commit | 6ce8211bab84308a17043dc4901d6a93b2777da8 | [log] [tgz] |
---|---|---|
author | Nico Kruber <nico@data-artisans.com> | Wed Jul 04 17:45:18 2018 +0200 |
committer | Nico Kruber <nico@data-artisans.com> | Thu Jul 05 12:20:23 2018 +0200 |
tree | c34ee4f17a8ae2f951d27787d7cf467433bd694f | |
parent | f857156ce7c5709be63cda0721968b1a9fe3f4bb [diff] |
[FLINK-9676][network] clarify contracts of BufferListener#notifyBufferAvailable() and fix a deadlock When recycling exclusive buffers of a RemoteInputChannel and recycling (other/floating) buffers to the buffer pool concurrently while the RemoteInputChannel is registered as a listener to the buffer pool and adding the exclusive buffer triggers a floating buffer to be recycled back to the same buffer pool, a deadlock would occur holding locks on LocalBufferPool#availableMemorySegments and RemoteInputChannel#bufferQueue but acquiring them in reverse order. One such instance would be: Task canceler thread -> RemoteInputChannel1#releaseAllResources -> recycle floating buffers -> lock(LocalBufferPool#availableMemorySegments) -> RemoteInputChannel2#notifyBufferAvailable -> try to lock(RemoteInputChannel2#bufferQueue) Task thread -> RemoteInputChannel2#recycle -> lock(RemoteInputChannel2#bufferQueue) -> bufferQueue#addExclusiveBuffer -> floatingBuffer#recycleBuffer -> try to lock(LocalBufferPool#availableMemorySegments) Therefore, we decouple the listener callback from lock around LocalBufferPool#availableMemorySegments and implicitly enforce that RemoteInputChannel2#bufferQueue takes precedence over this lock, i.e. must be acquired first and should never be taken after having locked on LocalBufferPool#availableMemorySegments. This closes #6257.
Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities.
Learn more about Flink at http://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 and Apache Storm
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") .timeWindow(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 mvn 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 3.0.3 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: http://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.