cwielenb/CNCF-Dataset huggingface dataset:
Project: Vector (Observability and Analysis / Observability)
File: 2020-03-19-apache-pulsar-sink.md

date: "2020-07-13" title: "New Apache Pulsar Sink" description: "Sink logs to the Apache Pulsar service" authors: ["binarylogic"] pr_numbers: [1665] release: "0.9.0" hideonrelease_notes: true badges: type: "new feature" domains: ["sinks"] sinks: ["pulsar"] We have a new ! This was a community contribution and effort. Thanks to for his help building this sink!
Project: HertzBeat (Observability and Analysis / Observability)
File: quickstart.md

id: quickstart title: Quick Start sidebar_label: Quick Start If you wish to deploy HertzBeat locally, please refer to the following Deployment Documentation for instructions. HertzBeat supports installation through source code, docker or package, cpu support X86/ARM64. Just one command to get started: ```docker run -d -p 1157:1157 -p 1158:1158 --name hertzbeat apache/hertzbeat``` ```or use quay.io (if dockerhub network connect timeout)``` ```docker run -d -p 1157:1157 -p 1158:1158 --name hertzbeat quay.io/tancloud/hertzbeat``` Access `http://localhost:1157` to start, default account: `admin/hertzbeat` Deploy collector clusters ``` docker run -d -e IDENTITY=custom-collector-name -e MANAGERHOST=127.0.0.1 -e MANAGERPORT=1158 --name hertzbeat-collector apache/hertzbeat-collector ``` `-e IDENTITY=custom-collector-name` : set the collector unique identity name. `-e MODE=public` : set the running mode(public or private), public cluster or private cloud-edge. `-e MANAGER_HOST=127.0.0.1` : set the main hertzbeat server ip. `-e MANAGER_PORT=1158` : set the main hertzbeat server port, default 1158. Detailed config refer to Download the release package `hertzbeat-xx.tar.gz` Configure the HertzBeat configuration yml file `hertzbeat/config/application.yml` (optional) Run command `$ ./bin/startup.sh ` or `bin/startup.bat` Access `http://localhost:1157` to start, default account: `admin/hertzbeat` Deploy collector clusters Download the release package `hertzbeat-collector-xx.tar.gz` to new machine Configure the collector configuration yml file `hertzbeat-collector/config/application.yml`: unique `identity` name, running `mode` (public or private), hertzbeat `manager-host`, hertzbeat `manager-port` ```yaml collector: dispatch: entrance: netty: enabled: true identity: ${IDENTITY:} mode: ${MODE:public} manager-host: ${MANAGER_HOST:127.0.0.1} manager-port: ${MANAGER_PORT:1158} ``` Run command `$ ./bin/startup.sh ` or `bin/startup.bat` Access `http://localhost:1157` and you will see the registered new collector in dashboard Detailed config refer to Local source code debugging needs to start the back-end project `manager` and the front-end project `web-app`. Backendneed `maven3+`, `java11`, `lombok`, start the `manager` service. Webneed `nodejs npm angular-cli` environment, Run `ng serve --open` in `web-app` directory after backend startup. Access `http://localhost:4200` to start, default account: `admin/hertzbeat` Detailed steps refer to Install and deploy the mysql database, iotdb/tdengine database and hertzbeat at one time through . Detailed steps refer to Install HertzBeat cluster in a Kubernetes cluster by Helm chart. Detailed steps refer to HAVE FUN
Project: Checkmk (Observability and Analysis / Observability)
File: 16675.md

key | value | date | 2024-04-04T10:58:02+00:00 version | 2.4.0b1 class | fix edition | cre component | checks level | 1 compatible | no This change is only relevant for plugin developers. Some rule specs allow the configuration of the `eval_type` to specify how the rules are meant to be evaluated in respect to each other. However, this configuration is not considered in some cases, instead Checkmk uses a predetermined evaluation type. For the affected rule specs this option was removed: AgentConfig NotificationParameters DiscoveryParameters
Project: Grafana (Observability and Analysis / Observability)
File: release-notes-7-5-8.md

