cwielenb/CNCF-Dataset huggingface dataset:
Project: Chef InSpec (Provisioning / Security & Compliance)
File: zfs_dataset.md

+++ title = "zfs_dataset resource" draft = false gh_repo = "inspec" platform = "linux" [menu] [menu.inspec] title = "zfs_dataset" identifier = "inspec/resources/os/zfsdataset.md zfsdataset resource" parent = "inspec/resources/os" +++ Use the `zfs_dataset` Chef InSpec audit resource to test the ZFS datasets on FreeBSD & Linux (Check for more details). {{< readfile file="content/inspec/reusable/md/inspec_installation.md" >}} This resource first became available in v1.16.0 of InSpec. A `zfs_dataset` resource block declares the ZFS dataset properties that should be tested: describe zfs_dataset('dataset') do it { should MATCHER 'value' } end where `('dataset')` is the name of the ZFS dataset (eg: `'tank/tmp'`) `MATCHER` is a valid matcher for this resource `'value'` is the value to be tested The following examples show how to use this Chef InSpec audit resource. describe zfs_dataset('tank/tmp') do it { should be_mounted } its('atime') { should eq 'on' } its('compression') { should eq 'lz4' } its('exec') { should eq 'off' } its('readonly') { should eq 'off' } its('setuid') { should eq 'off' } end This Chef InSpec audit resource has the matchers listed below, in addition to dynamically exposing all ZFS dataset properties available (see: `man zfs` for the list of supported properties). {{< readfile file="content/inspec/reusable/md/inspecmatcherslink.md" >}} This resource has the following special matchers. The `be_mounted` matcher tests if the dataset is accessible from the file system: it { should be_mounted }
Project: Beam (App Definition and Development / Streaming & Messaging)
File: spotify.md

title: "Spotify" icon: /images/logos/powered-by/spotify.png hasLink: "https://www.youtube.com/watch?v=sJ5EPAJUzTg&list=PL4dEBWmGSIU9OkXQU2OAXmITPLhiMSPRp&index=5" <!-- 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: KubeBlocks by ApeCloud (App Definition and Development / Database)
File: kbcli_dashboard_list.md

title: kbcli dashboard list List all dashboards. ``` kbcli dashboard list [flags] ``` ``` kbcli dashboard list ``` ``` -h, --help help for list ``` ``` --as string Username to impersonate for the operation. User could be a regular user or a service account in a namespace. --as-group stringArray Group to impersonate for the operation, this flag can be repeated to specify multiple groups. --as-uid string UID to impersonate for the operation. --cache-dir string Default cache directory (default "$HOME/.kube/cache") --certificate-authority string Path to a cert file for the certificate authority --client-certificate string Path to a client certificate file for TLS --client-key string Path to a client key file for TLS --cluster string The name of the kubeconfig cluster to use --context string The name of the kubeconfig context to use --disable-compression If true, opt-out of response compression for all requests to the server --insecure-skip-tls-verify If true, the server's certificate will not be checked for validity. This will make your HTTPS connections insecure --kubeconfig string Path to the kubeconfig file to use for CLI requests. --match-server-version Require server version to match client version -n, --namespace string If present, the namespace scope for this CLI request --request-timeout string The length of time to wait before giving up on a single server request. Non-zero values should contain a corresponding time unit (e.g. 1s, 2m, 3h). A value of zero means don't timeout requests. (default "0") -s, --server string The address and port of the Kubernetes API server --tls-server-name string Server name to use for server certificate validation. If it is not provided, the hostname used to contact the server is used --token string Bearer token for authentication to the API server --user string The name of the kubeconfig user to use ``` - List and open the KubeBlocks dashboards.
Project: Trivy (Provisioning / Security & Compliance)
File: docker.md

