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<!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" />  <meta name="description" content="A new open source Apache Hadoop ecosystem project, Apache Kudu completes Hadoop's storage layer to enable fast analytics on fast data" /> <meta name="author" content="Cloudera" /> <title>Apache Kudu - Blog</title>  <link rel="stylesheet" href="/css/bootstrap.min.css"/>  <link href="/css/kudu.css" rel="stylesheet"/> <link href="/css/asciidoc.css" rel="stylesheet"/> <link rel="shortcut icon" href="/img/logo-favicon.ico" /> <link rel="stylesheet" href="/css/font-awesome.min.css" /> <link rel="alternate" type="application/atom+xml" title="RSS Feed for Apache Kudu blog" href="/feed.xml" /> </head> <body> <div class="kudu-site container-fluid">  <nav class="navbar navbar-default"> <div class="container-fluid"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false" aria-controls="navbar"> <span class="sr-only">Toggle navigation</span> <span class="icon-bar"></span> <span class="icon-bar"></span> <span class="icon-bar"></span> </button> <a class="logo" href="/"><img src="/img/apachekudu_logo_0716_80px.png" srcset="/img/apachekudu_logo_0716_80px.png 1x, /img/apachekudu_logo_0716_160px.png 2x" alt="Apache Kudu"/></a> </div> <div id="navbar" class="collapse navbar-collapse"> <ul class="nav navbar-nav navbar-right"> <li > <a href="/">Home</a> </li> <li > <a href="/overview.html">Overview</a> </li> <li > <a href="/docs/">Documentation</a> </li> <li > <a href="/releases/">Releases</a> </li> <li class="active"> <a href="/blog/">Blog</a> </li>  <li class="dropdown"> <a href="/community.html" role="button" aria-haspopup="true" aria-expanded="false">Community <span class="caret"></span></a> <ul class="dropdown-menu"> <li class="dropdown-header">GET IN TOUCH</li> <li><a class="icon email" href="/community.html">Mailing Lists</a></li> <li><a class="icon slack" href="https://join.slack.com/t/getkudu/shared_invite/zt-244b4zvki-hB1q9IbAk6CqHNMZHvUALA">Slack Channel</a></li> <li role="separator" class="divider"></li> <li><a href="/community.html#meetups-user-groups-and-conference-presentations">Events and Meetups</a></li> <li><a href="/committers.html">Project Committers</a></li> <li><a href="/ecosystem.html">Ecosystem</a></li>  <li><a href="/community.html#contributions">How to Contribute</a></li> <li role="separator" class="divider"></li> <li class="dropdown-header">DEVELOPER RESOURCES</li> <li><a class="icon github" href="https://github.com/apache/incubator-kudu">GitHub</a></li> <li><a class="icon gerrit" href="http://gerrit.cloudera.org:8080/#/q/status:open+project:kudu">Gerrit Code Review</a></li> <li><a class="icon jira" href="https://issues.apache.org/jira/browse/KUDU">JIRA Issue Tracker</a></li> <li role="separator" class="divider"></li> <li class="dropdown-header">SOCIAL MEDIA</li> <li><a class="icon twitter" href="https://twitter.com/ApacheKudu">Twitter</a></li> <li><a href="https://www.reddit.com/r/kudu/">Reddit</a></li> <li role="separator" class="divider"></li> <li class="dropdown-header">APACHE SOFTWARE FOUNDATION</li> <li><a href="https://www.apache.org/security/" target="_blank">Security</a></li> <li><a href="https://www.apache.org/foundation/sponsorship.html" target="_blank">Sponsorship</a></li> <li><a href="https://www.apache.org/foundation/thanks.html" target="_blank">Thanks</a></li> <li><a href="https://www.apache.org/licenses/" target="_blank">License</a></li> </ul> </li> <li > <a href="/faq.html">FAQ</a> </li> </ul> </div> </div> </nav> <div class="row header"> <div class="col-lg-12"> <h2><a href="/blog">Apache Kudu Blog</a></h2> </div> </div> <div class="row-fluid"> <div class="col-lg-9">  <article> <header> <h1 class="entry-title"><a href="/2016/09/16/predicate-pushdown.html">Pushing Down Predicate Evaluation in Apache Kudu</a></h1> <p class="meta">Posted 16 Sep 2016 by Andrew Wong</p> </header> <div class="entry-content"> <p>I had the pleasure of interning with the Apache Kudu team at Cloudera this summer. This project was my summer contribution to Kudu: a restructuring of the scan path to speed up queries.