_build: list: false labels: products: cloud enterprise oss title: Release notes for Grafana 7.5.8 <!-- Auto generated by update changelog github action --> Datasource: Add support for maxconnsper_host in dataproxy settings. , Datasource: Add support for maxidleconnectionsperhost in dataproxy settings. , Instrumentation: Add metrics for outbound HTTP connections. , Snapshots: Remove dashboard links from dashboard snapshots. ,
Project: Vector (Observability and Analysis / Observability)
File: pgo.md

title: Profile-Guided Optimization description: How to optimize Vector performance with Profile-Guided Optimization short: PGO weight: 3 tags: ["pgo", "tuning", "rust", "performance"] Profile-Guided Optimization (PGO) is a compiler optimization technique where a program is optimized based on the runtime profile. According to the [tests], we see improvements of up to 15% more processed log events per second on some Vector workloads. The performance benefits depend on your typical workload - you can get better or worse results. More information about PGO in Vector you can read in the corresponding GitHub [issue]. There are two major kinds of PGO: Instrumentation and Sampling (also known as AutoFDO). In this guide, is described the Instrumentation PGO with Vector. In this guide we use [cargo-pgo] for building Vector with PGO. Install [cargo-pgo]. Check out the Vector repository. Go to the Vector source directory and run `cargo pgo build`. It will build the instrumented Vector version. Run instrumented Vector on your test load like `cargo pgo run -- -- -c vector.toml` and wait for some time to collect enough information from your workload. Usually, waiting several minutes is enough (but your case can be different). Stop Vector instance. The profile data will be generated in the `target/pgo-profiles` directory. Run `cargo pgo optimize`. It will build Vector with PGO optimization. A more detailed guide on how to apply PGO is in the Rust [documentation].
Project: Vector (Observability and Analysis / Observability)
File: 2020-03-09-kafka-sink-compression.md

date: "2020-04-13" title: "Compression Now Available In The Kafka Sink" description: "Improve throughput by compressing data before writing it to Kafka" authors: ["binarylogic"] pr_numbers: [1969] release: "0.9.0" hideonrelease_notes: true badges: type: "new feature" domains: ["sinks"] sinks: ["kafka"] Compression for Vector's is now available. Before we take credit for this feature, Vector uses under the hood, and to maintain consistency we just mapped the appropriate options. In addition, we added a that enables transparent pass-through of .
Project: Headlamp (Observability and Analysis / Observability)
File: base-url.md

title: Run Headlamp with a base-url linkTitle: Base URL Normally Headlamp runs at the root of the domain. Hower you can also ask to run it at a base-url like "/headlamp" for example. default at the root of the domain: `https://headlamp.example.com/`. base-url `https://example.com/headlamp/` Hosting multiple websites (apps) on the can lead to possible conflicts between the apps. Each app is able to access information and make requests of the other. Therefore each app needs to be tested together, trusted, and a compatible should be considered for each of them. If in doubt, host Headlamp on a separate origin (domain or port, don't use the `-base-url` option). ```bash ./backend/headlamp-server -dev -base-url /headlamp PUBLIC_URL="/headlamp" make run-frontend ``` Then go to http://localhost:3000/headlamp/ in your browser. ```bash cd frontend && npm install && npm run build && cd .. ./backend/headlamp-server -dev -base-url /headlamp -html-static-dir frontend/build ``` Then go to http://localhost:4466/headlamp/ in your browser. Append `--base-url /headlamp` to the docker run command. Note the extra "-". You can modify your kubernetes deployment file to add `-base-url /headlamp` to the containers args. ```yaml args: "-in-cluster" "-plugins-dir=/headlamp/plugins" "-base-url=/headlamp" ```
Project: Grafana Pyroscope (Observability and Analysis / Observability)
File: flamegraphs.md

title: "Flame graphs: Visualizing performance data" menuTitle: Flame graphs description: Learn about flame graphs to help visualize performance data. weight: 33 aliases: ../introduction/flamegraphs/ keywords: Pyroscope Profiling A fundamental aspect of continuous profiling is the flame graph, a convenient way to visualize performance data. These graphs provide a clear, intuitive understanding of resource allocation and bottlenecks within the application. Pyroscope extends this functionality with additional visualization formats like tree graphs and top lists. This diagram shows how code is turned into a flame graph. In this case Pyroscope would sample the stacktrace of your application to understand how many CPU cycles are being spent in each function. It would then aggregate this data and turn it into a flame graph. This is a very simplified example but it gives you an idea of how Pyroscope works. Horizontally, the flame graph represents 100% of the time that this application was running. The width of each node represents the amount of time spent in that function. The wider the node, the more time spent in that function. The narrower the node, the less time spent in that function. Vertically, the nodes in the flame graph represent the hierarchy of which functions were called and how much time was spent in each function. The top node is the root node and represents the total amount of time spent in the application. The nodes below it represent the functions that were called and how much time was spent in each function. The nodes below those represent the functions that were called from those functions and how much time was spent in each function. This continues until you reach the bottom of the flame graph. This is a CPU profile, but profiles can represent many other types of resource such as memory, network, disk, etc. To learn more about the flame graph in the Pyroscope UI, refer to . To learn more about the flame graph user interface in Grafana, refer to .
Project: Trickster (Observability and Analysis / Observability)
File: spinning-new-release.md

