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+ <li class="toctree-l3"><a href="#kafka-input-operator">KAFKA INPUT OPERATOR</a></li>
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+ <li><a class="toctree-l4" href="#introduction-about-kafka-input-operator">Introduction: About Kafka Input Operator</a></li>
+
+ <li><a class="toctree-l4" href="#why-is-it-needed">Why is it needed ?</a></li>
+
+ <li><a class="toctree-l4" href="#abstractkafkainputoperator">AbstractKafkaInputOperator</a></li>
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+ <li><a class="toctree-l4" href="#kafkaconsumer">KafkaConsumer</a></li>
+
+ <li><a class="toctree-l4" href="#pre-requisites">Pre-requisites</a></li>
+
+ <li><a class="toctree-l4" href="#offsetmanager">OffsetManager</a></li>
+
+ <li><a class="toctree-l4" href="#partitioning">Partitioning</a></li>
+
+ <li><a class="toctree-l4" href="#abstractsingleportkafkainputoperator">AbstractSinglePortKafkaInputOperator</a></li>
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+ <h1 id="kafka-input-operator">KAFKA INPUT OPERATOR</h1>
+<h3 id="introduction-about-kafka-input-operator">Introduction: About Kafka Input Operator</h3>
+<p>This is an input operator that consumes data from Kafka messaging system for further processing in Apex. Kafka Input Operator is an fault-tolerant and scalable Malhar Operator.</p>
+<h3 id="why-is-it-needed">Why is it needed ?</h3>
+<p>Kafka is a pull-based and distributed publish subscribe messaging system, topics are partitioned and replicated across
+nodes. Kafka input operator is needed when you want to read data from multiple
+partitions of a Kafka topic in parallel in an Apex application.</p>
+<h3 id="abstractkafkainputoperator">AbstractKafkaInputOperator</h3>
+<p>This is the abstract implementation that serves as base class for consuming messages from Kafka messaging system. This class doesn\u2019t have any ports.</p>
+<p><img alt="AbstractKafkaInput.png" src="../images/kafkainput/image00.png" /></p>
+<h4 id="configuration-parameters">Configuration Parameters</h4>
+<p><table>
+<col width="25%" />
+<col width="75%" />
+<tbody>
+<tr class="odd">
+<td align="left"><p>Parameter</p></td>
+<td align="left"><p>Description</p></td>
+</tr>
+<tr class="even">
+<td align="left"><p>maxTuplesPerWindow</p></td>
+<td align="left"><p>Controls the maximum number of messages emitted in each streaming window from this operator. Minimum value is 1. Default value = MAX_VALUE </p></td>
+</tr>
+<tr class="odd">
+<td align="left"><p>idempotentStorageManager</p></td>
+<td align="left"><p>This is an instance of IdempotentStorageManager. Idempotency ensures that the operator will process the same set of messages in a window before and after a failure. For example, let's say the operator completed window 10 and failed somewhere between window 11. If the operator gets restored at window 10 then it will process the same messages again in window 10 which it did in the previous run before the failure. Idempotency is important but comes with higher cost because at the end of each window the operator needs to persist some state with respect to that window. Default Value = com.datatorrent.lib.io.IdempotentStorageManager.<br>NoopIdempotentStorageManager</p></td>
+</tr>
+<tr class="even">
+<td align="left"><p>strategy</p></td>
+<td align="left"><p>Operator supports two types of partitioning strategies, ONE_TO_ONE and ONE_TO_MANY.</p>
+<p>ONE_TO_ONE: If this is enabled, the AppMaster creates one input operator instance per Kafka topic partition. So the number of Kafka topic partitions equals the number of operator instances.</p>
+<p>ONE_TO_MANY: The AppMaster creates K = min(initialPartitionCount, N) Kafka input operator instances where N is the number of Kafka topic partitions. If K is less than N, the remaining topic partitions are assigned to the K operator instances in round-robin fashion. If K is less than initialPartitionCount, the AppMaster creates one input operator instance per Kafka topic partition. For example, if initialPartitionCount = 5 and number of Kafka partitions(N) = 2 then AppMaster creates 2 Kafka input operator instances.
+Default Value = ONE_TO_ONE</p></td>
+</tr>
+<tr class="odd">
+<td align="left"><p>msgRateUpperBound</p></td>
+<td align="left"><p>Maximum messages upper bound. Operator repartitions when the <em>msgProcessedPS</em> exceeds this bound. <em>msgProcessedPS</em> is the average number of messages processed per second by this operator.</p></td>
+</tr>
+<tr class="even">
+<td align="left"><p>byteRateUpperBound</p></td>
+<td align="left"><p>Maximum bytes upper bound. Operator repartitions when the <em>bytesPS</em> exceeds this bound. <em>bytesPS</em> is the average number of bytes processed per second by this operator.</p>
+<p></p></td>
+</tr>
+<tr class="odd">
+<td align="left"><p>offsetManager</p></td>
+<td align="left"><p>This is an optional parameter that is useful when the application restarts or start at specific offsets (offsets are explained below)</p></td>
+</tr>
+<tr class="even">
+<td align="left"><p>repartitionInterval</p></td>
+<td align="left"><p>Interval specified in milliseconds. This value specifies the minimum time required between two repartition actions. Default Value = 30 Seconds</p></td>
+</tr>
+<tr class="odd">
+<td align="left"><p>repartitionCheckInterval</p></td>
+<td align="left"><p>Interval specified in milliseconds. This value specifies the minimum interval between two offset updates. Default Value = 5 Seconds</p></td>
+</tr>
+<tr class="even">
+<td align="left"><p>initialPartitionCount</p></td>
+<td align="left"><p>When the ONE_TO_MANY partition strategy is enabled, this value indicates the number of Kafka input operator instances. Default Value = 1</p></td>
+</tr>
+<tr class="odd">
+<td align="left"><p>consumer</p></td>
+<td align="left"><p>This is an instance of com.datatorrent.contrib.kafka.KafkaConsumer. Default Value = Instance of SimpleKafkaConsumer.</p></td>
+</tr>
+</tbody>
+</table></p>
+<h4 id="abstract-methods">Abstract Methods</h4>
+<p>void emitTuple(Message message): Abstract method that emits tuples
+extracted from Kafka message.</p>
+<h3 id="kafkaconsumer">KafkaConsumer</h3>
+<p>This is an abstract implementation of Kafka consumer. It sends the fetch
+requests to the leading brokers of Kafka partitions. For each request,
+it receives the set of messages and stores them into the buffer which is
+ArrayBlockingQueue. SimpleKafkaConsumer�which extends
+KafkaConsumer and serves the functionality of Simple Consumer API and
+HighLevelKafkaConsumer which extends KafkaConsumer and �serves the
+functionality of High Level Consumer API.</p>
+<h3 id="pre-requisites">Pre-requisites</h3>
+<p>This operator referred the Kafka Consumer API of version
+0.8.1.1. So, this operator will work with any 0.8.x and 0.7.x version of Apache Kafka.</p>
+<h4 id="configuration-parameters_1">Configuration Parameters</h4>
+<table>
+<col width="15%" />
+<col width="15%" />
+<col width="15%" />
+<col width="55%" />
+<tbody>
+<tr class="odd">
+<td align="left"><p>Parameter</p></td>
+<td align="left"><p>Type</p></td>
+<td align="left"><p>Default</p></td>
+<td align="left"><p>Description</p></td>
+</tr>
+<tr class="even">
+<td align="left"><p>zookeeper</p></td>
+<td align="left"><p>String</p></td>
+<td align="left"><p></p></td>
+<td align="left"><p>Specifies the zookeeper quorum of Kafka clusters that you want to consume messages from. zookeeper �is a string in the form of hostname1:port1,hostname2:port2,hostname3:port3 �where hostname1,hostname2,hostname3 are hosts and port1,port2,port3 are ports of zookeeper server. �If the topic name is the same across the Kafka clusters and want to consume data from these clusters, then configure the zookeeper as follows: c1::hs1:p1,hs2:p2,hs3:p3;c2::hs4:p4,hs5:p5,c3::hs6:p6</p>
+<p>where</p>
+<p>c1,c2,c3 indicates the cluster names, hs1,hs2,hs3,hs4,hs5,hs6 are zookeeper hosts and p1,p2,p3,p4,p5,p6 are corresponding ports. Here, cluster name is optional in case of single cluster</p></td>
+</tr>
+<tr class="odd">
+<td align="left"><p>cacheSize</p></td>
+<td align="left"><p>int</p></td>
+<td align="left"><p>1024</p></td>
+<td align="left"><p>Maximum of buffered messages hold in memory.</p></td>
+</tr>
+<tr class="even">
+<td align="left"><p>topic</p></td>
+<td align="left"><p>String</p></td>
+<td align="left"><p>default_topic</p></td>
+<td align="left"><p>Indicates the name of the topic.</p></td>
+</tr>
+<tr class="odd">
+<td align="left"><p>initialOffset</p></td>
+<td align="left"><p>String</p></td>
+<td align="left"><p>latest</p></td>