</p> </div> <div class="read-full"> <a class="btn btn-info" href="/2016/09/16/predicate-pushdown.html">Read full post...</a> </div> </article>  <article> <header> <h1 class="entry-title"><a href="/2016/08/31/intro-flume-kudu-sink.html">An Introduction to the Flume Kudu Sink</a></h1> <p class="meta">Posted 31 Aug 2016 by Ara Abrahamian</p> </header> <div class="entry-content"> <p>This post discusses the Kudu Flume Sink. First, I’ll give some background on why we considered using Kudu, what Flume does for us, and how Flume fits with Kudu in our project.</p> <h2 id="why-kudu">Why Kudu</h2> <p>Traditionally in the Hadoop ecosystem we’ve dealt with various <em>batch processing</em> technologies such as MapReduce and the many libraries and tools built on top of it in various languages (Apache Pig, Apache Hive, Apache Oozie and many others). The main problem with this approach is that it needs to process the whole data set in batches, again and again, as soon as new data gets added. Things get really complicated when a few such tasks need to get chained together, or when the same data set needs to be processed in various ways by different jobs, while all compete for the shared cluster resources.</p> <p>The opposite of this approach is <em>stream processing</em>: process the data as soon as it arrives, not in batches. Streaming systems such as Spark Streaming, Storm, Kafka Streams, and many others make this possible. But writing streaming services is not trivial. The streaming systems are becoming more and more capable and support more complex constructs, but they are not yet easy to use. All queries and processes need to be carefully planned and implemented.</p> <p>To summarize, <em>batch processing</em> is:</p> <ul> <li>file-based</li> <li>a paradigm that processes large chunks of data as a group</li> <li>high latency and high throughput, both for ingest and query</li> <li>typically easy to program, but hard to orchestrate</li> <li>well suited for writing ad-hoc queries, although they are typically high latency</li> </ul> <p>While <em>stream processing</em> is:</p> <ul> <li>a totally different paradigm, which involves single events and time windows instead of large groups of events</li> <li>still file-based and not a long-term database</li> <li>not batch-oriented, but incremental</li> <li>ultra-fast ingest and ultra-fast query (query results basically pre-calculated)</li> <li>not so easy to program, relatively easy to orchestrate</li> <li>impossible to write ad-hoc queries</li> </ul> <p>And a Kudu-based <em>near real-time</em> approach is:</p> <ul> <li>flexible and expressive, thanks to SQL support via Apache Impala (incubating)</li> <li>a table-oriented, mutable data store that feels like a traditional relational database</li> <li>very easy to program, you can even pretend it’s good old MySQL</li> <li>low-latency and relatively high throughput, both for ingest and query</li> </ul> <p>At Argyle Data, we’re dealing with complex fraud detection scenarios. We need to ingest massive amounts of data, run machine learning algorithms and generate reports. When we created our current architecture two years ago we decided to opt for a database as the backbone of our system. That database is Apache Accumulo. It’s a key-value based database which runs on top of Hadoop HDFS, quite similar to HBase but with some important improvements such as cell level security and ease of deployment and management. To enable querying of this data for quite complex reporting and analytics, we used Presto, a distributed query engine with a pluggable architecture open-sourced by Facebook. We wrote a connector for it to let it run queries against the Accumulo database. This architecture has served us well, but there were a few problems:</p> <ul> <li>we need to ingest even more massive volumes of data in real-time</li> <li>we need to perform complex machine-learning calculations on even larger data-sets</li> <li>we