - Build a Merkle tree for each replica - Compare the Merkle trees to discover differences Merkle trees are binary hash trees whose leaves are hashes of the individual key values. The leaf of a Cassandra Merkle tree is the hash of a row value. Each Parent node higher in the tree is a hash of its respective children. Because higher nodes in the Merkle tree represent data further down the tree, Casandra can check each branch independently without requiring the coordinator node to download the entire data set. For anti-entropy repair Cassandra uses a compact tree version with a depth of 15 (2^15 = 32K leaf nodes). For example, a node containing a million partitions with one damaged partition, about 30 partitions are streamed, which is the number that fall into each of the leaves of the tree. Cassandra works with smaller Merkle trees because they require less storage memory and can be transferred more quickly to other nodes during the comparison process. After the initiating node receives the Merkle trees from the participating peer nodes, the initiating node compares every tree to every other tree. If the initiating node detects a difference, it directs the differing nodes to exchange data for the conflicting range(s). The new data is written to SSTables. The comparison begins with the top node of the Merkle tree. If Cassandra detects no difference between corresponding tree nodes, the process goes on to compares the left leaves (child nodes), then the right leaves. A difference between corresponding leaves indicates inconsistencies between the data in each replica for the data range that corresponds to that leaf. Cassandra replaces all data that corresponds to the leaves below the differing leaf with the newest version of the data. Merkle tree building is quite resource intensive, stressing disk I/O and using memory. Some of the options discussed here help lessen the impact on the cluster performance. For details, see Repair in Cassandra. You can run the nodetool repair command on a specified node or on all nodes. The node that initiates the repair becomes the coordinator node for the operation. The coordinator node finds peer nodes with matching ranges of data and performs a major, or validation, compaction on each peer node. The validation compaction builds a Merkle tree and returns the tree to the initiating node. The initiating mode processes the Merkle trees as described. Full vs Incremental repair The process described above represents what occurs for a full repair of a node's data: Cassandra compares all SSTables for that node and makes necessary repairs. Cassandra 2.1 and later support incremental repair. An incremental repair persists data that has already been repaired, and only builds Merkle trees for unrepaired SSTables. This more efficient process depends on new metadata that marks the rows in an SSTable as repaired or unrepaired. If you run incremental repairs frequently, the repair process works with much smaller Merkle trees. The incremental repair process works with Merkle trees as described above. Once the process had reconciled the data and built new SSTables, the initiating node issues an anti-compaction command. Anti-compaction is the process of segregating repaired and unrepaired ranges into separate SSTables, unless the SSTable fits entirely within the repaired range. If it does, the process just updates the SSTable's repairedAt field. - Size-tiered compaction splits repaired and unrepaired data into separate pools for separate compactions. A major compaction generates two SSTables, one for each pool of data. - Leveled compaction performs size-tiered compaction on unrepaired data. After repair completes, Casandra moves data from the set of unrepaired SSTables to L0. Full repair is the default in Cassandra 2.1 and earlier. Parallel vs Sequential Sequential repair takes action on one node after another. Parallel repair repairs all nodes with the same replica data at the same time. Sequential repair takes a snapshot of each replica. Snapshots are hardlinks to existing SSTables. They are immutable and require almost no disk space. The snapshots are live until the repair is completed and then Cassandra removes them. The coordinator node compares the Merkle trees for one replica after the other, and makes required repairs from the snapshots. For example, for a table in a keyspace with a Replication factor RF=3 and replicas A, B and C, the repair command takes a snapshot of each replica immediately and then repairs each replica from the snapshots sequentially (using snapshot A to repair replica B, then snapshot A to repair replica C, then snapshot B to repair replica C). Parallel repair works on nodes A, B, and C all at once. During parallel repair, the dynamic snitch processes queries for this table using a replica in the snapshot that is not undergoing repair. Snapshots are hardlinks to existing SSTables. Snapshots are immutable and require almost no disk space. Repair requires intensive disk I/O because validation compaction occurs during Merkle tree construction. For any given replica set, only one replica at a time performs the validation compaction. Partitioner range ( -pr) nodetool repairon one node at a time, Cassandra may repair the same range of data several times (depending on the replication factor used in the keyspace). Using the partitioner range option, nodetool repaironly repairs a specified range of data once, rather than repeating the repair operation needlessly. This decreases the strain on network resources, although nodetool repairstill builds Merkle trees for each replica. nodetool repair -pron EVERY node in the cluster to repair all data. Otherwise, some ranges of data will not be repaired. Local ( -local, --in-local-dc) vs datacenter ( dc, --in-dc) vs Cluster-wide Consider carefully before using nodetool repair across datacenters, instead of within a local datacenter. When you run repair on a node using -local or --in-local-dc, the command runs only on nodes within the same datacenter as the node that runs it. Otherwise, the command runs repair processes on all nodes that contain replicas, even those in different datacenters. If run over multiple datacenters, nodetool repair increases network traffic between datacenters tremendously, and can cause cluster issues. If the local option is too limited, consider using the -dc or --in-dc options, limiting repairs to a specific datacenter. This does not repair replicas on nodes in other datacenters, but it can decrease network traffic while repairing more nodes than the local options. The nodetool repair -pr option is good for repairs across multiple datacenters, as the number of replicas in multiple datacenters can increase substantially. For example, if you start nodetool repair over two datacenters, DC1 and DC2, each with a replication factor of 3, repairmust build Merkle tables for 6 nodes. The number of Merkle Tree increases linearly for additional datacenters. -localrepairs: - The repairtool does not support the use of -localwith the -proption unless the datacenter's nodes have all the data for all ranges. - Also, the tool does not support the use of -localwith -inc(incremental repair). -dcparor --dc-parallelto repair datacenters in parallel. Endpoint range vs Subrange repair ( -st, --start-token, -et --end-token) A repair operation runs on all partition ranges on a node, or an endpoint range, unless you use the -st and -et (or -start-token and -end-token ) options to run subrange repairs. When you specify a start token and end token, nodetool repair works between these tokens, repairing only those partition ranges. Subrange repair is not a good strategy because it requires generated token ranges. However, if you know which partition has an error, you can target that partition range precisely for repair. This approach can relieve the problem known as overstreaming, which ties up resources by sending repairs to a range over and over. You can use subrange repair with Java to reduce overstreaming further. Send a Java describe_splits call to ask for a split containing 32k partitions can be iterated throughout the entire range incrementally or in parallel. Once the tokens are generated for the split, you can pass them to nodetool repair -st <start_token> -et <end_token>. Add the -local option to limit the repair to the local datacenter. This reduces cross datacenter transfer.

