cwielenb/CNCF-Dataset huggingface dataset:
Project: Druid (App Definition and Development / Database)
File: druid-vs-key-value.md

id: druid-vs-key-value title: "Apache Druid vs. Key/Value Stores (HBase/Cassandra/OpenTSDB)" <!-- ~ 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. --> Druid is highly optimized for scans and aggregations, it supports arbitrarily deep drill downs into data sets. This same functionality is supported in key/value stores in 2 ways: Pre-compute all permutations of possible user queries Range scans on event data When pre-computing results, the key is the exact parameters of the query, and the value is the result of the query. The queries return extremely quickly, but at the cost of flexibility, as ad-hoc exploratory queries are not possible with pre-computing every possible query permutation. Pre-computing all permutations of all ad-hoc queries leads to result sets that grow exponentially with the number of columns of a data set, and pre-computing queries for complex real-world data sets can require hours of pre-processing time. The other approach to using key/value stores for aggregations to use the dimensions of an event as the key and the event measures as the value. Aggregations are done by issuing range scans on this data. Timeseries specific databases such as OpenTSDB use this approach. One of the limitations here is that the key/value storage model does not have indexes for any kind of filtering other than prefix ranges, which can be used to filter a query down to a metric and time range, but cannot resolve complex predicates to narrow the exact data to scan. When the number of rows to scan gets large, this limitation can greatly reduce performance. It is also harder to achieve good locality with key/value stores because most dont support pushing down aggregates to the storage layer. For arbitrary exploration of data (flexible data filtering), Druid's custom column format enables ad-hoc queries without pre-computation. The format also enables fast scans on columns, which is important for good aggregation performance.
Project: Beam (App Definition and Development / Streaming & Messaging)
File: euphoria.md

title: "Euphoria API Roadmap" <!-- 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. --> Easy to use Java 8 DSL for the Beam Java SDK. Provides a high-level abstraction of Beam transformations, which is both easy to read and write. Can be used as a complement to existing Beam pipelines (convertible back and forth). You can have a glimpse of the API at . Issues: / Contact: Implementation of a join, that can handle large scale join of highly skewed data sets. This implementation breaks the large keys into multiple splits, using key distribution approximated by count min sketch data structure. In order to pick the right translation for the operator without user interference, we can leverage knowledge from previous pipeline runs. We want to provide a convenient and portable way to gather this knowledge. Implementation of an easy to use Fluent API on top of Euphoria DSL. An convenient API for multiple outputs. Introduce API for converting streams to tables (KStream <-> KTable approach) and various types of (windowed and unwindowed) joins on them.
Project: StarRocks (App Definition and Development / Database)
File: SHOW_BROKER.md

displayed_sidebar: "English" This statement is used to view broker that currently exists. :::tip Only users with the SYSTEM-level OPERATE privilege or the `cluster_admin` role can perform this operation. ::: ```sql SHOW BROKER ``` Note: LastStartTime represents the latest BE start-up time. LastHeartbeat represents the latest heartbeat. Alive indicates whether the node survives. ErrMsg is used to display error messages when the heartbeat fails.
Project: Harbor (Provisioning / Container Registry)
File: 12-03-LDAP-usergroup-delete.md

Test 12-03 LDAP Usergroup Delete ======= To verify admin user can delete an LDAP group User guide This test requires that a Harbor instance is running and available. An LDAP server is running and available, and enabled memberof overlay feature LDAP group config parameter are configured. ldapgroupbasedn ldapgroupfilter ldap_gid ldapgroupscope Login UI as admin user. In `Administration->User Group` page, Add an LDAP with a valid group DN with group name. In Project Member of library, assign this user group with a developer role to this user group. In `Administration->User Group` page, Delete the user group with a different name. Check Project Member of library, make sure there is no role for this user group. In step4 the user group is deleted, and all its project member information is removed too. None
Project: YDB (App Definition and Development / Database)
File: load-actors-vdisk.md

