Parallel Execution

This section describes how the parallel execution of programs can be configured in Flink. A Flink program consists of multiple tasks (transformations/operators, data sources, and sinks). A task is split into several parallel instances for execution and each parallel instance processes a subset of the task’s input data. The number of parallel instances of a task is called its parallelism.

If you want to use savepoints you should also consider setting a maximum parallelism (or max parallelism). When restoring from a savepoint you can change the parallelism of specific operators or the whole program and this setting specifies an upper bound on the parallelism. This is required because Flink internally partitions state into key-groups and we cannot have +Inf number of key-groups because this would be detrimental to performance.

Setting the Parallelism

The parallelism of a task can be specified in Flink on different levels:

Operator Level

The parallelism of an individual operator, data source, or data sink can be defined by calling its setParallelism() method. For example, like this:

Java

final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStream<String> text = [...];
DataStream<Tuple2<String, Integer>> wordCounts = text
    .flatMap(new LineSplitter())
    .keyBy(value -> value.f0)
    .window(TumblingEventTimeWindows.of(Time.seconds(5)))
    .sum(1).setParallelism(5);
wordCounts.print();
env.execute("Word Count Example");

Scala

val env = StreamExecutionEnvironment.getExecutionEnvironment
val text = [...]
val wordCounts = text
    .flatMap{ _.split(" ") map { (_, 1) } }
    .keyBy(_._1)
    .window(TumblingEventTimeWindows.of(Time.seconds(5)))
    .sum(1).setParallelism(5)
wordCounts.print()
env.execute("Word Count Example")

Python

env = StreamExecutionEnvironment.get_execution_environment()
text = [...]
word_counts = text 
    .flat_map(lambda x: x.split(" ")) \
    .map(lambda i: (i, 1), output_type=Types.TUPLE([Types.STRING(), Types.INT()])) \
    .key_by(lambda i: i[0]) \
    .window(TumblingEventTimeWindows.of(Time.seconds(5))) \
    .reduce(lambda i, j: (i[0], i[1] + j[1])) \
    .set_parallelism(5)
word_counts.print()
env.execute("Word Count Example")

Execution Environment Level

As mentioned here Flink programs are executed in the context of an execution environment. An execution environment defines a default parallelism for all operators, data sources, and data sinks it executes. Execution environment parallelism can be overwritten by explicitly configuring the parallelism of an operator.

The default parallelism of an execution environment can be specified by calling the setParallelism() method. To execute all operators, data sources, and data sinks with a parallelism of 3, set the default parallelism of the execution environment as follows:

Java

final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(3);
DataStream<String> text = [...];
DataStream<Tuple2<String, Integer>> wordCounts = [...];
wordCounts.print();
env.execute("Word Count Example");

Scala

val env = StreamExecutionEnvironment.getExecutionEnvironment
env.setParallelism(3)
val text = [...]
val wordCounts = text
    .flatMap{ _.split(" ") map { (_, 1) } }
    .keyBy(_._1)
    .window(TumblingEventTimeWindows.of(Time.seconds(5)))
    .sum(1)
wordCounts.print()
env.execute("Word Count Example")

Python

env = StreamExecutionEnvironment.get_execution_environment()
env.set_parallelism(3)
text = [...]
word_counts = text
    .flat_map(lambda x: x.split(" ")) \
    .map(lambda i: (i, 1), output_type=Types.TUPLE([Types.STRING(), Types.INT()])) \
    .key_by(lambda i: i[0]) \
    .window(TumblingEventTimeWindows.of(Time.seconds(5))) \
    .reduce(lambda i, j: (i[0], i[1] + j[1]))
word_counts.print()
env.execute("Word Count Example")

Client Level

The parallelism can be set at the Client when submitting jobs to Flink. The Client can either be a Java or a Scala program. One example of such a Client is Flink’s Command-line Interface (CLI).

For the CLI client, the parallelism parameter can be specified with -p. For example:

./bin/flink run -p 10 ../examples/*WordCount-java*.jar

In a Java/Scala program, the parallelism is set as follows:

Java

try {
    PackagedProgram program = new PackagedProgram(file, args);
    InetSocketAddress jobManagerAddress = RemoteExecutor.getInetFromHostport("localhost:6123");
    Configuration config = new Configuration();
    Client client = new Client(jobManagerAddress, config, program.getUserCodeClassLoader());
    // set the parallelism to 10 here
    client.run(program, 10, true);
} catch (ProgramInvocationException e) {
    e.printStackTrace();
}

Scala

try {
    PackagedProgram program = new PackagedProgram(file, args)
    InetSocketAddress jobManagerAddress = RemoteExecutor.getInetFromHostport("localhost:6123")
    Configuration config = new Configuration()
    Client client = new Client(jobManagerAddress, new Configuration(), program.getUserCodeClassLoader())
    // set the parallelism to 10 here
    client.run(program, 10, true)
} catch {
    case e: Exception => e.printStackTrace
}

Python

Still not supported in Python API.

System Level

A system-wide default parallelism for all execution environments can be defined by setting the parallelism.default property in Flink configuration file. See the Configuration documentation for details.

Setting the Maximum Parallelism

The maximum parallelism can be set in places where you can also set a parallelism (except client level and system level). Instead of calling setParallelism() you call setMaxParallelism() to set the maximum parallelism.

The default setting for the maximum parallelism is roughly operatorParallelism + (operatorParallelism / 2) with a lower bound of 128 and an upper bound of 32768.

Setting the maximum parallelism to a very large value can be detrimental to performance because some state backends have to keep internal data structures that scale with the number of key-groups (which are the internal implementation mechanism for rescalable state).

Changing the maximum parallelism explicitly when recovery from original job will lead to state incompatibility.