December 12, 2017 -
Aljoscha Krettek
(@aljoscha)
Mike Winters
(@wints)
The Apache Flink community is pleased to announce the 1.4.0 release. Over the past 5 months, the Flink community has been working hard to resolve more than 900 issues. See the complete changelog for more detail.
This is the fifth major release in the 1.x.y series. It is API-compatible with the other 1.x.y releases for APIs annotated with the @Public annotation.
We encourage everyone to download the release and check out the documentation.
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November 21, 2017 -
Stephan Ewen
(@StephanEwen)
Aljoscha Krettek
(@aljoscha)
Mike Winters
(@wints)
The Apache Flink 1.4.0 release is on track to happen in the next couple of weeks, and for all of the readers out there who haven’t been following the release discussion on Flink’s developer mailing list, we’d like to provide some details on what’s coming in Flink 1.4.0 as well as a preview of what the Flink community will save for 1.5.0.
Both releases include ambitious features that we believe will move Flink to an entirely new level in terms of the types of problems it can solve and applications it can support.
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August 5, 2017 -
The Apache Flink community released the second bugfix version of the Apache Flink 1.3 series.
This release includes more than 60 fixes and minor improvements for Flink 1.3.1. The list below includes a detailed list of all fixes.
We highly recommend all users to upgrade to Flink 1.3.2.
Important Notice: A user reported a bug in the FlinkKafkaConsumer (FLINK-7143) that is causing incorrect partition assignment in large Kafka deployments in the presence of inconsistent broker metadata.
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July 4, 2017 -
Apache Flink 1.2.0, released in February 2017, introduced support for rescalable state. This post provides a detailed overview of stateful stream processing and rescalable state in Flink. An Intro to Stateful Stream Processing # At a high level, we can consider state in stream processing as memory in operators that remembers information about past input and can be used to influence the processing of future input.
In contrast, operators in stateless stream processing only consider their current inputs, without further context and knowledge about the past.
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June 23, 2017 -
The Apache Flink community released the first bugfix version of the Apache Flink 1.3 series.
This release includes 50 fixes and minor improvements for Flink 1.3.0. The list below includes a detailed list of all fixes.
We highly recommend all users to upgrade to Flink 1.3.1.
<dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-java</artifactId> <version>1.3.1</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-streaming-java_2.10</artifactId> <version>1.3.1</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-clients_2.10</artifactId> <version>1.3.1</version> </dependency> You can find the binaries on the updated Downloads page.
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June 1, 2017 -
The Apache Flink community is pleased to announce the 1.3.0 release. Over the past 4 months, the Flink community has been working hard to resolve more than 680 issues. See the complete changelog for more detail.
This is the fourth major release in the 1.x.y series. It is API compatible with the other 1.x.y releases for APIs annotated with the @Public annotation.
Users can expect Flink releases now in a 4 month cycle.
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May 16, 2017 -
For some time, the Apache Flink community has provided scripts to build a Docker image to run Flink. Now, starting with version 1.2.1, Flink will have a Docker image on the Docker Hub. This image is maintained by the Flink community and curated by the Docker team to ensure it meets the quality standards for container images of the Docker community.
A community-maintained way to run Apache Flink on Docker and other container runtimes and orchestrators is part of the ongoing effort by the Flink community to make Flink a first-class citizen of the container world.
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April 26, 2017 -
The Apache Flink community released the first bugfix version of the Apache Flink 1.2 series.
This release includes many critical fixes for Flink 1.2.0. The list below includes a detailed list of all fixes.
We highly recommend all users to upgrade to Flink 1.2.1.
Please note that there are two unresolved major issues in Flink 1.2.1 and 1.2.0:
FLINK-6353 Restoring using CheckpointedRestoring does not work from 1.2 to 1.2 FLINK-6188 Some setParallelism() methods can’t cope with default parallelism <dependency> <groupId>org.
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March 30, 2017 -
Analyzing Data Streams with SQL # More and more companies are adopting stream processing and are migrating existing batch applications to streaming or implementing streaming solutions for new use cases. Many of those applications focus on analyzing streaming data. The data streams that are analyzed come from a wide variety of sources such as database transactions, clicks, sensor measurements, or IoT devices.
Apache Flink is very well suited to power streaming analytics applications because it provides support for event-time semantics, stateful exactly-once processing, and achieves high throughput and low latency at the same time.
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March 29, 2017 -
Stream processing can deliver a lot of value. Many organizations have recognized the benefit of managing large volumes of data in real-time, reacting quickly to trends, and providing customers with live services at scale. Streaming applications with well-defined business logic can deliver a competitive advantage.
Flink’s DataStream abstraction is a powerful API which lets you flexibly define both basic and complex streaming pipelines. Additionally, it offers low-level operations such as Async IO and ProcessFunctions.
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