need to support ad-hoc queries, plus long-term data warehouse functionality</li> </ul> <p>So, we’ve started gradually moving the core machine-learning pipeline to a streaming based solution. This way we can ingest and process larger data-sets faster in the real-time. But then how would we take care of ad-hoc queries and long-term persistence? This is where Kudu comes in. While the machine learning pipeline ingests and processes real-time data, we store a copy of the same ingested data in Kudu for long-term access and ad-hoc queries. Kudu is our <em>data warehouse</em>. By using Kudu and Impala, we can retire our in-house Presto connector and rely on Impala’s super-fast query engine.</p> <p>But how would we make sure data is reliably ingested into the streaming pipeline <em>and</em> the Kudu-based data warehouse? This is where Apache Flume comes in.</p> <h2 id="why-flume">Why Flume</h2> <p>According to their <a href="http://flume.apache.org/">website</a> “Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms.” As you can see, nowhere is Hadoop mentioned but Flume is typically used for ingesting data to Hadoop clusters.</p> <p><img src="https://blogs.apache.org/flume/mediaresource/ab0d50f6-a960-42cc-971e-3da38ba3adad" alt="png" /></p> <p>Flume has an extensible architecture. An instance of Flume, called an <em>agent</em>, can have multiple <em>channels</em>, with each having multiple <em>sources</em> and <em>sinks</em> of various types. Sources queue data in channels, which in turn write out data to sinks. Such <em>pipelines</em> can be chained together to create even more complex ones. There may be more than one agent and agents can be configured to support failover and recovery.</p> <p>Flume comes with a bunch of built-in types of channels, sources and sinks. Memory channel is the default (an in-memory queue with no persistence to disk), but other options such as Kafka- and File-based channels are also provided. As for the sources, Avro, JMS, Thrift, spooling directory source are some of the built-in ones. Flume also ships with many sinks, including sinks for writing data to HDFS, HBase, Hive, Kafka, as well as to other Flume agents.</p> <p>In the rest of this post I’ll go over the Kudu Flume sink and show you how to configure Flume to write ingested data to a Kudu table. The sink has been part of the Kudu distribution since the 0.8 release and the source code can be found <a href="https://github.com/apache/kudu/tree/master/java/kudu-flume-sink">here</a>.</p> <h2 id="configuring-the-kudu-flume-sink">Configuring the Kudu Flume Sink</h2> <p>Here is a sample flume configuration file:</p> <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>agent1.sources = source1 agent1.channels = channel1 agent1.sinks = sink1 agent1.sources.source1.type = exec agent1.sources.source1.command = /usr/bin/vmstat 1 agent1.sources.source1.channels = channel1 agent1.channels.channel1.type = memory agent1.channels.channel1.capacity = 10000 agent1.channels.channel1.transactionCapacity = 1000 agent1.sinks.sink1.type = org.apache.flume.sink.kudu.KuduSink agent1.sinks.sink1.masterAddresses = localhost agent1.sinks.sink1.tableName = stats agent1.sinks.sink1.channel = channel1 agent1.sinks.sink1.batchSize = 50 agent1.sinks.sink1.producer = org.apache.kudu.flume.sink.SimpleKuduEventProducer </code></pre></div></div> <p>We define a source called <code class="language-plaintext highlighter-rouge">source1</code> which simply executes a <code class="language-plaintext highlighter-rouge">vmstat</code> command to continuously generate virtual memory statistics for the machine and queue events into an in-memory <code class="language-plaintext highlighter-rouge">channel1</code> channel, which in turn is used for writing these events to a Kudu table called <code class="language-plaintext highlighter-rouge">stats</code>. We are using <code class="language-plaintext highlighter-rouge">org.apache.kudu.flume.sink.SimpleKuduEventProducer</code> as the producer. <code class="language-plaintext