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target="_blank">@ApacheFlink <small><span class="glyphicon glyphicon-new-window"></span></small></a></li> + + <!-- Visualizer --> + <li class=" hidden-md hidden-sm"><a href="/visualizer/" target="_blank">Plan Visualizer <small><span class="glyphicon glyphicon-new-window"></span></small></a></li> + + </ul> + </div><!-- /.navbar-collapse --> + </nav> + + </div> + <div class="col-sm-9"> + <div class="row-fluid"> + <div class="col-sm-12"> + <div class="row"> + <h1>Stream Processing for Everyone with SQL and Apache Flink</h1> + + <article> + <p>24 May 2016 by Fabian Hueske (<a href="https://twitter.com/fhueske";>@fhueske</a>)</p> + +<p>The capabilities of open source systems for distributed stream processing have evolved significantly over the last years. Initially, the first systems in the field (notably <a href="https://storm.apache.org";>Apache Storm</a>) provided low latency processing, but were limited to at-least-once guarantees, processing-time semantics, and rather low-level APIs. Since then, several new systems emerged and pushed the state of the art of open source stream processing in several dimensions. Today, users of Apache Flink or <a href="https://beam.incubator.apache.org";>Apache Beam</a> can use fluent Scala and Java APIs to implement stream processing jobs that operate in event-time with exactly-once semantics at high throughput and low latency.</p> + +<p>In the meantime, stream processing has taken off in the industry. We are witnessing a rapidly growing interest in stream processing which is reflected by prevalent deployments of streaming processing infrastructure such as <a href="https://kafka.apache.org";>Apache Kafka</a> and Apache Flink. The increasing number of available data streams results in a demand for people that can analyze streaming data and turn it into real-time insights. However, stream data analysis requires a special skill set including knowledge of streaming concepts such as the characteristics of unbounded streams, windows, time, and state as well as the skills to implement stream analysis jobs usually against Java or Scala APIs. People with this skill set are rare and hard to find.</p> + +<p>About six months ago, the Apache Flink community started an effort to add a SQL interface for stream data analysis. SQL is <em>the</em> standard language to access and process data. Everybody who occasionally analyzes data is familiar with SQL. Consequently, a SQL interface for stream data processing will make this technology accessible to a much wider audience. Moreover, SQL support for streaming data will also enable new use cases such as interactive and ad-hoc stream analysis and significantly simplify many applications including stream ingestion and simple transformations. In this blog post, we report on the current status, architectural design, and future plans of the Apache Flink community to implement support for SQL as a language for analyzing data streams.</p> + +<h2 id="where-did-we-come-from">Where did we come from?</h2> + +<p>With the <a href="http://flink.apache.org/news/2015/04/13/release-0.9.0-milestone1.html";>0.9.0-milestone1</a> release, Apache Flink added an API to process relational data with SQL-like expressions called the Table API. The central concept of this API is a Table, a structured data set or stream on which relational operations can be applied. The Table API is tightly integrated with the DataSet and DataStream API. A Table can be easily created from a DataSet or DataStream and can also be converted back into a DataSet or DataStream as the following example shows</p> + +<div class="highlight"><pre><code class="language-scala"><span class="k">val</span> <span class="n">execEnv</span> <span class="k">=</span> <span class="nc">ExecutionEnvironment</span><span class="o">.</span><span class="n">getExecutionEnvironment</span> +<span class="k">val</span> <span class="n">tableEnv</span> <span class="k">=</span> <span class="nc">TableEnvironment</span><span class="o">.</span><span class="n">getTableEnvironment</span><span class="o">(</span><span class="n">execEnv</span><span class="o">)</span> + +<span class="c1">// obtain a DataSet from somewhere</span> +<span class="k">val</span> <span class="n">tempData</span><span class="k">:</span> <span class="kt">DataSet</span><span class="o">[(</span><span class="kt">String</span>, <span class="kt">Long</span>, <span class="kt">Double</span><span class="o">)]</span> <span class="k">=</span> + +<span class="c1">// convert the DataSet to a Table</span> +<span class="k">val</span> <span class="n">tempTable</span><span class="k">:</span> <span class="kt">Table</span> <span class="o">=</span> <span class="n">tempData</span><span class="o">.</span><span class="n">toTable</span><span class="o">(</span><span class="n">tableEnv</span><span class="o">,</span> <span class="-Symbol">'location</span><span class="o">,</span> <span class="-Symbol">'time</span><span class="o">,</span> <span class="-Symbol">'tempF</span><span class="o">)</span> +<span class="c1">// compute your result</span> +<span class="k">val</span> <span class="n">avgTempCTable</span><span class="k">:</span> <span class="kt">Table</span> <span class="o">=</span> <span class="n">tempTable</span> + <span class="o">.</span><span class="n">where</span><span class="o">(</span><span class="-Symbol">'location</span><span class="o">.</span><span class="n">like</span><span class="o">(</span><span class="s">"room%"</span><span class="o">))</span> + <span class="o">.</span><span class="n">select</span><span class="o">(</span> + <span class="o">(</span><span class="-Symbol">'time</span> <span class="o">/</span> <span class="o">(</span><span class="mi">3600</span> <span class="o">*</span> <span class="mi">24</span><span class="o">))</span> <span class="n">as</span> <span class="-Symbol">'day</span><span class="o">,</span> + <span class="-Symbol">'Location</span> <span class="n">as</span> <span class="-Symbol">'room</span><span class="o">,</span> + <span class="o">((</span><span class="-Symbol">'tempF</span> <span class="o">-</span> <span class="mi">32</span><span class="o">)</span> <span class="o">*</span> <span class="mf">0.556</span><span class="o">)</span> <span class="n">as</span> <span class="-Symbol">'tempC</span> + <span class="o">)</span> + <span class="o">.</span><span class="n">groupBy</span><span class="o">(</span><span class="-Symbol">'day</span><span class="o">,</span> <span class="-Symbol">'room</span><span class="o">)</span> + <span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="-Symbol">'day</span><span class="o">,</span> <span class="-Symbol">'room</span><span class="o">,</span> <span class="-Symbol">'tempC</span><span class="o">.</span><span class="n">avg</span> <span class="n">as</span> <span class="-Symbol">'avgTempC</span><span class="o">)</span> +<span class="c1">// convert result Table back into a DataSet and print it</span> +<span class="n">avgTempCTable</span><span class="o">.</span><span class="n">toDataSet</span><span class="o">[</span><span class="kt">Row</span><span class="o">].</span><span class="n">print</span><span class="o">()</span></code></pre></div> + +<p>Although the example shows Scala code, there is also an equivalent Java version of the Table API. The following picture depicts the original architecture of the Table API.