cwielenb/CNCF-Dataset huggingface dataset:
Project: DragonflyDB (App Definition and Development / Database)
File: feature_request.md

name: Feature request about: Suggest an idea for Dragonfly DB title: '' labels: 'feature request' assignees: '' Did you search GitHub Issues and GitHub Discussions First? Many users may find their feature is already being discussed. Help us keep duplicates to a minimum by searching for your feature first to see if it is already in progress. Is your feature request related to a problem? Please describe. A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] Describe the solution you'd like A clear and concise description of what you want to happen. Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered. Additional context Add any other context or screenshots about the feature request here.
Project: Matano (Provisioning / Security & Compliance)
File: CONTRIBUTING.md

:+1::tada: First off, thanks for taking the time to contribute! :tada::+1: We love to receive contributions from our community! There are many ways to contribute, from writing tutorials, improving the documentation, submitting bug reports and feature requests or writing code which can be incorporated into Matano itself. Did you find a bug? Do not open up a GitHub issue if the bug is a security vulnerability and instead to refer to our . Ensure the bug was not already reported by searching on GitHub under . If you're unable to find an open issue addressing the problem, open a new one. Be sure to include a title and clear description, as much relevant information as possible demonstrating the expected behavior that is not occurring. Feature requests are welcome. If you find yourself wishing for a feature that doesn't exist in Matano, you are probably not alone. There are bound to be others out there with similar needs. Open an issue on our which describes the feature you would like to see, why you need it, and how it should work. We welcome code changes and pull requests from the community. Good pull requests - patches, improvements, new features - are a fantastic help. To get started developing, check out the . Please ask (feel free to open an issue or send a message in Discord) before embarking on any significant pull request (e.g. implementing features, refactoring code), otherwise you risk spending a lot of time working on something that the project's developers might not want to merge into the project. You can interact with the core team and community on .
Project: KICS (Provisioning / Security & Compliance)
File: 1bc1c685-e593-450e-88fb-19db4c82aa1d.md

title: IAM Password Without Minimum Length hide: toc: true navigation: true <style> .highlight .hll { background-color: #ff171742; } .md-content { max-width: 1100px; margin: 0 auto; } </style> Query id: 1bc1c685-e593-450e-88fb-19db4c82aa1d Query name: IAM Password Without Minimum Length Platform: Terraform Severity: <span style="color:#edd57e">Low</span> Category: Best Practices URL: IAM password should have the required minimum length<br> ```tf title="Positive test num. 1 - tf file" hl_lines="1 10" resource "awsiamaccountpasswordpolicy" "positive1" { requirelowercasecharacters = true require_numbers = true requireuppercasecharacters = true require_symbols = true allowuserstochangepassword = true } resource "awsiamaccountpasswordpolicy" "positive2" { minimumpasswordlength = 3 requirelowercasecharacters = true require_numbers = true requireuppercasecharacters = true require_symbols = true allowuserstochangepassword = true } ``` ```tf title="Negative test num. 1 - tf file" resource "awsiamaccountpasswordpolicy" "negative1" { minimumpasswordlength = 14 requirelowercasecharacters = true require_numbers = true requireuppercasecharacters = true require_symbols = true allowuserstochangepassword = true } ```
Project: Meshery (Provisioning / Automation & Configuration)
File: v0.4.17.md

layout: release date: 2020-10-29 tag: v0.4.17 General Translation of the kubernetes quick start pageto es, just added this @alonsolopezr (#1774) Getting the kinD quick start page, translation to es in the docs-page @alonsolopezr (#1758) [Docs] update resources page @chandrashritii (#1771) Bump the chart version and add mesheryconsul adapter @Aisuko (#1781) [provider-ui] - Add Integration test for Provider UI @ramrodo (#1778) Bug Fixes Generate meshery config dir at mesheryctl init @nitishm (#1780) fix: ubuntu sh is linked to dash @kushthedude (#1777) Documentation [Docs] Architecture diagrams @leecalcote (#1783)
Project: KICS (Provisioning / Security & Compliance)
File: 32d31f1f-0f83-4721-b7ec-1e6948c60145.md

