commit | 881b2bf046e510b1b6dddddf8c15af45926397f1 | [log] [tgz] |
---|---|---|
author | Timo Walther <twalthr@apache.org> | Thu Aug 11 11:18:46 2022 +0200 |
committer | Timo Walther <twalthr@apache.org> | Mon Aug 15 15:24:36 2022 +0200 |
tree | 0573e3c99972c07d7055538e9acd0970d7c9fadd | |
parent | b142860352721fe65b8fe7e4106cbcd2059714e5 [diff] |
[FLINK-28861][table] Fix bug in UID format for future migrations and make it configurable Before this commit, the UID format was not future-proof for migrations. The ExecNode version should not be in the UID, otherwise, operator migration won't be possible once plan migration is executed. See the FLIP-190 example that drops a version in the plan, once operator migration has been performed. Given that the plan feature is marked as @Experimental, this change should still be possible without providing backwards compatibility. However, the config option table.exec.uid.format allows for restoring the old format and solves other UID related issues on the way. This closes #20555.
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/
A streaming-first runtime that supports both batch processing and data streaming programs
Elegant and fluent APIs in Java and Scala
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)
Built-in support for iterative programs (BSP) in the DataSet (batch) API
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
case class WordWithCount(word: String, count: Long) val text = env.socketTextStream(host, port, '\n') val windowCounts = text.flatMap { w => w.split("\\s") } .map { w => WordWithCount(w, 1) } .keyBy("word") .window(TumblingProcessingTimeWindow.of(Time.seconds(5))) .sum("count") windowCounts.print()
case class WordWithCount(word: String, count: Long) val text = env.readTextFile(path) val counts = text.flatMap { w => w.split("\\s") } .map { w => WordWithCount(w, 1) } .groupBy("word") .sum("count") counts.writeAsCsv(outputPath)
Prerequisites for building Flink:
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
.
NOTE: Maven 3.3.x can build Flink, but will not properly shade away certain dependencies. Maven 3.1.1 creates the libraries properly. To build unit tests with Java 8, use Java 8u51 or above to prevent failures in unit tests that use the PowerMock runner.
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:
The IntelliJ IDE supports Maven out of the box and offers a plugin for Scala development.
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 found 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.
Apache Flink is an open source project of The Apache Software Foundation (ASF). The Apache Flink project originated from the Stratosphere research project.