</p> + +<center> +<img src="/img/blog/stream-sql/old-table-api.png" style="width:75%;margin:15px" /> +</center> + +<p>A Table is created from a DataSet or DataStream and transformed into a new Table by applying relational transformations such as <code>filter</code>, <code>join</code>, or <code>select</code> on them. Internally, a logical table operator tree is constructed from the applied Table transformations. When a Table is translated back into a DataSet or DataStream, the respective translator translates the logical operator tree into DataSet or DataStream operators. Expressions like <code>'location.like("room%")</code> are compiled into Flink functions via code generation.</p> + +<p>However, the original Table API had a few limitations. First of all, it could not stand alone. Table API queries had to be always embedded into a DataSet or DataStream program. Queries against batch Tables did not support outer joins, sorting, and many scalar functions which are commonly used in SQL queries. Queries against streaming tables only supported filters, union, and projections and no aggregations or joins. Also, the translation process did not leverage query optimization techniques except for the physical optimization that is applied to all DataSet programs.</p> + +<h2 id="table-api-joining-forces-with-sql">Table API joining forces with SQL</h2> + +<p>The discussion about adding support for SQL came up a few times in the Flink community. With Flink 0.9 and the availability of the Table API, code generation for relational expressions, and runtime operators, the foundation for such an extension seemed to be there and SQL support the next logical step. On the other hand, the community was also well aware of the multitude of dedicated âSQL-on-Hadoopâ solutions in the open source landscape (<a href="https://hive.apache.org";>Apache Hive</a>, <a href="https://drill.apache.org";>Apache Drill</a>, <a href="http://impala.io";>Apache Impala</a>, <a href="https://tajo.apache.org";>Apache Tajo</a>, just to name a few). Given these alternatives, we figured that time would be better spent improving Flink in other ways than implementing yet another SQL-on-Hadoop solution.</p> + +<p>However, with the growing popularity of stream processing and the increasing adoption of Flink in this area, the Flink community saw the need for a simpler API to enable more users to analyze streaming data. About half a year ago, we decided to take the Table API to the next level, extend the stream processing capabilities of the Table API, and add support for SQL on streaming data. What we came up with was a revised architecture for a Table API that supports SQL (and Table API) queries on streaming and static data sources. We did not want to reinvent the wheel and decided to build the new Table API on top of <a href="https://calcite.apache.org";>Apache Calcite</a>, a popular SQL parser and optimizer framework. Apache Calcite is used by many projects including Apache Hive, Apache Drill, Cascading, and many <a href="https://calcite.apache.org/docs/powered_by.html";>more</a>. Moreover, the Calcite community put <a href="https://calcite.apache.org/docs/stream.html";>SQL on streams</a> on their roadmap which makes it a perfect fit for Flinkâs SQL interface.</p> + +<p>Calcite is central in the new design as the following architecture sketch shows:</p> + +<center> +<img src="/img/blog/stream-sql/new-table-api.png" style="width:75%;margin:15px" /> +</center> + +<p>The new architecture features two integrated APIs to specify relational queries, the Table API and SQL. Queries of both APIs are validated against a catalog of registered tables and converted into Calciteâs representation for logical plans. In this representation, stream and batch queries look exactly the same. Next, Calciteâs cost-based optimizer applies transformation rules and optimizes the logical plans. Depending on the nature of the sources (streaming or static) we use different rule sets. Finally, the optimized plan is translated into a regular Flink DataStream or DataSet program. This step involves again code generation to compile relational expressions into Flink functions.</p> + +<p>The new architecture of the Table API maintains the basic principles of the original Table API and improves it. It keeps a uniform interface for relational queries on streaming and static data. In addition, we take advantage of Calciteâs query optimization framework and SQL parser. The design builds upon Flinkâs established APIs, i.e., the DataStream API that offers low-latency, high-throughput stream processing with exactly-once semantics and consistent results due to event-time processing, and the DataSet API with robust and efficient in-memory operators and pipelined data exchange. Any improvements to Flinkâs core APIs and engine will automatically improve the execution of Table API and SQL queries.</p> + +<p>With this effort, we are adding SQL support for both streaming and static data to Flink. However, we do not want to see this as a competing solution to dedicated, high-performance SQL-on-Hadoop solutions, such as Impala, Drill, and Hive. Instead, we see the sweet spot of Flinkâs SQL integration primarily in providing access to streaming analytics to a wider audience. In addition, it will facilitate integrated applications that use Flinkâs APIâs as well as SQL while being executed on a single runtime engine.</p> + +<h2 id="how-will-flinks-sql-on-streams-look-like">How will Flinkâs SQL on streams look like?</h2> + +<p>So far we discussed the motivation for and architecture of Flinkâs stream SQL interface, but how will it actually look like? The new SQL interface is integrated into the Table API. DataStreams, DataSets, and external data sources can be registered as tables at the <code>TableEnvironment</code> in order to make them queryable with SQL. The <code>TableEnvironment.sql()</code> method states a SQL query and returns its result as a Table. The following example shows a complete program that reads a streaming table from a JSON encoded Kafka topic, processes it with a SQL query and writes the resulting stream into another Kafka topic. Please note that the KafkaJsonSource and KafkaJsonSink are under development and not available yet. In the future, TableSources and TableSinks can be persisted to and loaded from files to ease reuse of source and sink definitions and to reduce boilerplate code.</p> + +<div class="highlight"><pre><code class="language-scala"><span class="c1">// get environments</span> +<span class="k">val</span> <span class="n">execEnv</span> <span class="k">=</span> <span class="nc">StreamExecutionEnvironment</span><span class="o">.</span><span class="n">getExecutionEnvironment</span> +<span class="k">val</span> <span class="n">tableEnv</span> <span class="k">=</span> <span class="nc">TableEnvironment</span><span class="o">.</span><span class="n">getTableEnvironment</span><span class="o">(</span><span class="n">execEnv</span><span class="o">)</span> + +<span class="c1">// configure Kafka connection</span> +<span class="k">val</span> <span class="n">kafkaProps</span> <span class="k">=</span> <span class="o">...