highlighter-rouge">SimpleKuduEventProducer</code> is the built-in and default producer, but it’s implemented as a showcase for how to write Flume events into Kudu tables. For any serious functionality we’d have to write a custom producer. We need to make this producer and the <code class="language-plaintext highlighter-rouge">KuduSink</code> class available to Flume. We can do that by simply copying the <code class="language-plaintext highlighter-rouge">kudu-flume-sink-<VERSION>.jar</code> jar file from the Kudu distribution to the <code class="language-plaintext highlighter-rouge">$FLUME_HOME/plugins.d/kudu-sink/lib</code> directory in the Flume installation. The jar file contains <code class="language-plaintext highlighter-rouge">KuduSink</code> and all of its dependencies (including Kudu java client classes).</p> <p>At a minimum, the Kudu Flume Sink needs to know where the Kudu masters are (<code class="language-plaintext highlighter-rouge">agent1.sinks.sink1.masterAddresses = localhost</code>) and which Kudu table should be used for writing Flume events to (<code class="language-plaintext highlighter-rouge">agent1.sinks.sink1.tableName = stats</code>). The Kudu Flume Sink doesn’t create this table, it has to be created before the Kudu Flume Sink is started.</p> <p>You may also notice the <code class="language-plaintext highlighter-rouge">batchSize</code> parameter. Batch size is used for batching up to that many Flume events and flushing the entire batch in one shot. Tuning batchSize properly can have a huge impact on ingest performance of the Kudu cluster.</p> <p>Here is a complete list of KuduSink parameters:</p> <table> <thead> <tr> <th>Parameter Name</th> <th>Default</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>masterAddresses</td> <td>N/A</td> <td>Comma-separated list of “host:port” pairs of the masters (port optional)</td> </tr> <tr> <td>tableName</td> <td>N/A</td> <td>The name of the table in Kudu to write to</td> </tr> <tr> <td>producer</td> <td>org.apache.kudu.flume.sink.SimpleKuduEventProducer</td> <td>The fully qualified class name of the Kudu event producer the sink should use</td> </tr> <tr> <td>batchSize</td> <td>100</td> <td>Maximum number of events the sink should take from the channel per transaction, if available</td> </tr> <tr> <td>timeoutMillis</td> <td>30000</td> <td>Timeout period for Kudu operations, in milliseconds</td> </tr> <tr> <td>ignoreDuplicateRows</td> <td>true</td> <td>Whether to ignore errors indicating that we attempted to insert duplicate rows into Kudu</td> </tr> </tbody> </table> <p>Let’s take a look at the source code for the built-in producer class:</p> <div class="language-java highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kd">public</span> <span class="kd">class</span> <span class="nc">SimpleKuduEventProducer</span> <span class="kd">implements</span> <span class="nc">KuduEventProducer</span> <span class="o">{</span> <span class="kd">private</span> <span class="kt">byte</span><span class="o">[]</span> <span class="n">payload</span><span class="o">;</span> <span class="kd">private</span> <span class="nc">KuduTable</span> <span class="n">table</span><span class="o">;</span> <span class="kd">private</span> <span class="nc">String</span> <span class="n">payloadColumn</span><span class="o">;</span> <span class="kd">public</span> <span class="nf">SimpleKuduEventProducer</span><span class="o">(){</span> <span class="o">}</span> <span class="nd">@Override</span> <span class="kd">public</span> <span class="kt">void</span> <span class="nf">configure</span><span class="o">(</span><span class="nc">Context</span> <span class="n">context</span><span class="o">)</span> <span class="o">{</span> <span class="n">payloadColumn</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="na">getString</span><span class="o">(</span><span class="s">"payloadColumn"</span><span class="o">,</span><span class="s">"payload"</span><span class="o">);</span> <span class="o">}</span> <span class="nd">@Override</span> <span class="kd">public</span> <span