+<td align="left"><p>Indicates the type of offset i.e, \u201cearliest or latest\u201d. If initialOffset is \u201clatest\u201d, then the operator consumes messages from latest point of Kafka queue. If initialOffset is \u201cearliest\u201d, then the operator consumes messages starting from message queue. This can be overridden by OffsetManager.</p></td>
+</tr>
+</tbody>
+</table>
+
+<h4 id="abstract-methods_1">Abstract Methods</h4>
+<ol>
+<li>void commitOffset(): Commit the offsets at checkpoint.</li>
+<li>Map <KafkaPartition, Long> getCurrentOffsets(): Return the current
+ offset status.</li>
+<li>resetPartitionsAndOffset(Set <KafkaPartition> partitionIds,
+ Map <KafkaPartition, Long> startOffset): Reset the partitions with
+ parittionIds and offsets with startOffset.</li>
+</ol>
+<h4 id="configuration-parameters-for-simplekafkaconsumer">Configuration Parameters�for SimpleKafkaConsumer</h4>
+<table>
+<col width="25%" />
+<col width="15%" />
+<col width="15%" />
+<col width="45%" />
+<tbody>
+<tr class="odd">
+<td align="left"><p>Parameter</p></td>
+<td align="left"><p>Type</p></td>
+<td align="left"><p>Default</p></td>
+<td align="left"><p>Description</p></td>
+</tr>
+<tr class="even">
+<td align="left"><p>bufferSize</p></td>
+<td align="left"><p>int</p></td>
+<td align="left"><p>1 MB</p></td>
+<td align="left"><p>Specifies the maximum total size of messages for each fetch request.</p></td>
+</tr>
+<tr class="odd">
+<td align="left"><p>metadataRefreshInterval</p></td>
+<td align="left"><p>int</p></td>
+<td align="left"><p>30 Seconds</p></td>
+<td align="left"><p>Interval in between refresh the metadata change(broker change) in milliseconds. Enabling metadata refresh guarantees an automatic reconnect when a new broker is elected as the host. A value of -1 disables this feature.</p></td>
+</tr>
+<tr class="even">
+<td align="left"><p>metadataRefreshRetryLimit</p></td>
+<td align="left"><p>int</p></td>
+<td align="left"><p>-1</p></td>
+<td align="left"><p>Specifies the maximum brokers' metadata refresh retry limit. -1 means unlimited retry.</p></td>
+</tr>
+</tbody>
+</table>
+
+<h3 id="offsetmanager">OffsetManager</h3>
+<p>This is an interface for offset management and is useful when consuming data
+from specified offsets. Updates the offsets for all the Kafka partitions
+periodically. Below is the code snippet:��������</p>
+<pre><code class="java">public interface OffsetManager
+{
+ public Map<KafkaPartition, Long> loadInitialOffsets();
+ public void updateOffsets(Map<KafkaPartition, Long> offsetsOfPartitions);
+}
+</code></pre>
+
+<h4 id="abstract-methods_2">Abstract Methods</h4>
+<p>Map <KafkaPartition, Long> loadInitialOffsets(): Specifies the initial offset for consuming messages; called at the activation stage.</p>
+<p>updateOffsets(Map <KafkaPartition, Long> offsetsOfPartitions): �This
+method is called at every repartitionCheckInterval to update offsets.</p>
+<h3 id="partitioning">Partitioning</h3>
+<p>The logical instance of the KafkaInputOperator acts as the Partitioner
+as well as a StatsListener. This is because the
+AbstractKafkaInputOperator implements both the
+com.datatorrent.api.Partitioner and com.datatorrent.api.StatsListener
+interfaces and provides an implementation of definePartitions(...) and
+processStats(...) which makes it auto-scalable.</p>
+<h4 id="response-processstatsbatchedoperatorstats-stats">Response processStats(BatchedOperatorStats stats)</h4>
+<p>The application master invokes this method on the logical instance with
+the stats (tuplesProcessedPS, bytesPS, etc.) of each partition.
+Re-partitioning happens based on whether any new Kafka partitions added for
+the topic or bytesPS and msgPS cross their respective upper bounds.</p>
+<h4 id="definepartitions">DefinePartitions</h4>
+<p>Based on the repartitionRequired field of the Response object which is
+returned by processStats(...) method, the application master invokes
+definePartitions(...) on the logical instance which is also the
+partitioner instance. Dynamic partition can be disabled by setting the
+parameter repartitionInterval value to a negative value.</p>
+<h3 id="abstractsingleportkafkainputoperator">AbstractSinglePortKafkaInputOperator</h3>
+<p>This class extends AbstractKafkaInputOperator and having single output
+port, will emit the messages through this port.</p>
+<h4 id="ports">Ports</h4>
+<p>outputPort <T>: Tuples extracted from Kafka messages are emitted through
+this port.</p>
+<h4 id="abstract-methods_3">Abstract Methods</h4>
+<p>T getTuple(Message msg) : Converts the Kafka message to tuple.</p>
+<h3 id="concrete-classes">Concrete Classes</h3>
+<ol>
+<li>
+<p>KafkaSinglePortStringInputOperator :
+This class extends AbstractSinglePortKafkaInputOperator and getTuple() method extracts string from Kafka message.</p>
+</li>
+<li>
+<p>KafkaSinglePortByteArrayInputOperator:
+This class extends AbstractSinglePortKafkaInputOperator and getTuple() method extracts byte array from Kafka message.</p>
+</li>
+</ol>
+<h3 id="application-example">Application Example</h3>
+<p>This section builds an Apex application using Kafka input operator.
+Below is the code snippet:</p>
+<pre><code class="java">@ApplicationAnnotation(name = "KafkaApp")
+public class ExampleKafkaApplication implements StreamingApplication
+{
+@Override
+public void populateDAG(DAG dag, Configuration entries)
+{
+ KafkaSinglePortByteArrayInputOperator input = dag.addOperator("MessageReader", new KafkaSinglePortByteArrayInputOperator());
+
+ ConsoleOutputOperator output = dag.addOperator("Output", new ConsoleOutputOperator());
+
+ dag.addStream("MessageData", input.outputPort, output.input);
+}
+}
+</code></pre>
+
+<p>Below is the configuration for \u201ctest\u201d Kafka topic name and
+\u201clocalhost:2181\u201d is the zookeeper forum:</p>
+<pre><code class="xml"><property>
+<name>dt.operator.MessageReader.prop.topic</name>
+<value>test</value>
+</property>
+
+<property>
+<name>dt.operator.KafkaInputOperator.prop.zookeeper</nam>
+<value>localhost:2181</value>
+</property>
+</code></pre>
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+
+ <link rel="stylesheet" href="./css/theme.css" type="text/css" />
+ <link rel="stylesheet" href="./css/theme_extra.css" type="text/css" />
+ <link rel="stylesheet" href="./css/highlight.css">
+
+
+ <script src="./js/jquery-2.1.1.min.js"></script>
+ <script src="./js/modernizr-2.8.3.min.js"></script>
+ <script type="text/javascript" src="./js/highlight.pack.js"></script>
+ <script src="./js/theme.js"></script>
+ <script>var base_url = '.';</script>
+ <script data-main="./mkdocs/js/search.js" src="./mkdocs/js/require.js"></script>
+
+
+
+</head>
+
+<body class="wy-body-for-nav" role="document">
+
+ <div class="wy-grid-for-nav">
+
+
+ <nav data-toggle="wy-nav-shift" class="wy-nav-side stickynav">
+ <div class="wy-side-nav-search">
+ <a href="." class="icon icon-home"> Apache Apex Malhar Documentation</a>
+ <div role="search">
+ <form id ="rtd-search-form" class="wy-form" action="./search.html" method="get">
+ <input type="text" name="q" placeholder="Search docs" />
+ </form>
+</div>
+ </div>
+
+ <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
+ <ul class="current">
+
+ <li>
+ <li class="toctree-l1 ">
+ <a class="" href=".">Apache Apex Malhar</a>
+
+ </li>
+<li>
+
+ <li>
+ <ul class="subnav">
+ <li><span>Operators</span></li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="operators/kafkaInputOperator/">Kafka Input</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="operators/file_splitter/">File Splitter</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="operators/block_reader/">Block Reader</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="operators/file_output/">File Output</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="operators/enricher/">Enricher</a>
+
+ </li>
+
+
+ </ul>
+<li>
+
+ </ul>
+ </div>
+
+ </nav>
+
+ <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
+
+
+ <nav class="wy-nav-top" role="navigation" aria-label="top navigation">
+ <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+ <a href=".">Apache Apex Malhar Documentation</a>
+ </nav>
+
+
+ <div class="wy-nav-content">
+ <div class="rst-content">
+ <div role="navigation" aria-label="breadcrumbs navigation">
+ <ul class="wy-breadcrumbs">
+ <li><a href=".">Docs</a> »</li>
+
+
+ <li class="wy-breadcrumbs-aside">
+
+ </li>
+ </ul>
+ <hr/>
+</div>
+ <div role="main">
+ <div class="section">
+
+
+ <h1 id="search">Search Results</h1>
+
+ <form id="content_search" action="search.html">
+ <span role="status" aria-live="polite" class="ui-helper-hidden-accessible"></span>
+ <input name="q" id="mkdocs-search-query" type="text" class="search_input search-query ui-autocomplete-input" placeholder="Search the Docs" autocomplete="off" autofocus>
+ </form>
+
+ <div id="mkdocs-search-results">
+ Searching...