Users with push access to trickstercache/trickster (Maintainers and owners) can spin releases. To spin a new Trickster release, clone the repo, checkout the commit ID for the release, tag it with a release in semantic version format (`vX.Y.Z`), and push the tag back to the GitHub repository. GitHub actions will detect the publishing of the new tag (so long as it's in the proper format) and cut a full release for the tag automatically. The cut release will be published as a Draft, giving the Maintainer the opportunity to craft release notes in advance of the actual release. Once a Trickster release is cut, the Maintainer must follow the directions in the project to separately push the release to Docker Hub via automation.
Project: Loggie (Observability and Analysis / Observability)
File: CHANGELOG-6.0.md

See . 6.0 has removed support for the `_all` field. Only use `true` or `false` for boolean values, not `0` or `1` or `on` or `off`. Notice that 6.0 and future versions will default to single type indices, i.e. you may not use multiple types when e.g. adding an index with a mapping. See .
Project: Botkube (Observability and Analysis / Observability)
File: botkube_telemetry.md

title: botkube telemetry Configure collection of anonymous analytics ``` -h, --help help for telemetry ``` ``` -v, --verbose int/string[=simple] Prints more verbose output. Allowed values: 0 - disable, 1 - simple, 2 - trace (default 0 - disable) ``` - Botkube CLI - Disable Botkube telemetry - Enable Botkube telemetry
Project: Promscale (Observability and Analysis / Observability)
File: multinode.md

Using Promscale to write and read data to a multi-node deployment of TimescaleDB is straightforward. To get started, you have to follow to set up a multi-node TimescaleDB cluster. Then, you point Promscale to connect to the access node of the cluster. That's it! Promscale will automatically connect to the cluster and set up any table/objecte/roles that it needs. When querying Promscale data from SQL, simply connect to the access node as well. When adding nodes to a TimescaleDB cluster that is already being written to by Promscale, you should run the `addpromnode(node_name)` function after running the standard `adddatanode()` function. For example: ```sql SELECT adddatanode('examplenodename', host => 'examplehostaddress'); CALL addpromnode('examplenodename'); ``` Note: `addpromnode` should be run by a database superuser e.g. "postgres".
Project: Grafana (Observability and Analysis / Observability)
File: release-notes-8-4-0.md