</span> +<span class="c1">// define a JSON encoded Kafka topic as external table</span> +<span class="k">val</span> <span class="n">sensorSource</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">KafkaJsonSource</span><span class="o">[(</span><span class="kt">String</span>, <span class="kt">Long</span>, <span class="kt">Double</span><span class="o">)](</span> + <span class="s">"sensorTopic"</span><span class="o">,</span> + <span class="n">kafkaProps</span><span class="o">,</span> + <span class="o">(</span><span class="s">"location"</span><span class="o">,</span> <span class="s">"time"</span><span class="o">,</span> <span class="s">"tempF"</span><span class="o">))</span> + +<span class="c1">// register external table</span> +<span class="n">tableEnv</span><span class="o">.</span><span class="n">registerTableSource</span><span class="o">(</span><span class="s">"sensorData"</span><span class="o">,</span> <span class="n">sensorSource</span><span class="o">)</span> + +<span class="c1">// define query in external table</span> +<span class="k">val</span> <span class="n">roomSensors</span><span class="k">:</span> <span class="kt">Table</span> <span class="o">=</span> <span class="n">tableEnv</span><span class="o">.</span><span class="n">sql</span><span class="o">(</span> + <span class="s">"SELECT STREAM time, location AS room, (tempF - 32) * 0.556 AS tempC "</span> <span class="o">+</span> + <span class="s">"FROM sensorData "</span> <span class="o">+</span> + <span class="s">"WHERE location LIKE 'room%'"</span> + <span class="o">)</span> + +<span class="c1">// define a JSON encoded Kafka topic as external sink</span> +<span class="k">val</span> <span class="n">roomSensorSink</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">KafkaJsonSink</span><span class="o">(...)</span> + +<span class="c1">// define sink for room sensor data and execute query</span> +<span class="n">roomSensors</span><span class="o">.</span><span class="n">toSink</span><span class="o">(</span><span class="n">roomSensorSink</span><span class="o">)</span> +<span class="n">execEnv</span><span class="o">.</span><span class="n">execute</span><span class="o">()</span></code></pre></div> + +<p>You might have noticed that this example left out the most interesting aspects of stream data processing: window aggregates and joins. How will these operations be expressed in SQL? Well, that is a very good question. The Apache Calcite community put out an excellent proposal that discusses the syntax and semantics of <a href="https://calcite.apache.org/docs/stream.html";>SQL on streams</a>. It describes Calciteâs stream SQL as <em>âan extension to standard SQL, not another âSQL-likeâ languageâ</em>. This has several benefits. First, people who are familiar with standard SQL will be able to analyze data streams without learning a new syntax. Queries on static tables and streams are (almost) identical and can be easily ported. Moreover it is possible to specify queries that reference static and streaming tables at the same time which goes well together with Flinkâs vision to handle batch processing as a special case of stream processing, i.e., as processing finite strea ms. Finally, using standard SQL for stream data analysis means following a well established standard that is supported by many tools.</p> + +<p>Although we havenât completely fleshed out the details of how windows will be defined in Flinkâs SQL syntax and Table API, the following examples show how a tumbling window query could look like in SQL and the Table API.</p> + +<h3 id="sql-following-the-syntax-proposal-of-calcites-streaming-sql-document">SQL (following the syntax proposal of Calciteâs streaming SQL document)</h3> + +<div class="highlight"><pre><code class="language-sql"><span class="k">SELECT</span> <span class="n">STREAM</span> + <span class="n">TUMBLE_END</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="nb">INTERVAL</span> <span class="s1">'1'</span> <span class="k">DAY</span><span class="p">)</span> <span class="k">AS</span> <span class="k">day</span><span class="p">,</span> + <span class="k">location</span> <span class="k">AS</span> <span class="n">room</span><span class="p">,</span> + <span class="k">AVG</span><span class="p">((</span><span class="n">tempF</span> <span class="o">-</span> <span class="mi">32</span><span class="p">)</span> <span class="o">*</span> <span class="mi">0</span><span class="p">.</span><span class="mi">556</span><span class="p">)</span> <span class="k">AS</span> <span class="n">avgTempC</span> +<span class="k">FROM</span> <span class="n">sensorData</span> +<span class="k">WHERE</span> <span class="k">location</span> <span class="k">LIKE</span> <span class="s1">'room%'</span> +<span class="k">GROUP</span> <span class="k">BY</span> <span class="n">TUMBLE</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="nb">INTERVAL</span> <span class="s1">'1'</span> <span class="k">DAY</span><span class="p">),</span> <span class="k">location</span></code></pre></div> + +<h3 id="table-api">Table API</h3> + +<div class="highlight"><pre><code class="language-scala"><span class="k">val</span> <span class="n">avgRoomTemp</span><span class="k">:</span> <span class="kt">Table</span> <span class="o">=</span> <span class="n">tableEnv</span><span class="o">.</span><span class="n">ingest</span><span class="o">(</span><span class="s">"sensorData"</span><span class="o">)</span> + <span class="o">.</span><span class="n">where</span><span class="o">(</span><span class="-Symbol">'location</span><span class="o">.</span><span class="n">like</span><span class="o">(</span><span class="s">"room%"</span><span class="o">))</span> + <span class="o">.</span><span class="n">partitionBy</span><span class="o">(</span><span class="-Symbol">'location</span><span class="o">)</span> + <span class="o">.</span><span class="n">window</span><span class="o">(</span><span class="nc">Tumbling</span> <span class="n">every</span> <span class="nc">Days</span><span class="o">(</span><span class="mi">1</span><span class="o">)</span> <span class="n">on</span> <span class="-Symbol">'time</span> <span class="n">as</span> <span class="-Symbol">'w</span><span class="o">)</span> + <span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="-Symbol">'w</span><span class="o">.</span><span class="n">end</span><span class="o">,</span> <span class="-Symbol">'location</span><span class="o">,</span> <span class="o">,</span> <span class="o">((</span><span class="-Symbol">'tempF</span> <span class="o">-</span> <span class="mi">32</span><span class="o">)</span> <span class="o">*</span> <span class="mf">0.556</span><span class="o">).</span><span class="n">avg</span> <span class="n">as</span> <span class="-Symbol">'avgTempCs</span><span class="o">)</span></code></pre></div> + +<h2 id="whats-up-next">Whatâs up next?</h2> + +<p>The Flink community is actively working on SQL support for the next minor version Flink 1.1.0. In the first version, SQL (and Table API) queries on streams will be limited to selection, filter, and union operators. Compared to Flink 1.0.0, the revised Table API will support many more scalar functions and be able to read tables from external sources and write them back to external sinks. A lot of work went into reworking the architecture of the Table API and integrating Apache Calcite.</p> + +<p>In Flink 1.2.0, the feature set of SQL on streams will be significantly extended. Among other things, we plan to support different types of window aggregates and maybe also streaming joins. For this effort, we want to closely collaborate with the Apache Calcite community and help extending Calciteâs support for relational operations on streaming data when necessary.