May 11, 2016 -
Today, the Flink community released Flink version 1.0.3, the third bugfix release of the 1.0 series.
We recommend all users updating to this release by bumping the version of your Flink dependencies to 1.0.3 and updating the binaries on the server. You can find the binaries on the updated Downloads page.
Fixed Issues # Bug # [FLINK-3790] [streaming] Use proper hadoop config in rolling sink [FLINK-3840] Remove Testing Files in RocksDB Backend [FLINK-3835] [optimizer] Add input id to JSON plan to resolve ambiguous input names [hotfix] OptionSerializer.
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April 22, 2016 -
Today, the Flink community released Flink version 1.0.2, the second bugfix release of the 1.0 series.
We recommend all users updating to this release by bumping the version of your Flink dependencies to 1.0.2 and updating the binaries on the server. You can find the binaries on the updated Downloads page.
Fixed Issues # Bug # [FLINK-3657] [dataSet] Change access of DataSetUtils.countElements() to ‘public’ [FLINK-3762] [core] Enable Kryo reference tracking [FLINK-3732] [core] Fix potential null deference in ExecutionConfig#equals() [FLINK-3760] Fix StateDescriptor.
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April 14, 2016 -
We are happy to announce that the call for submissions for Flink Forward 2016 is now open! The conference will take place September 12-14, 2016 in Berlin, Germany, bringing together the open source stream processing community. Most Apache Flink committers will attend the conference, making it the ideal venue to learn more about the project and its roadmap and connect with the community.
The conference welcomes submissions on everything Flink-related, including experiences with using Flink, products based on Flink, technical talks on extending Flink, as well as connecting Flink with other open source or proprietary software.
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April 6, 2016 -
With the ubiquity of sensor networks and smart devices continuously collecting more and more data, we face the challenge to analyze an ever growing stream of data in near real-time. Being able to react quickly to changing trends or to deliver up to date business intelligence can be a decisive factor for a company’s success or failure. A key problem in real time processing is the detection of event patterns in data streams.
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April 6, 2016 -
Today, the Flink community released Flink version 1.0.1, the first bugfix release of the 1.0 series.
We recommend all users updating to this release by bumping the version of your Flink dependencies to 1.0.1 and updating the binaries on the server. You can find the binaries on the updated Downloads page.
Fixed Issues # Bug [FLINK-3179] - Combiner is not injected if Reduce or GroupReduce input is explicitly partitioned [FLINK-3472] - JDBCInputFormat.
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March 8, 2016 -
The Apache Flink community is pleased to announce the availability of the 1.0.0 release. The community put significant effort into improving and extending Apache Flink since the last release, focusing on improving the experience of writing and executing data stream processing pipelines in production.
Flink version 1.0.0 marks the beginning of the 1.X.X series of releases, which will maintain backwards compatibility with 1.0.0. This means that applications written against stable APIs of Flink 1.
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February 11, 2016 -
Today, the Flink community released Flink version 0.10.2, the second bugfix release of the 0.10 series.
We recommend all users updating to this release by bumping the version of your Flink dependencies to 0.10.2 and updating the binaries on the server.
Issues fixed # FLINK-3242: Adjust StateBackendITCase for 0.10 signatures of state backends FLINK-3236: Flink user code classloader as parent classloader from Flink core classes FLINK-2962: Cluster startup script refers to unused variable FLINK-3151: Downgrade to Netty version 4.
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December 18, 2015 -
With 2015 ending, we thought that this would be good time to reflect on the amazing work done by the Flink community over this past year, and how much this community has grown.
Overall, we have seen Flink grow in terms of functionality from an engine to one of the most complete open-source stream processing frameworks available. The community grew from a relatively small and geographically focused team, to a truly global, and one of the largest big data communities in the the Apache Software Foundation.
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December 11, 2015 -
Apache Storm was one of the first distributed and scalable stream processing systems available in the open source space offering (near) real-time tuple-by-tuple processing semantics. Initially released by the developers at Backtype in 2011 under the Eclipse open-source license, it became popular very quickly. Only shortly afterwards, Twitter acquired Backtype. Since then, Storm has been growing in popularity, is used in production at many big companies, and is the de-facto industry standard for big data stream processing.
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December 4, 2015 -
The data analysis space is witnessing an evolution from batch to stream processing for many use cases. Although batch can be handled as a special case of stream processing, analyzing never-ending streaming data often requires a shift in the mindset and comes with its own terminology (for example, “windowing” and “at-least-once”/”exactly-once” processing). This shift and the new terminology can be quite confusing for people being new to the space of stream processing.
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