Generates a write-only load on the VDisk. Simulates a Distributed Storage Proxy. The test outputs the VDisk write performance in operations per second. {% include notitle %} {% include %} | Parameter | Description | | | `VDiskId` | Parameters of the VDisk used to generate load.<ul><li>`GroupID`: Group ID.</li><li>`GroupGeneration`: Group generation.</li><li>`Ring`: Group ring ID.</li><li>`Domain`: Ring fail domain ID.</li><li>`VDisk`: Index of the VDisk in the fail domain.</li></ul> | | `GroupInfo` | Description of the group hosting theloaded VDisk (of the appropriate generation). | | `TabletId` | ID of the tablet that generates the load. It must be unique for each load actor. | | `Channel` | ID of the channel inside the tablet that will be specified in the BLOB write and garbage collection commands. | | `DurationSeconds` | The total test time in seconds; when it expires, the load stops automatically. | | `WriteIntervals` | Setting up the ofintervals between therecords. | | `WriteSizes` | Size of the data to write. It is selected randomly for each request from the `Min`-`Max` range. You can set multiple `WriteSizes` ranges, in which case a value from a specific range will be selected based on its `Weight`. | | `InFlightPutsMax` | Maximum number of concurrentBLOB write queries against the VDisk (TEvVPut queries); if omitted, the number of queries is unlimited. | | `InFlightPutBytesMax` | Maximum number of bytes in theconcurrent BLOB write queries against the VDisk (TEvVPut-requests). | | `PutHandleClass` | Class of data writes to the disk subsystem. If the `TabletLog` value is set, the write operation has the highest priority. | | `BarrierAdvanceIntervals` | Setting up the ofintervals between the advance of thegarbage collection barrier and the write step. | | `StepDistance` | Distance between the currently written step `Gen:Step` of the BLOB and its currently collected step. The higher is the value, the more data is stored. Data is written from `Step = X` and deleted from all the BLOBs where `Step = X - StepDistance`. The `Step` is periodically incremented by one (with the `BarrierAdvanceIntervals` period). | {% include %} <!-- ## {#examples} -->
Project: ArangoDB (App Definition and Development / Database)
File: failure_type.md

+++ title = "`failure_type<EC, EP = void>`" description = "Type sugar for constructing an unsuccessful result or outcome." +++ Type sugar for constructing an unsuccessful result or outcome. Generally not constructed directly, but via the free function {{% api "failure(T &&, ...)" %}}. Detectable using {{% api "isfailuretype<T>" %}}. This is a regular wrapper type, with defaulted default, copy and move constructor, defaulted assignment, and defaulted destructor. Member type aliases `errortype` and `exceptiontype` indicate `EC` and `E`. There is an explicit initialising constructor taking any types `U` and `V` which will forward construct the contained `errortype` and `exceptiontype` respectively. It also has an optional parameter `spare_storage`, if you wish to specify a spare storage value. There are two tagged initialising constructors taking `inplacetypet<errortype>` or `inplacetypet<exceptiontype>`, and a `U` which will forward construct the contained `errortype` and `exceptiontype` respectively. These both also have an optional parameter `spare_storage`, if you wish to specify a spare storage value. There are `.error()` and `.exception()` reference observers with the usual constexpr lvalue, const lvalue, rvalue and const rvalue overloads. One can discover which or both of these is valid using the usual `.haserror()` and `.hasexception()` observers. There is a `.spare_storage()` observer which returns the spare storage value with which the failure type sugar was constructed. There are specialisations `failuretype<EC, void>` and `failuretype<void, E>` which store nothing for the voided type and do not provide their observer functions. Requires: Nothing. Namespace: `BOOSTOUTCOMEV2_NAMESPACE` Header: `<boost/outcome/success_failure.hpp>`
Project: Meshery (Provisioning / Automation & Configuration)
File: v0.6.0-rc-5g.md