class="kt">void</span> <span class="nf">configure</span><span class="o">(</span><span class="nc">ComponentConfiguration</span> <span class="n">conf</span><span class="o">)</span> <span class="o">{</span> <span class="o">}</span> <span class="nd">@Override</span> <span class="kd">public</span> <span class="kt">void</span> <span class="nf">initialize</span><span class="o">(</span><span class="nc">Event</span> <span class="n">event</span><span class="o">,</span> <span class="nc">KuduTable</span> <span class="n">table</span><span class="o">)</span> <span class="o">{</span> <span class="k">this</span><span class="o">.</span><span class="na">payload</span> <span class="o">=</span> <span class="n">event</span><span class="o">.</span><span class="na">getBody</span><span class="o">();</span> <span class="k">this</span><span class="o">.</span><span class="na">table</span> <span class="o">=</span> <span class="n">table</span><span class="o">;</span> <span class="o">}</span> <span class="nd">@Override</span> <span class="kd">public</span> <span class="nc">List</span><span class="o"><</span><span class="nc">Operation</span><span class="o">></span> <span class="nf">getOperations</span><span class="o">()</span> <span class="kd">throws</span> <span class="nc">FlumeException</span> <span class="o">{</span> <span class="k">try</span> <span class="o">{</span> <span class="nc">Insert</span> <span class="n">insert</span> <span class="o">=</span> <span class="n">table</span><span class="o">.</span><span class="na">newInsert</span><span class="o">();</span> <span class="nc">PartialRow</span> <span class="n">row</span> <span class="o">=</span> <span class="n">insert</span><span class="o">.</span><span class="na">getRow</span><span class="o">();</span> <span class="n">row</span><span class="o">.</span><span class="na">addBinary</span><span class="o">(</span><span class="n">payloadColumn</span><span class="o">,</span> <span class="n">payload</span><span class="o">);</span> <span class="k">return</span> <span class="nc">Collections</span><span class="o">.</span><span class="na">singletonList</span><span class="o">((</span><span class="nc">Operation</span><span class="o">)</span> <span class="n">insert</span><span class="o">);</span> <span class="o">}</span> <span class="k">catch</span> <span class="o">(</span><span class="nc">Exception</span> <span class="n">e</span><span class="o">){</span> <span class="k">throw</span> <span class="k">new</span> <span class="nf">FlumeException</span><span class="o">(</span><span class="s">"Failed to create Kudu Insert object!"</span><span class="o">,</span> <span class="n">e</span><span class="o">);</span> <span class="o">}</span> <span class="o">}</span> <span class="nd">@Override</span> <span class="kd">public</span> <span class="kt">void</span> <span class="nf">close</span><span class="o">()</span> <span class="o">{</span> <span class="o">}</span> <span class="o">}</span> </code></pre></div></div> <p><code class="language-plaintext highlighter-rouge">SimpleKuduEventProducer</code> implements the <code class="language-plaintext highlighter-rouge">org.apache.kudu.flume.sink.KuduEventProducer</code> interface, which itself looks like this:</p> <div class="language-java highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kd">public</span> <span class="kd">interface</span> <span class="nc">KuduEventProducer</span> <span class="kd">extends</span> <span class="nc">Configurable</span><span class="o">,</span> <span class="nc">ConfigurableComponent</span> <span class="o">{</span> <span class="cm">/** * Initialize the event producer. * @param event to be written to Kudu * @param table the KuduTable object used for creating Kudu Operation objects */</span> <span class="kt">void</span> <span class="nf">initialize</span><span class="o">(</span><span class="nc">Event</span> <span class="n">event</span><span class="o">,</span> <span class="nc">KuduTable</span> <span class="n">table</span><span class="o">);</span> <span class="cm">/** * Get the operations that should be