title: Stack Without Template hide: toc: true navigation: true <style> .highlight .hll { background-color: #ff171742; } .md-content { max-width: 1100px; margin: 0 auto; } </style> Query id: 32d31f1f-0f83-4721-b7ec-1e6948c60145 Query name: Stack Without Template Platform: Ansible Severity: <span style="color:#edd57e">Low</span> Category: Build Process URL: AWS CloudFormation should have a template defined through the attribute template, templateurl or attribute templatebody<br> ```yaml title="Positive test num. 1 - yaml file" hl_lines="40 2 30 15" name: create a stack, pass in the template via an URL amazon.aws.cloudformation: stack_name: "ansible-cloudformation" state: present region: us-east-1 disable_rollback: true template_parameters: KeyName: jmartin DiskType: ephemeral InstanceType: m1.small ClusterSize: 3 tags: Stack: ansible-cloudformation name: create a stack, pass in the template via an URL v2 amazon.aws.cloudformation: stack_name: "ansible-cloudformation" state: present region: us-east-1 disable_rollback: true template_url: https://s3.amazonaws.com/my-bucket/cloudformation.template template_body: "{{ lookup('template', 'cloudformation.j2') }}" template_parameters: KeyName: jmartin DiskType: ephemeral InstanceType: m1.small ClusterSize: 3 tags: Stack: ansible-cloudformation name: Create a stack set with instances in two accounts community.aws.cloudformationstackset: name: my-stack description: Test stack in two accounts state: present template_url: https://s3.amazonaws.com/my-bucket/cloudformation.template template_body: "{{ lookup('template', 'cloudformation.j2') }}" accounts: [1234567890, 2345678901] regions: us-east-1 name: Create a stack set with instances in two accounts v2 community.aws.cloudformationstackset: name: my-stack description: Test stack in two accounts state: present accounts: [1234567890, 2345678901] regions: us-east-1 ``` ```yaml title="Negative test num. 1 - yaml file" name: create a stack, pass in the template body via lookup template v3 amazon.aws.cloudformation: stack_name: ansible-cloudformation state: present region: us-east-1 disable_rollback: true template_body: "{{ lookup('template', 'cloudformation.j2') }}" template_parameters: KeyName: jmartin DiskType: ephemeral InstanceType: m1.small ClusterSize: 3 tags: Stack: ansible-cloudformation name: create a stack, pass in the template via an URL v4 amazon.aws.cloudformation: stack_name: ansible-cloudformation state: present region: us-east-1 disable_rollback: true template_url: https://s3.amazonaws.com/my-bucket/cloudformation.template template_parameters: KeyName: jmartin DiskType: ephemeral InstanceType: m1.small ClusterSize: 3 tags: Stack: ansible-cloudformation name: Create a stack set with instances in two accounts v5 community.aws.cloudformationstackset: name: my-stack description: Test stack in two accounts state: present template_url: https://s3.amazonaws.com/my-bucket/cloudformation.template accounts: [1234567890, 2345678901] regions: us-east-1 ```
Project: StarRocks (App Definition and Development / Database)
File: md5.md

displayed_sidebar: "English" Uses the MD5 Message-Digest Algorithm to calculate the 128-bit checksum of a string. The checksum is represented by a 32-character hexadecimal string. ```sql md5(expr) ``` `expr`: the string to calculate. It must be of the VARCHAR type. Returns a checksum of the VARCHAR type, which is a 32-character hexadecimal string. If the input is NULL, NULL is returned. ```sql select md5('abc'); ``` ```plaintext +-+ | md5('abc') | +-+ | 900150983cd24fb0d6963f7d28e17f72 | +-+ 1 row in set (0.01 sec) ``` ```sql select md5(null); ``` ```plaintext +--+ | md5(NULL) | +--+ | NULL | +--+ 1 row in set (0.00 sec) ``` MD5, ENCRYPTION
Project: Meshery (Provisioning / Automation & Configuration)
File: fluentbit-skt.md

