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/
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A streaming-first runtime that supports both batch processing and data streaming programs
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Elegant and fluent APIs in Java
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A runtime that supports very high throughput and low event latency at the same time
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Support for event time and out-of-order processing in the DataStream API, based on the Dataflow Model
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Flexible windowing (time, count, sessions, custom triggers) across different time semantics (event time, processing time)
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Fault-tolerance with exactly-once processing guarantees
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Natural back-pressure in streaming programs
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Libraries for Graph processing (batch), Machine Learning (batch), and Complex Event Processing (streaming)
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Custom memory management for efficient and robust switching between in-memory and out-of-core data processing algorithms
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Compatibility layers for Apache Hadoop MapReduce
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Integration with YARN, HDFS, HBase, and other components of the Apache Hadoop ecosystem
// 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();
}// 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(wordWintCount -> wordWintCount.word)
.sum("count").returns(TypeInformation.of(WordWithCount.class));
windowCounts.print();
env.execute();Prerequisites for building Flink:
- Unix-like environment (we use Linux, Mac OS X, Cygwin, WSL)
- Git
- Maven (we require version 3.8.6)
- Java 11
git clone https://github.com/apache/flink.git
cd flink
./mvnw clean package -DskipTests # this will take up to 10 minutes
Flink is now installed in build-target.
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
The IntelliJ IDE supports Maven out of the box and offers a plugin for Scala development.
- IntelliJ download: https://www.jetbrains.com/idea/
- IntelliJ Scala Plugin: https://plugins.jetbrains.com/plugin/?id=1347
Check out our Setting up IntelliJ guide for details.
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)
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.
The documentation of Apache Flink is located on the website: https://flink.apache.org
or in the docs/ directory of the source code.
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.
Most Flink connectors have been externalized to individual repos under the Apache Software Foundation:
- flink-connector-aws
- flink-connector-cassandra
- flink-connector-elasticsearch
- flink-connector-gcp-pubsub
- flink-connector-hbase
- flink-connector-jdbc
- flink-connector-kafka
- flink-connector-mongodb
- flink-connector-opensearch
- flink-connector-prometheus
- flink-connector-pulsar
- flink-connector-rabbitmq
Apache Flink is an open source project of The Apache Software Foundation (ASF). The Apache Flink project originated from the Stratosphere research project.