Trivy supports the scanners listed in the table below. | Scanner | Supported | | :-: | :-: | | [Misconfiguration] | | | [Secret] | | It supports the following configurations. | Config | Supported | | :--: | :-: | | Dockerfile | | | Containerfile | | | Compose | - | Trivy recursively searches directories and scans all found Docker files. The secret scan is performed on plain text files, with no special treatment for Dockerfile.
Project: Meshery (Provisioning / Automation & Configuration)
File: v0.6.0-rc.5u.md

layout: release date: 2022-06-09 tag: v0.6.0-rc.5u language: en [dde] UI Change to adapt to the exposed server version @sudo-NithishKarthik (#5596) fix: Service mesh provisioning @sudo-NithishKarthik (#5595) exposed meshery server version in dde @warunicorn19 (#5591) Fix panic @Revolyssup (#5593) Fix NATS connectivity test @MUzairS15 (#5583) [UI] Pattern file bug fixes @Chadha93 (#5587) [UI] Settings button style fix for lifecycle @Chadha93 (#5588) material-ui/core from 4.12.3 to 4.12.4 @Harshit-Dandriyal (#5585) [UI] Pattern file bug fixes @Chadha93 (#5587) Bump google.golang.org/grpc from 1.46.0 to 1.47.0 @dependabot (#5560) Thank you to our contributors for making this release possible: @Chadha93, @Harshit-Dandriyal, @MUzairS15, @Revolyssup, @dependabot, @l5io, @leecalcote, @sudo-NithishKarthik, @warunicorn19 and Nithish
Project: Buildpacks (App Definition and Development / Application Definition & Image Build)
File: feature.md

name: Feature request about: Suggest a new feature or an improvement to existing functionality title: '' labels: type/enhancement, status/triage assignees: '' <!-- A concise description of what problem the feature solves and why solving it matters. Ex. My shoelaces won't stay tied and I keep tripping... --> <!-- A clear and concise description of what you want to happen. Ex. We could have velcro on the shoes instead of laces...--> <!-- A clear and concise description of any alternative solutions or features you've considered. --> [ ] This feature should be documented somewhere <!-- Add any other context or screenshots about the feature request here. -->
Project: Meshery (Provisioning / Automation & Configuration)
File: broker.md

layout: default title: Broker permalink: concepts/architecture/broker type: components redirect_from: architecture/broker abstract: "Meshery broker component facilitates data streaming between kubernetes cluster components and outside world." language: en list: include Broker is a custom Kubernetes controller that provides data streaming across independent components of Meshery whether those components are running inside or outside of the Kubernetes cluster. [Meshery Log Viewer]({{ site.baseurl }}/assets/img/architecture/meshery-log-viewer.svg) It is recommended to run one broker instance for each kubernetes cluster, However the instance itself can be scaled up based on the incoming data volume in each of the cluster. The scaling is independent of the number of instances running. We leverage on the kubernetes functionality in terms of the High-Availability behaviour. Meaning, the broker instance gets instantiated/restarted on its own when an issue occurs. In part, Meshery-Operator is also responsible for keeping the broker functional. All the messages that are published to the broker is persisted in-memory within the broker instance until it get consumed. Persistent-volume/Disk-space is not currently being used by the Broker. The Broker instance is deployed inside the kubernetes cluster as a `Statefulset`. In the case where the broker doesn't seem to work, here are a few steps to troubleshoot the instance: Make sure the pods corresponding to the `Statefulset` is up and running. Make sure the kubernetes cluster has support for kubernetes `Service` type `LoadBalancer` or `NodePort`. Ensure connectivity between the Meshery-Server and the Broker service endpoint.
Project: Apache Heron (App Definition and Development / Streaming & Messaging)
File: ADOPTERS.md

<!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you 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. --> Twitter isn't the only company using Heron. We're sure the following list isn't complete, so please if your company should be included, or if you'd like us to feature a link to a blog post or article about how you're using Heron. Or even better, add the links yourself (please use HTTPS URLs if possible) and ! * https://blog.twitter.com/2016/open-sourcing-twitter-heron TODO
Project: Backstage (App Definition and Development / Application Definition & Image Build)
File: switching-sqlite-postgres.md

