cwielenb/CNCF-Dataset huggingface dataset:
Project: Pixie (Observability and Analysis / Observability)
File: task.md

name: Task about: Task for work that needs to be done, without user-facing impact Describe the Task An explanation of the work that needs to be done, and why. This could be improving tests, cleaning up technical debt, improving the build system, etc. This should be understandable to other developers, not just yourself! Subtasks The list of concrete tasks which must be done to complete this task. These may be low-level. [x] Here is an example of a completed task. [ ] Here is an example of an uncompleted task.
Project: Litmus (Observability and Analysis / Chaos Engineering)
File: COMMERCIAL_SUPPORT.md

Here is a list of third-party companies and individuals who provide products or services related to LitmusChaos. LitmusChaos is a CNCF project which does not endorse any company. If you are a commercial support provider for LitmusChaos and wish to add your company, please raise a PR against this document. The list is provided in alphabetical order.
Project: Embrace (Observability and Analysis / Observability)
File: CONTRIBUTING.md

Contributing ============ Before code can be accepted all contributors must complete our . If you would like to contribute code you can do so through GitHub by forking the repository and sending a pull request. When submitting code, please make every effort to follow existing conventions and style in order to keep the code as readable as possible. Please also run and fix `swiftlint` by using: ```sh $ swift run swiftlint --fix ``` If you have any trouble to start contributing, please or reach out in our Finally, feel free to open a .
Project: HertzBeat (Observability and Analysis / Observability)
File: alert_telegram.md

id: alert_telegram title: Alert Telegram Bot Notification sidebar_label: Alert Telegram bot notification keywords: [open source monitoring tool, open source alerter, open source Telegram bot notification] Send an alarm message after the threshold is triggered, and notify the recipient through the Telegram robot. Use to create your own bot and get an access token `Token` Get the `User ID` of the recipient Use the recipient account you want to notify to send a message to the newly created Bot account, Visit ```https://api.telegram.org/bot<TOKEN>/getUpdates```, `use the Bot Token from the previous step to replace the <TOKEN>`, and respond to the first in the `Json` data A `result.message.from.id` value is the recipient's `User ID` ```json { "ok": true, "result": [ { "update_id": 632299191, "message": { "from":{ "id": "User ID" }, "chat":{ }, "date": 1673858065, "text": "111" } } ] } ``` Record and save the `Token` and `User Id` we got Alarm Notification->Add Recipient->Select Telegram Robot Notification Method->Set Robot Token and UserId-> OK Configure the associated alarm notification strategy [Add notification strategy] -> [Associate the recipient just set] -> [OK] Note Adding a new recipient does not mean that it has taken effect and can receive alarm information. It is also necessary to configure the associated alarm notification strategy, that is, specify which messages are sent to which recipients. Telegram did not receive the robot warning notification Please check whether the alarm information has been triggered in the alarm center Please check whether the robot Token and UserId are configured correctly, and whether the alarm policy association has been configured UserId should be the UserId of the recipient of the message Other questions can be fed back through the communication group ISSUE!
Project: Grafana (Observability and Analysis / Observability)
File: styling.md

is our default-to-be approach to styling React components. It provides a way for styles to be a consequence of properties and state of a component. For styling components, use . To access the theme in your styles, use the `useStyles` hook. It provides basic memoization and access to the theme object. Please remember to put `getStyles` function at the end of the file! ```tsx import React from 'react'; import { GrafanaTheme2 } from '@grafana/data'; import { useStyles2 } from '@grafana/ui'; import { css } from '@emotion/css'; const Foo = (props: FooProps) => { const styles = useStyles2(getStyles); // Use styles with classNames return <div className={styles}>...</div>; }; const getStyles = (theme: GrafanaTheme2) => css({ padding: theme.spacing(1, 2), // will result in 8px 16px padding }); ``` In more complex cases, especially when you need to style multiple DOM elements in one component, or when using styles that depend on properties and/or state you can have your getStyles function return an object with many class names and use to compose them. Let's say you need to style a component that has a different background depending on the `isActive` property : ```tsx import { css, cx } from '@emotion/css'; import React from 'react'; import { GrafanaTheme2 } from '@grafana/data'; import { useStyles2 } from '@grafana/ui'; interface ComponentAProps { isActive: boolean; } const ComponentA = ({ isActive }: ComponentAProps) => { const styles = useStyles2(getStyles); return ( <div className={cx(styles.wrapper, isActive && styles.active)}> As red as you can get <i className={styles.icon} /> </div> ); }; // Mind, that you can pass multiple arguments, theme included const getStyles = (theme: GrafanaTheme2) => { return { wrapper: css({ background: theme.colors.background.secondary, }), active: css({ background: theme.colors.primary.main, text: theme.colors.primary.contrastText, }), icon: css({ fontSize: theme.typography.bodySmall.fontSize, }), }; }; ``` For more information about themes at Grafana please see the .
Project: Grafana (Observability and Analysis / Observability)
File: 5-chore.md