written out to Kudu as a result of this * event. This list is written to Kudu using the Kudu client API. * @return List of {@link org.kududb.client.Operation} which * are written as such to Kudu */</span> <span class="nc">List</span><span class="o"><</span><span class="nc">Operation</span><span class="o">></span> <span class="nf">getOperations</span><span class="o">();</span> <span class="cm">/* * Clean up any state. This will be called when the sink is being stopped. */</span> <span class="kt">void</span> <span class="nf">close</span><span class="o">();</span> <span class="o">}</span> </code></pre></div></div> <p><code class="language-plaintext highlighter-rouge">public void configure(Context context)</code> is called when an instance of our producer is instantiated by the KuduSink. SimpleKuduEventProducer’s implementation looks for a producer parameter named <code class="language-plaintext highlighter-rouge">payloadColumn</code> and uses its value (“payload” if not overridden in Flume configuration file) as the column which will hold the value of the Flume event payload. If you recall from above, we had configured the KuduSink to listen for events generated from the <code class="language-plaintext highlighter-rouge">vmstat</code> command. Each output row from that command will be stored as a new row containing a <code class="language-plaintext highlighter-rouge">payload</code> column in the <code class="language-plaintext highlighter-rouge">stats</code> table. <code class="language-plaintext highlighter-rouge">SimpleKuduEventProducer</code> does not have any configuration parameters, but if it had any we would define them by prefixing it with <code class="language-plaintext highlighter-rouge">producer.</code> (<code class="language-plaintext highlighter-rouge">agent1.sinks.sink1.producer.parameter1</code> for example).</p> <p>The main producer logic resides in the <code class="language-plaintext highlighter-rouge">public List<Operation> getOperations()</code> method. In SimpleKuduEventProducer’s implementation we simply insert the binary body of the Flume event into the Kudu table. Here we call Kudu’s <code class="language-plaintext highlighter-rouge">newInsert()</code> to initiate an insert, but could have used <code class="language-plaintext highlighter-rouge">Upsert</code> if updating an existing row was also an option, in fact there’s another producer implementation available for doing just that: <code class="language-plaintext highlighter-rouge">SimpleKeyedKuduEventProducer</code>. Most probably you will need to write your own custom producer in the real world, but you can base your implementation on the built-in ones.</p> <p>In the future, we plan to add more flexible event producer implementations so that creation of a custom event producer is not required to write data to Kudu. See <a href="https://gerrit.cloudera.org/#/c/4034/">here</a> for a work-in-progress generic event producer for Avro-encoded Events.</p> <h2 id="conclusion">Conclusion</h2> <p>Kudu is a scalable data store which lets us ingest insane amounts of data per second. Apache Flume helps us aggregate data from various sources, and the Kudu Flume Sink lets us easily store the aggregated Flume events into Kudu. Together they enable us to create a data warehouse out of disparate sources.</p> <p><em>Ara Abrahamian is a software engineer at Argyle Data building fraud detection systems using sophisticated machine learning methods. Ara is the original author of the Flume Kudu Sink that is included in the Kudu distribution. You can follow him on Twitter at <a href="https://twitter.com/ara_e">@ara_e</a>.</em></p> </div> <div class="read-full"> <a class="btn btn-info" href="/2016/08/31/intro-flume-kudu-sink.html">Read full post...