+ </div>
+
+
+ </div>
+ </div>
+ <footer>
+
+
+ <hr/>
+
+ <div role="contentinfo">
+ <!-- Copyright etc -->
+
+ </div>
+
+ Built with <a href="http://www.mkdocs.org">MkDocs</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+</footer>
+
+ </div>
+ </div>
+
+ </section>
+
+ </div>
+
+<div class="rst-versions" role="note" style="cursor: pointer">
+ <span class="rst-current-version" data-toggle="rst-current-version">
+
+
+
+ </span>
+</div>
+
+</body>
+</html>
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/searchbox.html
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/searchbox.html b/docs/malhar-3.5/searchbox.html
new file mode 100644
index 0000000..177fcb3
--- /dev/null
+++ b/docs/malhar-3.5/searchbox.html
@@ -0,0 +1,5 @@
+<div role="search">
+ <form id ="rtd-search-form" class="wy-form" action="{{ base_url }}/search.html" method="get">
+ <input type="text" name="q" placeholder="Search docs" />
+ </form>
+</div>
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/sitemap.xml
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/sitemap.xml b/docs/malhar-3.5/sitemap.xml
new file mode 100644
index 0000000..ea3ee75
--- /dev/null
+++ b/docs/malhar-3.5/sitemap.xml
@@ -0,0 +1,46 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
+
+
+ <url>
+ <loc>/</loc>
+ <lastmod>2016-08-29</lastmod>
+ <changefreq>daily</changefreq>
+ </url>
+
+
+
+
+ <url>
+ <loc>/operators/kafkaInputOperator/</loc>
+ <lastmod>2016-08-29</lastmod>
+ <changefreq>daily</changefreq>
+ </url>
+
+ <url>
+ <loc>/operators/file_splitter/</loc>
+ <lastmod>2016-08-29</lastmod>
+ <changefreq>daily</changefreq>
+ </url>
+
+ <url>
+ <loc>/operators/block_reader/</loc>
+ <lastmod>2016-08-29</lastmod>
+ <changefreq>daily</changefreq>
+ </url>
+
+ <url>
+ <loc>/operators/file_output/</loc>
+ <lastmod>2016-08-29</lastmod>
+ <changefreq>daily</changefreq>
+ </url>
+
+ <url>
+ <loc>/operators/enricher/</loc>
+ <lastmod>2016-08-29</lastmod>
+ <changefreq>daily</changefreq>
+ </url>
+
+
+
+</urlset>
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/toc.html
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/toc.html b/docs/malhar-3.5/toc.html
new file mode 100644
index 0000000..6cd2fc9
--- /dev/null
+++ b/docs/malhar-3.5/toc.html
@@ -0,0 +1,23 @@
+{% if nav_item.children %}
+ <ul class="subnav">
+ <li><span>{{ nav_item.title }}</span></li>
+
+ {% for nav_item in nav_item.children %}
+ {% include 'toc.html' %}
+ {% endfor %}
+ </ul>
+{% else %}
+ <li class="toctree-l1 {% if nav_item.active%}current{%endif%}">
+ <a class="{% if nav_item.active%}current{%endif%}" href="{{ nav_item.url }}">{{ nav_item.title }}</a>
+ {% if nav_item == current_page %}
+ <ul>
+ {% for toc_item in toc %}
+ <li class="toctree-l3"><a href="{{ toc_item.url }}">{{ toc_item.title }}</a></li>
+ {% for toc_item in toc_item.children %}
+ <li><a class="toctree-l4" href="{{ toc_item.url }}">{{ toc_item.title }}</a></li>
+ {% endfor %}
+ {% endfor %}
+ </ul>
+ {% endif %}
+ </li>
+{% endif %}
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/versions.html
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/versions.html b/docs/malhar-3.5/versions.html
new file mode 100644
index 0000000..d12d197
--- /dev/null
+++ b/docs/malhar-3.5/versions.html
@@ -0,0 +1,15 @@
+<div class="rst-versions" role="note" style="cursor: pointer">
+ <span class="rst-current-version" data-toggle="rst-current-version">
+ {% if repo_name == 'GitHub' %}
+ <a href="{{ repo_url }}" class="icon icon-github" style="float: left; color: #fcfcfc"> GitHub</a>
+ {% elif repo_name == 'Bitbucket' %}
+ <a href="{{ repo_url }}" class="icon icon-bitbucket" style="float: left; color: #fcfcfc"> BitBucket</a>
+ {% endif %}
+ {% if previous_page %}
+ <span><a href="{{ previous_page.url }}" style="color: #fcfcfc;">« Previous</a></span>
+ {% endif %}
+ {% if next_page %}
+ <span style="margin-left: 15px"><a href="{{ next_page.url }}" style="color: #fcfcfc">Next »</a></span>
+ {% endif %}
+ </span>
+</div>
[12/12] apex-site git commit: Adding malhar-3.5.0 documentation
Posted by th...@apache.org.