name: Chore about: Create an issue for a chore needing completion labels: 'type: chore' <!-- Please use this template to create your chore issue. You can use this template if you spot an out-of-date README, discover a misspelling, or happen upon a deeply nested 7-layer for-loop that could be better handled another way. Please use this template for your non-bug related fixes/updates/refactors. Questions should be posted to: https://community.grafana.com Use query inspector to troubleshoot issues: https://bit.ly/2XNF6YS How to record and attach gif: https://bit.ly/2Mi8T6K --> What is the chore?: Is there anything else we need to know?:
Project: Kuberhealthy (Observability and Analysis / Observability)
File: CONTRIBUTING.md

If you would like to contribute code to this project you can do so through GitHub by forking the repository and sending a pull request. Your commits so that the DCO bot will accept them. This means using `git commit -s` when comitting. If you are making a large change, you should submit a proposal issue first so that you don't risk your feature being denied Fork the Kuberhealthy/Kuberhealthy repository on github Clone your fork to your machine Create a branch describing your change Develop your feature and push changes to a branch in your fork Add your name to the `CONTRIBUTORS.md` file Open a pull request from your branch branch in your fork to the master branch on github.com/kuberhealthy/kuberhealthy Wait for a project maintainer to address your change and merge your code. Keep an eye on your open PR. Celebrate! The code must be formatted with `go fmt` The change must include tests for new functionality created The code must pass all Github CI tests
Project: Headlamp (Observability and Analysis / Observability)
File: lib_k8s_limitRange.LimitRange.md

title: "Class: LimitRange" linkTitle: "LimitRange" slug: "libk8slimitRange.LimitRange" .LimitRange `any` `LimitRange` new LimitRange(`json`) | Name | Type | | : | : | | `json` | | makeKubeObject<KubeLimitRange\>('LimitRange').constructor `Static` apiEndpoint: `Object` | Name | Type | | : | : | | `scale?` | { `get`: (`namespace`: `string`, `name`: `string`, `clusterName?`: `string`) => `Promise`<`any`\> ; `put`: (`body`: { `metadata`: ; `spec`: { `replicas`: `number` } }, `clusterName?`: `string`) => `Promise`<`any`\> } | | `scale.get` | (`namespace`: `string`, `name`: `string`, `clusterName?`: `string`) => `Promise`<`any`\> | | `scale.put` | (`body`: { `metadata`: ; `spec`: { `replicas`: `number` } }, `clusterName?`: `string`) => `Promise`<`any`\> | _ `Static` className: `string` makeKubeObject<KubeLimitRange\>('LimitRange').className `get` spec(): `any` `any` `Static` apiList(`onList`, `onError?`, `opts?`): `any` | Name | Type | | : | : | | `onList` | (`arg`: `any`[]) => `void` | | `onError?` | (`err`: ) => `void` | | `opts?` | | `any` makeKubeObject<KubeLimitRange\>('LimitRange').apiList _ `Static` `Optional` getAuthorization(`arg`, `resourceAttrs?`): `any` | Name | Type | | : | : | | `arg` | `string` | | `resourceAttrs?` | | `any` makeKubeObject<KubeLimitRange\>('LimitRange').getAuthorization _ `Static` getErrorMessage(`err?`): ``null`` \| `string` | Name | Type | | : | : | | `err?` | ``null`` \| | ``null`` \| `string` makeKubeObject<KubeLimitRange\>('LimitRange').getErrorMessage _ `Static` useApiGet(`onGet`, `name`, `namespace?`, `onError?`): `void` | Name | Type | | : | : | | `onGet` | (...`args`: `any`) => `void` | | `name` | `string` | | `namespace?` | `string` | | `onError?` | (`err`: ) => `void` | `void` makeKubeObject<KubeLimitRange\>('LimitRange').useApiGet _ `Static` useApiList(`onList`, `onError?`, `opts?`): `any` | Name | Type | | : | : | | `onList` | (`arg`: `any`[]) => `void` | | `onError?` | (`err`: ) => `void` | | `opts?` | | `any` makeKubeObject<KubeLimitRange\>('LimitRange').useApiList _ `Static` useGet(`name`, `namespace?`): , (`item`: `any`) => `void`, (`err`: ``null`` \| ) => `void`] | Name | Type | | : | : | | `name` | `string` | | `namespace?` | `string` | , (`item`: `any`) => `void`, (`err`: ``null`` \| ) => `void`] makeKubeObject<KubeLimitRange\>('LimitRange').useGet _ `Static` useList(`opts?`): , (`items`: `any`) => `void`] | Name | Type | | : | : | | `opts?` | | , (`items`: `any`) => `void`] makeKubeObject<KubeLimitRange\>('LimitRange').useList
Project: HertzBeat (Observability and Analysis / Observability)
File: windows.md