layout: integration title: Fluentbit Skt subtitle: Collaborative and visual infrastructure as code for Fluentbit Skt image: /assets/img/integrations/fluentbit-skt/icons/color/fluentbit-skt-color.svg permalink: extensibility/integrations/fluentbit-skt docURL: https://docs.meshery.io/extensibility/integrations/fluentbit-skt description: integrations-category: Observability and Analysis integrations-subcategory: Logging registrant: Artifact Hub components: featureList: [ "Fluent Bit Management: Deploy and destroy Fluent Bit DaemonSet automatically.", "Fluentd Management: Deploy and destroy Fluentd StatefulSet automatically.", "Custom Configuration: Select input/filter/output plugins via labels." ] howItWorks: "Fluent Bit will be deployed as a DaemonSet while Fluentd will be deployed as a StatefulSet. " howItWorksDetails: "Although both Fluent Bit and Fluentd can collect, process(parse and filter) and then forward log to the final destinations, still they have strengths in different aspects. Fluent Bit is a good choice as a logging agent because of its lightweight and efficiency, while Fluentd is more powerful to perform advanced processing on logs because of its rich plugins. Fluent Bit only mode: If you just need to collect logs and send logs to the final destinations, all you need is Fluent Bit. Fluent Bit + Fluentd mode: If you also need to perform some advanced processing on the logs collected or send to more sinks, then you also need Fluentd. Fluentd only mode: If you need to receive logs through networks like HTTP or Syslog and then process and send the log to the final sinks, you only need Fluentd. Fluent Operator includes CRDs and controllers for both Fluent Bit and Fluentd which allows you to config your log processing pipelines in the 3 modes mentioned above as you wish." language: en list: include type: extensibility category: integrations
Project: Kui (App Definition and Development / Application Definition & Image Build)
File: kui_shell_plugin_kubectl.KubeStatusCondition.md

/ / KubeStatusCondition .KubeStatusCondition `Optional` lastProbeTime: `string` lastTransitionTime: `string` lastUpdateTime: `string` message: `string` `Optional` phase: `string` `Optional` reason: `string` status: `string` \| `boolean` `Optional` type: `string`
Project: Beam (App Definition and Development / Streaming & Messaging)
File: bbc.md

title: "BBC" icon: /images/logos/powered-by/bbc.png hasLink: "https://www.youtube.com/watch?v=KG7HdjUNhHY" <!-- Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -->
Project: Beam (App Definition and Development / Streaming & Messaging)
File: 36_ai_kfp_orchestration.md