https://docs.datastax.com/en/cassandra/2.1/cassandra/operations/opsRepairNodesManualRepair.html

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[array(['../images/ops_inc_repair.png', None], dtype=object)]

docs.datastax.com

Set-SPCentral Administration Syntax Set-SPCentralAdministration -Port <Int32> [-AssignmentCollection <SPAssignmentCollection>] [-Confirm] [-WhatIf] [-SecureSocketsLayer] [<CommonParameters>] Description The Set-SPCentralAdministration cmdlet sets the port for the Central Administration site. For permissions and the most current information about Windows PowerShell for SharePoint Products, see the online documentation at (). Examples ------------------EXAMPLE------------------ C:\PS>Set-SPCentralAdministration -Port 8282 This example sets the port for the Central Administration web application on the local farm to 8282. Required Parameters Specifies the administration port for the Central Administration site. The type must be a valid port number; for example, 8080. {{Fill SecureSocketsLayer Description}} Displays a message that describes the effect of the command instead of executing the command. For more information, type the following command: get-help about_commonparameters

https://docs.microsoft.com/en-us/powershell/module/sharepoint-server/Set-SPCentralAdministration?view=sharepoint-ps

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docs.microsoft.com

Bug Check 0x9C: MACHINE_CHECK_EXCEPTION The MACHINE_CHECK_EXCEPTION bug check has a value of 0x0000009C. This bug check indicates that a fatal machine check exception has occurred. Important This topic is for programmers. If you are a customer who has received a blue screen error code while using your computer, see Troubleshoot blue screen errors. MACHINE_CHECK_EXCEPTION Parameters The four parameters that are listed in the message have different meanings, depending on the processor type. If the processor is based on an older x86-based architecture and has the Machine Check Exception (MCE) feature but not the Machine Check Architecture (MCA) feature (for example, the Intel Pentium processor), the parameters have the following meaning. If the processor is based on a newer x86-based architecture and has the MCA feature and the MCE feature (for example, any Intel Processor of family 6 or higher, such as Pentium Pro, Pentium IV, or Xeon), or if the processor is an x64-based processor, the parameters have the following meaning. On an Itanium-based processor, the parameters have the following meaning. Note Parameter 1 indicates the type of violation. Remarks In Windows Vista and later operating systems, this bug check occurs only in the following circumstances. - WHEA is not fully initialized. - All processors that rendezvous have no errors in their registers. For other circumstances, this bug check has been replaced with bug Check 0x124: WHEA_UNCORRECTABLE_ERROR in Windows Vista and later operating systems. For more information about Machine Check Architecture (MCA), see the Intel or AMD Web sites.

https://docs.microsoft.com/en-us/windows-hardware/drivers/debugger/bug-check-0x9c--machine-check-exception

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docs.microsoft.com

BurnEngine™ is a framework from ThemeBurn.com that adds advanced layout and cms features to OpeCart themes. It has unified control panel, which delivers consistent experience across different themes and stores. BurnEngine stands as a layer between OpenCart and the theme – it manages a huge amount of options to customize your shop, while keeping backward compatibility with third party OpenCart modules and extensions. Why relying on BurnEngine™ BurnEngine helps you control every aspect of the theme – appearance, performance, data migration etc. Most importantly – it offers a single codebase. The main benefit from the single codebase is that all of the themes build on BurnEngine receive bug fixes and feature additions at once. Every BurnEngine compatible theme gets regular updates, regardless of its initial release date. This mitigates the negative effect of low (or even lacking) support for old themes that is typical for the theme market. BurnEngine does not limit itself to cms settings only. It brings system-wide features like performance optimizations, SEO tools, ecommerce settings and many more. It has also internal plugin system, which guarantees that every installed BurnEngine extension is 100% compatible with all of the themes. The question `Is extension X compatible with my theme?` becomes thus obsolete. BurnEngine is not a trendy, little tested technology that just came out of the wild. It lies on years of experience and testing from ThemeBurn team. It was incorporated under-the-hood in ThemeBurn’s themes much before its release as BurnEngine™ brand and powers many stores that operate for a long time. ThemeBurn’s long-term engagement guarantees your investment when relying on a BurnEngine powered theme. By using a BurnEngine compatible theme you can count on continuous support and new features, because BurnEngine evolves separately from the theme. The BurnEngine release cycle is not targeted at specific theme. Every new BurnEngine version adds fixes and options to all existing themes that are compatible with it. You won’t be forced to buy new themes in order to get improved functionality. How to obtain BurnEngine™ BurnEngine is not installed separately from your theme – it is included in your theme package. You don’t have to take additional actions to activate it. You can have unlimited themes that rely on BurnEngine – they are all managed from a single control panel. If you have bought a themeburn’s theme (Pavilion, Kiddos etc.) you already have BurnEngine – just proceed to installation.