id: windows title: MonitoringWindows operating system monitoring sidebar_label: Windows operating system keywords: [open source monitoring tool, open source windows monitoring tool, monitoring windows metrics] Collect and monitor the general performance Metrics of Windows operating system through SNMP protocol. Note You need to start SNMP service for Windows server. References: | Parameter name | Parameter help description | | -- | -- | | Monitoring Host | Monitored IPV4, IPV6 or domain name. NoteWithout protocol header (eg: https://, http://) | | Monitoring name | Identify the name of this monitoring. The name needs to be unique | | Port | Port provided by Windows SNMP service. The default is 161 | | SNMP version | SNMP protocol version V1 V2c V3 | | SNMP community Word | SNMP agreement community name(Community Name). It is used to realize the authentication of SNMP network administrator when accessing SNMP management agent. Similar to password, the default value is public | | Timeout | Protocol connection timeout | | Collection interval | Interval time of monitor periodic data collection, unit: second, and the minimum interval that can be set is 30 seconds | | Whether to detect | Whether to detect and check the availability of monitoring before adding monitoring. Adding and modifying operations will continue only after the detection is successful | | Description remarks | For more information about identifying and describing this monitoring, users can note information here | | Metric name | Metric unit | Metric help description | | -- | -- | -- | | name | none | Host name | | descr | none | Operating system description | | uptime | none | System running time | | numUsers | number | Current number of users | | services | number | Current number of services | | processes | number | Current number of processes | | responseTime | ms | Collection response time |
Project: SkyWalking (Observability and Analysis / Observability)
File: Import-Project-Eclipse-RequireItems-Exception.md

When importing the SkyWalking project to Eclipse, the following errors may occur: Software being installed: Checkstyle configuration plugin for M2Eclipse 1.0.0.201705301746 (com.basistech.m2e.code.quality.checkstyle.feature.feature.group 1.0.0.201705301746) Missing requirement: Checkstyle configuration plugin for M2Eclipse 1.0.0.201705301746 (com.basistech.m2e.code.quality.checkstyle.feature.feature.group 1.0.0.201705301746) requires 'net.sf.eclipsecs.core 5.2.0' but it could not be found The Eclipse Checkstyle Plug-in has not been installed. Download the plug-in at the link here: https://sourceforge.net/projects/eclipse-cs/?source=typ_redirect Eclipse Checkstyle Plug-in version 8.7.0.201801131309 is required. Plug-in notification: The Eclipse Checkstyle plug-in integrates the Checkstyle Java code auditor into the Eclipse IDE. The plug-in provides real-time feedback to the user on rule violations, including checking against coding style and error-prone code constructs.
Project: Headlamp (Observability and Analysis / Observability)
File: lib_k8s_endpoints.KubeEndpoint.md

title: "Interface: KubeEndpoint" linkTitle: "KubeEndpoint" slug: "libk8sendpoints.KubeEndpoint" .KubeEndpoint `KubeEndpoint` `Optional` apiVersion: `string` . _ kind: `string` Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. In CamelCase. Cannot be updated. `see` . _ metadata: . _ subsets: []
Project: Grafana Loki (Observability and Analysis / Observability)
File: SECURITY.md

Join the [kubernetes-security-announce] group for security and vulnerability announcements. You can also subscribe to an RSS feed of the above using . Instructions for reporting a vulnerability can be found on the [Kubernetes Security and Disclosure Information] page. Information about supported Kubernetes versions can be found on the [Kubernetes version and version skew support policy] page on the Kubernetes website.
Project: Vector (Observability and Analysis / Observability)
File: gcp_chronicle_unstructured.md

title: GCP Chronicle Unstructured description: Store unstructured log events in short: GCP Chronicle Unstructured component_kind: sink layout: component tags: ["gcp", "chronicle", "component", "sink"] {{/* This doc is generated using: The template in layouts/docs/component.html The relevant CUE data in cue/reference/components/... */}}
Project: Beats (Observability and Analysis / Observability)
File: testing.md

