Apache Flink
Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities.
Learn more about Flink at https://flink.apache.org/
Features
A streaming-first runtime that supports both batch processing and data streaming programs
Elegant and fluent APIs in Java
A runtime that supports very high throughput and low event latency at the same time
Support for event time and out-of-order processing in the DataStream API, based on the Dataflow Model
Flexible windowing (time, count, sessions, custom triggers) across different time semantics (event time, processing time)
Fault-tolerance with exactly-once processing guarantees
Natural back-pressure in streaming programs
Libraries for Graph processing (batch), Machine Learning (batch), and Complex Event Processing (streaming)
Custom memory management for efficient and robust switching between in-memory and out-of-core data processing algorithms
Compatibility layers for Apache Hadoop MapReduce
Integration with YARN, HDFS, HBase, and other components of the Apache Hadoop ecosystem
Streaming Example
// pojo class WordWithCount
public class WordWithCount {
public String word;
public int count;
public WordWithCount() {}
public WordWithCount(String word, int count) {
this.word = word;
this.count = count;
}
}
// main method
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStreamSource<String> text = env.socketTextStream(host, port);
DataStream<WordWithCount> windowCounts = text
.flatMap(
(FlatMapFunction<String, String>) (line, collector)
-> Arrays.stream(line.split("\\s")).forEach(collector::collect)
).returns(String.class)
.map(word -> new WordWithCount(word, 1)).returns(TypeInformation.of(WordWithCount.class))
.keyBy(wordWithCnt -> wordWithCnt.word)
.window(TumblingProcessingTimeWindows.of(Duration.ofSeconds(5)))
.sum("count").returns(TypeInformation.of(WordWithCount.class));
windowCounts.print();
env.execute();
}
Batch Example
// pojo class WordWithCount
public class WordWithCount {
public String word;
public int count;
public WordWithCount() {}
public WordWithCount(String word, int count) {
this.word = word;
this.count = count;
}
}
// main method
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.BATCH);
FileSource<String> source = FileSource.forRecordStreamFormat(new TextLineInputFormat(), new Path("MyInput.txt")).build();
DataStreamSource<String> text = env.fromSource(source, WatermarkStrategy.noWatermarks(), "MySource");
DataStream<WordWithCount> windowCounts = text
.flatMap((FlatMapFunction<String, String>) (line, collector) -> Arrays
.stream(line.split("\\s"))
.forEach(collector::collect)).returns(String.class)
.map(word -> new WordWithCount(word, 1)).returns(TypeInformation.of(WordWithCount.class))
.keyBy(wordWithCount -> wordWithCount.word)
.sum("count").returns(TypeInformation.of(WordWithCount.class));
windowCounts.print();
env.execute();
Building Apache Flink from Source
Prerequisites for building Flink:
- Unix-like environment (we use Linux, Mac OS X, Cygwin, WSL)
- Git
- Maven (we require version 3.9.16)
- Java (version 11, 17, or 21)
Basic Build Instructions
First, clone the repository:
git clone https://github.com/apache/flink.git
cd flink
Then, choose one of the following commands based on your preferred Java version:
For Java 11
./mvnw clean package -DskipTests -Djdk11 -Pjava11-target
For Java 17 (Default)
./mvnw clean package -DskipTests -Djdk17 -Pjava17-target
For Java 21
./mvnw clean package -DskipTests -Djdk21 -Pjava21-target
The build process will take approximately 10 minutes to complete.
Flink will be installed in build-target.
Notes
- Make sure your JAVA_HOME environment variable points to the correct JDK version
- The build command uses Maven wrapper (mvnw) which ensures the correct Maven version is used
- The -DskipTests flag skips running tests to speed up the build process
- Each Java version requires its corresponding profile (-Pjava-target) and JDK flag (-Djdk)
Developing Flink
The Flink committers use IntelliJ IDEA to develop the Flink codebase.
We recommend IntelliJ IDEA for developing projects that involve Scala code.
Minimal requirements for an IDE are:
- Support for Java and Scala (also mixed projects)
- Support for Maven with Java and Scala
IntelliJ IDEA
The IntelliJ IDE supports Maven out of the box and offers a plugin for Scala development.
Check out our DEVELOPMENT.md guide for detailed IDE setup instructions.
Eclipse Scala IDE
NOTE: From our experience, this setup does not work with Flink
due to deficiencies of the old Eclipse version bundled with Scala IDE 3.0.3 or
due to version incompatibilities with the bundled Scala version in Scala IDE 4.4.1.
We recommend to use IntelliJ instead (see above)
Support
Don’t hesitate to ask!
Contact the developers and community on the mailing lists if you need any help.
Open an issue if you find a bug in Flink.
Documentation
The documentation of Apache Flink is located on the website: https://flink.apache.org
or in the docs/ directory of the source code.
Fork and Contribute
This is an active open-source project. We are always open to people who want to use the system or contribute to it.
Contact us if you are looking for implementation tasks that fit your skills.
This article describes how to contribute to Apache Flink.
Externalized Connectors
Most Flink connectors have been externalized to individual repos under the Apache Software Foundation:
About
Apache Flink is an open source project of The Apache Software Foundation (ASF).
The Apache Flink project originated from the Stratosphere research project.
Apache Flink 是一个开源流处理框架，具备强大的流处理和批处理能力，可以在它自己的分布式运行时上运行你的程序。