Adding malhar-3.5.0 documentation
Project: http://git-wip-us.apache.org/repos/asf/apex-site/repo
Commit: http://git-wip-us.apache.org/repos/asf/apex-site/commit/37ae8b76
Tree: http://git-wip-us.apache.org/repos/asf/apex-site/tree/37ae8b76
Diff: http://git-wip-us.apache.org/repos/asf/apex-site/diff/37ae8b76
Branch: refs/heads/asf-site
Commit: 37ae8b7678d2487028ed2c65aa7d08495ce74527
Parents: d86c144
Author: Thomas Weise <th...@datatorrent.com>
Authored: Mon Aug 29 12:58:48 2016 -0700
Committer: Thomas Weise <th...@datatorrent.com>
Committed: Mon Aug 29 12:58:48 2016 -0700
----------------------------------------------------------------------
docs/malhar-3.5/__init__.py | 0
docs/malhar-3.5/__init__.pyc | Bin 0 -> 163 bytes
docs/malhar-3.5/base.html | 118 +++++
docs/malhar-3.5/breadcrumbs.html | 25 +
docs/malhar-3.5/css/highlight.css | 124 +++++
docs/malhar-3.5/css/theme.css | 12 +
docs/malhar-3.5/css/theme_extra.css | 154 ++++++
docs/malhar-3.5/favicon.ico | Bin 0 -> 25597 bytes
docs/malhar-3.5/fonts/fontawesome-webfont.eot | Bin 0 -> 37405 bytes
docs/malhar-3.5/fonts/fontawesome-webfont.svg | 399 ++++++++++++++
docs/malhar-3.5/fonts/fontawesome-webfont.ttf | Bin 0 -> 79076 bytes
docs/malhar-3.5/fonts/fontawesome-webfont.woff | Bin 0 -> 43572 bytes
docs/malhar-3.5/footer.html | 23 +
docs/malhar-3.5/images/malhar-operators.png | Bin 0 -> 109734 bytes
docs/malhar-3.5/img/favicon.ico | Bin 0 -> 1150 bytes
docs/malhar-3.5/index.html | 255 +++++++++
docs/malhar-3.5/js/highlight.pack.js | 2 +
docs/malhar-3.5/js/jquery-2.1.1.min.js | 4 +
docs/malhar-3.5/js/modernizr-2.8.3.min.js | 1 +
docs/malhar-3.5/js/theme.js | 55 ++
docs/malhar-3.5/main.html | 10 +
docs/malhar-3.5/mkdocs/js/lunr-0.5.7.min.js | 7 +
docs/malhar-3.5/mkdocs/js/lunr.min.js | 7 +
docs/malhar-3.5/mkdocs/js/mustache.min.js | 1 +
docs/malhar-3.5/mkdocs/js/require.js | 36 ++
.../mkdocs/js/search-results-template.mustache | 4 +
docs/malhar-3.5/mkdocs/js/search.js | 88 ++++
docs/malhar-3.5/mkdocs/js/text.js | 390 ++++++++++++++
docs/malhar-3.5/mkdocs/search_index.json | 524 +++++++++++++++++++
.../operators/block_reader/index.html | 407 ++++++++++++++
docs/malhar-3.5/operators/enricher/index.html | 503 ++++++++++++++++++
.../malhar-3.5/operators/file_output/index.html | 342 ++++++++++++
.../operators/file_splitter/index.html | 359 +++++++++++++
.../images/blockreader/classdiagram.png | Bin 0 -> 48613 bytes
.../images/blockreader/flowdiagram.png | Bin 0 -> 48160 bytes
.../images/blockreader/fsreaderexample.png | Bin 0 -> 29927 bytes
.../blockreader/totalBacklogProcessing.png | Bin 0 -> 55944 bytes
.../images/fileoutput/FileRotation.png | Bin 0 -> 26067 bytes
.../operators/images/fileoutput/diagram1.png | Bin 0 -> 30754 bytes
.../images/filesplitter/baseexample.png | Bin 0 -> 14493 bytes
.../images/filesplitter/classdiagram.png | Bin 0 -> 14513 bytes
.../images/filesplitter/inputexample.png | Bin 0 -> 16012 bytes
.../operators/images/filesplitter/sequence.png | Bin 0 -> 17020 bytes
.../operators/images/kafkainput/image00.png | Bin 0 -> 36143 bytes
.../operators/kafkaInputOperator/index.html | 462 ++++++++++++++++
docs/malhar-3.5/search.html | 178 +++++++
docs/malhar-3.5/searchbox.html | 5 +
docs/malhar-3.5/sitemap.xml | 46 ++
docs/malhar-3.5/toc.html | 23 +
docs/malhar-3.5/versions.html | 15 +
50 files changed, 4579 insertions(+)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/__init__.py
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/__init__.py b/docs/malhar-3.5/__init__.py
new file mode 100644
index 0000000..e69de29
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/__init__.pyc
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/__init__.pyc b/docs/malhar-3.5/__init__.pyc
new file mode 100644
index 0000000..5d767d8
Binary files /dev/null and b/docs/malhar-3.5/__init__.pyc differ
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/base.html
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/base.html b/docs/malhar-3.5/base.html
new file mode 100644
index 0000000..87c9f8a
--- /dev/null
+++ b/docs/malhar-3.5/base.html
@@ -0,0 +1,118 @@
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
+<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
+<head>
+ <meta charset="utf-8">
+ <meta http-equiv="X-UA-Compatible" content="IE=edge">
+ <meta name="viewport" content="width=device-width, initial-scale=1.0">
+ {% if page_description %}<meta name="description" content="{{ page_description }}">{% endif %}
+ {% if site_author %}<meta name="author" content="{{ site_author }}">{% endif %}
+ {% block htmltitle %}
+ <title>{% if page_title %}{{ page_title }} - {% endif %}{{ site_name }}</title>
+ {% endblock %}
+
+ {% if favicon %}<link rel="shortcut icon" href="{{ favicon }}">
+ {% else %}<link rel="shortcut icon" href="{{ base_url }}/img/favicon.ico">{% endif %}
+
+ {# CSS #}
+ <link href='https://fonts.googleapis.com/css?family=Lato:400,700|Roboto+Slab:400,700|Inconsolata:400,700' rel='stylesheet' type='text/css'>
+
+ <link rel="stylesheet" href="{{ base_url }}/css/theme.css" type="text/css" />
+ <link rel="stylesheet" href="{{ base_url }}/css/theme_extra.css" type="text/css" />
+ <link rel="stylesheet" href="{{ base_url }}/css/highlight.css">
+ {%- for path in extra_css %}
+ <link href="{{ path }}" rel="stylesheet">
+ {%- endfor %}
+
+ {% if current_page %}
+ <script>
+ // Current page data
+ var mkdocs_page_name = {{ page_title|tojson|safe }};
+ var mkdocs_page_input_path = {{ current_page.input_path|tojson|safe }};
+ var mkdocs_page_url = {{ current_page.abs_url|tojson|safe }};
+ </script>
+ {% endif %}
+ <script src="{{ base_url }}/js/jquery-2.1.1.min.js"></script>
+ <script src="{{ base_url }}/js/modernizr-2.8.3.min.js"></script>
+ <script type="text/javascript" src="{{ base_url }}/js/highlight.pack.js"></script>
+ <script src="{{ base_url }}/js/theme.js"></script>
+
+ {%- block extrahead %} {% endblock %}
+
+ {%- for path in extra_javascript %}
+ <script src="{{ path }}"></script>
+ {%- endfor %}
+
+ {% if google_analytics %}
+ <script>
+ (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+ (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+ m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+
+ ga('create', '{{ google_analytics[0] }}', '{{ google_analytics[1] }}');
+ ga('send', 'pageview');
+ </script>
+ {% endif %}
+</head>
+
+<body class="wy-body-for-nav" role="document">
+
+ <div class="wy-grid-for-nav">
+
+ {# SIDE NAV, TOGGLES ON MOBILE #}
+ <nav data-toggle="wy-nav-shift" class="wy-nav-side stickynav">
+ <div class="wy-side-nav-search">
+ <a href="{{ homepage_url }}" class="icon icon-home"> {{ site_name }}</a>
+ {% include "searchbox.html" %}
+ </div>
+
+ <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
+ <ul class="current">
+ {% for nav_item in nav %}
+ <li>{% include "toc.html" %}<li>
+ {% endfor %}
+ </ul>
+ </div>
+
+ </nav>
+
+ <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
+
+ {# MOBILE NAV, TRIGGLES SIDE NAV ON TOGGLE #}
+ <nav class="wy-nav-top" role="navigation" aria-label="top navigation">
+ <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+ <a href="{{ homepage_url }}">{{ site_name }}</a>
+ </nav>
+
+ {# PAGE CONTENT #}
+ <div class="wy-nav-content">
+ <div class="rst-content">
+ {% include "breadcrumbs.html" %}
+ <div role="main">
+ <div class="section">
+ {% block content %}
+ {{ content }}
+ {% endblock %}
+ </div>
+ </div>
+ {%- block footer %}
+ {% include "footer.html" %}
+ {% endblock %}
+ </div>
+ </div>
+
+ </section>
+
+ </div>
+
+{% include "versions.html" %}
+
+</body>
+</html>
+{% if current_page and current_page.is_homepage %}
+<!--
+MkDocs version : {{ mkdocs_version }}