</p> + +<p>If this post made you curious and you want to try out Flinkâs SQL interface and the new Table API, we encourage you to do so! Simply clone the SNAPSHOT <a href="https://github.com/apache/flink/tree/master";>master branch</a> and check out the <a href="https://ci.apache.org/projects/flink/flink-docs-master/apis/table.html";>Table API documentation for the SNAPSHOT version</a>. Please note that the branch is under heavy development, and hence some code examples in this blog post might not work. We are looking forward to your feedback and welcome contributions.</p> + + </article> + </div> + + <div class="row"> + <div id="disqus_thread"></div> + <script type="text/javascript"> + /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */ + var disqus_shortname = 'stratosphere-eu'; // required: replace example with your forum shortname + + /* * * DON'T EDIT BELOW THIS LINE * * */ + (function() { + var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true; + dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js'; + (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); + })(); + </script> + </div> + </div> +</div> + </div> + </div> + + <hr /> + + <div class="row"> + <div class="footer text-center col-sm-12"> + <p>Copyright © 2014-2016 <a href="http://apache.org";>The Apache Software Foundation</a>. 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target="_blank">@ApacheFlink <small><span class="glyphicon glyphicon-new-window"></span></small></a></li> + + <!-- Visualizer --> + <li class=" hidden-md hidden-sm"><a href="/visualizer/" target="_blank">Plan Visualizer <small><span class="glyphicon glyphicon-new-window"></span></small></a></li> + + </ul> + </div><!-- /.navbar-collapse --> + </nav> + + </div> + <div class="col-sm-9"> + <div class="row-fluid"> + <div class="col-sm-12"> + <div class="row"> + <h1>Announcing Apache Flink 1.1.0</h1> + + <article> + <p>08 Aug 2016</p> + +<div class="alert alert-success"><strong>Important</strong>: The Maven artifacts published with version 1.1.0 on Maven central have a Hadoop dependency issue. It is highly recommended to use <strong>1.1.1</strong> or <strong>1.1.1-hadoop1</strong> as the Flink version.</div> + +<p>The Apache Flink community is pleased to announce the availability of Flink 1.1.0.</p> + +<p>This release is the first major release in the 1.X.X series of releases, which maintains API compatibility with 1.0.0. This means that your applications written against stable APIs of Flink 1.0.0 will compile and run with Flink 1.1.0. 95 contributors provided bug fixes, improvements, and new features such that in total more than 450 JIRA issues could be resolved. See the <a href="/blog/release_1.1.0-changelog.html">complete changelog</a> for more details.</p> + +<p><strong>We encourage everyone to <a href="http://flink.apache.org/downloads.html";>download the release</a> and <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.1/";>check out the documentation</a>. Feedback through the Flink <a href="http://flink.apache.org/community.html#mailing-lists";>mailing lists</a> is, as always, very welcome!</strong></p> + +<p>Some highlights of the release are listed in the following sections.</p> + +<h2 id="connectors">Connectors</h2> + +<p>The <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.1/apis/streaming/connectors/index.html";>streaming connectors</a> are a major part of Flinkâs DataStream API. This release adds support for new external systems and further improves on the available connectors.</p> + +<h3 id="continuous-file-system-sources">Continuous File System Sources</h3> + +<p>A frequently requested feature for Flink 1.0 was to be able to monitor directories and process files continuously. Flink 1.1 now adds support for this via <code>FileProcessingMode</code>s:</p> + +<div class="highlight"><pre><code class="language-java"><span class="n">DataStream</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">stream</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="na">readFile</span><span class="o">(</span> + <span class="n">textInputFormat</span><span class="o">,</span> + <span class="s">"hdfs:///file-path"</span><span class="o">,</span> + <span class="n">FileProcessingMode</span><span class="o">.</span><span class="na">PROCESS_CONTINUOUSLY</span><span class="o">,</span> + <span class="mi">5000</span><span class="o">,</span> <span class="c1">// monitoring interval (millis)</span> + <span class="n">FilePathFilter</span><span class="o">.</span><span class="na">createDefaultFilter</span><span class="o">());</span> <span class="c1">// file path filter</span></code></pre></div> + +<p>This will monitor <code>hdfs:///file-path</code> every <code>5000</code> milliseconds. Check out the <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.1/apis/streaming/index.html#data-sources";>DataSource documentation for more details</a>.</p> + +<h3 id="kinesis-source-and-sink">Kinesis Source and Sink</h3> + +<p>Flink 1.1 adds a Kinesis connector for both consuming (<code>FlinkKinesisConsumer</code>) from and producing (<code>FlinkKinesisProduer</code>) to <a href="https://aws.amazon.com/kinesis/";>Amazon Kinesis Streams</a>, which is a managed service purpose-built to make it easy to work with streaming data on AWS.</p> + +<div class="highlight"><pre><code class="language-java"><span class="n">DataStream</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">kinesis</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="na">addSource</span><span class="o">(</span> + <span class="k">new</span> <span class="n">FlinkKinesisConsumer</span><span class="o"><>(</span><span class="s">"stream-name"</span><span class="o">,</span> <span class="n">schema</span><span class="o">,</span> <span class="n">config</span><span class="o">));</span></code></pre></div> + +<p>Check out the <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.1/apis/streaming/connectors/kinesis.html";>Kinesis connector documentation for more details</a>.</p> + +<h3 id="cassandra-sink">Cassandra Sink</h3> + +<p>The <a href="http://wiki.apache.org/cassandra/GettingStarted";>Apache Cassandra</a> sink allows you to write from Flink to Cassandra. Flink can provide exactly-once guarantees if the query is idempotent, meaning it can be applied multiple times without changing the result.</p> + +<div class="highlight"><pre><code class="language-java"><span class="n">CassandraSink</span><span class="o">.</span><span class="na">addSink</span><span class="o">(</span><span class="n">input</span><span class="o">)</span></code></pre></div> + +<p>Check out the <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.1/apis/streaming/connectors/cassandra.html";>Cassandra Sink documentation for more details</a>.</p> + +<h2 id="table-api-and-sql">Table API and SQL</h2> + +<p>The Table API is a SQL-like expression language for relational stream and batch processing that can be easily embedded in Flinkâs DataSet and DataStream APIs (for both Java and Scala).</p> + +<div class="highlight"><pre><code class="language-java"><span class="n">Table</span> <span class="n">custT</span> <span class="o">=</span> <span class="n">tableEnv</span> + <span class="o">.