http://docs.themeburn.com/burnengine/concepts/

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docs.themeburn.com

VS Code¶ If you use VS Code, you can also install the Imandra IDE Extension plugin to help with development, providing things like completion and syntax highlighting as well as full asynchronous proof checking and semantic editing, meaning you can see proved theorems, counterexamples and instances within the IDE as you type. In order to install the standard extension, first follow the instructions above for installing Imandra. Then in VSCode itself VSCode go to the extensions view by following the instructions about the Extension Marketplace and searching for Imandra IDE. The extension will open automatically when a file with extension .iml or .ire is opened. The extension will look for the correct version of ocamlmerlin on the opam switch associated with the folder in which the opened .iml or .ire file resides (defaulting to the current global switch). We recommend that the current global switch is that produced by the recommended installation of Imandra, as that contains all the artifacts to facilitate correct Imandra type inference, asynchronous proof checking and other language server features. Below are example images of the type information the Imandra IDE provides in VSCode. With Simple Installation¶ If you have used the Simple installation instructions for Imandra then the VSCode extension should work automatically. With Manual Installation¶ If you have used the Manual installation instructions for Imandra then it is necessary to modify some of the settings in VSCode by hand. Pressing CMD+ , takes you to the settings section of VSCode. It is necessary to alter the following settings: imandra_merlinand enter here the result of typing which imandra-merlinin a terminal where you installed Imandra. So for example if you had installed Imandra in ~/imandrayou would add for this setting: ~/imandra/imandra-merlin imandra-vscode-serverand enter here the result of typing which imandra-vscode-serverthen -serverthen which imandra_network_clientin a terminal where you installed Imandra. So for example if you had installed Imandra in ~/imandrayou would add for this setting: ~/imandra/imandra-vscode-server -server ~/imandra/imandra_network_client As is shown in the following screen shot.

https://docs.imandra.ai/imandra-docs/notebooks/installation-vscode/

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[array(['https://storage.googleapis.com/imandra-assets/images/docs/ImandraVSCodeManualOpam.png', 'Example settings screen'], dtype=object) ]

docs.imandra.ai

Identity models and authentication in Microsoft Teams Microsoft Teams support all the identity models that are available with Office 365. Supported identity models include: Cloud Identity: In this model, a user is created and managed in Office 365 and stored in Azure Active Directory, and the password is verified by. Configurations Depending on your organization’s decisions of which identity model to implement and use, the implementation requirements may vary. Refer to the requirements table below to ensure that your deployment meets these prerequisites. If you have already deployed Office 365 and have already implemented the identity and authentication method, you may skip these steps. Refer to Choosing a sign-in model for Office 365 and Understanding Office 365 identity and Azure Active Directory guides for additional details. Multi-Factor Authentication Office 365 plans support Multi-Factor Authentication (MFA) that increases the security of user logins to Office 365 services. With MFA for Office 365, users are required to acknowledge a phone call, text message, or an app notification on their smartphone after correctly entering their password. Only after this second authentication factor has been satisfied, can a user sign in. Multi Factor authentication is supported with any Office 365 plan that includes Microsoft Teams. The Office 365 subscription plans that include Microsoft Teams are discussed later in the Licensing section below. Once the users are enrolled for MFA, the next time a user signs in, they will see a message that asks them to set up their second authentication factor. Supported authentication methods are: Feedback

https://docs.microsoft.com/en-us/MicrosoftTeams/identify-models-authentication?redirectSourcePath=%252fen-us%252farticle%252fModern-authentication-and-Microsoft-Teams-desktop-clients-71467704-92DB-4253-A086-5F63C4A886CA

