The flink-yarn-session command was added in Amazon EMR version 5.5.0 as a wrapper for the yarn-session.sh script to simplify execution. If you use an earlier version of Amazon EMR, substitute bash -c "/usr/lib/flink/bin/yarn-session.sh -d" for Arguments in the console or Args. in the AWS CLI command.
The flink-yarn-session command with arguments appropriate for your application. For example, flink-yarn-session -d starts a Flink session within your YARN cluster in a detached state (-d). See YARN setup in the latest Flink documentation for argument details.
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Use the add-steps command to add a Flink job to a long-running cluster. The following example command specifies Args="flink-yarn-session", "-d" to start a Flink session within your YARN cluster in a detached state (-d). See YARN setup in the latest Flink documentation for argument details.
In Zeppelin 0.9, we refactor the Flink interpreter in Zeppelin to support the latest version of Flink. Only Flink 1.10+ is supported, old versions of flink won't work.Apache Flink is supported in Zeppelin with the Flink interpreter group which consists of the five interpreters listed below.
For beginner, we would suggest you to play Flink in Zeppelin docker. First you need to download Flink, because there's no Flink binary distribution shipped with Zeppelin. e.g. Here we download Flink 1.12.2 to/mnt/disk1/flink-1.12.2,and we mount it to Zeppelin docker container and run the following command to start Zeppelin docker.
After running the above command, you can open :8080 to play Flink in Zeppelin. We only verify the flink local mode in Zeppelin docker, other modes may not due to network issues.-p 8081:8081 is to expose Flink web ui, so that you can access Flink web ui via :8081.
The Flink interpreter can be configured with properties provided by Zeppelin (as following table).You can also add and set other Flink properties which are not listed in the table. For a list of additional properties, refer to Flink Available Properties. Property Default Description FLINK_HOME Location of Flink installation. It is must be specified, otherwise you can not use Flink in Zeppelin HADOOP_CONF_DIR Location of hadoop conf, this is must be set if running in yarn mode HIVE_CONF_DIR Location of hive conf, this is must be set if you want to connect to hive metastore flink.execution.mode local Execution mode of Flink, e.g. local | remote | yarn | yarn-application flink.execution.remote.host Host name of running JobManager. Only used for remote mode flink.execution.remote.port Port of running JobManager. Only used for remote mode jobmanager.memory.process.size 1024m Total memory size of JobManager, e.g. 1024m. It is official Flink property taskmanager.memory.process.size 1024m Total memory size of TaskManager, e.g. 1024m. It is official Flink property taskmanager.numberOfTaskSlots 1 Number of slot per TaskManager local.number-taskmanager 4 Total number of TaskManagers in local mode yarn.application.name Zeppelin Flink Session Yarn app name yarn.application.queue default queue name of yarn app zeppelin.flink.uiWebUrl User specified Flink JobManager url, it could be used in remote mode where Flink cluster is already started, or could be used as url template, e.g. -server:8443/gateway/cluster-topo/yarn/proxy/{{applicationId}}/ where {{applicationId}} is placeholder of yarn app id zeppelin.flink.run.asLoginUser true Whether run Flink job as the Zeppelin login user, it is only applied when running Flink job in hadoop yarn cluster and shiro is enabled flink.udf.jars Flink udf jars (comma separated), Zeppelin will register udf in these jars automatically for user. These udf jars could be either local files or hdfs files if you have hadoop installed. The udf name is the class name. flink.udf.jars.packages Packages (comma separated) that would be searched for the udf defined in flink.udf.jars. Specifying this can reduce the number of classes to scan, otherwise all the classes in udf jar will be scanned. flink.execution.jars Additional user jars (comma separated), these jars could be either local files or hdfs files if you have hadoop installed. It can be used to specify Flink connector jars or udf jars (no udf class auto-registration like flink.udf.jars) flink.execution.packages Additional user packages (comma separated), e.g. org.apache.flink:flink-json:1.10.0 zeppelin.flink.concurrentBatchSql.max 10 Max concurrent sql of Batch Sql (%flink.bsql) zeppelin.flink.concurrentStreamSql.max 10 Max concurrent sql of Stream Sql (%flink.ssql) zeppelin.pyflink.python python Python binary executable for PyFlink table.exec.resource.default-parallelism 1 Default parallelism for Flink sql job zeppelin.flink.scala.color true Whether display Scala shell output in colorful format
zeppelin.flink.enableHive false Whether enable hive zeppelin.flink.hive.version 2.3.4 Hive version that you would like to connect zeppelin.flink.module.enableHive false Whether enable hive module, hive udf take precedence over Flink udf if hive module is enabled. zeppelin.flink.maxResult 1000 max number of row returned by sql interpreter zeppelin.flink.job.check_interval 1000 Check interval (in milliseconds) to check Flink job progress flink.interpreter.close.shutdown_cluster true Whether shutdown Flink cluster when closing interpreter zeppelin.interpreter.close.cancel_job true Whether cancel Flink job when closing interpreter
Running Flink in local mode will start a MiniCluster in local JVM. By default, the local MiniCluster use port 8081, so make sure this port is available in your machine,otherwise you can configure rest.port to specify another port. You can also specify local.number-taskmanager and flink.tm.slot to customize the number of TM and number of slots per TM.Because by default it is only 4 TM with 1 slot in this MiniCluster which may not be enough for some cases.