hide_menu: true labels: products: cloud enterprise oss title: Release notes for Grafana 8.4.0 <!-- Auto generated by update changelog github action --> API: Extract OpenAPI specification from source code using go-swagger. , AccessControl: Disable user remove and user update roles when they do not have the permissions. , AccessControl: Provisioning for teams. , API: Add usage stats preview endpoint. , Alerting: Move slow queries in the scheduler to another goroutine. , Alerting: Use time.Ticker instead of alerting.Ticker in ngalert. , Alerting: add custom grouping to Alert Panel. , Analytics: Add user id tracking to google analytics. , Angular: Add AngularJS plugin support deprecation plan to docs site. , Auth: implement autosignup for auth.jwt. , Azure Monitor Logs: Order subscriptions in resource picker by name. , Azure monitor Logs: Optimize data fetching in resource picker. , AzureMonitor: Filter list of resources by resourceType. , BarChart: color by field, x time field, bar radius, label skipping. , Chore: Implement OpenTelemetry in Grafana. , Cloud Monitoring: Adds metric type to Metric drop down options. , CloudWatch: Add Data Lifecycle Manager metrics and dimension. , CloudWatch: Add Missing Elasticache Host-level metrics. , CloudWatch: Add all ElastiCache Redis Metrics. , CloudWatch: Add new AWS/ES metrics. , Cloudwatch: Add syntax highlighting and autocomplete for "Metric Search". , Explore: Support custom display label for exemplar links for Prometheus datasource. , Hotkeys: Make time range absolute/permanent. , Playlists: Enable sharing direct links to playlists. , SQLStore: Prevent concurrent migrations. , SSE: Add Mode to drop NaN/Inf/Null in Reduction operations. , Setting: Support configuring feature toggles with bools instead of just passing an array. , TimeSeries: Add support for negative Y and constant transform. , Transformations: Add 'JSON' field type to ConvertFieldTypeTransformer. , Auth: Guarantee consistency of signed SigV4 headers. , CloudWatch: Fix MetricName resetting on Namespace change. , Cloudwatch : Fixed resetting metric name when changing namespace in Metric Query. , Explore: Avoid locking timepicker when range is inverted. , Instrumentation: Fix HTTP request instrumentation of authentication failures. , LibraryPanels: Prevent long descriptions and names from obscuring the delete button. , OAuth: Fix parsing of ID token if header contains non-string value. , Panel Edit: Visualization search now works correctly with special characters. , Provisioning: Fix duplicate validation when multiple organizations have been configured. , QueryField: Fix issue with undo history when suggestion is inserted (#28656). , TablePanel: Do not prefix columns with frame name if multiple frames and override active. , AngularJS plugin support is now in a deprecated state, meaning it will be removed in a future release. Currently, that is planned for version 10 (in 2023). The documentation site has an with more details on why, when, and how. Issue
Project: M3 (Observability and Analysis / Observability)
File: pull_request_template.md

<!-- Thanks for sending a pull request! Here are some tips for you: If this is your first time, please read our contributor guidelines: https://github.com/m3db/m3/blob/master/CONTRIBUTING.md and developer notes https://github.com/m3db/m3/blob/master/DEVELOPMENT.md Please prefix the name of the pull request with the component you are updating in the format "[component] Change title" (for example "[dbnode] Support out of order writes") and also label this pull request according to what type of issue you are addressing. Furthermore, if this is a WIP or DRAFT PR, please create a draft PR instead: https://github.blog/2019-02-14-introducing-draft-pull-requests/ Ensure you have added or ran the appropriate tests for your PR: read more at https://github.com/m3db/m3/blob/master/DEVELOPMENT.md#testing-changes Follow the instructions for writing a changelog note: read more at https://github.com/m3db/m3/blob/master/DEVELOPMENT.md#updating-the-changelog --> What this PR does / why we need it: <!-- If you have an issue this change addresses, please add the following details: *Automatically closes linked issue when PR is merged. Usage: `Fixes #<issue number>`, or `Fixes (paste link of issue)`. --> Fixes # Special notes for your reviewer: Does this PR introduce a user-facing and/or backwards incompatible change?: <!-- If no, just write "NONE" in the release-note block below. If yes, a release note is required: Enter your extended release note in the block below. If the PR requires additional action from users switching to the new release, include the string "action required". --> ```release-note ``` Does this PR require updating code package or user-facing documentation?: <!-- If no, just write "NONE" in the documentation-note block below. If yes, describe which documentation you updated. Note: Any changes that significantly updates how a code package is architectured or functions requires code package documentation updates in the form of updates to the README.md file in that package. --> ```documentation-note ```
Project: Checkmk (Observability and Analysis / Observability)
File: 16406.md

key | value | date | 2024-03-07T13:04:36+00:00 version | 2.4.0b1 class | fix edition | cre component | omd level | 2 compatible | yes While upgrading with `cmk-update-config`, the user can be prompted with questions about the next update steps. This questioning can be disabled by using one of the conflict options `install`, `keepold` or `abort`. Due to a regression in the 2.3.0b1 the options `install` and `keepold` do not supress these questions. In particular, if there is a problem while `Verifying the Checkmk configuration...`, then the update of Checkmk on Checkmk appliances will exit with a traceback. Upgrading to the 2.3.0b1 is thus only possible here, if all problems are fixed beforehand.
Project: Kubecost (Observability and Analysis / Continuous Optimization)
File: MAINTAINERS.md