create kubernetes cluster using kind, check for details deploy ELK stack, check for details A slightly modified (comparing to beats/deploy/kubernetes/filebeat-kubernetes.yaml) all-in-one filebeat manifest resides under `01_playground` directory. Modifications: the daemonset executes an infinite sleep command instead of starting filebeat. variables `ELASTICSEARCHHOST`, `ELASTICSEARCHPORT`, `ELASTICSEARCHUSERNAME`, `ELASTICSEARCHPASSWORD` variables are set according to local kind EK stack. Note: In case of using Elastic Cloud deployment configure the variables `ELASTICCLOUDID` and `ELASTICCLOUDAUTH` accordingly. Deploy filebeat: ```bash cd filebeat/_meta/test/docs kubectl apply -f 01_playground/filebeat.yaml ``` Build filebeat binary and copy it in the running filebeat pod. Under beats/filebeat execute: ```bash GOOS=linux GOARCH=amd64 go build kubectl cp ./filebeat `kubectl get pod -n kube-system -l k8s-app=filebeat -o jsonpath='{.items[].metadata.name}'`:/usr/share/filebeat/ -n kube-system ```` The above command only copies filebeat binary. For configuration files updates modify `filebeat.yml`, defined in the `ConfigMap` and execute again: ``` kubectl apply -f 01_playground/filebeat.yaml ``` Note: Filebeat on kubernetes can be running with one of the configurations: Filebeat use to read container logs under the given path in combination with to enrich logs with relevant Kubernetes pod metadata. Filebeat use autodiscover . Start filebeat. ```bash kubectl exec `kubectl get pod -n kube-system -l k8s-app=filebeat -o jsonpath='{.items[].metadata.name}'` -n kube-system -- bash -c "filebeat -e -c /etc/filebeat.yml" ``` Filebeat will launch and the process logs will appear in the terminal. You can as well exec in filebeat pod with bash command and then run filebeat. This gives the flexibility to easily start and stop the process.
Project: Headlamp (Observability and Analysis / Observability)
File: lib_k8s_daemonSet.DaemonSet.md

title: "Class: DaemonSet" linkTitle: "DaemonSet" slug: "libk8sdaemonSet.DaemonSet" .DaemonSet `any` `DaemonSet` new DaemonSet(`json`) | Name | Type | | : | : | | `json` | | makeKubeObject<KubeDaemonSet\>('DaemonSet').constructor `Static` apiEndpoint: `Object` | Name | Type | | : | : | | `scale?` | { `get`: (`namespace`: `string`, `name`: `string`, `clusterName?`: `string`) => `Promise`<`any`\> ; `put`: (`body`: { `metadata`: ; `spec`: { `replicas`: `number` } }, `clusterName?`: `string`) => `Promise`<`any`\> } | | `scale.get` | (`namespace`: `string`, `name`: `string`, `clusterName?`: `string`) => `Promise`<`any`\> | | `scale.put` | (`body`: { `metadata`: ; `spec`: { `replicas`: `number` } }, `clusterName?`: `string`) => `Promise`<`any`\> | _ `Static` className: `string` makeKubeObject<KubeDaemonSet\>('DaemonSet').className `get` spec(): `any` `any` _ `get` status(): `any` `any` getContainers(): [] [] _ getNodeSelectors(): `string`[] `string`[] _ `Static` apiList(`onList`, `onError?`, `opts?`): `any` | Name | Type | | : | : | | `onList` | (`arg`: `any`[]) => `void` | | `onError?` | (`err`: ) => `void` | | `opts?` | | `any` makeKubeObject<KubeDaemonSet\>('DaemonSet').apiList _ `Static` `Optional` getAuthorization(`arg`, `resourceAttrs?`): `any` | Name | Type | | : | : | | `arg` | `string` | | `resourceAttrs?` | | `any` makeKubeObject<KubeDaemonSet\>('DaemonSet').getAuthorization _ `Static` getErrorMessage(`err?`): ``null`` \| `string` | Name | Type | | : | : | | `err?` | ``null`` \| | ``null`` \| `string` makeKubeObject<KubeDaemonSet\>('DaemonSet').getErrorMessage _ `Static` useApiGet(`onGet`, `name`, `namespace?`, `onError?`): `void` | Name | Type | | : | : | | `onGet` | (...`args`: `any`) => `void` | | `name` | `string` | | `namespace?` | `string` | | `onError?` | (`err`: ) => `void` | `void` makeKubeObject<KubeDaemonSet\>('DaemonSet').useApiGet _ `Static` useApiList(`onList`, `onError?`, `opts?`): `any` | Name | Type | | : | : | | `onList` | (`arg`: `any`[]) => `void` | | `onError?` | (`err`: ) => `void` | | `opts?` | | `any` makeKubeObject<KubeDaemonSet\>('DaemonSet').useApiList _ `Static` useGet(`name`, `namespace?`): , (`item`: `any`) => `void`, (`err`: ``null`` \| ) => `void`] | Name | Type | | : | : | | `name` | `string` | | `namespace?` | `string` | , (`item`: `any`) => `void`, (`err`: ``null`` \| ) => `void`] makeKubeObject<KubeDaemonSet\>('DaemonSet').useGet _ `Static` useList(`opts?`): , (`items`: `any`) => `void`] | Name | Type | | : | : | | `opts?` | | , (`items`: `any`) => `void`] makeKubeObject<KubeDaemonSet\>('DaemonSet').useList
Project: Litmus (Observability and Analysis / Chaos Engineering)
File: okteto.md