</span><span class="na">toTable</span><span class="o">(</span><span class="n">custDs</span><span class="o">,</span> <span class="s">"name, zipcode"</span><span class="o">)</span> + <span class="o">.</span><span class="na">where</span><span class="o">(</span><span class="s">"zipcode = '12345'"</span><span class="o">)</span> + <span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"name"</span><span class="o">)</span></code></pre></div> + +<p>An initial version of this API was already available in Flink 1.0. For Flink 1.1, the community put a lot of work into reworking the architecture of the Table API and integrating it with <a href="https://calcite.apache.org";>Apache Calcite</a>.</p> + +<p>In this first version, SQL (and Table API) queries on streams are limited to selection, filter, and union operators. Compared to Flink 1.0, the revised Table API supports many more scalar functions and is able to read tables from external sources and write them back to external sinks.</p> + +<div class="highlight"><pre><code class="language-java"><span class="n">Table</span> <span class="n">result</span> <span class="o">=</span> <span class="n">tableEnv</span><span class="o">.</span><span class="na">sql</span><span class="o">(</span> + <span class="s">"SELECT STREAM product, amount FROM Orders WHERE product LIKE '%Rubber%'"</span><span class="o">);</span></code></pre></div> +<p>A more detailed introduction can be found in the <a href="http://flink.apache.org/news/2016/05/24/stream-sql.html";>Flink blog</a> and the <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.1/apis/table.html";>Table API documentation</a>.</p> + +<h2 id="datastream-api">DataStream API</h2> + +<p>The DataStream API now exposes <strong>session windows</strong> and <strong>allowed lateness</strong> as first-class citizens.</p> + +<h3 id="session-windows">Session Windows</h3> + +<p>Session windows are ideal for cases where the window boundaries need to adjust to the incoming data. This enables you to have windows that start at individual points in time for each key and that end once there has been a <em>certain period of inactivity</em>. The configuration parameter is the session gap that specifies how long to wait for new data before considering a session as closed.</p> + +<center> +<img src="/img/blog/session-windows.svg" style="height:400px" /> +</center> + +<div class="highlight"><pre><code class="language-java"><span class="n">input</span><span class="o">.</span><span class="na">keyBy</span><span class="o">(<</span><span class="n">key</span> <span class="n">selector</span><span class="o">>)</span> + <span class="o">.</span><span class="na">window</span><span class="o">(</span><span class="n">EventTimeSessionWindows</span><span class="o">.</span><span class="na">withGap</span><span class="o">(</span><span class="n">Time</span><span class="o">.</span><span class="na">minutes</span><span class="o">(</span><span class="mi">10</span><span class="o">)))</span> + <span class="o">.<</span><span class="n">windowed</span> <span class="n">transformation</span><span class="o">>(<</span><span class="n">window</span> <span class="n">function</span><span class="o">>);</span></code></pre></div> + +<h3 id="support-for-late-elements">Support for Late Elements</h3> + +<p>You can now specify how a windowed transformation should deal with late elements and how much lateness is allowed. The parameter for this is called <em>allowed lateness</em>. This specifies by how much time elements can be late.</p> + +<div class="highlight"><pre><code class="language-java"><span class="n">input</span><span class="o">.</span><span class="na">keyBy</span><span class="o">(<</span><span class="n">key</span> <span class="n">selector</span><span class="o">>).</span><span class="na">window</span><span class="o">(<</span><span class="n">window</span> <span class="n">assigner</span><span class="o">>)</span> + <span class="o">.</span><span class="na">allowedLateness</span><span class="o">(<</span><span class="n">time</span><span class="o">>)</span> + <span class="o">.<</span><span class="n">windowed</span> <span class="n">transformation</span><span class="o">>(<</span><span class="n">window</span> <span class="n">function</span><span class="o">>);</span></code></pre></div> + +<p>Elements that arrive within the allowed lateness are still put into windows and are considered when computing window results. If elements arrive after the allowed lateness they will be dropped. Flink will also make sure that any state held by the windowing operation is garbage collected once the watermark passes the end of a window plus the allowed lateness.</p> + +<p>Check out the <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.1/apis/streaming/windows.html";>Windows documentation for more details</a>.</p> + +<h2 id="scala-api-for-complex-event-processing-cep">Scala API for Complex Event Processing (CEP)</h2> + +<p>Flink 1.0 added the initial version of the CEP library. The core of the library is a Pattern API, which allows you to easily specify patterns to match against in your event stream. While in Flink 1.0 this API was only available for Java, Flink 1.1. now exposes the same API for Scala, allowing you to specify your event patterns in a more concise manner.</p> + +<p>A more detailed introduction can be found in the <a href="http://flink.apache.org/news/2016/04/06/cep-monitoring.html";>Flink blog</a> and the <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.1/apis/streaming/libs/cep.html";>CEP documentation</a>.</p> + +<h2 id="graph-generators-and-new-gelly-library-algorithms">Graph generators and new Gelly library algorithms</h2> + +<p>This release includes many enhancements and new features for graph processing. Gelly now provides a collection of scalable graph generators for common graph types, such as complete, cycle, grid, hypercube, and RMat graphs. A variety of new graph algorithms have been added to the Gelly library, including Global and Local Clustering Coefficient, HITS, and similarity measures (Jaccard and Adamic-Adar).</p> + +<p>For a full list of new graph processing features, check out the <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.1/apis/batch/libs/gelly.html";>Gelly documentation</a>.</p> + +<h2 id="metrics">Metrics</h2> + +<p>Flinkâs new metrics system allows you to easily gather and expose metrics from your user application to external systems. You can add counters, gauges, and histograms to your application via the runtime context:</p> + +<div class="highlight"><pre><code class="language-java"><span class="n">Counter</span> <span class="n">counter</span> <span class="o">=</span> <span class="n">getRuntimeContext</span><span class="o">()</span> + <span class="o">.</span><span class="na">getMetricGroup</span><span class="o">()</span> + <span class="o">.</span><span class="na">counter</span><span class="o">(</span><span class="s">"my-counter"</span><span class="o">);</span></code></pre></div> + +<p>All registered metrics will be exposed via reporters. Out of the box, Flinks comes with support for JMX, Ganglia, Graphite, and statsD. In addition to your custom metrics, Flink exposes many internal metrics like checkpoint sizes and JVM stats.</p> + +<p>Check out the <a href="https://ci.apache.org/projects/flink/flink-docs-release-1.1/apis/metrics.html";>Metrics documentation for more details</a>.