Running Flink in remote mode will connect to an existing Flink cluster which could be standalone cluster or yarn session cluster. Besides specifying flink.execution.mode to be remote, you also need to specifyflink.execution.remote.host and flink.execution.remote.port to point to Flink job manager's rest api address.
Scala is the default language of Flink on Zeppelin%flink), and it is also the entry point of Flink interpreter. Underneath Flink interpreter will create Scala shell which would create several built-in variables, including ExecutionEnvironmentStreamExecutionEnvironment and so on. So don't create these Flink environment variables again, otherwise you might hit weird issues. The Scala code you write in Zeppelin will be submitted to this Scala shell.
Here are the builtin variables created in Flink Scala shell.
In Flink Sql-client, either you run streaming sql or run batch sql in one session. You can not run them together. But in Zeppelin, you can do that. %flink.ssql is used for running streaming sql, while %flink.bsql is used for running batch sql. Batch/Streaming Flink jobs run in the same Flink session cluster.
PyFlink is Python entry point of Flink on Zeppelin, internally Flink interpreter will create Python shell whichwould create Flink's environment variables (including ExecutionEnvironment, StreamExecutionEnvironment and so on).To be noticed, the java environment behind Pyflink is created in Scala shell.That means underneath Scala shell and Python shell share the same environment.These are variables created in Python shell.
%flink.pyflink is much simple and easy, you don't need to do anything except the above setting,but its function is also limited. We suggest you to use %flink.ipyflink which provides almost the same user experience like jupyter.
The format is artifactGroup:artifactId:version, if you have multiple packages,then separate them with comma. flink.execution.packages requires internet accessible.So if you can not access internet, you need to use flink.execution.jars instead.
If your Zeppelin machine can not access internet or your dependencies are not deployed to maven repository,then you can use flink.execution.jars to specify the jar files you depend on (each jar file is separated with comma)
Zeppelin would scan this jar, find out all the udf classes and then register them automatically for you. The udf name is the class name. For example, here's the output of show functions after specifing the above udf jars in flink.udf.jars
By default, Zeppelin would scan all the classes in this jar, so it would be pretty slow if your jar is very big specially when your udf jar has other dependencies. So in this case we would recommend you to specify flink.udf.jars.packages to specify the package to scan,this can reduce the number of classes to scan and make the udf detection much faster.
Remember, you can always pull down the blog-wc-batch branch of flink-exploration github project if that would be easier. There is even a complete text version of Green Eggs and Ham to word count on. If you run it, comment below on the number of times green, eggs, and ham were present. To help make sure it all ran correctly, there were 82 instances of not in the text.
In the Cluster properties section, click Add Properties foreach optionalcluster propertyto add to your cluster. You can add flink prefixed propertiesto configure Flink properties in /etc/flink/conf/flink-conf.yaml thatwill act as defaults for Flink applications that you run on the cluster.
In the Custom cluster metadata section, click Add Metadata to addoptional metadata. For example, addflink-start-yarn-session true to run the Flink YARN daemon(/usr/bin/flink-yarn-daemon) in the background on the cluster masternode to start a Flink YARN session (see Flink session mode).
When creating Dataproc clusters withimage versions 2.0.67+ and 2.1.15+, you can use the --properties flag toto configure Flink properties in /etc/flink/conf/flink-conf.yaml that willact as defaults for Flink applications that you run on the cluster.
You can set flink:historyserver.archive.fs.dirto specify the Cloud Storage location to write Flink job historyfiles (this location will be used by the Flink History Server runningon the Flink cluster). 006ab0faaa
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