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:
bin/start-scala-shell.sh local
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 Batch and Streaming. Two different ExecutionEnvironments are automatically prebound after startup. Use “benv” and “senv” to access the Batch and Streaming environment respectively.
The following example will execute the wordcount program in the Scala shell:
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()
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:
Scala-Flink> benv.execute("MyProgram")
Similar to the the batch program above, we can execute a streaming program through the DataStream API:
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")
Note, that in the Streaming case, the print operation does not trigger execution directly.
The Flink Shell comes with command history and auto-completion.
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.
bin/start-scala-shell.sh [local | remote <host> <port> | yarn] --addclasspath <path/to/jar.jar>
To get an overview of what options the Scala Shell provides, please use
bin/start-scala-shell.sh --help
To use the shell with an integrated Flink cluster just execute:
bin/start-scala-shell.sh local
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:
bin/start-scala-shell.sh remote <hostname> <portnumber>
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:
bin/start-scala-shell.sh yarn -n 2
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:
bin/start-scala-shell.sh yarn
Flink Scala Shell
Usage: start-scala-shell.sh [local|remote|yarn] [options] <args>...
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] <host> <port>
Starts Flink scala shell connecting to a remote cluster
<host>
Remote host name as string
<port>
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 [in MB]
-nm <value> | --name <value>
Set a custom name for the application on YARN
-qu <arg> | --queue <arg>
Specifies YARN queue
-s <arg> | --slots <arg>
Number of slots per TaskManager
-tm <arg> | --taskManagerMemory <arg>
Memory per TaskManager container [in MB]
-a <path/to/jar> | --addclasspath <path/to/jar>
Specifies additional jars to be used in Flink
--configDir <value>
The configuration directory.
-h | --help
Prints this usage text