Prompt: How can I orchestrate Apache Beam pipelines with Kubeflow? Response: You can use Apache Beam for various tasks within your machine learning project, including data validation, preprocessing, model validation, and model deployment and inference. However, a comprehensive ML workflow also involves additional steps like data exploration, feature engineering, and model training. Additionally, ML workflows require metadata and artifact tracking for reproducibility and auditability. Popular tools for building and managing ML workflows include , , and . Kubeflow is an open-source ML platform tailored for MLOps, which comprises best practices and guiding principles to streamline the development and maintenance of ML workflows. Kubeflow provides a set of tools for building, deploying, and managing end-to-end ML pipelines in the form of DAGs. The DAGs orchestrate the scheduling and execution of pipeline steps and facilitate the transfer of execution parameters, metadata, and artifacts between steps. You can integrate an Apache Beam pipeline into a Kubeflow pipeline, where the Apache Beam pipeline DAG serves as a node within the Kubeflow pipeline DAG. To run an Apache Beam pipeline as a part of a Kubeflow Pipeline (KFP), you need to: Create KFP components by specifying interfaces and containerizing the component implementation. Create the KFP pipeline by connecting components and defining how inputs and outputs are exchanged between them. Compile and run the KFP pipeline by submitting it to a KFP client endpoint. Here is an example of a KFP pipeline orchestrating an Apache Beam preprocessing pipeline: ``` kfp pipeline.py components preprocess Dockerfile component.yaml requirements.txt src preprocess.py requirements.txt ``` Apache Beam pipelines can be used as KFP components consisting of a YAML specification `component.yaml` and a Python source file `preprocess.py`. The YAML file defines the input and output arguments of the component, while the Python file contains the Apache Beam pipeline code. Since KFP provides the input and output arguments as command-line arguments, you need to use the `ArgumentParser` class. You containerize the logic and prerequisites of the pipeline components, and then push the resulting container image to a container registry. In the final stage, the `compiler` method transforms the KFP pipeline into a JSON file, and the `pipeline.py` script submits it to a KFP client endpoint. ```python Compiler().compile(pipelinefunc=pipeline, packagepath="pipeline.json") client = kfp.Client() experiment = client.create_experiment("KFP orchestration example") runresult = client.runpipeline( experiment_id=experiment.id, job_name="KFP orchestration job", pipelinepackagepath="pipeline.json", params=run_arguments) ``` For a complete example of orchestrating an Apache Beam preprocessing pipeline with Kubeflow, refer to the .
Project: Podman (App Definition and Development / Application Definition & Image Build)
File: health-cmd.md

Set or alter a healthcheck command for a container. The command is a command to be executed inside the container that determines the container health. The command is required for other healthcheck options to be applied. A value of none disables existing healthchecks. Multiple options can be passed in the form of a JSON array; otherwise, the command is interpreted as an argument to /bin/sh -c.
Project: Falco (Provisioning / Security & Compliance)
File: 20190909-psp-rules-support.md

<!-- toc --> * * * <!-- tocstop --> We want to make it easier for K8s Cluster Operators to Author Pod Security Policies by providing a way to read a PSP, convert it to a set of Falco rules, and then run Falco with those rules. PSPs provide a rich powerful framework to restrict the behavior of pods and apply consistent security policies across a cluster, but its difficult to know the gap between what you want your security policy to be and what your cluster is actually doing. Additionally, since PSPs enforce once applied, they might prevent pods from running, and the process of tuning a PSP live on a cluster can be disruptive and painful. That's where Falco comes in. We want to make it possible for Falco to perform a "dry run" evaluation of a PSP, translating it to Falco rules that observe the behaviour of deployed pods and sending alerts for violations, without blocking. This helps accelerate the authoring cycle, providing a complete authoring framework for PSPs without deploying straight to the cluster. Transparently read a candidate PSP into an equivalent set of Falco rules that can look for the conditions in the PSP. The PSP is converted into a set of Falco rules which can be either saved as a file for later use/inspection, or loaded directly so that they can monitor system calls and k8s audit activity. Falco will not automatically read PSPs from a cluster, will not install PSPs, and will not provide guidance on the parts of your infrastructure that are already covered by PSPs. This feature only helps with the testing part of a candidate PSP. For coming up with an initial PSP, you can use tools like . The use case here is for cluster operators who want to author PSPs, but don't want to just put it in a cluster and see what breaks. For example, if your PSP sets privileged to false, but it turns out some of your pods are running privileged, they won't be able to start. With this feature, they could iterate without enforcement until they have a PSP that matches the actual behaviour of their cluster. Some of that will come from changing the PSP, some of that will come from changing the behaviour of the cluster. The important part is that it's not mistakenly preventing things from running while you're figuring it out. You'll be able to run falco with a `--psp` argument that provides a single PSP yaml file. Falco will automatically convert the PSP into an equivalent set of Falco rules, load the rules, and then run with the loaded rules. You can optionally provide a `--psp_save=<path>` command line option to save the converted rules to a file. No diagrams yet. We'll use as the templating engine. For the most part, we can rely on the existing framework of rules, filter expressions, and output expressions that already exist in Falco. One significant change will be that filter fields can extract more than one "value" per event, and we'll need to define new operators to perform set comparisons between values in an event and values in the comparison right-hand-side. This will rely heavily on existing support for in Falco.
Project: KICS (Provisioning / Security & Compliance)
File: a5bf1a1c-92c7-401c-b4c6-ebdc8b686c01.md