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docs.microsoft.com

For information about the Revenue Recognition feature that generates data in this export and how to configure this feature, visit Revenue Recognition Revenue Recognition Schedules The Revenue Recognition Schedules export provides a list of all schedules from each charge or credit and the corresponding revenue amortization information. This export will only appear in the admin console for for sites that have enabled the feature. Invoice Status Filter All Every invoice generated within your Recurly site, regardless of status. Open All invoices that have not received a payment attempt. This status has a value of 'pending' in the export and does not include invoice with a 'past_due' status. This status only exists for manual invoices. If you see this status for an automatically collected invoice, it may mean that the payment attempt had a transaction communication error. This filter will also include all credit invoices which have not been fully allocated to charge invoices. Closed All successfully collected invoices. This status has a value of 'paid' in the export and does not include the 'failed' status. It is important to note that the associated credit card, PayPal, or Amazon transaction with a paid invoice may not be settled with the gateway. Allow at least 1 to 3 business days for transaction settlement with the gateway. This will also include all credit invoices which have been fully allocated to charge invoices. Past Due All automatic invoices that attempted collection, but payment failed, or manual invoices that have reached their due date. Payment for automatic invoices will be retried automatically through the dunning process. Failed All invoices that have either experienced 20 declines, or have been through the dunning process without successful payment. These invoices will not be retried. It is important to note that failed invoices will clear the owed balance from the account and will not attempt any future collections, but the charge line items will still exist in the account history. Time Range Filter Created The revenue recognition schedules export uses the invoice_created_at date column for the time range filter. In other words, all schedules from invoices created in the selected time range will be included in the results. Export Contents revenue_schedule_id 224934254870915 the unique identifier of the revenue schedule invoice_number 192880 The invoice number from the Recurly UI. invoice_date 2017-06-01 00:00:00 UTC Creation date of the invoice. invoice_state paid, processing, open, failed the current state of the invoice account_code 11734 Account code being charged for this invoice. accounting_code monthly_fee Internal accounting code for a specific invoice line item. This value will only populate if you define an accounting code for the line item. Accounting codes can be defined for all line items. Plan set-up fees and plan free trial charges will inherit the plan's accounting code. line_item_id 3a6ae2555a2e2a76c077604bf5b90457 Unique internal identifier for the adjustment. Also called line_item_uuid or adjustment_uuid in other exports. line_item_start_date 2017-06-01 12:00:00 UTC Bill cycle start date for a specific invoice line item. Equivalent to line_item_start_date in the deprecated Invoices export. line_item_end_date 2017-07-01 12:00:00 UTC Bill cycle end date for a specific invoice line item. This date will not exist for custom charges (one-time). origin plan, add_on, credit, charge the original source for a line item. A credit created from an original charge will have the value of the charge's origin. (plan = subscription fee, plan_trial = trial period 0 amount charge, setup_fee = subscription setup fee, add_on = subscription add-on fee, debit = custom charge through the UI or Adjustments API, one_time = custom charge through the Transactions API, credit = custom credit days 31 number of days between line_item_start_date and line_item_end_date amount_per_day 4.88 the total amount divided by the number of days total_amount 1000 total amount of the charge to be recognized for this schedule, in other words "subtotal after discounts) currency USD currency of the line item schedule_type at_range_start, evenly the way in which revenue is recognized for that schedule, as defined in the plan settings for revenue recognition revenue_recognition_date 2017-06-05 00:00:00 UTC the date revenue is recognized. only populated if schedule type is at_range_start or at_rate_end deferred_revenue_balance evenly reflects the total remaining deferred revenue to be recognized as of the date the export is requested For example, if you specify an export date range of Dec 1- 31, the deferred revenue balance will calculate all remaining revenue on the schedule yet to be recognized as of December 31st. arrears 0 the portion of the line item that applies to the past, i.e. before time range "start date" month_1 20 the total amount recognized in a specific month. Note that the first month will be the current month month_2 20 the total amount recognized in a specific month. The second column will be the month after the current month month_3, etc. 20 the total amount recognized in a specific month. There will be a column for each month after the current month, creating 12 columns of months in total future_revenue 90 revenue to be recognized in the future past 12 months from the current month invoice_origin purchase, refund The event that created the invoice. Invoices issued before the Credit Invoices feature was enabled on your site will have either purchase or refund as the value. Once Credit Invoices is enabled, you can see new origins like renewal, immediate_change, and write_off.

https://docs.recurly.com/docs/revenue-recognition-export

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docs.recurly.com

STV_WLM_QUERY_STATE Records the current state of queries being tracked by WLM. STV_WLM_QUERY_STATE is visible to all users. Superusers can see all rows; regular users can see only their own data. For more information, see Visibility of Data in System Tables and Views. Table Columns Sample Query The following query displays all currently executing queries in service classes greater than 4. For a list of service class IDs, see WLM Service Class IDs. select xid, query, trim(state), queue_time, exec_time from stv_wlm_query_state where service_class > 4; This query returns the following sample output: xid | query | btrim | queue_time | exec_time -------+-------+---------+------------+----------- 100813 | 25942 | Running | 0 | 1369029 100074 | 25775 | Running | 0 | 2221589242

https://docs.aws.amazon.com/redshift/latest/dg/r_STV_WLM_QUERY_STATE.html

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docs.aws.amazon.com

This page provides information on the V-Ray IES Light. Page Contents Overview The V-Ray IES Light is a V-Ray specific light source plugin that can be used to create physically accurate area lights. UI Paths: V-Ray Lights Toolbar > IES Light Extensions > V-Ray > V-Ray Lights > IES Light Main Enabled () – Turns the VRayLight on and off. Color – Specifies the color of the light. Intensity – Specifies the strength of the light.. The Diameter parameter is only available when the Circle and Sphere shapes are selected..