id: switching-sqlite-postgres title: Switching Backstage from SQLite to PostgreSQL description: How to get ready for deploying Backstage to production with PostgreSQL The default `@backstage/create-app` database is SQLite, an in-memory database that's perfect for initial experimentation as it requires no environment setup. Once you're ready to deploy Backstage in production, or to have a more persistent development setup, you can switch the Backstage database to PostgreSQL. Backstage uses the library, making it fairly easy to switch between database backends. First, add PostgreSQL to your `backend` package: ```bash yarn --cwd packages/backend add pg ``` Next, modify `app-config.yaml` in the root folder to add PostgreSQL configuration for the backend: ```yaml title="app-config.yaml" backend: database: client: better-sqlite3 connection: ':memory:' client: pg connection: host: ${POSTGRES_HOST} port: ${POSTGRES_PORT} user: ${POSTGRES_USER} password: ${POSTGRES_PASSWORD} ``` If you have an `app-config.local.yaml` for local development, a similar update should be made there. You can set the `POSTGRES_` environment variables prior to launching Backstage, or remove the `${...}` values and simply set actual values directly for development. The Backstage App is now ready to start up with a PostgreSQL backing database. If you want to override the default connection pool settings then use the below configuration: ```yaml title="app-config.local.yaml" backend: database: client: better-sqlite3 connection: ':memory:' client: pg connection: host: ${POSTGRES_HOST} port: ${POSTGRES_PORT} user: ${POSTGRES_USER} password: ${POSTGRES_PASSWORD} knexConfig: pool: min: 3 max: 12 acquireTimeoutMillis: 60000 idleTimeoutMillis: 60000 ``` By default, each plugin will get its own logical database, to ensure that there's no conflict in table names throughout the plugins that you install and to keep their concerns separate for other use cases further down the line. If you are limited in that you can only make use of a single database, you can use a special option `pluginDivisionMode` with `client: pg` in the config to create separate instead of creating separate databases. You can enable this using the following config: ```yaml backend: database: client: pg pluginDivisionMode: schema # defaults to database, but changing this to schema means plugins will be given their own schema (in the specified/default database) ```
Project: YugabyteDB (App Definition and Development / Database)
File: hgetall.md

title: HGETALL linkTitle: HGETALL description: HGETALL menu: preview: parent: api-yedis weight: 2130 aliases: /preview/api/redis/hgetall /preview/api/yedis/hgetall type: docs `HGETALL key` This command fetches the full content of all fields and all values of the hash that is associated with the given `key`. If the given `key` does not exist, and empty list is returned. If the given `key` is associated with non-hash data, an error is raised. Returns list of fields and values. You can do this as shown below. ```sh $ HSET yugahash area1 "Africa" ``` ``` 1 ``` ```sh $ HSET yugahash area2 "America" ``` ``` 1 ``` ```sh $ HGETALL yugahash ``` ``` 1) area1 2) "Africa" 3) area2 4) "America" ``` , , , , , , , , , ,
Project: StarRocks (App Definition and Development / Database)
File: array_map.md