layout: release date: 2022-03-28 tag: v0.6.0-rc-5g language: en added targetarch to dockerfile @warunicorn19 (#5289) changed ui @warunicorn19 (#5288) [Docs] DDE: readme @leecalcote (#5284) Fix: failing deployement of argo-rollout due to invalid name @Abhishek-kumar09 (#5272) Fix empty dependson @Revolyssup (#5271) Fix falsy error message in bash script @Revolyssup (#5246) [DDE] Failing build for UI @sudo-NithishKarthik (#5268) initial ui for DDE @warunicorn19 (#5265) [Fix] Use relative path for build files generated by react @sudo-NithishKarthik (#5261) ui-dde to ui @warunicorn19 (#5259) Correcting UI build @hershd23 (#5256) Bump minimist from 1.2.5 to 1.2.6 in /install/docker-desktop-extension/ui-dde @dependabot (#5253) [DDE] Reinitialize Extension @leecalcote (#5252) Reduces Dockerfile for DDE to only build dde-ui @hershd23 (#5251) [Meshery] Handling unit tests failure @piyushsingariya (#5245) [CI] Fix golangci-lint to avoid legacy lint errors @alphaX86 (#5270) fix popper z-index problems with second tooltip @Abhishek-kumar09 (#5286) [UI]Added EventListner in SliderButton @Harshit-Dandriyal (#5277) Fixed routing to Lifecycle menu @MUzairS15 (#5276) [UI]Resolved ui bug of lagging side list Navs on hovering @Harshit-Dandriyal (#5275) [UI/RJSF] remove custom memoized i/p fields and use widgets instead @Abhishek-kumar09 (#5263) [UI] fixed meshery-ui not showing up after following make build-ui(Meshery docs) @Harshit-Dandriyal (#5237) [Chore] Security: vulnerability patches in dependencies @leecalcote (#5258) Bump minimist from 1.2.5 to 1.2.6 in /provider-ui @dependabot (#5254) Sanitize the pattern before validation for failing K8s components @Revolyssup (#5273) Fix: retrieve the cytoscape element Id from extenstion interface @Abhishek-kumar09 (#5248) [CI] DDE Build Updates @leecalcote (#5290) [CI] Fix golangci-lint to avoid legacy lint errors @alphaX86 (#5270) [Chore] Reducing noise level of info log @leecalcote (#5257) [CI] Docker Desktop Extension: refactor for DDE build only @leecalcote (#5264) Bump minimist from 1.2.5 to 1.2.6 in /provider-ui @dependabot (#5254) [CI] Chore: remove make command for go1.16 @leecalcote (#5247) [Docs]Added top margin so that in-page anchor tags align with menu bar @Harshit-Dandriyal (#5266) [Docs] Promote oam REST endpoint from experimental @alceil (#5233) [Chore] Security: vulnerability patches in dependencies @leecalcote (#5258) [Chore] Security: vulnerability patches in dependencies @leecalcote (#5258) Thank you to our contributors for making this release possible: @Abhishek-kumar09, @Harshit-Dandriyal, @MUzairS15, @Revolyssup, @alceil, @alphaX86, @dependabot, @dependabot[bot], @hershd23, @l5io, @leecalcote, @piyushsingariya, @sudo-NithishKarthik and @warunicorn19
Project: Checkov (Provisioning / Security & Compliance)
File: Azure Pipelines.md