https://docs.chaosgroup.com/pages/viewpage.action?pageId=30835180

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docs.chaosgroup.com

We have provided two distinct methods of installing Codecov Enterprise. We highly suggest using Docker, which is the easiest and quickest deployment option. There are two main methods when deploying with Docker Compose, with varying degrees of configuration, availability, and scale. It is recommended to read the Codecov Enterprise Deployment Strategies documentation. However, if you're just seeking a trial/proof of concept deployment of Codecov Enterprise, see Deploying with Docker Compose. Full deployment scripts using terraform can be found for AWS, GCP, and Azure (coming soon) here: Supported pathways currently in progress, if you would like a custom deployment / orchestration, please reach out to us directly at [email protected] Linux / bare metal deprecated As of January 22nd, 2019, we have deprecated support for Linux / bare metal. In line with industry best practices, we recommend placing your enterprise install of Codecov behind your company's firewall, or otherwise perform other access controls such that it is only accessible by trusted staff and employees. Other Best Practices

https://docs.codecov.io/docs/install-guide

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docs.codecov.io

February 2013 Volume 28 Number 02 Patterns in Practice - Data Design for Adding Functionality to a Class By Peter Vogel | February 2013 In my last column, I described a common business problem: A SalesOrder with multiple OrderLines, with each OrderLine specifying a Product being bought by a Customer and the SalesOptions the customer has chosen to apply to that OrderLine/Product combination (e.g. giftwrapping, expediting). Those SalesOptions affect how that OrderLine/Product combination will be processed, including calculating the price for that purchase. In that column, I looked at a couple of patterns that might support a solution that was maintainable, extendable, testable and understandable. In the end, I decided that calculating the price should be handled by the decorator pattern because it would allow the SalesOptions to interact as required by the organization’s business rules (e.g. expediting increases the price of the Product by a set percentage after all other discounts are applied and ignoring any costs associated with giftwrapping). To deal with the rest of the processing required by the SalesOptions, I decided my best choice was to implement some version of the Roles pattern. This column will look at the data design required to support implementing the solution. Basic Tables I’m old fashioned enough to begin to develop any solution by designing the database tables that I’ll need in my relational database (of course, if this organization had enough sales orders they might need a big data solution—but that isn’t the case here). Obviously, I’ll need a table listing valid SalesOptions. The primary key for this SalesOptions table is the SalesOptionId and the table has at least one other column: the SalesOption description, which is displayed in the user interface when the user is selecting or reviewing the SalesOptions for an OrderLine. Any data about the SalesOption that doesn’t vary from one Product to another will also go in this table. Because not all SalesOptions can be applied to all Products, I also need a table of valid SalesOptions and Product combinations (the company sells both “things” and services, for instance, and you can’t gift wrap a service). This ValidSalesOptionsForProducts table would have a primary key made up of two columns: the ProductId and SalesOptionId. The table might have some additional columns to hold data related to the relationship between a particular Product and SalesOption. If there’s no data associated with a Product/SalesOption combination, however, there’s another data design possible. If most SalesOptions apply to most Products, it would be more efficient to create an InvalidSalesOptionsForProduct table that lists just the Products and SalesOptions that can’t be combined. If the organization has thousands of products, a table of exceptions would be more efficient than a table of allowed combinations. And before I get tons of comments about using natural keys/real data as the primary keys of my table: You’re perfectly welcome to give the ValidSalesOptionsForProduct table a meaningless primary key (a GUID or Identity key of some kind) and apply a unique index to the combination of SalesOptionId and ProductId. SQL Server’s hashing mechanisms for building an index will almost certainly give your better performance whenever you need to use that meaningless key as the foreign key of some other table. But, for this discussion, I don’t need that meaningless primary key so I will ignore that option. That also applies to the next table that I’ll discuss. For any particular OrderLine, I’ll also need a table to record the SalesOptions that have been applied to it. That SalesOptionForOrderLine table will have a primary key, or unique index, consisting of the OrderId, the OrderLineId, and the SalesOptionId. Supporting SalesOptions Finally, each SalesOption will have data associated with its application to a particular Product/OrderLine. For instance, if the user selects the expediting SalesOption, the user needs to select the level of expediting (NextDay or Urgent); for the giftwrapping SalesOption the user will need to specify the type of giftwrapping. There are at least two different data designs that would support recording this information. One design is to simply add the required columns to the SalesOptionForOrderLine table: a GiftwrapId column for the giftwrapping option and the ExpediteLevel for the expediting option. For any particular SalesOption most of those columns would be Null; in other words, for the giftwrapping SalesOption the GiftwrapId column will have a valid value but the ExpediteLevel column will hold a Null value. In the other design, each SalesOption would have its own SalesOptionData table (including ExpediteSalesOptionData and GiftwrapSalesOptionData). Each of these tables would have as its primary key the SalesOrderId and OrderLineId and the columns required by the SalesOption (e.g. the ExpediteSalesOptionData table would have an ExpediteLevel column, the GiftwrapSalesOptionData table would have GiftwrapId column). There’s no universally right answer here—the right answer will depend on the way the business works. For instance, having a separate SalesOptionsData table for each SalesOption requires me to use one of two data access plans when processing the OrderLines in a SalesOrder. - For each OrderLine, make a separate trip to the database to retrieve the row from the relevant SalesOptionData table (more trips = worse performance). - Fetch all of the rows from all of the relevant SalesOptionsData table by joining the OrderLine to all of the SalesOptionsData