is a development platform for Cloud Native applications, built to help teams get to production faster and more confidently. Okteto allows development teams to: Deploy pre-configured development and test environments with one click. Stop running docker-compose up on every machine. Quickly onboard new developers. Eliminate integration issues by developing the same way applications run in production. Get self-service access to their Kubernetes infrastructure. When building Okteto Cloud and Okteto Enterprise, we realized that just testing things end to end wasn't enough. As our user base started to grow, we realized that the resiliency of our systems was as important as it being easy to use. We started by inducing chaos in our microservices, to ensure that they all survived restarts, and that we didn't have any single points of failure. Now we are evolving towards simulating more complex failures, spanning not only our services, but also the underlying Kubernetes infrastructure. We use Litmus as part of our release testing criteria. We'll deploy the new version of our application, and run integration testing while inducing chaos. In the future, we are planning on using litmus to also simulate failures in the Kubernetes API and cluster failures. Litmus makes it easier to give every engineer in our organization the tools to chaos test. By integrating it directly in our catalog, we can also make that flow available to everyone else. More information on this .
Project: OpenMetrics (Observability and Analysis / Observability)
File: CONTRIBUTORS.md

Richard "RichiH" Hartmann (Grafana Labs) Brian Brazil (Robust Perception) Paul Dix (InfluxDB) Morgan McLean (Google) Sumeer Bhola (Google) Bogdan Drutu (Splunk) Rob Skillington (Chronosphere) Ben Kochie (GitLab)
Project: Netdata (Observability and Analysis / Observability)
File: mysql_galera_cluster_status.md

This alert indicates the current status of the Galera node cluster component in your MySQL or MariaDB database. Receiving this alert means that there is a potential issue with the cluster, such as a network partition that has caused the cluster to split into multiple components. Check the status of the Galera cluster First, you need to determine the current status of the cluster to understand the severity of the issue. Check the value of the alert. Refer to the table in the given alert description to see which state your cluster is in. Verify cluster connectivity If your cluster is in a non-primary state or disconnected, you should verify if all the nodes in your cluster can communicate with each other. You can use tools like `ping`, `traceroute`, or `mtr` to test connectivity between the cluster nodes. If there is a network issue, get in touch with your network administrator to resolve it. Examine node logs Check the logs on each node for any indication of issues or error messages that can help identify the root cause of the problem. The logs are usually located in the `/var/log/mysqld.log` file or in the `/var/log/mysql/error.log` file. Look for lines that contain "ERROR" or "WARNING" as a starting point. Inspect Galera cluster settings Analyze your Galera cluster configuration file (`/etc/my.cnf` or `/etc/mysql/my.cnf`) to make sure you have the correct settings, including the initial `wsrepclusteraddress` value, which defines the initial list of nodes in the cluster. If you find any misconfiguration, correct it and restart your database service. Force a new primary component If you have a split-brain scenario, where multiple parts of the cluster are claiming to be the primary component, you need to force a new primary component. To do this, you can use the `SET GLOBAL wsrepprovideroptions='pc.bootstrap=YES';` statement on one of the nodes that has the most up-to-date data. This action will force that node to act as the new primary component. To minimize the risks of cluster issues, ensure the following: Use reliable and redundant network connections between nodes. Configure Galera cluster settings correctly. Regularly monitor the cluster status and review logs. Use the latest stable version of the Galera cluster software. [MariaDB Galera Cluster Documentation]( https://mariadb.com/kb/en/getting-started-with-mariadb-galera-cluster/)