+Build Date UTC : {{ build_date_utc }}
+-->
+{% endif %}
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/breadcrumbs.html
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/breadcrumbs.html b/docs/malhar-3.5/breadcrumbs.html
new file mode 100644
index 0000000..01960e6
--- /dev/null
+++ b/docs/malhar-3.5/breadcrumbs.html
@@ -0,0 +1,25 @@
+<div role="navigation" aria-label="breadcrumbs navigation">
+ <ul class="wy-breadcrumbs">
+ <li><a href="{{ homepage_url }}">Docs</a> »</li>
+ {% if current_page %}
+ {% for doc in current_page.ancestors %}
+ {% if doc.link %}
+ <li><a href="{{ doc.link|e }}">{{ doc.title }}</a> »</li>
+ {% else %}
+ <li>{{ doc.title }} »</li>
+ {% endif %}
+ {% endfor %}
+ {% endif %}
+ {% if current_page %}<li>{{ current_page.title }}</li>{% endif %}
+ <li class="wy-breadcrumbs-aside">
+ {% if repo_url %}
+ {% if repo_name == 'GitHub' %}
+ <a href="{{ repo_url }}" class="icon icon-github"> Edit on GitHub</a>
+ {% elif repo_name == 'Bitbucket' %}
+ <a href="{{ repo_url }}" class="icon icon-bitbucket"> Edit on BitBucket</a>
+ {% endif %}
+ {% endif %}
+ </li>
+ </ul>
+ <hr/>
+</div>
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/css/highlight.css
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/css/highlight.css b/docs/malhar-3.5/css/highlight.css
new file mode 100644
index 0000000..0ae40a7
--- /dev/null
+++ b/docs/malhar-3.5/css/highlight.css
@@ -0,0 +1,124 @@
+/*
+This is the GitHub theme for highlight.js
+
+github.com style (c) Vasily Polovnyov <va...@whiteants.net>
+
+*/
+
+.hljs {
+ display: block;
+ overflow-x: auto;
+ color: #333;
+ -webkit-text-size-adjust: none;
+}
+
+.hljs-comment,
+.diff .hljs-header,
+.hljs-javadoc {
+ color: #998;
+ font-style: italic;
+}
+
+.hljs-keyword,
+.css .rule .hljs-keyword,
+.hljs-winutils,
+.nginx .hljs-title,
+.hljs-subst,
+.hljs-request,
+.hljs-status {
+ color: #333;
+ font-weight: bold;
+}
+
+.hljs-number,
+.hljs-hexcolor,
+.ruby .hljs-constant {
+ color: #008080;
+}
+
+.hljs-string,
+.hljs-tag .hljs-value,
+.hljs-phpdoc,
+.hljs-dartdoc,
+.tex .hljs-formula {
+ color: #d14;
+}
+
+.hljs-title,
+.hljs-id,
+.scss .hljs-preprocessor {
+ color: #900;
+ font-weight: bold;
+}
+
+.hljs-list .hljs-keyword,
+.hljs-subst {
+ font-weight: normal;
+}
+
+.hljs-class .hljs-title,
+.hljs-type,
+.vhdl .hljs-literal,
+.tex .hljs-command {
+ color: #458;
+ font-weight: bold;
+}
+
+.hljs-tag,
+.hljs-tag .hljs-title,
+.hljs-rule .hljs-property,
+.django .hljs-tag .hljs-keyword {
+ color: #000080;
+ font-weight: normal;
+}
+
+.hljs-attribute,
+.hljs-variable,
+.lisp .hljs-body,
+.hljs-name {
+ color: #008080;
+}
+
+.hljs-regexp {
+ color: #009926;
+}
+
+.hljs-symbol,
+.ruby .hljs-symbol .hljs-string,
+.lisp .hljs-keyword,
+.clojure .hljs-keyword,
+.scheme .hljs-keyword,
+.tex .hljs-special,
+.hljs-prompt {
+ color: #990073;
+}
+
+.hljs-built_in {
+ color: #0086b3;
+}
+
+.hljs-preprocessor,
+.hljs-pragma,
+.hljs-pi,
+.hljs-doctype,
+.hljs-shebang,
+.hljs-cdata {
+ color: #999;
+ font-weight: bold;
+}
+
+.hljs-deletion {
+ background: #fdd;
+}
+
+.hljs-addition {
+ background: #dfd;
+}
+
+.diff .hljs-change {
+ background: #0086b3;
+}
+
+.hljs-chunk {
+ color: #aaa;
+}
[11/12] apex-site git commit: Adding malhar-3.5.0 documentation
Posted by th...@apache.org.
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/css/theme.css
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+/*
+ * This file is copied from the upstream ReadTheDocs Sphinx
+ * theme. To aid upgradability this file should *not* be edited.
+ * modifications we need should be included in theme_extra.css.
+ *
+ * https://github.com/rtfd/readthedocs.org/blob/master/media/css/sphinx_rtd_theme.css
+ */
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[02/12] apex-site git commit: Adding malhar-3.5.0 documentation
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+ <a class="" href="../..">Apache Apex Malhar</a>
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+ <li><span>Operators</span></li>
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+ <a class="" href="../kafkaInputOperator/">Kafka Input</a>
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+ <a class="" href="../file_splitter/">File Splitter</a>
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+ <a class="current" href="./">Block Reader</a>
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+ <ul>
+
+ <li class="toctree-l3"><a href="#block-reader">Block Reader</a></li>
+
+ <li><a class="toctree-l4" href="#why-is-it-needed">Why is it needed?</a></li>
+
+ <li><a class="toctree-l4" href="#class-diagram">Class Diagram</a></li>
+
+ <li><a class="toctree-l4" href="#abstractblockreader">AbstractBlockReader</a></li>
+
+ <li><a class="toctree-l4" href="#example-application">Example Application</a></li>
+
+
+ <li class="toctree-l3"><a href="#abstractfsreadaheadlinereader">AbstractFSReadAheadLineReader</a></li>
+
+ <li><a class="toctree-l4" href="#readaheadlinereadercontext">ReadAheadLineReaderContext</a></li>
+
+
+ <li class="toctree-l3"><a href="#abstractfslinereader">AbstractFSLineReader</a></li>
+
+ <li><a class="toctree-l4" href="#linereadercontext">LineReaderContext</a></li>
+
+
+ <li class="toctree-l3"><a href="#fsslicereader">FSSliceReader</a></li>
+
+ <li><a class="toctree-l4" href="#fixedbytesreadercontext">FixedBytesReaderContext</a></li>
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+ <li><a class="toctree-l4" href="#configuration_1">Configuration</a></li>
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+ <h1 id="block-reader">Block Reader</h1>
+<p>This is a scalable operator that reads and parses blocks of data sources into records. A data source can be a file or a message bus that contains records and a block defines a chunk of data in the source by specifying the block offset and the length of the source belonging to the block. </p>
+<h2 id="why-is-it-needed">Why is it needed?</h2>
+<p>A Block Reader is needed to parallelize reading and parsing of a single data source, for example a file. Simple parallelism of reading data sources can be achieved by multiple partitions reading different source of same type (for files see <a href="https://github.com/apache/incubator-apex-malhar/blob/master/library/src/main/java/com/datatorrent/lib/io/fs/AbstractFileInputOperator.java">AbstractFileInputOperator</a>) but Block Reader partitions can read blocks of same source in parallel and parse them for records ensuring that no record is duplicated or missed.</p>
+<h2 id="class-diagram">Class Diagram</h2>
+<p><img alt="BlockReader class diagram" src="../images/blockreader/classdiagram.png" /></p>
+<h2 id="abstractblockreader">AbstractBlockReader</h2>
+<p>This is the abstract implementation that serves as the base for different types of data sources. It defines how a block metadata is processed. The flow diagram below describes the processing of a block metadata.</p>
+<p><img alt="BlockReader flow diagram" src="../images/blockreader/flowdiagram.png" /></p>
+<h3 id="ports">Ports</h3>
+<ul>
+<li>
+<p>blocksMetadataInput: input port on which block metadata are received.</p>
+</li>
+<li>
+<p>blocksMetadataOutput: output port on which block metadata are emitted if the port is connected. This port is useful when a downstream operator that receives records from block reader may also be interested to know the details of the corresponding blocks.</p>
+</li>
+<li>
+<p>messages: output port on which tuples of type <code>com.datatorrent.lib.io.block.AbstractBlockReader.ReaderRecord</code> are emitted. This class encapsulates a <code>record</code> and the <code>blockId</code> of the corresponding block.</p>