table with outer joins (more outer joins = worse performance). This solution also requires me to rewrite this query every time a new SalesOption is added to the application. If the number of OrderLines being processed at any one time is small (such as one SalesOrder’s worth of OrderLines which is, typically, less than six OrderLines), I could live with the performance hit that comes with either data access plan. I could also live with either data access plan if any particular part of the organization only needs to process a small number of specific SalesOptions (if the shipping department only needs to retrieve the information for the Expedite SalesOption). However, if the number of OrderLines being processed at any one time is large ( if I process many SalesOrders at a time) or if there’s a part of the organization that needs to handle all of the SalesOptions applied to an OrderLine then the performance impact could be crippling. Looking at the business, I can see that any part of the organization will typically only be interested in a few specific SalesOptions applied to a Product/OrderLine combination. The one exception is the order taking application—however, it only works with one SalesOrder’s worth of OrderLines at a time. However, there are several places in the organization where many Orders are processed at once. The shipping department, for instance, prepares a day’s worth of shipping at a time to support combining shipments and reducing costs. That second fact drives me to adding nullable columns to the SalesOptionForOrderLine table. I admit to having another reason for adding nullable columns to the SalesOptionForOrderLine table. I also know that the amount of data associated with a SalesOption is typically small. If I used individual tables I’d end up with tables that have only one or two columns (other than their primary key columns). I have a visceral objection to that though I’m not sure that I could justify it. Putting all of this together, it means that, on the data side, adding a new SalesOption consists of adding: - A row to the SalesOptions table - Multiple rows to the ValidSalesOptionsForProduct table - Additional columns to the SalesOptionForOrderLine table Next Steps And, of course, adding a new SalesOption requires creating the appropriate role object to hold the code for processing the SalesOption. So that’s next month’s column—the object model. One of the things that you may have noticed is that I’ve frequently referred to the way that the organization works both in the design phase and in the implementation phase (for me, the implementation phase includes deciding on the details of the design). That’s another one of the assumptions of this column: conditions alter cases. Your deep understanding of how your organization works is critical not only in selecting the right pattern for your application but also in deciding what makes sense when it comes to implementing that pattern. That’s one of the reasons that I appreciate patterns so much: they’re a support rather than a straightjacket and allow me to tailor the solution to my clients’ needs. Sidebar: Which Class Should Manage SalesOptions? One of the comments made by a reader on the original column suggested that the OrderLine should be responsible for managing the SalesOptions rather than the Products. That’s a good question. Certainly, the data design that ties OrderLines to SalesOptions suggests that it’s a reusable choice. But it really leads to more interesting question for this column: What would be the basis for deciding where to put control of the SalesOption role objects? The question is made a little harder to decide because, in this organization, an OrderLine always has a Product assigned to it; disentangling the two business entities is hard to do. The reason that I felt that the Product should manage the SalesOptions was because of the necessity of validating SalesOptions against the ValidSalesOptionsForProduct table—I assumed would be handled by code in the Product class and that the rest of the SalesOptions code would go in the Product also. However, I’m not sure that’s a compelling argument; The code in an OrderLine class could validate the Product associated with the OrderLine as easily as the Product class could because an OrderLine always knows what Product is associated with it. One way to make the decision would be to look at the way that the business is run. If, after assigning a SalesOption to a Product/Orderline combination, is it possible to move that Product to another OrderLine in the SalesOrder? Or to change the Product assigned to the OrderLine? If either of those changes is possible, what happens to the SalesOptions? Do the SalesOptions follow the Product to another OrderLine or stay with the OrderLine? If you replace the Product on an OrderLine with a new Product, would it always retain the SalesOptions assigned to the original Product? The SalesOptions stay with the OrderLine, it suggests that the OrderLine is responsible for the SalesOptions. Another way to answer the question is to look at how the classes will be used elsewhere in the business’ processes. If I know that, somewhere in the organization, Products and SalesOptions needed to be processed even when the Products aren’t associated with an OrderLine then I will have a compelling reason for keeping the responsibility of processing the SalesOption with the Product. I do have one scenario where Products and SalesOptions are processed independently of an OrderLine: Some SalesOptions have different processing, depending on which Product the SalesOption is assigned to and regardless of the state of the OrderLine involved. For instance, expediting a “thing” is different from expediting a “service”; expediting a thing means shipping it earlier, while expediting a service means the team delivering the service goes to the customer’s site earlier. As a result, when an application asks for the Expediting role object for a Product, different Products will provide a different Role object. There are other solutions to this problem, of course. For instance, should delivering a thing and a service earlier both be called “expediting”? Having different Strategy objects (one for things and one for services) might also resolve the problem. However, rather than make the OrderLine responsible for determining which Expediting role object to use with a Product or create a complex Expediting role object that can handle all Products, I’ll turn the responsibility for returning the right role object over to the Product. Peter Vogel is the principal system architect in PH&V Information Services, specializing in SharePoint and service-oriented architecture (SOA) development, with expertise in user interface design. In addition, Peter is the author of four books on programming and wrote Learning Tree International’s courses on SOA design ASP.NET development taught in North America, Europe, Africa and Asia.