layout: default published: true title: Azure Pipelines configuration scanning nav_order: 20 Checkov supports the evaluation of policies on your Azure Pipelines files. When using checkov to scan a directory that contains Azure Pipelines templates it will validate if the file is compliant with Azure Pipelines best practices such as usage of digest for container image version and more. Full list of Azure Pipelines policies checks can be found . ```yaml trigger: master resources: repositories: repository: AzureDevOps type: git endpoint: AzureDevOps name: AzureDevOps/AzureDevOps jobs: job: RunInContainer pool: vmImage: 'ubuntu-18.04' container: ubuntu:20.04 steps: script: printenv ``` ```bash checkov -d . --framework azure_pipelines ``` ```bash _| | _ | | / | '_ \ / _ \/ | |/ / _ \ \ / / | (| | | | / (| < (_) \ V / \_|_| |_|\|\|_|\_\/ \/ By Prisma Cloud | version: x.x.x azure_pipelines scan results: Passed checks: 1, Failed checks: 1, Skipped checks: 0 Check: CKVAZUREPIPELINES1: "Ensure container job uses a non latest version tag" PASSED for resource: /azure-pipelines.yml.jobs.jobs.CKVAZUREPIPELINES1[12:20] File: /azure-pipelines.yml:12-21 Check: CKVAZUREPIPELINES2: "Ensure container job uses a version digest" FAILED for resource: /azure-pipelines.yml.jobs.jobs.CKVAZUREPIPELINES2[12:20] File: /azure-pipelines.yml:12-21 12 | - job: RunInContainer 13 | pool: 14 | vmImage: 'ubuntu-18.04' 15 | 16 | container: ubuntu:20.04 17 | 18 | steps: 19 | - script: printenv ```
Project: KICS (Provisioning / Security & Compliance)
File: 5744cbb8-5946-4b75-a196-ade44449525b.md

title: HPA Targeted Deployments With Configured Replica Count hide: toc: true navigation: true <style> .highlight .hll { background-color: #ff171742; } .md-content { max-width: 1100px; margin: 0 auto; } </style> Query id: 5744cbb8-5946-4b75-a196-ade44449525b Query name: HPA Targeted Deployments With Configured Replica Count Platform: Kubernetes Severity: <span style="color:#5bc0de">Info</span> Category: Availability URL: Deployments targeted by HorizontalPodAutoscaler should not have a statically configured replica count set<br> ```yaml title="Positive test num. 1 - yaml file" hl_lines="10" apiVersion: apps/v1 kind: Deployment metadata: name: php-apache-1 spec: selector: matchLabels: run: php-apache replicas: 1 template: metadata: labels: run: php-apache spec: containers: name: php-apache image: k8s.gcr.io/hpa-example ports: containerPort: 80 resources: limits: cpu: 500m requests: cpu: 200m apiVersion: autoscaling/v2beta2 kind: HorizontalPodAutoscaler metadata: name: php-apache spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: php-apache-1 minReplicas: 1 maxReplicas: 10 metrics: type: Resource resource: name: cpu target: type: Utilization averageUtilization: 50 ``` ```yaml title="Negative test num. 1 - yaml file" apiVersion: apps/v1 kind: Deployment metadata: name: php-apache spec: selector: matchLabels: run: php-apache template: metadata: labels: run: php-apache spec: containers: name: php-apache image: k8s.gcr.io/hpa-example ports: containerPort: 80 resources: limits: cpu: 500m requests: cpu: 200m ```
Project: ArangoDB (App Definition and Development / Database)
File: poke_exception.md

+++ title = "Custom exception ptr" description = "" weight = 40 +++ If you merely want `result` to capture stack backtraces without calling a memory allocator and retaining any triviality of copy which is important for optimisation, you already have everything you need. But let's keep going by intercepting any construction of our localised `outcome` from our localised `result`, retrieving any stored backtrace and using it to synthesise an exception ptr with a message text including the backtrace. Firstly let us look at the function which synthesises the exception ptr: {{% snippet "errorcodeextended.cpp" "errorcodeextended4" %}} If the localised `outcome` being constructed is errored, try fetching the TLS slot for the unique 16-bit value in its spare storage. If that is valid, symbolise the stack backtrace into a string and make an exception ptr with a runtime error with that string. Finally, override the payload/exception member in our just-copy-constructed localised `outcome` with the new exception ptr. <hr> As the reference documentation for {{% api "void overrideoutcomeexception(basic_outcome<T, EC, EP, NoValuePolicy> *, U &&) noexcept" %}} points out, you almost certainly never want to use this function if there is any other alternative. It is worth explaining what is meant by this. In this section, we always synthesise an exception ptr from the stored state and error code at the exact point of transition from `result` based APIs to `outcome` based APIs. This is acceptable only because we know that our code enforces that discipline. If one were designing a library facility, one could not assume such discipline in the library user. One would probably be better off making the exception ptr synthesis lazy via a custom no-value policy which generates the stacktrace-containing error message only on demand e.g. `.exception()` observation, or a `.value()` observation where no value is available. Such a design is however more indeterminate than the design presented in this section, because the indeterminacy is less predictable than in this design. Ultimately which strategy you adopt depends on how important absolute determinism is to your Outcome-based application.
Project: kiosk (Provisioning / Automation & Configuration)
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: StarRocks (App Definition and Development / Database)
File: Flink_connector_faq.md

