Project: Flink (App Definition and Development / Streaming & Messaging)
File: parallel.md

title: "Parallel Execution" weight: 31 type: docs aliases: /dev/parallel.html <!-- 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 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 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. The parallelism of a task can be specified in Flink on different levels: The parallelism of an individual operator, data source, or data sink can be defined by calling its `setParallelism()` method. For example, like this: {{< tabs "d35e6dcb-7630-4e12-9ab7-2798c1c36002" >}} {{< tab "Java" >}} ```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"); ``` {{< /tab >}} {{< tab "Scala" >}} ```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") ``` {{< /tab >}} {{< tab "Python" >}} ```python env = StreamExecutionEnvironment.getexecutionenvironment() 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") ``` {{< /tab >}} {{< /tabs >}} As mentioned 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()`

To execute all operators, data sources, and data sinks with a parallelism of `3`, set the default parallelism of the execution environment as follows: {{< tabs "f4258714-1999-4a03-b6ea-52b9c9910f6c" >}} {{< tab "Java" >}} ```java final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.setParallelism(3); DataStream<String> text = [...]; DataStream<Tuple2<String, Integer>> wordCounts = [...]; wordCounts.print(); env.execute("Word Count Example"); ``` {{< /tab >}} {{< tab "Scala" >}} ```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") ``` {{< /tab >}} {{< tab "Python" >}} ```python env = StreamExecutionEnvironment.getexecutionenvironment() 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") ``` {{< /tab >}} {{< /tabs >}} 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: {{< tabs "54c4adac-7461-40e9-b85c-69bbc0b3a36d" >}} {{< tab "Java" >}} ```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(); } ``` {{< /tab >}} {{< tab "Scala" >}} ```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 } ``` {{< /tab >}} {{< tab "Python" >}} ```python Still not supported in Python API. ``` {{< /tab >}} {{< /tabs >}} A system-wide default parallelism for all execution environments can be defined by setting the `parallelism.default` property in . See the documentation for details. 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`. {{< hint warning >}} 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. {{< /hint >}} {{< top >}}