https://docs.microsoft.com/en-us/archive/msdn-magazine/2013/february/patterns-in-practice-data-design-for-adding-functionality-to-a-class

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Configure and manage Azure Active Directory authentication with SQL This article shows you how to create and populate Azure AD, and then use Azure AD with Azure SQL Database, managed instance, and SQL Data Warehouse. For an overview, see Azure Active Directory Authentication. Note This article applies to Azure SQL server, and to both SQL Database and SQL Data Warehouse databases that are created on the Azure SQL server. For simplicity, SQL Database is used when referring to both SQL Database and SQL Data Warehouse. Important Connecting to SQL Server running on an Azure VM is not supported using an Azure Active Directory account. Use a domain Active Directory account instead. Create and populate an Azure AD Create an Azure AD How Azure subscriptions are associated with Azure AD.. Create an Azure AD administrator for Azure SQL server Each Azure SQL server (which hosts a SQL Database or SQL Data Warehouse). For more information about the server administrator accounts, see Managing Databases and Logins in Azure SQL Database. Azure SQL server administrator account), cannot create Azure AD-based users, because they do not have permission to validate proposed database users with the Azure AD. Provision an Azure Active Directory administrator for your managed instance Important Only follow these steps if you are provisioning a managed instance. This operation can only be executed by Global/Company administrator or a Privileged Role Administrator in Azure AD. Following steps describe the process of granting permissions for users with different privileges in directory. Note For Azure AD admins for MI created prior to GA, but continue operating post GA, there is no functional change to the existing behavior. For more information, see the New Azure AD admin functionality for MI section for more details. Your managed instance needs permissions to read Azure AD to successfully accomplish tasks such as authentication of users through security group membership or creation of new users. For this to work, you need to grant permissions to managed instance to read Azure AD. There are two ways to do it: from Portal and PowerShell. The following steps both methods. In the Azure portal, in the upper-right corner, select your connection to drop down a list of possible Active Directories. Choose the correct Active Directory as the default Azure AD. This step links the subscription associated with Active Directory with Managed Instance making sure that the same subscription is used for both Azure AD and the Managed Instance. Navigate to Managed Instance and select one that you want to use for Azure AD integration. Select the banner on top of the Active Directory admin page and grant permission to the current user. If you're logged in as Global/Company administrator in Azure AD, you can do it from the Azure portal or using PowerShell with the script below. # Gives Azure Active Directory read permission to a Service Principal representing the'." } After the operation is successfully completed, the following notification will show up in the top-right corner: Now you can choose your Azure AD admin for your managed instance. For that, on the Active Directory admin page, select Set admin command. In the AAD Server. At the top of the Active Directory admin page, select Save. The process of changing the administrator may take several minutes. Then the new administrator appears in the Active Directory admin box. After provisioning an Azure AD admin for your managed instance, you can begin to create Azure AD server principals (logins) with the CREATE LOGIN syntax. For more information, see managed instance overview. Tip To later remove an Admin, at the top of the Active Directory admin page, select Remove admin, and then select Save. New Azure AD admin functionality for MI The table below summarizes the functionality for the public preview Azure AD login admin for MI, versus a new functionality delivered with GA for Azure AD logins. As a best practice for existing Azure AD admins for MI created before GA, and still operating post GA, reset the Azure AD admin using the Azure portal “Remove admin” and “Set admin” option for the same Azure AD user or group. Known issues with the Azure AD login GA for MI If an Azure AD login exists in the master database for MI, created using the T-SQL command CREATE LOGIN [myaadaccount] FROM EXTERNAL PROVIDER, it can't be set up as an Azure AD admin for MI. You'll experience an error setting the login as an Azure AD admin using the Azure portal, PowerShell, or CLI commands to create the Azure AD login. - The login must be dropped in the master database using the command DROP LOGIN [myaadaccount], before the account can be created as an Azure AD admin. - Set up the Azure AD admin account in the Azure portal after the DROP LOGINsucceeds. - If you can't set up the Azure AD admin account, check in the master database of the managed instance for the login. Use the following command: SELECT * FROM sys.server_principals - Setting up an Azure AD admin for MI will automatically create a login in the master database for this account. Removing the Azure AD admin will automatically drop the login from the master database. Individual Azure AD guest users are not supported as Azure AD admins for MI. Guest users must be part of an Azure AD group to be set up as Azure AD admin. Currently, the Azure portal blade doesn't gray out guest users for another Azure AD, allowing users to continue with the admin setup. Saving guest users as an Azure AD admin will cause the setup to fail. - If you wish to make a guest user an Azure AD admin for MI, include the guest user in an Azure AD group, and set this group as an Azure AD admin. Cmdlets used to provision and manage Azure AD admin for SQL managed instance: The following command gets information about an Azure AD administrator for a managed instance named ManagedInstanceName01 associated with the resource group ResourceGroup01. Remove-AzSqlInstanceActiveDirectoryAdministrator -ResourceGroupName "ResourceGroup01" -InstanceName "ManagedInstanceName01" -Confirm -PassThru Provision an Azure Active Directory administrator for your Azure SQL Database server Important Only follow these steps if