</p> + +<h2 id="list-of-contributors">List of Contributors</h2> + +<p>The following 95 people contributed to this release:</p> + +<ul> + <li>Abdullah Ozturk</li> + <li>Ajay Bhat</li> + <li>Alexey Savartsov</li> + <li>Aljoscha Krettek</li> + <li>Andrea Sella</li> + <li>Andrew Palumbo</li> + <li>Chenguang He</li> + <li>Chiwan Park</li> + <li>David Moravek</li> + <li>Dominik Bruhn</li> + <li>Dyana Rose</li> + <li>Fabian Hueske</li> + <li>Flavio Pompermaier</li> + <li>Gabor Gevay</li> + <li>Gabor Horvath</li> + <li>Geoffrey Mon</li> + <li>Gordon Tai</li> + <li>Greg Hogan</li> + <li>Gyula Fora</li> + <li>Henry Saputra</li> + <li>Ignacio N. Lucero Ascencio</li> + <li>Igor Berman</li> + <li>Ismaël MejÃa</li> + <li>Ivan Mushketyk</li> + <li>Jark Wu</li> + <li>Jiri Simsa</li> + <li>Jonas Traub</li> + <li>Josh</li> + <li>Joshi</li> + <li>Joshua Herman</li> + <li>Ken Krugler</li> + <li>Konstantin Knauf</li> + <li>Lasse Dalegaard</li> + <li>Li Fanxi</li> + <li>MaBiao</li> + <li>Mao Wei</li> + <li>Mark Reddy</li> + <li>Martin Junghanns</li> + <li>Martin Liesenberg</li> + <li>Maximilian Michels</li> + <li>Michal Fijolek</li> + <li>Márton Balassi</li> + <li>Nathan Howell</li> + <li>Niels Basjes</li> + <li>Niels Zeilemaker</li> + <li>Phetsarath, Sourigna</li> + <li>Robert Metzger</li> + <li>Scott Kidder</li> + <li>Sebastian Klemke</li> + <li>Shahin</li> + <li>Shannon Carey</li> + <li>Shannon Quinn</li> + <li>Stefan Richter</li> + <li>Stefano Baghino</li> + <li>Stefano Bortoli</li> + <li>Stephan Ewen</li> + <li>Steve Cosenza</li> + <li>Sumit Chawla</li> + <li>Tatu Saloranta</li> + <li>Tianji Li</li> + <li>Till Rohrmann</li> + <li>Todd Lisonbee</li> + <li>Tony Baines</li> + <li>Trevor Grant</li> + <li>Ufuk Celebi</li> + <li>Vasudevan</li> + <li>Yijie Shen</li> + <li>Zack Pierce</li> + <li>Zhai Jia</li> + <li>chengxiang li</li> + <li>chobeat</li> + <li>danielblazevski</li> + <li>dawid</li> + <li>dawidwys</li> + <li>eastcirclek</li> + <li>erli ding</li> + <li>gallenvara</li> + <li>kl0u</li> + <li>mans2singh</li> + <li>markreddy</li> + <li>mjsax</li> + <li>nikste</li> + <li>omaralvarez</li> + <li>philippgrulich</li> + <li>ramkrishna</li> + <li>sahitya-pavurala</li> + <li>samaitra</li> + <li>smarthi</li> + <li>spkavuly</li> + <li>subhankar</li> + <li>twalthr</li> + <li>vasia</li> + <li>xueyan.li</li> + <li>zentol</li> + <li>å«ä¹</li> +</ul> + + </article> + </div> + + <div class="row"> + <div id="disqus_thread"></div> + <script type="text/javascript"> + /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */ + var disqus_shortname = 'stratosphere-eu'; // required: replace example with your forum shortname + + /* * * DON'T EDIT BELOW THIS LINE * * */ + (function() { + var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true; + dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js'; + (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); + })(); + </script> + </div> + </div> +</div> + </div> + </div> + + <hr /> + + <div class="row"> + <div class="footer text-center col-sm-12"> + <p>Copyright © 2014-2016 <a href="http://apache.org";>The Apache Software Foundation</a>. 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+ </script> + </body> +</html> http://git-wip-us.apache.org/repos/asf/flink-web/blob/9ec0a879/content/news/2016/08/11/release-1.1.1.html ---------------------------------------------------------------------- diff --git a/content/news/2016/08/11/release-1.1.1.html b/content/news/2016/08/11/release-1.1.1.html new file mode 100644 index 0000000..c37f747 --- /dev/null +++ b/content/news/2016/08/11/release-1.1.1.html @@ -0,0 +1,223 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> + <meta http-equiv="X-UA-Compatible" content="IE=edge"> + <meta name="viewport" content="width=device-width, initial-scale=1"> + <!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags --> + <title>Apache Flink: Flink 1.1.1 Released</title> + <link rel="shortcut icon" href="/favicon.ico" type="image/x-icon"> + <link rel="icon" href="/favicon.ico" type="image/x-icon"> + + <!-- Bootstrap --> + <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/css/bootstrap.min.css";> + <link rel="stylesheet" href="/css/flink.css"> + <link rel="stylesheet" href="/css/syntax.css"> + + <!-- Blog RSS feed --> + <link href="/blog/feed.xml" rel="alternate" type="application/rss+xml" title="Apache Flink Blog: RSS feed" /> + + <!-- jQuery (necessary for Bootstrap's JavaScript plugins) --> + <!-- We need to load Jquery in the header for custom google analytics event tracking--> + <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.2/jquery.min.js";></script> + + <!-- HTML5 shim and Respond.js for IE8 support of HTML5 elements and media queries --> + <!-- WARNING: Respond.js doesn't work if you view the page via file:// --> + <!--[if lt IE 9]> + <script src="https://oss.maxcdn.com/html5shiv/3.7.2/html5shiv.min.js";></script> + <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js";></script> + <![endif]--> + </head> + <body> + + + <!-- Main content. --> + <div class="container"> + <div class="row"> + + + <div id="sidebar" class="col-sm-3"> + <!-- Top navbar. --> + <nav class="navbar navbar-default"> + <!-- The logo. --> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#bs-example-navbar-collapse-1"> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <div class="navbar-logo"> + <a href="/"> + <img alt="Apache Flink" src="/img/navbar-brand-logo.png" width="147px" height="73px"> + </a> + </div> + </div><!-- /.navbar-header --> + + <!-- The navigation links. --> + <div class="collapse navbar-collapse" id="bs-example-navbar-collapse-1"> + <ul class="nav navbar-nav navbar-main"> + + <!-- Downloads --> + <li class=""><a class="btn btn-info" href="/downloads.html">Download Flink</a></li> + + <!-- Overview --> + <li><a href="/index.html">Home</a></li> + + <!-- Intro --> + <li><a href="/introduction.html">Introduction to Flink</a></li> + + <!-- Use cases --> + <li><a href="/usecases.html">Flink Use Cases</a></li> + + <!-- Powered by --> + <li><a href="/poweredby.html">Powered by Flink</a></li> + + <!-- Ecosystem --> + <li><a href="/ecosystem.html">Ecosystem</a></li> + + <!-- Community --> + <li><a href="/community.html">Community & Project Info</a></li> + + <!-- Contribute --> + <li><a href="/how-to-contribute.html">How to Contribute</a></li> + + <!-- Blog --> + <li class=" active hidden-md hidden-sm"><a href="/blog/"><b>Flink Blog</b></a></li> + + <hr /> + + + + <!-- Documentation --> + <!-- <li> + <a href="http://ci.apache.org/projects/flink/flink-docs-release-1.1"; 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+ </script> + </body> +</html> http://git-wip-us.apache.org/repos/asf/flink-web/blob/9ec0a879/content/news/2016/08/24/ff16-keynotes-panels.html ---------------------------------------------------------------------- diff --git a/content/news/2016/08/24/ff16-keynotes-panels.html b/content/news/2016/08/24/ff16-keynotes-panels.html new file mode 100644 index 0000000..1166e2f --- /dev/null +++ b/content/news/2016/08/24/ff16-keynotes-panels.html @@ -0,0 +1,212 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> + <meta http-equiv="X-UA-Compatible" content="IE=edge"> + <meta name="viewport" content="width=device-width, initial-scale=1"> + <!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags --> + <title>Apache Flink: Flink Forward 2016: Announcing Schedule, Keynotes, and Panel Discussion</title> + <link rel="shortcut icon" href="/favicon.ico" type="image/x-icon"> + <link rel="icon" href="/favicon.ico" type="image/x-icon"> + + <!