title: SQL DB Instance Backup Disabled hide: toc: true navigation: true <style> .highlight .hll { background-color: #ff171742; } .md-content { max-width: 1100px; margin: 0 auto; } </style> Query id: a5bf1a1c-92c7-401c-b4c6-ebdc8b686c01 Query name: SQL DB Instance Backup Disabled Platform: GoogleDeploymentManager Severity: <span style="color:#ff7213">Medium</span> Category: Backup URL: Checks if backup configuration is enabled for all Cloud SQL Database instances<br> ```yaml title="Positive test num. 1 - yaml file" hl_lines="5" resources: name: sql-instance type: sqladmin.v1beta4.instance properties: settings: tier: db-custom-1-3840 ``` ```yaml title="Positive test num. 2 - yaml file" hl_lines="7" resources: name: sql-instance type: sqladmin.v1beta4.instance properties: settings: tier: db-custom-1-3840 backupConfiguration: binaryLogEnabled: true ``` ```yaml title="Positive test num. 3 - yaml file" hl_lines="8" resources: name: sql-instance type: sqladmin.v1beta4.instance properties: settings: tier: db-custom-1-3840 backupConfiguration: enabled: false ``` ```yaml title="Negative test num. 1 - yaml file" resources: name: sql-instance type: sqladmin.v1beta4.instance properties: settings: tier: db-custom-1-3840 backupConfiguration: enabled: true ```
Project: Chef InSpec (Provisioning / Security & Compliance)
File: etc_hosts_deny.md

+++ title = "etchostsdeny resource" draft = false gh_repo = "inspec" platform = "linux" [menu] [menu.inspec] title = "etchostsdeny" identifier = "inspec/resources/os/etchostsdeny.md etchostsdeny resource" parent = "inspec/resources/os" +++ Use the `etchostsdeny` Chef InSpec audit resource to test rules for rejecting daemon and client traffic defined in /etc/hosts.deny. {{< readfile file="content/inspec/reusable/md/inspec_installation.md" >}} This resource first became available in v1.39.1 of InSpec. An `etchostsdeny` rule specifies one or more daemons mapped to one or more clients, with zero or more options for rejecting traffic when found. Use the where clause to match a property to one or more rules in the hosts.deny file: describe etchostsdeny.where { daemon == 'value' } do its ('client_list') { should include ['values'] } its ('options') { should include ['values'] } end Use the optional constructor parameter to give an alternative path to hosts.deny: describe etchostsdeny(hosts_path).where { daemon == 'value' } do its ('client_list') { should include ['values'] } its ('options') { should include ['values'] } end where `daemon` is a daemon that will be rejected to pass traffic in. `client_list` is a list of clients that will be rejected to pass traffic in. `options` is a list of tasks that are to be done with the rule when traffic is found. The `daemon` property returns a string containing the daemon that is allowed in the rule. describe etchostsdeny.where { client_list == ['127.0.1.154', '[:fff:fAb0::]'] } do its('daemon') { should eq ['vsftpd', 'sshd'] } end The `client_list` property returns a 2d string array where each entry contains the clients specified for the rule. describe etchostsdeny.where { daemon == 'sshd' } do its('client_list') { should include ['192.168.0.0/16', '[abcd::0000:1234]'] } end The `options` property returns a 2d string array where each entry contains any options specified for the rule. describe etchostsdeny.where { daemon == 'sshd' } do its('options') { should include ['deny', 'echo "REJECTED"'] } end {{< readfile file="content/inspec/reusable/md/inspecmatcherslink.md" >}}