+</li>
+</ul>
+<h3 id="readercontext">readerContext</h3>
+<p>This is one of the most important fields in the block reader. It is of type <code>com.datatorrent.lib.io.block.ReaderContext</code> and is responsible for fetching bytes that make a record. It also lets the reader know how many total bytes were consumed which may not be equal to the total bytes in a record because consumed bytes also include bytes for the record delimiter which may not be a part of the actual record.</p>
+<p>Once the reader creates an input stream for the block (or uses the previous opened stream if the current block is successor of the previous block) it initializes the reader context by invoking <code>readerContext.initialize(stream, blockMetadata, consecutiveBlock);</code>. Initialize method is where any implementation of <code>ReaderContext</code> can perform all the operations which have to be executed just before reading the block or create states which are used during the lifetime of reading the block.</p>
+<p>Once the initialization is done, <code>readerContext.next()</code> is called repeatedly until it returns <code>null</code>. It is left to the <code>ReaderContext</code> implementations to decide when a block is completely processed. In cases when a record is split across adjacent blocks, reader context may decide to read ahead of the current block boundary to completely fetch the split record (examples- <code>LineReaderContext</code> and <code>ReadAheadLineReaderContext</code>). In other cases when there isn't a possibility of split record (example- <code>FixedBytesReaderContext</code>), it returns <code>null</code> immediately when the block boundary is reached. The return type of <code>readerContext.next()</code> is of type <code>com.datatorrent.lib.io.block.ReaderContext.Entity</code> which is just a wrapper for a <code>byte[]</code> that represents the record and total bytes used in fetching the record.</p>
+<h3 id="abstract-methods">Abstract methods</h3>
+<ul>
+<li>
+<p><code>STREAM setupStream(B block)</code>: creating a stream for a block is dependent on the type of source which is not known to AbstractBlockReader. Sub-classes which deal with a specific data source provide this implementation.</p>
+</li>
+<li>
+<p><code>R convertToRecord(byte[] bytes)</code><a name="convertToRecord"></a>: this converts the array of bytes into the actual instance of record type.</p>
+</li>
+</ul>
+<h3 id="auto-scalability">Auto-scalability</h3>
+<p>Block reader can auto-scale, that is, depending on the backlog (total number of all the blocks which are waiting in the <code>blocksMetadataInput</code> port queue of all partitions) it can create more partitions or reduce them. Details are discussed in the last section which covers the <a href="#partitioning">partitioner and stats-listener</a>.</p>
+<h3 id="configuration">Configuration</h3>
+<ol>
+<li><a name="maxReaders"></a><strong>maxReaders</strong>: when auto-scaling is enabled, this controls the maximum number of block reader partitions that can be created.</li>
+<li><a name="minReaders"></a><strong>minReaders</strong>: when auto-scaling is enabled, this controls the minimum number of block reader partitions that should always exist.</li>
+<li><a name="collectStats"></a><strong>collectStats</strong>: this enables or disables auto-scaling. When it is set to <code>true</code> the stats (number of blocks in the queue) are collected and this triggers partitioning; otherwise auto-scaling is disabled.</li>
+<li><strong>intervalMillis</strong>: when auto-scaling is enabled, this specifies the interval at which the reader will trigger the logic of computing the backlog and auto-scale.</li>
+</ol>
+<h2 id="abstractfsblockreader"><a name="AbstractFSBlockReader"></a> AbstractFSBlockReader</h2>
+<p>This abstract implementation deals with files. Different types of file systems that are implementations of <code>org.apache.hadoop.fs.FileSystem</code> are supported. The user can override <code>getFSInstance()</code> method to create an instance of a specific <code>FileSystem</code>. By default, filesystem instance is created from the filesytem URI that comes from the default hadoop configuration.</p>
+<pre><code class="java">protected FileSystem getFSInstance() throws IOException
+{
+ return FileSystem.newInstance(configuration);
+}
+</code></pre>
+
+<p>It uses this filesystem instance to setup a stream of type <code>org.apache.hadoop.fs.FSDataInputStream</code> to read the block.</p>
+<pre><code class="java">@Override
+protected FSDataInputStream setupStream(BlockMetadata.FileBlockMetadata block) throws IOException
+{
+ return fs.open(new Path(block.getFilePath()));
+}
+</code></pre>
+
+<p>All the ports and configurations are derived from the super class. It doesn't provide an implementation of <a href="#convertToRecord"><code>convertToRecord(byte[] bytes)</code></a> method which is delegated to concrete sub-classes.</p>
+<h3 id="example-application">Example Application</h3>
+<p>This simple dag demonstrates how any concrete implementation of <code>AbstractFSBlockReader</code> can be plugged into an application. </p>
+<p><img alt="Application with FSBlockReader" src="../images/blockreader/fsreaderexample.png" /></p>
+<p>In the above application, file splitter creates block metadata for files which are sent to block reader. Partitions of the block reader parses the file blocks for records which are filtered, transformed and then persisted to a file (created per block). Therefore block reader is parallel partitioned with the 2 downstream operators - filter/converter and record output operator. The code which implements this dag is below.</p>
+<pre><code class="java">public class ExampleApplication implements StreamingApplication
+{
+ @Override
+ public void populateDAG(DAG dag, Configuration configuration)
+ {
+ FileSplitterInput input = dag.addOperator("File-splitter", new FileSplitterInput());
+ //any concrete implementation of AbstractFSBlockReader based on the use-case can be added here.
+ LineReader blockReader = dag.addOperator("Block-reader", new LineReader());
+ Filter filter = dag.addOperator("Filter", new Filter());
+ RecordOutputOperator recordOutputOperator = dag.addOperator("Record-writer", new RecordOutputOperator());
+
+ dag.addStream("file-block metadata", input.blocksMetadataOutput, blockReader.blocksMetadataInput);
+ dag.addStream("records", blockReader.messages, filter.input);
+ dag.addStream("filtered-records", filter.output, recordOutputOperator.input);
+ }
+
+ /**
+ * Concrete implementation of {@link AbstractFSBlockReader} for which a record is a line in the file.
+ */
+ public static class LineReader extends AbstractFSBlockReader.AbstractFSReadAheadLineReader<String>
+ {
+
+ @Override
+ protected String convertToRecord(byte[] bytes)
+ {
+ return new String(bytes);
+ }
+ }
+
+ /**
+ * Considers any line starting with a '.' as invalid. Emits the valid records.
+ */
+ public static class Filter extends BaseOperator
+ {
+ public final transient DefaultOutputPort<AbstractBlockReader.ReaderRecord<String>> output = new DefaultOutputPort<>();
+ public final transient DefaultInputPort<AbstractBlockReader.ReaderRecord<String>> input = new DefaultInputPort<AbstractBlockReader.ReaderRecord<String>>()
+ {
+ @Override
+ public void process(AbstractBlockReader.ReaderRecord<String> stringRecord)
+ {
+ //filter records and transform
+ //if the string starts with a '.' ignore the string.
+ if (!StringUtils.startsWith(stringRecord.getRecord(), ".")) {
+ output.emit(stringRecord);
+ }
+ }
+ };
+ }
+
+ /**
+ * Persists the valid records to corresponding block files.