displayed_sidebar: "English" array_map() is a higher-order SQL function that can take lambda functions as arguments.It applies the lambda function to the input arrays and returns a new array. array_map() can accept multiple arrays and can be nested with lambda functions to capture a variable. For more information about lambda functions, see . This function is supported from v2.5. The alias is transform(). ```Haskell arraymap(lambdafunction, arr1, arr2...) arraymap(arr1, arr2..., lambdafunction) ``` Lambda functions can only be used as the first or last parameter of a higher-order function. Either position does not affect the calculation. The number of arrays must be the same as the input parameters in the lambda function. Otherwise, an error is returned. All the arrays must have the same number of elements. Otherwise, an error is returned. Example 1: Increment every element of the array by 100. ```Plain select array_map(x -> x+100,[1,2,3]); ++ | array_map(x -> x + 100, [1, 2, 3]) | ++ | [101,102,103] | ++ ``` Example 2: Sum array1 [1,2,3] and array2 [11,12,13] element by element. ```Plain select array_map((x,y) -> x + y, [1,2,3], [11,12,13]); +--+ | array_map((x, y) -> x + y, [1, 2, 3], [11, 12, 13]) | +--+ | [12,14,16] | +--+ ``` Example 3: Determine whether the element in `x` is greater than 1.5. If yes, log(x) is returned for the element. If not, (x+y) is returned for the corresponding element. ```Plain select array_map((x,y) -> if(x>1.5,log(x),x+y), [1,2,3], [11,12,13]); +--+ | array_map((x, y) -> if(x > 1.5, log(x), x + y), [1, 2, 3], [11, 12, 13]) | +--+ | [12,0.6931471805599453,1.0986122886681098] | +--+ ``` Example 4: Use array_map() to capture variables. The returned element whose value is 1 indicates the condition is met. ```Plain -- In the example table, last_avg is the average score in the last exam. score represents the scores of three subjects in the current exam. ++-++ | id | last_avg | score | ++-++ | 1 | 55 | [50,60,70] | | 2 | 73 | [70,65,75] | | 3 | 89 | [88,92,90] | ++-++ -- Find the score that is higher than the average score of the last exam. select arraymap(x -> x > lastavg, score) from test_tbl; +--+ | arraymap(x -> x > lastavg, score) | +--+ | [0,1,1] | | [0,0,1] | | [0,1,1] | +--+ ``` Example 5: Use nested lambda functions. ```Plain select arraymap(x -> arraymap(x->x+100, x),[[1,2.3],[4,3,2]]); +-+ | arraymap(x -> arraymap(x -> x + 100, x), [[1, 2.3], [4, 3, 2]]) | +-+ | [[101,102.3],[104,103,102]] | +-+ ``` Example 6: An error is returned because the numbers of arrays and lambda parameters are inconsistent. The lambda function requires only one array but two arrays are passed in. ```Plain select array_map(x -> x,[1],[2,4]); ERROR 1064 (HY000): Lambda arguments should equal to lambda input arrays. ```
Project: Podman (App Definition and Development / Application Definition & Image Build)
File: tty.md

Allocate a pseudo-TTY. The default is false. When set to true, Podman allocates a pseudo-tty and attach to the standard input of the container. This can be used, for example, to run a throwaway interactive shell. NOTE: The --tty flag prevents redirection of standard output. It combines STDOUT and STDERR, it can insert control characters, and it can hang pipes. This option is only used when run interactively in a terminal. When feeding input to Podman, use -i only, not -it.
Project: StarRocks (App Definition and Development / Database)
File: synchronize_mysql_into_sr.md

displayed_sidebar: "English" A Flink job reports the error `Could not execute SQL statement. Reason:org.apache.flink.table.api.ValidationException: One or more required options are missing.` A possible reason is that the required configuration information is missing in multiple sets of rules, such as `[table-rule.1]` and `[table-rule.2]`, in the SMT configuration file config_prod.conf. You can check whether each set of rules, such as `[table-rule.1]` and `[table-rule.2]` is configured with the required database, table, and Flink connector information. Flink automatically restarts failed tasks through the and . For example, if you need to enable the checkpointing mechanism and use the default restart strategy, which is the fixed delay restart strategy, you can configure the following information in the configuration file flink-conf.yaml: ```Bash execution.checkpointing.interval: 300000 state.backend: filesystem state.checkpoints.dir: file:///tmp/flink-checkpoints-directory ``` Parameter description: NOTE For more detailed parameter descriptions in Flink documentation, see . `execution.checkpointing.interval`: the base time interval of checkpointing. Unit: millisecond. To enable the checkpointing mechanism, you need to set this parameter to a value greater than `0`. `state.backend`: specifies the state backend to determine how the state is represented internally, and how and where it is persisted upon checkpointing. Common values are `filesystem` or `rocksdb`. After the checkpointing mechanism is enabled, the state is persisted upon checkpoints to prevent data loss and ensure data consistency after recovery. For more information on state, see . `state.checkpoints.dir`: the directory to which checkpoints are written to. You can manually trigger a when stopping a Flink job (a savepoint is a consistent image of the execution state of a streaming Flink job, and is created based on the checkpointing mechanism). Later, you can restore the Flink job from the specified savepoint. Stop the Flink job with a savepoint. The following command automatically triggers a savepoint for the Flink job `jobId` and stops the Flink job. Additionally, you can specify a target file system directory to store the savepoint. ```Bash bin/flink stop --type [native/canonical] --savepointPath [:targetDirectory] :jobId ``` Parameter description: `jobId`: You can view the Flink job ID from the Flink WebUI or by running `flink list -running` on the command line. `targetDirectory`: You can specify `state.savepoints.dir` as the default directory for storing savepoints in the Flink configuration file flink-conf.yml. When a savepoint is triggered, the savepoint is stored in this default directory and you do not need to specify a directory . ```Bash state.savepoints.dir: [file:// or hdfs://]/home/user/savepoints_dir ``` Resubmit the Flink job with the preceding savepoint specified. ```Bash ./flink run -c com.starrocks.connector.flink.tools.ExecuteSQL -s savepoints_dir/savepoints-xxxxxxxx flink-connector-starrocks-xxxx.jar -f flink-create.all.sql ```
Project: Chef InSpec (Provisioning / Security & Compliance)
File: google_cloud_scheduler_jobs.md