</a> </div> </article>  <article> <header> <h1 class="entry-title"><a href="/2016/08/23/new-range-partitioning-features.html">New Range Partitioning Features in Kudu 0.10</a></h1> <p class="meta">Posted 23 Aug 2016 by Dan Burkert</p> </header> <div class="entry-content"> <p>Kudu 0.10 is shipping with a few important new features for range partitioning. These features are designed to make Kudu easier to scale for certain workloads, like time series. This post will introduce these features, and discuss how to use them to effectively design tables for scalability and performance.</p> </div> <div class="read-full"> <a class="btn btn-info" href="/2016/08/23/new-range-partitioning-features.html">Read full post...</a> </div> </article>  <article> <header> <h1 class="entry-title"><a href="/2016/08/23/apache-kudu-0-10-0-released.html">Apache Kudu 0.10.0 released</a></h1> <p class="meta">Posted 23 Aug 2016 by Todd Lipcon</p> </header> <div class="entry-content"> <p>The Apache Kudu team is happy to announce the release of Kudu 0.10.0!</p> <p>This latest version adds several new features, including:</p> </div> <div class="read-full"> <a class="btn btn-info" href="/2016/08/23/apache-kudu-0-10-0-released.html">Read full post...</a> </div> </article>  <article> <header> <h1 class="entry-title"><a href="/2016/08/16/weekly-update.html">Apache Kudu Weekly Update August 16th, 2016</a></h1> <p class="meta">Posted 16 Aug 2016 by Todd Lipcon</p> </header> <div class="entry-content"> <p>Welcome to the twentieth edition of the Kudu Weekly Update. This weekly blog post covers ongoing development and news in the Apache Kudu project.</p> </div> <div class="read-full"> <a class="btn btn-info" href="/2016/08/16/weekly-update.html">Read full post...</a> </div> </article>  <nav> <hr/> <ul class="pager"> <li class="previous"><a href="/blog/page/8"><span aria-hidden="true">←</span> Newer posts</a></li> <li class="next"><a href="/blog/page/10">Older posts <span aria-hidden="true">→</span></a></li> </ul> </nav> </div> <div class="col-lg-3 recent-posts"> <h3>Recent posts</h3> <ul> <li> <a href="/2024/11/13/apache-kudu-1-17-1-release.html">Apache Kudu 1.17.1 Released</a> </li> <li> <a href="/2024/03/07/introducing-auto-incrementing-column.html">Introducing Auto-incrementing Column in Kudu</a> </li> <li> <a href="/2023/09/07/apache-kudu-1-17-0-released.html">Apache Kudu 1.17.0 Released</a> </li> <li> <a href="/2022/06/17/apache-kudu-1-16-0-released.html">Apache Kudu 1.16.0 Released</a> </li> <li> <a href="/2021/06/22/apache-kudu-1-15-0-released.html">Apache Kudu 1.15.0 Released</a> </li> <li> <a href="/2021/01/28/apache-kudu-1-14-0-release.html">Apache Kudu 1.14.0 Released</a> </li> <li> <a href="/2021/01/15/bloom-filter-predicate.html">Optimized joins & filtering with Bloom filter predicate in Kudu</a> </li> <li> <a href="/2020/09/21/apache-kudu-1-13-0-release.html">Apache Kudu 1.13.0 released</a> </li> <li> <a href="/2020/08/11/fine-grained-authz-ranger.html">Fine-Grained Authorization with Apache Kudu and Apache Ranger</a> </li> <li> <a href="/2020/07/30/building-near-real-time-big-data-lake.html">Building Near Real-time Big Data Lake</a> </li> <li> <a href="/2020/05/18/apache-kudu-1-12-0-release.html">Apache Kudu 1.12.0 released</a> </li> <li> <a href="/2019/11/20/apache-kudu-1-11-1-release.html">Apache Kudu 1.11.1 released</a> </li> <li> <a href="/2019/11/20/apache-kudu-1-10-1-release.html">Apache Kudu 1.10.1 released</a> </li> <li> <a href="/2019/07/09/apache-kudu-1-10-0-release.html">Apache Kudu 1.10.0 Released</a> </li> <li> <a href="/2019/04/30/location-awareness.html">Location Awareness in Kudu</a> </li> </ul> </div> </div> <footer class="footer"> <div class="row"> <div class="col-md-9"> <p class="small"> Copyright © 2023 The Apache Software Foundation. </p> <p class="small"> Apache Kudu, Kudu, Apache, the Apache feather logo, and the Apache Kudu project logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries. </p> </div> <div class="col-md-3"> <a class="pull-right" href="https://www.apache.org/events/current-event.html"> <img src="https://www.apache.org/events/current-event-234x60.png"/> </a> </div> </div> </footer> </div> <script src="/js/jquery.min.js"></script> <script> // Try to detect touch-screen devices. 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