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| <h1> |
| <a href="/2015/04/13/announcing-flink-0.9.0-milestone1-preview-release/">Announcing Flink 0.9.0-milestone1 preview release</a> |
| </h1> |
| |
| |
| |
| April 13, 2015 - |
| |
| |
| |
| |
| |
| <p><p>The Apache Flink community is pleased to announce the availability of |
| the 0.9.0-milestone-1 release. The release is a preview of the |
| upcoming 0.9.0 release. It contains many new features which will be |
| available in the upcoming 0.9 release. Interested users are encouraged |
| to try it out and give feedback. As the version number indicates, this |
| release is a preview release that contains known issues.</p> |
| <p>You can download the release |
| <a href="http://flink.apache.org/downloads.html#preview">here</a> and check out the |
| latest documentation |
| <a href="//nightlies.apache.org/flink/flink-docs-master/">here</a>. Feedback |
| through the Flink <a href="http://flink.apache.org/community.html#mailing-lists">mailing |
| lists</a> is, as |
| always, very welcome!</p> |
| <h2 id="new-features"> |
| New Features |
| <a class="anchor" href="#new-features">#</a> |
| </h2> |
| <h3 id="table-api"> |
| Table API |
| <a class="anchor" href="#table-api">#</a> |
| </h3> |
| <p>Flink’s new Table API offers a higher-level abstraction for |
| interacting with structured data sources. The Table API allows users |
| to execute logical, SQL-like queries on distributed data sets while |
| allowing them to freely mix declarative queries with regular Flink |
| operators. Here is an example that groups and joins two tables:</p> |
| <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-scala" data-lang="scala"><span class="line"><span class="cl"><span class="k">val</span> <span class="n">clickCounts</span> <span class="k">=</span> <span class="n">clicks</span> |
| </span></span><span class="line"><span class="cl"> <span class="o">.</span><span class="n">groupBy</span><span class="o">(</span>'user<span class="o">).</span><span class="n">select</span><span class="o">(</span>'userId<span class="o">,</span> 'url<span class="o">.</span><span class="n">count</span> <span class="n">as</span> 'count<span class="o">)</span> |
| </span></span><span class="line"><span class="cl"> |
| </span></span><span class="line"><span class="cl"><span class="k">val</span> <span class="n">activeUsers</span> <span class="k">=</span> <span class="n">users</span><span class="o">.</span><span class="n">join</span><span class="o">(</span><span class="n">clickCounts</span><span class="o">)</span> |
| </span></span><span class="line"><span class="cl"> <span class="o">.</span><span class="n">where</span><span class="o">(</span>'id <span class="o">===</span> 'userId <span class="o">&&</span> 'count <span class="o">></span> <span class="mi">10</span><span class="o">).</span><span class="n">select</span><span class="o">(</span>'username<span class="o">,</span> 'count<span class="o">,</span> <span class="o">...)</span> |
| </span></span></code></pre></div><p>Tables consist of logical attributes that can be selected by name |
| rather than physical Java and Scala data types. This alleviates a lot |
| of boilerplate code for common ETL tasks and raises the abstraction |
| for Flink programs. Tables are available for both static and streaming |
| data sources (DataSet and DataStream APIs).</p> |
| <p>Check out the Table guide for Java and Scala |
| <a href="//nightlies.apache.org/flink/flink-docs-master/apis/batch/libs/table.html">here</a>.</p> |
| <h3 id="gelly-graph-processing-api"> |
| Gelly Graph Processing API |
| <a class="anchor" href="#gelly-graph-processing-api">#</a> |
| </h3> |
| <p>Gelly is a Java Graph API for Flink. It contains a set of utilities |
| for graph analysis, support for iterative graph processing and a |
| library of graph algorithms. Gelly exposes a Graph data structure that |
| wraps DataSets for vertices and edges, as well as methods for creating |
| graphs from DataSets, graph transformations and utilities (e.g., in- |
| and out- degrees of vertices), neighborhood aggregations, iterative |
| vertex-centric graph processing, as well as a library of common graph |
| algorithms, including PageRank, SSSP, label propagation, and community |
| detection.</p> |
| <p>Gelly internally builds on top of Flink’s <a href="//nightlies.apache.org/flink/flink-docs-master/apis/batch/iterations.html">delta |
| iterations</a>. Iterative |
| graph algorithms are executed leveraging mutable state, achieving |