-- Bootstrap --> + <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/css/bootstrap.min.css";> + <link rel="stylesheet" href="/css/flink.css"> + <link rel="stylesheet" href="/css/syntax.css"> + + <!-- Blog RSS feed --> + <link href="/blog/feed.xml" rel="alternate" type="application/rss+xml" title="Apache Flink Blog: RSS feed" /> + + <!-- jQuery (necessary for Bootstrap's JavaScript plugins) --> + <!-- We need to load Jquery in the header for custom google analytics event tracking--> + <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.2/jquery.min.js";></script> + + <!-- HTML5 shim and Respond.js for IE8 support of HTML5 elements and media queries --> + <!-- WARNING: Respond.js doesn't work if you view the page via file:// --> + <!--[if lt IE 9]> + <script src="https://oss.maxcdn.com/html5shiv/3.7.2/html5shiv.min.js";></script> + <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js";></script> + <![endif]--> + </head> + <body> + + + <!-- Main content. --> + <div class="container"> + <div class="row"> + + + <div id="sidebar" class="col-sm-3"> + <!-- Top navbar. --> + <nav class="navbar navbar-default"> + <!-- The logo. --> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#bs-example-navbar-collapse-1"> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <div class="navbar-logo"> + <a href="/"> + <img alt="Apache Flink" src="/img/navbar-brand-logo.png" width="147px" height="73px"> + </a> + </div> + </div><!-- /.navbar-header --> + + <!-- The navigation links. --> + <div class="collapse navbar-collapse" id="bs-example-navbar-collapse-1"> + <ul class="nav navbar-nav navbar-main"> + + <!-- Downloads --> + <li class=""><a class="btn btn-info" href="/downloads.html">Download Flink</a></li> + + <!-- Overview --> + <li><a href="/index.html">Home</a></li> + + <!-- Intro --> + <li><a href="/introduction.html">Introduction to Flink</a></li> + + <!-- Use cases --> + <li><a href="/usecases.html">Flink Use Cases</a></li> + + <!-- Powered by --> + <li><a href="/poweredby.html">Powered by Flink</a></li> + + <!-- Ecosystem --> + <li><a href="/ecosystem.html">Ecosystem</a></li> + + <!-- Community --> + <li><a href="/community.html">Community & Project Info</a></li> + + <!-- Contribute --> + <li><a href="/how-to-contribute.html">How to Contribute</a></li> + + <!-- Blog --> + <li class=" active hidden-md hidden-sm"><a href="/blog/"><b>Flink Blog</b></a></li> + + <hr /> + + + + <!-- Documentation --> + <!-- <li> + <a href="http://ci.apache.org/projects/flink/flink-docs-release-1.1"; 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This year's event will include 2 days of talks from stream processing experts at Google, MapR, Alibaba, Netflix, Cloudera, and more. Following the talks is a full day of hands-on Flink training.</p> + +<p>Ted Dunning has been announced as a keynote speaker at the event. Ted is the VP of Incubator at <a href="http://www.apache.org";>Apache Software Foundation</a>, the Chief Application Architect at <a href="http://www.mapr.com";>MapR Technologies</a>, and a mentor on many recent projects. He'll present <a href="http://flink-forward.org/kb_sessions/keynote-tba/";>"How Can We Take Flink Forward?"</a> on the second day of the conference.</p> + +<p>Following Ted's keynote there will be a panel discussion on <a href="http://flink-forward.org/kb_sessions/panel-large-scale-streaming-in-production/";>"Large Scale Streaming in Production"</a>. As stream processing systems become more mainstream, companies are looking to empower their users to take advantage of this technology. We welcome leading stream processing experts Xiaowei Jiang <a href="http://www.alibaba.com";>(Alibaba)</a>, Monal Daxini <a href="http://www.netflix.com";>(Netflix)</a>, Maxim Fateev <a href="http://www.uber.com";>(Uber)</a>, and Ted Dunning <a href="http://www.mapr.com";>(MapR Technologies)</a> on stage to talk about the challenges they have faced and the solutions they have discovered while implementing stream processing systems at very large scale. The panel will be moderated by Jamie Grier <a href="http://www.data-artisans.com";>(data Artisans)</a>.</p> + +<p>The welcome keynote on Monday, September 12, will be presented by data Artisans' co-founders Kostas Tzoumas and Stephan Ewen. They will talk about <a href="http://flink-forward.org/kb_sessions/keynote-tba-2/";>"The maturing data streaming ecosystem and Apache Flinkâs accelerated growth"</a>. In this talk, Kostas and Stephan discuss several large-scale stream processing use cases that the data Artisans team has seen over the past year.</p> + +<p>And one more recent addition to the program: Maxim Fateev of Uber will present <a href="http://flink-forward.org/kb_sessions/beyond-the-watermark-on-demand-backfilling-in-flink/";>"Beyond the Watermark: On-Demand Backfilling in Flink"</a>. Flinkâs time-progress model is built around a single watermark, which is incompatible with Uberâs business need for generating aggregates retroactively. Maxim's talk covers Uber's solution for on-demand backfilling.</p> + +<p>We hope to see many community members at Flink Forward 2016. Registration is available online: <a href="http://flink-forward.org/registration/";>flink-forward.org/registration</a> +</p> + + </article> + </div> + + <div class="row"> + <div id="disqus_thread"></div> + <script type="text/javascript"> + /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */ + var disqus_shortname = 'stratosphere-eu'; // required: replace example with your forum shortname + + /* * * DON'T EDIT BELOW THIS LINE * * */ + (function() { + var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true; + dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js'; + (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); + })(); + </script> + </div> + </div> +</div> + </div> + </div> + + <hr /> + + <div class="row"> + <div class="footer text-center col-sm-12"> + <p>Copyright © 2014-2016 <a href="http://apache.org";>The Apache Software Foundation</a>. 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+ </script> + </body> +</html> http://git-wip-us.apache.org/repos/asf/flink-web/blob/9ec0a879/content/news/2016/09/05/release-1.1.2.html ---------------------------------------------------------------------- diff --git a/content/news/2016/09/05/release-1.1.2.html b/content/news/2016/09/05/release-1.1.2.html new file mode 100644 index 0000000..6fd3a11 --- /dev/null +++ b/content/news/2016/09/05/release-1.1.2.html @@ -0,0 +1,266 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> + <meta http-equiv="X-UA-Compatible" content="IE=edge"> + <meta name="viewport" content="width=device-width, initial-scale=1"> + <!-- The above 3 meta tags *must* come first in the head; any other head content must come *after* these tags --> + <title>Apache Flink: Apache Flink 1.1.2 Released</title> + <link rel="shortcut icon" href="/favicon.ico" type="image/x-icon"> + <link rel="icon" href="/favicon.ico" type="image/x-icon"> + + <!-- Bootstrap --> + <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/css/bootstrap.min.css";> + <link rel="stylesheet" href="/css/flink.css"> + <link rel="stylesheet" href="/css/syntax.css"> + + <!-- Blog RSS feed --> + <link href="/blog/feed.xml" rel="alternate" type="application/rss+xml" title="Apache Flink Blog: RSS feed" /> + + <!