+ */
+ public static class RecordOutputOperator extends AbstractFileOutputOperator<AbstractBlockReader.ReaderRecord<String>>
+ {
+ @Override
+ protected String getFileName(AbstractBlockReader.ReaderRecord<String> tuple)
+ {
+ return Long.toHexString(tuple.getBlockId());
+ }
+
+ @Override
+ protected byte[] getBytesForTuple(AbstractBlockReader.ReaderRecord<String> tuple)
+ {
+ return tuple.getRecord().getBytes();
+ }
+ }
+}
+</code></pre>
+
+<p>Configuration to parallel partition block reader with its downstream operators.</p>
+<pre><code class="xml"> <property>
+ <name>dt.operator.Filter.port.input.attr.PARTITION_PARALLEL</name>
+ <value>true</value>
+ </property>
+ <property>
+ <name>dt.operator.Record-writer.port.input.attr.PARTITION_PARALLEL</name>
+ <value>true</value>
+ </property>
+</code></pre>
+
+<h2 id="abstractfsreadaheadlinereader">AbstractFSReadAheadLineReader</h2>
+<p>This extension of <a href="#AbstractFSBlockReader"><code>AbstractFSBlockReader</code></a> parses lines from a block and binds the <code>readerContext</code> field to an instance of <code>ReaderContext.ReadAheadLineReaderContext</code>.</p>
+<p>It is abstract because it doesn't provide an implementation of <a href="#convertToRecord"><code>convertToRecord(byte[] bytes)</code></a> since the user may want to convert the bytes that make a line into some other type. </p>
+<h3 id="readaheadlinereadercontext">ReadAheadLineReaderContext</h3>
+<p>In order to handle a line split across adjacent blocks, ReadAheadLineReaderContext always reads beyond the block boundary and ignores the bytes till the first end-of-line character of all the blocks except the first block of the file. This ensures that no line is missed or incomplete.</p>
+<p>This is one of the most common ways of handling a split record. It doesn't require any further information to decide if a line is complete. However, the cost of this consistent way to handle a line split is that it always reads from the next block.</p>
+<h2 id="abstractfslinereader">AbstractFSLineReader</h2>
+<p>Similar to <code>AbstractFSReadAheadLineReader</code>, even this parses lines from a block. However, it binds the <code>readerContext</code> field to an instance of <code>ReaderContext.LineReaderContext</code>.</p>
+<h3 id="linereadercontext">LineReaderContext</h3>
+<p>This handles the line split differently from <code>ReadAheadLineReaderContext</code>. It doesn't always read from the next block. If the end of the last line is aligned with the block boundary then it stops processing the block. It does read from the next block when the boundaries are not aligned, that is, last line extends beyond the block boundary. The result of this is an inconsistency in reading the next block.</p>
+<p>When the boundary of the last line of the previous block was aligned with its block, then the first line of the current block is a valid line. However, in the other case the bytes from the block start offset to the first end-of-line character should be ignored. Therefore, this means that any record formed by this reader context has to be validated. For example, if the lines are of fixed size then size of each record can be validated or if each line begins with a special field then that knowledge can be used to check if a record is complete.</p>
+<p>If the validations of completeness fails for a line then <a href="#convertToRecord"><code>convertToRecord(byte[] bytes)</code></a> should return null.</p>
+<h2 id="fsslicereader">FSSliceReader</h2>
+<p>A concrete extension of <a href="#AbstractFSBlockReader"><code>AbstractFSBlockReader</code></a> that reads fixed-size <code>byte[]</code> from a block and emits the byte array wrapped in <code>com.datatorrent.netlet.util.Slice</code>.</p>
+<p>This operator binds the <code>readerContext</code> to an instance of <code>ReaderContext.FixedBytesReaderContext</code>.</p>
+<h3 id="fixedbytesreadercontext">FixedBytesReaderContext</h3>
+<p>This implementation of <code>ReaderContext</code> never reads beyond a block boundary which can result in the last <code>byte[]</code> of a block to be of a shorter length than the rest of the records.</p>
+<h3 id="configuration_1">Configuration</h3>
+<p><strong>readerContext.length</strong>: length of each record. By default, this is initialized to the default hdfs block size.</p>
+<h2 id="partitioner-and-statslistener">Partitioner and StatsListener</h2>
+<p>The logical instance of the block reader acts as the Partitioner (unless a custom partitioner is set using the operator attribute - <code>PARTITIONER</code>) as well as a StatsListener. This is because the
+<code>AbstractBlockReader</code> implements both the <code>com.datatorrent.api.Partitioner</code> and <code>com.datatorrent.api.StatsListener</code> interfaces and provides an implementation of <code>definePartitions(...)</code> and <code>processStats(...)</code> which make it auto-scalable.</p>
+<h3 id="processstats">processStats <a name="processStats"></a></h3>
+<p>The application master invokes <code>Response processStats(BatchedOperatorStats stats)</code> method on the logical instance with the stats (<code>tuplesProcessedPSMA</code>, <code>tuplesEmittedPSMA</code>, <code>latencyMA</code>, etc.) of each partition. The data which this operator is interested in is the <code>queueSize</code> of the input port <code>blocksMetadataInput</code>.</p>
+<p>Usually the <code>queueSize</code> of an input port gives the count of waiting control tuples plus data tuples. However, if a stats listener is interested only in the count of data tuples then that can be expressed by annotating the class with <code>@DataQueueSize</code>. In this case <code>AbstractBlockReader</code> itself is the <code>StatsListener</code> which is why it is annotated with <code>@DataQueueSize</code>.</p>
+<p>The logical instance caches the queue size per partition and at regular intervals (configured by <code>intervalMillis</code>) sums these values to find the total backlog which is then used to decide whether re-partitioning is needed. The flow-diagram below describes this logic.</p>
+<p><img alt="Processing of total-backlog" src="../images/blockreader/totalBacklogProcessing.png" /></p>
+<p>The goal of this logic is to create as many partitions within bounds (see <a href="#maxReaders"><code>maxReaders</code></a> and <a href="#minReaders"><code>minReaders</code></a> above) to quickly reduce this backlog or if the backlog is small then remove any idle partitions.</p>
+<h3 id="definepartitions">definePartitions</h3>
+<p>Based on the <code>repartitionRequired</code> field of the <code>Response</code> object which is returned by <em><a href="#processStats">processStats</a></em> method, the application master invokes </p>
+<pre><code class="java">Collection<Partition<AbstractBlockReader<...>>> definePartitions(Collection<Partition<AbstractBlockReader<...>>> partitions, PartitioningContext context)
+</code></pre>
+
+<p>on the logical instance which is also the partitioner instance. The implementation calculates the difference between required partitions and the existing count of partitions. If this difference is negative, then equivalent number of partitions are removed otherwise new partitions are created. </p>
+<p>Please note auto-scaling can be disabled by setting <a href="#collectStats"><code>collectStats</code></a> to <code>false</code>. If the use-case requires only static partitioning, then that can be achieved by setting <a href="https://github.com/chandnisingh/incubator-apex-core/blob/master/common/src/main/java/com/datatorrent/common/partitioner/StatelessPartitioner.java"><code>StatelessPartitioner</code></a> as the operator attribute- <code>PARTITIONER</code> on the block reader.</p>
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+ <li><span>Operators</span></li>
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+ <a class="" href="../kafkaInputOperator/">Kafka Input</a>
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+ <a class="" href="../file_output/">File Output</a>
+
+ </li>
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+ <li class="toctree-l1 current">
+ <a class="current" href="./">Enricher</a>
+
+ <ul>
+
+ <li class="toctree-l3"><a href="#pojo-enricher">POJO Enricher</a></li>
+
+ <li><a class="toctree-l4" href="#operator-objective">Operator Objective</a></li>
+
+ <li><a class="toctree-l4" href="#operator-usecase">Operator Usecase</a></li>
+
+ <li><a class="toctree-l4" href="#operator-information">Operator Information</a></li>
+
+ <li><a class="toctree-l4" href="#properties-attributes-and-ports">Properties, Attributes and Ports</a></li>
+
+ <li><a class="toctree-l4" href="#platform-attributes-that-influences-operator-behavior">Platform Attributes that influences operator behavior</a></li>
+
+ <li><a class="toctree-l4" href="#ports">Ports</a></li>
+
+
+ <li class="toctree-l3"><a href="#limitations">Limitations</a></li>
+
+
+ <li class="toctree-l3"><a href="#example">Example</a></li>
+
+
+ <li class="toctree-l3"><a href="#advanced">Advanced</a></li>
+
+ <li><a class="toctree-l4" href="#caching-mechanism-in-pojoenricher">Caching mechanism in POJOEnricher</a></li>
+
+ <li><a class="toctree-l4" href="#partitioning-of-pojoenricher">Partitioning of POJOEnricher</a></li>
+
+
+ </ul>
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+ <div class="section">
+
+ <h1 id="pojo-enricher">POJO Enricher</h1>
+<h2 id="operator-objective">Operator Objective</h2>
+<p>This operator receives an POJO (<a href="https://en.wikipedia.org/wiki/Plain_Old_Java_Object">Plain Old Java Object</a>) as an incoming tuple and uses an external source to enrich the data in
+the incoming tuple and finally emits the enriched data as a new enriched POJO.</p>
+<p>POJOEnricher supports enrichment from following external sources:</p>
+<ol>
+<li><strong>JSON File Based</strong> - Reads the file in memory having content stored in JSON format and use that to enrich the data. This can be done using FSLoader implementation.</li>
+<li><strong>JDBC Based</strong> - Any JDBC store can act as an external entity to which enricher can request data for enriching incoming tuples. This can be done using JDBCLoader implementation.</li>
+</ol>
+<p>POJO Enricher does not hold any state and is <strong>idempotent</strong>, <strong>fault-tolerant</strong> and <strong>statically/dynamically partitionable</strong>.</p>
+<h2 id="operator-usecase">Operator Usecase</h2>
+<ol>
+<li>Bank <strong><em>transaction records</em></strong> usually contains customerId. For further analysis of transaction one wants the customer name and other customer related information.
