Flink comes with an integrated interactive Scala Shell. It can be used in a local setup as well as in a cluster setup.
To use the shell with an integrated Flink cluster just execute:
{% highlight bash %} bin/start-scala-shell.sh local {% endhighlight %}
in the root directory of your binary Flink directory. To run the Shell on a cluster, please see the Setup section below.
The shell supports DataSet, DataStream, Table API and SQL. Four different Environments are automatically prebound after startup. Use “benv” and “senv” to access the Batch and Streaming ExecutionEnvironment respectively. Use “btenv” and “stenv” to access BatchTableEnvironment and StreamTableEnvironment respectively.
The following example will execute the wordcount program in the Scala shell:
{% highlight scala %} Scala-Flink> val text = benv.fromElements( “To be, or not to be,--that is the question:--”, “Whether 'tis nobler in the mind to suffer”, “The slings and arrows of outrageous fortune”, “Or to take arms against a sea of troubles,”) Scala-Flink> val counts = text .flatMap { .toLowerCase.split(“\W+”) } .map { (, 1) }.groupBy(0).sum(1) Scala-Flink> counts.print() {% endhighlight %}
The print() command will automatically send the specified tasks to the JobManager for execution and will show the result of the computation in the terminal.
It is possible to write results to a file. However, in this case you need to call execute, to run your program:
{% highlight scala %} Scala-Flink> benv.execute(“MyProgram”) {% endhighlight %}
Similar to the batch program above, we can execute a streaming program through the DataStream API:
{% highlight scala %} Scala-Flink> val textStreaming = senv.fromElements( “To be, or not to be,--that is the question:--”, “Whether 'tis nobler in the mind to suffer”, “The slings and arrows of outrageous fortune”, “Or to take arms against a sea of troubles,”) Scala-Flink> val countsStreaming = textStreaming .flatMap { .toLowerCase.split(“\W+”) } .map { (, 1) }.keyBy(0).sum(1) Scala-Flink> countsStreaming.print() Scala-Flink> senv.execute(“Streaming Wordcount”) {% endhighlight %}
Note, that in the Streaming case, the print operation does not trigger execution directly.
The Flink Shell comes with command history and auto-completion.
The example below is a wordcount program using Table API:
Note, that using $ as a prefix for the class name of TableFunction is a workaround of the issue that scala incorrectly generated inner class name.
The following example is a wordcount program written in SQL:
It is possible to add external classpaths to the Scala-shell. These will be sent to the Jobmanager automatically alongside your shell program, when calling execute.
Use the parameter -a <path/to/jar.jar> or --addclasspath <path/to/jar.jar> to load additional classes.
{% highlight bash %} bin/start-scala-shell.sh [local | remote | yarn] --addclasspath <path/to/jar.jar> {% endhighlight %}
To get an overview of what options the Scala Shell provides, please use
{% highlight bash %} bin/start-scala-shell.sh --help {% endhighlight %}
To use the shell with an integrated Flink cluster just execute:
{% highlight bash %} bin/start-scala-shell.sh local {% endhighlight %}
To use it with a running cluster start the scala shell with the keyword remote and supply the host and port of the JobManager with:
{% highlight bash %} bin/start-scala-shell.sh remote {% endhighlight %}
The shell can deploy a Flink cluster to YARN, which is used exclusively by the shell. The number of YARN containers can be controlled by the parameter -n <arg>. The shell deploys a new Flink cluster on YARN and connects the cluster. You can also specify options for YARN cluster such as memory for JobManager, name of YARN application, etc.
For example, to start a Yarn cluster for the Scala Shell with two TaskManagers use the following:
{% highlight bash %} bin/start-scala-shell.sh yarn -n 2 {% endhighlight %}
For all other options, see the full reference at the bottom.
If you have previously deployed a Flink cluster using the Flink Yarn Session, the Scala shell can connect with it using the following command:
{% highlight bash %} bin/start-scala-shell.sh yarn {% endhighlight %}
{% highlight bash %} Flink Scala Shell Usage: start-scala-shell.sh [local|remote|yarn] [options] ...
Command: local [options] Starts Flink scala shell with a local Flink cluster -a <path/to/jar> | --addclasspath <path/to/jar> Specifies additional jars to be used in Flink Command: remote [options] Starts Flink scala shell connecting to a remote cluster Remote host name as string Remote port as integer
-a <path/to/jar> | --addclasspath <path/to/jar> Specifies additional jars to be used in Flink Command: yarn [options] Starts Flink scala shell connecting to a yarn cluster -n arg | --container arg Number of YARN container to allocate (= Number of TaskManagers) -jm arg | --jobManagerMemory arg Memory for JobManager container with optional unit (default: MB) -nm | --name Set a custom name for the application on YARN -qu | --queue Specifies YARN queue -s | --slots Number of slots per TaskManager -tm | --taskManagerMemory Memory per TaskManager container with optional unit (default: MB) -a <path/to/jar> | --addclasspath <path/to/jar> Specifies additional jars to be used in Flink --configDir The configuration directory. -h | --help Prints this usage text {% endhighlight %}
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