+++ title = "googlecloudscheduler_jobs resource" draft = false gh_repo = "inspec" platform = "gcp" [menu] [menu.inspec] title = "googlecloudscheduler_jobs" identifier = "inspec/resources/gcp/googlecloudschedulerjobs.md googlecloudschedulerjobs resource" parent = "inspec/resources/gcp" +++ A `googlecloudscheduler_jobs` is used to test a Google Job resource ```ruby googlecloudscheduler_jobs(project: 'chef-gcp-inspec', region: ).names.each do |name| describe googlecloudscheduler_job(project: 'chef-gcp-inspec', region: us-central1, name: name) do it { should exist } its('description') { should cmp 'A description' } its('schedule') { should cmp '/8 *' } its('timezone') { should cmp 'America/NewYork' } its('httptarget.httpmethod') { should cmp 'POST' } its('http_target.uri') { should cmp 'https://example.com/ping' } end end ``` Properties that can be accessed from the `googlecloudscheduler_jobs` resource: See the resource for more information. `names` : an array of `googlecloudscheduler_job` name `descriptions` : an array of `googlecloudscheduler_job` description `schedules` : an array of `googlecloudscheduler_job` schedule `time_zones` : an array of `googlecloudschedulerjob` timezone `attempt_deadlines` : an array of `googlecloudschedulerjob` attemptdeadline `retry_configs` : an array of `googlecloudschedulerjob` retryconfig `pubsub_targets` : an array of `googlecloudschedulerjob` pubsubtarget `appenginehttp_targets` : an array of `googlecloudschedulerjob` appenginehttptarget `http_targets` : an array of `googlecloudschedulerjob` httptarget `regions` : an array of `googlecloudscheduler_job` region This resource supports all of the above properties as filter criteria, which can be used with `where` as a block or a method.
Project: StarRocks (App Definition and Development / Database)
File: array_min.md

displayed_sidebar: "English" Obtains the minimum value of all data in an ARRAY and return this result. ```Haskell array_min(array(type)) ``` ```plain text mysql> select array_min([113, 11, 12]); ++ | array_min([113,11,12]) | ++ | 11 | ++ mysql> select array_min([11.33, 11.11, 12.324]); ++ | array_min([11.33,11.11,12.324]) | ++ | 11.11 | ++ mysql> select array_min([cast('2020-02-25 11:35:17' as datetime), cast('2019-08-25 17:07:10' as datetime), cast('2025-08-25 17:07:10' as datetime)]); +--+ | array_min([CAST('2020-02-25 11:35:17' AS DATETIME),CAST('2019-08-25 17:07:10' AS DATETIME),CAST('2025-08-25 17:07:10' AS DATETIME)]) | +--+ | 2019-08-25 17:07:10 | +--+ ``` ARRAY_MIN,ARRAY
Project: SPIFFE (Provisioning / Key Management)
File: AGUSTIN_MARTINEZ_FAYO.md

Agustin is one of the longest serving contributors to the SPIRE project - having been involved since 2017 (before the project was even officially announced), and has been a maintainer for that entire time. He comes to the project from with a background in InfoSec and vulnerability assessment. He continues to be actively involved in significant architectural efforts to improve SPIRE, including (currently) support for SPIRE on Windows. GitHub Handle: @amartinezfayo Email Address: amartinezfayo@gmail.com LinkedIn Profile: https://www.linkedin.com/in/agustin-martinez-fayo-0a8a411/ Current Affiliation: Hewlett Packard Enterprise