you are provisioning an Azure SQL Database server or Data Warehouse. The following two procedures show you how to provision an Azure Active Directory administrator for your Azure SQL server in the Azure portal and by using PowerShell. Azure portal. (The Azure SQL server can be hosting either Azure SQL Database or Azure SQL Data Warehouse.) In the left banner select All services, and in the filter type in SQL server. Select Sql Servers. Note On this page, before you select SQL servers, you can select the star next to the name to favorite the category and add SQL servers to the left navigation bar. SQL Data Warehouse.) SQL Server authentication user. If present, the Azure AD admin setup will fail; rolling back its creation and indicating that such an admin (name) already exists. Since such a SQL Server authentication user is not part of the Azure AD, any effort to connect to the server using Azure AD authentication fails. To later remove an Admin, at the top of the Active Directory admin page, select Remove admin, and then select Save. PowerShell for Azure SQL Database and Azure SQL Data Warehouse Azure SQL Database and Azure SQL Data Warehouse: Azure SQL Azure SQL Database or Azure SQL Data Warehouse using Azure AD identities, you must install the following software: - .NET Framework 4.6 or later from. - Azure Active Directory Authentication Library for SQL Server (ADALSQL.DLL) is available in multiple languages (both x86 and amd64) from the download center at Microsoft Active Directory Authentication Library for Microsoft SQL Server. You can meet these requirements by: - Installing either SQL Server 2016 Management Studio or SQL Server Data Tools for Visual Studio 2015 meets the .NET Framework 4.6 requirement. - SSMS installs the x86 version of ADALSQL.DLL. - SSDT installs the amd64 version of ADALSQL.DLL. - The latest Visual Studio from Visual Studio Downloads meets the .NET Framework 4.6 requirement, but does not install the required amd64 version of ADALSQL.DLL. Create contained database users in your database mapped to Azure AD identities Important Managed instance now supports Azure AD server principals (logins), which enables you to create logins from Azure AD users, groups, or applications. Azure AD server principals (logins) provides the ability to authenticate to your managed instance without requiring database users to be created as a contained database user. For more information, see managed instance Overview. For syntax on creating Azure AD server principals (logins), see CREATE LOGIN.. For more information about contained database users, see Contained Database Users- Making Your Database Portable. Note Database users (with the exception of administrators) cannot be created using the Azure portal. RBAC roles are not propagated to SQL Server, SQL Database, or SQL Data Warehouse. Azure RBAC roles are used for managing Azure Resources, and do not apply to database permissions. For example, the SQL Server Contributor role does not grant access to connect to the SQL Database or SQL Data Warehouse. The access permission must be granted directly in the database using Transact-SQL statements. Warning Special characters like colon : or ampersand & when included as user names in the T-SQL CREATE LOGIN and CREATE USER statements are not supported.. AAD AAD tenant: they prevent the user from accessing the external provider. Updating the CA policies to allow access to the application '00000002-0000-0000-c000-000000000000' (the application ID of the AAD Azure SQL. Azure AD users are marked in the database metadata with type E (EXTERNAL_USER) and for groups with type X (EXTERNAL_GROUPS). For more information, see sys.database_principals. Connect to the user database or data warehouse by” option is only supported for Universal with MFA connection options, otherwise it is greyed out.) Active Directory password authentication Use this method when connecting with an Azure AD principal name using the Azure AD managed domain. You can also use it for federated accounts without access to the domain, for example when working remotely.. Start Management Studio or Data Tools and in the Connect to Server (or Connect to Database Engine) dialog box, in the Authentication box, select Active Directory - Password. In the User name box, type your Azure Active Directory user name in the format [email protected]. User names must be an account from the Azure Active Directory or an account from a domain federate with the Azure Active Directory. In the Password box, type your user password for the Azure Active Directory account or federated domain account. Select the Options button, and on the Connection Properties page, in the Connect to database box, type the name of the user database you want to connect to. (See the graphic in the previous option.) integrated authentication and an Azure AD identity, connect to Azure SQL Database or Azure SQL Data Warehouse by obtaining a token from Azure Active Directory (AAD). It enables sophisticated scenarios including Next steps -. Feedback

https://docs.microsoft.com/en-us/azure/sql-database/sql-database-aad-authentication-configure?branch=pr-en-us-16983

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chainer.functions.roi_max_align_2d¶ chainer.functions. roi_max_align_2d(x, rois, roi_indices, outsize, spatial_scale, sampling_ratio=None)[source]¶ Spatial Region of Interest (ROI) max align function. This function acts similarly to roi_max_pooling_2d(), but it computes maximum of input spatial patch with bilinear interpolation for each channel with the region of interest. - Parameters x (Variable) – Input variable. The shape is expected to be 4 dimensional: (n: batch, c: channel, h, height, w: width). rois (Variable) – Input roi variable. The shape is expected to be (n: data size,. sampling_ratio ((int, int) or int) – Sampling step for the alignment. It must be an integer over \(1\) or None, and the value is automatically decided when Noneis passed. Use of different ratio in height and width axis is also supported by passing tuple of int as (sampling_ratio_h, sampling_ratio_w). sampling_ratio=sand sampling_ratio=(s, s)are equivalent. - Returns Output variable. - Return type - See the original paper proposing ROIAlign: Mask R-CNN.

https://docs.chainer.org/en/stable/reference/generated/chainer.functions.roi_max_align_2d.html

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