| similar performance with specialized graph processing systems.</p> |
| <p>Gelly will eventually subsume Spargel, Flink’s Pregel-like API. Check |
| out the Gelly guide |
| <a href="//nightlies.apache.org/flink/flink-docs-master/apis/batch/libs/gelly.html">here</a>.</p> |
| <h3 id="flink-machine-learning-library"> |
| Flink Machine Learning Library |
| <a class="anchor" href="#flink-machine-learning-library">#</a> |
| </h3> |
| <p>This release includes the first version of Flink’s Machine Learning |
| library. The library’s pipeline approach, which has been strongly |
| inspired by scikit-learn’s abstraction of transformers and estimators, |
| makes it easy to quickly set up a data processing pipeline and to get |
| your job done.</p> |
| <p>Flink distinguishes between transformers and learners. Transformers |
| are components which transform your input data into a new format |
| allowing you to extract features, cleanse your data or to sample from |
| it. Learners on the other hand constitute the components which take |
| your input data and train a model on it. The model you obtain from the |
| learner can then be evaluated and used to make predictions on unseen |
| data.</p> |
| <p>Currently, the machine learning library contains transformers and |
| learners to do multiple tasks. The library supports multiple linear |
| regression using a stochastic gradient implementation to scale to |
| large data sizes. Furthermore, it includes an alternating least |
| squares (ALS) implementation to factorizes large matrices. The matrix |
| factorization can be used to do collaborative filtering. An |
| implementation of the communication efficient distributed dual |
| coordinate ascent (CoCoA) algorithm is the latest addition to the |
| library. The CoCoA algorithm can be used to train distributed |
| soft-margin SVMs.</p> |
| <h3 id="flink-on-yarn-leveraging-apache-tez"> |
| Flink on YARN leveraging Apache Tez |
| <a class="anchor" href="#flink-on-yarn-leveraging-apache-tez">#</a> |
| </h3> |
| <p>We are introducing a new execution mode for Flink to be able to run |
| restricted Flink programs on top of <a href="http://tez.apache.org">Apache |
| Tez</a>. This mode retains Flink’s APIs, |
| optimizer, as well as Flink’s runtime operators, but instead of |
| wrapping those in Flink tasks that are executed by Flink TaskManagers, |
| it wraps them in Tez runtime tasks and builds a Tez DAG that |
| represents the program.</p> |
| <p>By using Flink on Tez, users have an additional choice for an |
| execution platform for Flink programs. While Flink’s distributed |
| runtime favors low latency, streaming shuffles, and iterative |
| algorithms, Tez focuses on scalability and elastic resource usage in |
| shared YARN clusters.</p> |
| <p>Get started with Flink on Tez |
| <a href="//nightlies.apache.org/flink/flink-docs-master/setup/flink_on_tez.html">here</a>.</p> |
| <h3 id="reworked-distributed-runtime-on-akka"> |
| Reworked Distributed Runtime on Akka |
| <a class="anchor" href="#reworked-distributed-runtime-on-akka">#</a> |
| </h3> |
| <p>Flink’s RPC system has been replaced by the widely adopted |
| <a href="http://akka.io">Akka</a> framework. Akka’s concurrency model offers the |
| right abstraction to develop a fast as well as robust distributed |
| system. By using Akka’s own failure detection mechanism the stability |
| of Flink’s runtime is significantly improved, because the system can |
| now react in proper form to node outages. Furthermore, Akka improves |
| Flink’s scalability by introducing asynchronous messages to the |
| system. These asynchronous messages allow Flink to be run on many more |
| nodes than before.</p> |
| <h3 id="exactly-once-processing-on-kafka-streaming-sources"> |
| Exactly-once processing on Kafka Streaming Sources |
| <a class="anchor" href="#exactly-once-processing-on-kafka-streaming-sources">#</a> |
| </h3> |
| <p>This release introduces stream processing with exacly-once delivery |
| guarantees for Flink streaming programs that analyze streaming sources |
| that are persisted by <a href="http://kafka.apache.org">Apache Kafka</a>. The |
| system is internally tracking the Kafka offsets to ensure that Flink |
| can pick up data from Kafka where it left off in case of an failure.</p> |
| <p>Read |
| <a href="//nightlies.apache.org/flink/flink-docs-master/apis/streaming_guide.html#apache-kafka">here</a> |
| on how to use the persistent Kafka source.</p> |
| <h3 id="improved-yarn-support"> |