Official list of . are granted Triage permissions for the OpenCost repositories. The describes the process for becoming a committer and maintainer of the project. | Maintainer | GitHub ID | Affiliation | Email | | | | -- | -- | | Ajay Tripathy | @AjayTripathy | Kubecost | <Ajay@kubecost.com> | | Alex Meijer | @ameijer | Kubecost | <ameijer@kubecost.com> | | Artur Khantimirov | @r2k1 | Microsoft | | | Matt Bolt | @mbolt35 | Kubecost | <matt@kubecost.com> | | Matt Ray | @mattray | Kubecost | <mattray@kubecost.com> | | Niko Kovacevic | @nikovacevic | Kubecost | <niko@kubecost.com> | | Sean Holcomb | @Sean-Holcomb | Kubecost | <Sean@kubecost.com> | | Thomas Evans | @teevans | Kubecost | <thomas@kubecost.com> | We would like to acknowledge previous committers and their huge contributions to our collective success: | Maintainer | GitHub ID | Affiliation | Email | | | | -- | -- | | Michael Dresser | @michaelmdresser | Kubecost | <michaelmdresser@gmail.com> |
Project: Tracetest (Observability and Analysis / Observability)
File: bug_report.md

name: Bug report about: Create a report to help us improve title: '' labels: 'bug, triage' assignees: '' Describe the bug A clear and concise description of what the bug is. To Reproduce Steps to reproduce the behavior: Go to '...' Click on '....' Scroll down to '....' See error Expected behavior A clear and concise description of what you expected to happen. Screenshots If applicable, add screenshots to help explain your problem. Browser (please complete the following information): Browser: [Firefox] Version [e.g. 99] Additional context Add any other context about the problem here.
Project: Grafana (Observability and Analysis / Observability)
File: 1-feature_requests.md

name: Feature Requests about: Suggest an enhancement or new feature for the Grafana project <!-- Please only use this template for submitting feature requests --> Why is this needed: [Add a description of the problem the feature is trying to solve.] What would you like to be added: [Add a brief description of what the feature or enhancement should do.] Who is this feature for? [Add information on what kind of user the feature is for.]
Project: Vector (Observability and Analysis / Observability)
File: 2019-11-19-arm-support-on-linux.md

date: "2020-03-31" title: "ARMv7 and ARM64 Support on Linux" description: "These architectures are widely used in embedded devices and servers" authors: ["binarylogic"] pr_numbers: [1054, 1292] release: "0.6.0" hideonrelease_notes: false badges: type: "new feature" domains: ["platforms"] platforms: ["arm64", "arm7"] aliases: ["/blog/arm-support-on-linux"] Vector now supports on the Linux platform! These architectures are widely used in embedded devices and recently started to get traction on servers. To get started, you can follow the installation instructions for your preferred method: (select the ARM tab) (select the ARM tab) (select the ARM tab) <!--more--> It's worth noting that Vector ships a fully static binary without dependencies. This makes installation as simple as copying the Vector binary onto your machine. There are no dependencies to install or environment changes required. In addition to providing archives for these architectures, we went the extra mile to ensure , , and support them as well. If you're wondering how this benefits you, here are a couple of popular use cases: An interesting usecase for ARM support are the [new AWS `M6g`, `C6g`, and `R6g` instances][urls.awsarmg2_announcement]. These instances are based on Amazon's ARM-based Graviton2 processors. Amazon claims they "deliver up to 40% improved price/performance over current generation `M5`, `C5`, and `R5` instances". ARM architectures are widely used on IoT devices. Vector is the perfect candidate for resource constrained environments like this, especially given Vector's superior memory efficiency. If you're using a vendor-based data collector it's likely they lack support for the ARM architectures, especially the new ARM64 architecture. This limits your flexibility, and in the case of AWS, can have direct cost implications. Supporting these platforms, properly, is Vector's core competency.
Project: Falcon (Observability and Analysis / Observability)
File: 2017-01-01-expression_info_by_id.md

category: Expression apiurl: '/api/v1/expression/#{expression_id}' title: "Get Expression Info by id" type: 'GET' sample_doc: 'expression.html' layout: default Required ex. /api/v1/expression/5 ```Status: 200``` ```{ "action": { "id": 5, "uic": "taipei", "url": "", "callback": 0, "beforecallbacksms": 0, "beforecallbackmail": 0, "aftercallbacksms": 0, "aftercallbackmail": 0 }, "expression": { "id": 5, "expression": "each(metric=agent.alive endpoint=docker-agent)", "func": "all(#3)", "op": "==", "right_value": "0", "max_step": 3, "priority": 2, "note": "this is a test exp", "action_id": 177, "create_user": "root", "pause": 1 } }```