wangyang0918 commented on a change in pull request #14238: URL: https://github.com/apache/flink/pull/14238#discussion_r532339513
########## File path: docs/deployment/resource-providers/yarn.md ########## @@ -0,0 +1,238 @@ +--- +title: "Apache Hadoop YARN" +nav-title: YARN +nav-parent_id: resource_providers +nav-pos: 4 +--- +<!-- +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. +--> + +* This will be replaced by the TOC +{:toc} + +## Getting Started + +This *Getting Started* section guides you through setting up a fully functional Flink Cluster on YARN. + +### Introduction + +[Apache Hadoop YARN](https://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html) is a resource provider popular with many data processing frameworks. +Flink services are submitted to YARN's ResourceManager, which spawns containers on machines managed by YARN NodeManagers. Flink deploys its JobManager and TaskManager instances into such containers. + +Flink can dynamically allocate and de-allocate TaskManager resources depending on the number of processing slots required by the job(s) running on the JobManager. + +### Preparation + +This *Getting Started* section assumes a functional YARN environment, starting from version 2.4.1. YARN environments are provided most conveniently through services such as Amazon EMR, Google Cloud DataProc or products like Cloudera. [Manually setting up a YARN environment locally](https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/SingleCluster.html) or [on a cluster](https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/ClusterSetup.html) is not recommended for following through this *Getting Started* tutorial. + +- Make sure your YARN cluster is ready for accepting Flink applications by running `yarn top`. It should show no error messages. +- Download a recent Flink distribution from the [download page]({{ site.download_url }}) and unpack it. +- **Important** Make sure that the `HADOOP_CLASSPATH` environment variable is set up (it can be checked by running `echo $HADOOP_CLASSPATH`). If not, set it up using + +{% highlight bash %} +export HADOOP_CLASSPATH=`hadoop classpath` +{% endhighlight %} + +### Starting a Flink Session on YARN + +Once you've made sure that the `HADOOP_CLASSPATH` environment variable is set, you can launch a Flink on YARN session, and submit an example job: + +{% highlight bash %} + +# we assume to be in the root directory of the unzipped Flink distribution + +# (0) export HADOOP_CLASSPATH +export HADOOP_CLASSPATH=`hadoop classpath` + +# (1) Start YARN Session +./bin/yarn-session.sh --detached + +# (2) You can now access the Flink Web Interface through the URL printed in the last lines of the command output, or through the YARN ResourceManager web UI. + +# (3) Submit example job +./bin/flink run ./examples/streaming/TopSpeedWindowing.jar + +# (4) Stop YARN session (replace the application id based on the output of the yarn-session.sh command) +echo "stop" | ./bin/yarn-session.sh -yid application_XXXXX_XXX +{% endhighlight %} + +Congratulations! You have successfully run a Flink application by deploying Flink on YARN. + +{% top %} + +## Deployment Modes Supported by Flink on YARN + +For production use, we recommend deploying Flink Applications in the [Per-job or Application Mode]({% link deployment/index.md %}#deployment-modes), as these modes provide a better isolation for the Applications. + +### Application Mode + +Application Mode will launch a Flink cluster on YARN, where the main() method of the application jar gets executed on the JobManager in YARN. +The cluster will shut down as soon as the application has finished. You can manually stop the cluster using `yarn application -kill <ApplicationId>` or by cancelling the Flink job. + +{% highlight bash %} +./bin/flink run-application -t yarn-application ./examples/streaming/TopSpeedWindowing.jar +{% endhighlight %} + + +Once an Application Mode cluster is deployed, you can interact with it for operations like cancelling or taking a savepoint. + +{% highlight bash %} +# List running job on the cluster +./bin/flink list -t yarn-application -Dyarn.application.id=application_XXXX_YY +# Cancel running job +./bin/flink cancel -t yarn-application -Dyarn.application.id=application_XXXX_YY <jobId> +{% endhighlight %} + +Note that cancelling your job on an Application Cluster will stop the cluster. + +To unlock the full potential of the application mode, consider using it with the `yarn.provided.lib.dirs` configuration option +and pre-upload your application jar to a location accessible by all nodes in your cluster. In this case, the +command could look like: + +{% highlight bash %} +./bin/flink run-application -t yarn-application \ + -Dyarn.provided.lib.dirs="hdfs://myhdfs/my-remote-flink-dist-dir" \ + hdfs://myhdfs/jars/my-application.jar +{% endhighlight %} + +The above will allow the job submission to be extra lightweight as the needed Flink jars and the application jar +are going to be picked up by the specified remote locations rather than be shipped to the cluster by the +client. + +### Per-Job Cluster Mode + +The Per-job Cluster mode will launch a Flink cluster on YARN, then run the provided application jar locally and finally submit the JobGraph to the JobManager on YARN. If you pass the `--detached` argument, the client will stop once the submission is accepted. + +The YARN cluster will stop once the job has stopped. + +{% highlight bash %} +./bin/flink run -t yarn-per-job --detached ./examples/streaming/TopSpeedWindowing.jar +{% endhighlight %} + +Once a Per-Job Cluster is deployed, you can interact with it for operations like cancelling or taking a savepoint. + +{% highlight bash %} +# List running job on the cluster +./bin/flink list -t yarn-per-job -Dyarn.application.id=application_XXXX_YY +# Cancel running job +./bin/flink cancel -t yarn-per-job -Dyarn.application.id=application_XXXX_YY <jobId> +{% endhighlight %} + +Note that cancelling your job on an Per-Job Cluster will stop the cluster. + + +### Session Mode + +We describe deployment with the Session Mode in the [Getting Started](#getting-started) guide at the top of the page. + +The Session Mode has two operation modes: +- **attached mode** (default): The `yarn-session.sh` client submits the Flink cluster to YARN, but the client keeps running, tracking the state of the cluster. If the cluster fails, the client will show the error. If the client gets terminated, it will signal the cluster to shut down as well. +- **detached mode** (`-d` or `--detached`): The `yarn-session.sh` client submits the Flink cluster to YARN, then the client returns. Another invocation of the client, or YARN tools is needed to stop the Flink cluster. + +The session mode will create a hidden YARN properties file in `/tmp/.yarn-properties-<username>`, which will be picked up for cluster discovery by the command line interface when submitting a job. + +You can also **manually specifiy the target YARN cluster** in the command line interface when submitting a Flink job. Here's an example: + +``` +./bin/flink run -t yarn-session -Dyarn.application.id=application_XXXX_YY ./examples/streaming/TopSpeedWindowing.jar +``` + +You can **re-attach to a YARN session** using the following command: + +``` +./bin/yarn-session.sh -yid application_XXXX_YY +``` + +Besides passing [configuration]({% link deployment/config.md %}) via the `conf/flink-conf.yaml` file, you can also pass any configuration at submission time to the `./bin/yarn-session.sh` client using `-Dkey=value` arguments. + +The YARN session client also has a few "shortcut arguments" for commonly used settings. They can be listed with `./bin/yarn-session.sh -h`. + +{% top %} + +## Flink on YARN Reference + +### Configuring Flink on YARN + +The YARN-specific configurations are listed on the [configuration page]({% link deployment/config.md %}#yarn). + +The following configuration parameters are managed by Flink on YARN, as they might get overwritten by the framework at runtime: +- `jobmanager.rpc.address` (dynamically set to the address of the JobManager container by Flink on YARN) +- `io.tmp.dirs` (If not set, Flink sets the temporary directories defined by YARN) +- `high-availability.cluster-id` (automatically generated ID to distinguish multiple clusters in the HA service) + +If you need to pass additional Hadoop configuration files to Flink, you can do so via the `HADOOP_CONF_DIR` environment variable, which accepts a directory name containing Hadoop configuration files. By default, all required Hadoop configuration files are loaded from the classpath via the `HADOOP_CLASSPATH` environment variable. + +### Resource Allocation Behavior + +A JobManager running on YARN will request additional TaskManagers, if it can not run all submitted jobs with the existing resources. In particular when running in Session Mode, the JobManager will, if needed, allocate additional TaskManagers as additional jobs are submitted. Unused TaskManagers are freeed up again after a timeout. + +The memory configurations for JobManager and TaskManager processes will be respected by the YARN implementation. The number of reported VCores is by default equal to the number of configured slots per TaskManager. The [yarn.containers.vcores]({% link deployment/config.md %}#yarn-containers-vcores) allows overwriting the number of vcores with a custom value. In order for this parameter to work you should enable CPU scheduling in your YARN cluster. + +Failed containers (including the JobManager) are replaced by YARN. The maximum number of JobManager container restarts is configured via [yarn.application-attempts]({% link deployment/config.md %}#yarn-application-attempts) (default 1). The YARN Application will fail once all attempts are exhausted. + +### High-Availability on YARN + +High-Availability on YARN is achieved through a combination of YARN and a [high availability service]({% link deployment/ha/index.md %}). + +Once a HA service is configured, it will persist JobManager metadata and perform leader elections. + +YARN is taking care of restarting failed JobManagers. The maximum number of JobManager restarts is defined through two configuration parameters. First Flink's [yarn.application-attempts]({% link deployment/config.md %}#yarn-application-attempts) configuration will default 2. This value is limited by YARN's [yarn.resourcemanager.am.max-attempts](https://hadoop.apache.org/docs/r2.4.1/hadoop-yarn/hadoop-yarn-common/yarn-default.xml), which also defaults to 2. + +Note that Flink is managing the `high-availability.cluster-id` configuration parameter when running on YARN. **You should not overwrite this parameter when running an HA cluster on YARN**. The cluster ID is used to distinguish multiple HA clusters in the HA backend (for example Zookeeper). Overwriting this configuration parameter can lead to multiple YARN clusters affecting each other. Review comment: ```suggestion Note that Flink is managing the `high-availability.cluster-id` configuration parameter when running on YARN. **You should not overwrite this parameter when running a HA cluster on YARN**. The cluster ID is used to distinguish multiple HA clusters in the HA backend (for example Zookeeper). Overwriting this configuration parameter can lead to multiple YARN clusters affecting each other. ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org