-- jQuery (necessary for Bootstrap's JavaScript plugins) --> + <!-- We need to load Jquery in the header for custom google analytics event tracking--> + <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.2/jquery.min.js";></script> + + <!-- HTML5 shim and Respond.js for IE8 support of HTML5 elements and media queries --> + <!-- WARNING: Respond.js doesn't work if you view the page via file:// --> + <!--[if lt IE 9]> + <script src="https://oss.maxcdn.com/html5shiv/3.7.2/html5shiv.min.js";></script> + <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js";></script> + <![endif]--> + </head> + <body> + + + <!-- Main content. --> + <div class="container"> + <div class="row"> + + + <div id="sidebar" class="col-sm-3"> + <!-- Top navbar. --> + <nav class="navbar navbar-default"> + <!-- The logo. --> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#bs-example-navbar-collapse-1"> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <div class="navbar-logo"> + <a href="/"> + <img alt="Apache Flink" src="/img/navbar-brand-logo.png" width="147px" height="73px"> + </a> + </div> + </div><!-- /.navbar-header --> + + <!-- The navigation links. --> + <div class="collapse navbar-collapse" id="bs-example-navbar-collapse-1"> + <ul class="nav navbar-nav navbar-main"> + + <!-- Downloads --> + <li class=""><a class="btn btn-info" href="/downloads.html">Download Flink</a></li> + + <!-- Overview --> + <li><a href="/index.html">Home</a></li> + + <!-- Intro --> + <li><a href="/introduction.html">Introduction to Flink</a></li> + + <!-- Use cases --> + <li><a href="/usecases.html">Flink Use Cases</a></li> + + <!-- Powered by --> + <li><a href="/poweredby.html">Powered by Flink</a></li> + + <!-- Ecosystem --> + <li><a href="/ecosystem.html">Ecosystem</a></li> + + <!-- Community --> + <li><a href="/community.html">Community & Project Info</a></li> + + <!-- Contribute --> + <li><a href="/how-to-contribute.html">How to Contribute</a></li> + + <!-- Blog --> + <li class=" active hidden-md hidden-sm"><a href="/blog/"><b>Flink Blog</b></a></li> + + <hr /> + + + + <!-- Documentation --> + <!-- <li> + <a href="http://ci.apache.org/projects/flink/flink-docs-release-1.1"; 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target="_blank">@ApacheFlink <small><span class="glyphicon glyphicon-new-window"></span></small></a></li> + + <!-- Visualizer --> + <li class=" hidden-md hidden-sm"><a href="/visualizer/" target="_blank">Plan Visualizer <small><span class="glyphicon glyphicon-new-window"></span></small></a></li> + + </ul> + </div><!-- /.navbar-collapse --> + </nav> + + </div> + <div class="col-sm-9"> + <div class="row-fluid"> + <div class="col-sm-12"> + <div class="row"> + <h1>Apache Flink 1.1.2 Released</h1> + + <article> + <p>05 Sep 2016</p> + +<p>The Apache Flink community released another bugfix version of the Apache Flink 1.1. series.</p> + +<p>We recommend all users to upgrade to Flink 1.1.2.</p> + +<div class="highlight"><pre><code class="language-xml"><span class="nt"><dependency></span> + <span class="nt"><groupId></span>org.apache.flink<span class="nt"></groupId></span> + <span class="nt"><artifactId></span>flink-java<span class="nt"></artifactId></span> + <span class="nt"><version></span>1.1.2<span class="nt"></version></span> +<span class="nt"></dependency></span> +<span class="nt"><dependency></span> + <span class="nt"><groupId></span>org.apache.flink<span class="nt"></groupId></span> + <span class="nt"><artifactId></span>flink-streaming-java_2.10<span class="nt"></artifactId></span> + <span class="nt"><version></span>1.1.2<span class="nt"></version></span> +<span class="nt"></dependency></span> +<span class="nt"><dependency></span> + <span class="nt"><groupId></span>org.apache.flink<span class="nt"></groupId></span> + <span class="nt"><artifactId></span>flink-clients_2.10<span class="nt"></artifactId></span> + <span class="nt"><version></span>1.1.2<span class="nt"></version></span> +<span class="nt"></dependency></span></code></pre></div> + +<p>You can find the binaries on the updated <a href="http://flink.apache.org/downloads.html";>Downloads page</a>.</p> + +<h2>Release Notes - Flink - Version 1.1.2</h2> + +<ul> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4236";>FLINK-4236</a>] - Flink Dashboard stops showing list of uploaded jars if main method cannot be looked up +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4309";>FLINK-4309</a>] - Potential null pointer dereference in DelegatingConfiguration#keySet() +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4334";>FLINK-4334</a>] - Shaded Hadoop1 jar not fully excluded in Quickstart +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4341";>FLINK-4341</a>] - Kinesis connector does not emit maximum watermark properly +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4402";>FLINK-4402</a>] - Wrong metrics parameter names in documentation +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4409";>FLINK-4409</a>] - class conflict between jsr305-1.3.9.jar and flink-shaded-hadoop2-1.1.1.jar +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4411";>FLINK-4411</a>] - [py] Chained dual input children are not properly propagated +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4412";>FLINK-4412</a>] - [py] Chaining does not properly handle broadcast variables +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4425";>FLINK-4425</a>] - "Out Of Memory" during savepoint deserialization +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4454";>FLINK-4454</a>] - Lookups for JobManager address in config +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4480";>FLINK-4480</a>] - Incorrect link to elastic.co in documentation +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4486";>FLINK-4486</a>] - JobManager not fully running when yarn-session.sh finishes +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4488";>FLINK-4488</a>] - Prevent cluster shutdown after job execution for non-detached jobs +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4514";>FLINK-4514</a>] - ExpiredIteratorException in Kinesis Consumer on long catch-ups to head of stream +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4526";>FLINK-4526</a>] - ApplicationClient: remove redundant proxy messages +</li> + +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-3866";>FLINK-3866</a>] - StringArraySerializer claims type is immutable; shouldn't +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-3899";>FLINK-3899</a>] - Document window processing with Reduce/FoldFunction + WindowFunction +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4302";>FLINK-4302</a>] - Add JavaDocs to MetricConfig +</li> +<li>[<a href="https://issues.apache.org/jira/browse/FLINK-4495";>FLINK-4495</a>] - Running multiple jobs on yarn (without yarn-session) +</li> +</ul> + + + </article> + </div> + + <div class="row"> + <div id="disqus_thread"></div> + <script type="text/javascript"> + /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */ + var disqus_shortname = 'stratosphere-eu'; // required: replace example with your forum shortname + + /* * * DON'T EDIT BELOW THIS LINE * * */ + (function() { + var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true; + dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js'; + (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); 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