| Improved YARN support |
| <a class="anchor" href="#improved-yarn-support">#</a> |
| </h3> |
| <p>Flink’s YARN client contains several improvements, such as a detached |
| mode for starting a YARN session in the background, the ability to |
| submit a single Flink job to a YARN cluster without starting a |
| session, including a “fire and forget” mode. Flink is now also able to |
| reallocate failed YARN containers to maintain the size of the |
| requested cluster. This feature allows to implement fault-tolerant |
| setups on top of YARN. There is also an internal Java API to deploy |
| and control a running YARN cluster. This is being used by system |
| integrators to easily control Flink on YARN within their Hadoop 2 |
| cluster.</p> |
| <p>See the YARN docs |
| <a href="//nightlies.apache.org/flink/flink-docs-master/setup/yarn_setup.html">here</a>.</p> |
| <h2 id="more-improvements-and-fixes"> |
| More Improvements and Fixes |
| <a class="anchor" href="#more-improvements-and-fixes">#</a> |
| </h2> |
| <ul> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1605">FLINK-1605</a>: |
| Flink is not exposing its Guava and ASM dependencies to Maven |
| projects depending on Flink. We use the maven-shade-plugin to |
| relocate these dependencies into our own namespace. This allows |
| users to use any Guava or ASM version.</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1605">FLINK-1417</a>: |
| Automatic recognition and registration of Java Types at Kryo and the |
| internal serializers: Flink has its own type handling and |
| serialization framework falling back to Kryo for types that it cannot |
| handle. To get the best performance Flink is automatically registering |
| all types a user is using in their program with Kryo.Flink also |
| registers serializers for Protocol Buffers, Thrift, Avro and YodaTime |
| automatically. Users can also manually register serializers to Kryo |
| (<a href="https://issues.apache.org/jira/browse/FLINK-1399">https://issues.apache.org/jira/browse/FLINK-1399</a>)</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1296">FLINK-1296</a>: Add |
| support for sorting very large records</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1679">FLINK-1679</a>: |
| “degreeOfParallelism” methods renamed to “parallelism”</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1501">FLINK-1501</a>: Add |
| metrics library for monitoring TaskManagers</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1760">FLINK-1760</a>: Add |
| support for building Flink with Scala 2.11</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1648">FLINK-1648</a>: Add |
| a mode where the system automatically sets the parallelism to the |
| available task slots</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1622">FLINK-1622</a>: Add |
| groupCombine operator</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1589">FLINK-1589</a>: Add |
| option to pass Configuration to LocalExecutor</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1504">FLINK-1504</a>: Add |
| support for accessing secured HDFS clusters in standalone mode</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1478">FLINK-1478</a>: Add |
| strictly local input split assignment</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1512">FLINK-1512</a>: Add |
| CsvReader for reading into POJOs.</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1461">FLINK-1461</a>: Add |
| sortPartition operator</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1450">FLINK-1450</a>: Add |
| Fold operator to the Streaming api</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1389">FLINK-1389</a>: |
| Allow setting custom file extensions for files created by the |
| FileOutputFormat</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1236">FLINK-1236</a>: Add |
| support for localization of Hadoop Input Splits</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1179">FLINK-1179</a>: Add |
| button to JobManager web interface to request stack trace of a |
| TaskManager</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1105">FLINK-1105</a>: Add |
| support for locally sorted output</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1688">FLINK-1688</a>: Add |
| socket sink</p> |
| </li> |
| <li> |
| <p><a href="https://issues.apache.org/jira/browse/FLINK-1436">FLINK-1436</a>: |
| Improve usability of command line interface</p> |
| </li> |
| </ul> |
| </p> |
| </article> |
| |
| |
| |
| |
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