displayed_sidebar: "English" Issue description: The time generated by localtimestap function is normal in Flink. But it became 8 hours late when sunk to StarRocks. Flink server and StarRocks server are located in the same timezone, namely Asia/Shanghai UTC/GMT+08:00. Flink version is 1.12. Driver: flink-connector-jdbc_2.11. Can I ask how to resolve this issue? Solution: Please try configure the time parameter 'server-time-zone' = 'Asia/Shanghai' in Flink sink table. You may also add &serverTimezone=Asia/Shanghai in jdbc url. An example is shown below: ```sql CREATE TABLE sk ( sid int, local_dtm TIMESTAMP, curr_dtm TIMESTAMP ) WITH ( 'connector' = 'jdbc', 'url' = 'jdbc:mysql://192.168.110.66:9030/sys_device?characterEncoding=utf-8&serverTimezone=Asia/Shanghai', 'table-name' = 'sink', 'driver' = 'com.mysql.jdbc.Driver', 'username' = 'sr', 'password' = 'sr123', 'server-time-zone' = 'Asia/Shanghai' ); ``` Issue description: ```SQL failed to query wartermark offset, err: Local: Bad message format ``` Solution: Kafka communication needs the hostname. Users need to configure the host name resolution /etc/hosts in StarRocks cluster nodes. Solution: You can use StarRocks Tools to export the statements. This is a normal phenomenon, as large blocks of memory allocated to the database from the operating system are reserved during allocation and deferred during release in order to reuse the memory and make memory allocation more convenient. It is recommended that users validate the test environment by monitoring memory usage over a longer period of time to see if the memory can be released. Issue description: This package needs to be obtained through Aliyun mirror address. Solution: Please make sure that the mirror part of `/etc/maven/settings.xml` is all configured to be obtained through Aliyun mirror address. If it is, change it to the following: <mirror> <id>aliyunmaven </id> <mirrorf>central</mirrorf> <name>aliyun public repo</name> <url>https: //maven.aliyun.com/repository/public</url> </mirror> Issue description: ```plain text +-+--+ | Option | Required | Default | Type | Description | +-+ | sink.buffer-flush. | NO | 300000 | String | the flushing time interval, | | interval-ms | | | | range: [1000ms, 3600000ms] | +-+--+ ``` If this parameter is set as 15s with checkpoint interval being equal to 5 mins, does this value still take effect? Solution: Whichever of the three thresholds is reached first, that one will take effect first. This is not affected by the checkpoint interval value which only works for exactly once. Interval-ms is used by atleastonce.
Project: ClickHouse (App Definition and Development / Database)
File: v24.1.8.22-stable.md

sidebar_position: 1 sidebar_label: 2024 Fix possible incorrect result of aggregate function `uniqExact` (). Fix consecutive keys optimization for nullable keys (). Fix bug when reading system.parts using UUID (issue 61220). (). Fix client `-s` argument (). Fix string search with const position (). Fix crash in `multiSearchAllPositionsCaseInsensitiveUTF8` for incorrect UTF-8 (). Backported in :. (). Backported in : ... (). Backported in :. (). NO CL ENTRY: 'Revert "Backport to 24.1: Fix bug when reading system.parts using UUID (issue 61220)."'. (). Speed up cctools building ().