+Such information is present in another database. One could enrich the transaction's record with customer information using POJOEnricher.</li>
+<li><strong><em>Call Data Record (CDR)</em></strong> contains only mobile/telephone numbers of the customer. Customer information is missing in CDR. POJO Enricher can be used to enrich
+CDR with customer data for further analysis.</li>
+</ol>
+<h2 id="operator-information">Operator Information</h2>
+<ol>
+<li>Operator location: <strong><em>malhar-contrib</em></strong></li>
+<li>Available since: <strong><em>3.4.0</em></strong></li>
+<li>Operator state: <strong><em>Evolving</em></strong></li>
+<li>Java Packages:<ul>
+<li>Operator: <strong><em><a href="https://www.datatorrent.com/docs/apidocs/com/datatorrent/contrib/enrich/POJOEnricher.html">com.datatorrent.contrib.enrich.POJOEnricher</a></em></strong></li>
+<li>FSLoader: <strong><em><a href="https://www.datatorrent.com/docs/apidocs/com/datatorrent/contrib/enrich/FSLoader.html">com.datatorrent.contrib.enrich.FSLoader</a></em></strong></li>
+<li>JDBCLoader: <strong><em><a href="https://www.datatorrent.com/docs/apidocs/com/datatorrent/contrib/enrich/JDBCLoader.html">com.datatorrent.contrib.enrich.JDBCLoader</a></em></strong></li>
+</ul>
+</li>
+</ol>
+<h2 id="properties-attributes-and-ports">Properties, Attributes and Ports</h2>
+<h3 id="properties-of-pojoenricher"><a name="props"></a>Properties of POJOEnricher</h3>
+<table>
+<thead>
+<tr>
+<th><strong>Property</strong></th>
+<th><strong>Description</strong></th>
+<th><strong>Type</strong></th>
+<th><strong>Mandatory</strong></th>
+<th><strong>Default Value</strong></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><em>includeFields</em></td>
+<td>List of fields from database that needs to be added to output POJO.</td>
+<td>List<String></td>
+<td>Yes</td>
+<td>N/A</td>
+</tr>
+<tr>
+<td><em>lookupFields</em></td>
+<td>List of fields from input POJO which will form a <em>unique composite</em> key for querying to store</td>
+<td>List<String></td>
+<td>Yes</td>
+<td>N/A</td>
+</tr>
+<tr>
+<td><em>store</em></td>
+<td>Backend Store from which data should be queried for enrichment</td>
+<td><a href="#backendStore">BackendStore</a></td>
+<td>Yes</td>
+<td>N/A</td>
+</tr>
+<tr>
+<td><em>cacheExpirationInterval</em></td>
+<td>Cache entry expiry in ms. After this time, the lookup to store will be done again for given key</td>
+<td>int</td>
+<td>No</td>
+<td>1 * 60 * 60 * 1000 (1 hour)</td>
+</tr>
+<tr>
+<td><em>cacheCleanupInterval</em></td>
+<td>Interval in ms after which cache will be removed for any stale entries.</td>
+<td>int</td>
+<td>No</td>
+<td>1 * 60 * 60 * 1000 (1 hour)</td>
+</tr>
+<tr>
+<td><em>cacheSize</em></td>
+<td>Number of entry in cache after which eviction will start on each addition based on LRU</td>
+<td>int</td>
+<td>No</td>
+<td>1000</td>
+</tr>
+</tbody>
+</table>
+<h4 id="properties-of-fsloader-backendstore"><a name="backendStore"></a>Properties of FSLoader (BackendStore)</h4>
+<table>
+<thead>
+<tr>
+<th><strong>Property</strong></th>
+<th><strong>Description</strong></th>
+<th><strong>Type</strong></th>
+<th><strong>Mandatory</strong></th>
+<th><strong>Default Value</strong></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><em>fileName</em></td>
+<td>Path of the file, the data from which will be used for enrichment. See <a href="#JSONFileFormat">here</a> for JSON File format.</td>
+<td>String</td>
+<td>Yes</td>
+<td>N/A</td>
+</tr>
+</tbody>
+</table>
+<h4 id="properties-of-jdbcloader-backendstore">Properties of JDBCLoader (BackendStore)</h4>
+<table>
+<thead>
+<tr>
+<th><strong>Property</strong></th>
+<th><strong>Description</strong></th>
+<th><strong>Type</strong></th>
+<th><strong>Mandatory</strong></th>
+<th><strong>Default Value</strong></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><em>databaseUrl</em></td>
+<td>Connection string for connecting to JDBC</td>
+<td>String</td>
+<td>Yes</td>
+<td>N/A</td>
+</tr>
+<tr>
+<td><em>databaseDriver</em></td>
+<td>JDBC Driver class for connection to JDBC Store. This driver should be there in classpath</td>
+<td>String</td>
+<td>Yes</td>
+<td>N/A</td>
+</tr>
+<tr>
+<td><em>tableName</em></td>
+<td>Name of the table from which data needs to be retrieved</td>
+<td>String</td>
+<td>Yes</td>
+<td>N/A</td>
+</tr>
+<tr>
+<td><em>connectionProperties</em></td>
+<td>Command seperated list of advanced connection properties that need to be passed to JDBC Driver. For eg. <em>prop1:val1,prop2:val2</em></td>
+<td>String</td>
+<td>No</td>
+<td>null</td>
+</tr>
+<tr>
+<td><em>queryStmt</em></td>
+<td>Select statement which will be used to query the data. This is optional parameter in case of advanced query.</td>
+<td>String</td>
+<td>No</td>
+<td>null</td>
+</tr>
+</tbody>
+</table>
+<h3 id="platform-attributes-that-influences-operator-behavior">Platform Attributes that influences operator behavior</h3>
+<table>
+<thead>
+<tr>
+<th><strong>Attribute</strong></th>
+<th><strong>Description</strong></th>
+<th><strong>Type</strong></th>
+<th><strong>Mandatory</strong></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><em>input.TUPLE_CLASS</em></td>
+<td>TUPLE_CLASS attribute on input port which tells operator the class of POJO which will be incoming</td>
+<td>Class or FQCN</td>
+<td>Yes</td>
+</tr>
+<tr>
+<td><em>output.TUPLE_CLASS</em></td>
+<td>TUPLE_CLASS attribute on output port which tells operator the class of POJO which need to be emitted</td>
+<td>Class or FQCN</td>
+<td>Yes</td>
+</tr>
+</tbody>
+</table>
+<h3 id="ports">Ports</h3>
+<table>
+<thead>
+<tr>
+<th><strong>Port</strong></th>
+<th><strong>Description</strong></th>
+<th><strong>Type</strong></th>
+<th><strong>Mandatory</strong></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><em>input</em></td>
+<td>Tuple which needs to be enriched are received on this port</td>
+<td>Object (POJO)</td>
+<td>Yes</td>
+</tr>
+<tr>
+<td><em>output</em></td>
+<td>Tuples that are enriched from external source are emitted from on this port</td>
+<td>Object (POJO)</td>
+<td>No</td>
+</tr>
+</tbody>
+</table>
+<h2 id="limitations">Limitations</h2>
+<p>Current POJOEnricher contains following limitation:</p>
+<ol>
+<li>FSLoader loads the file content in memory. Though it loads only the composite key and composite value in memory, a very large amount of data would bloat the memory and make the operator go OOM. In case the filesize is large, allocate sufficient memory to the POJOEnricher.</li>
+<li>Incoming POJO should be a subset of outgoing POJO.</li>
+<li><a href="#props">includeFields</a> property should contains fields having same name in database column as well as outgoing POJO. For eg. If name of the database column is "customerName", then outgoing POJO should contains a field with the same name and same should be added to includeFields.</li>
+<li><a href="#props">lookupFields</a> property should contains fields having same name in database column as well as incoming POJO. For eg. If name of the database column is "customerId", then incoming POJO should contains a field with the same name and same should be added to lookupFields.</li>
+</ol>
+<h2 id="example">Example</h2>
+<p>Example for POJOEnricher can be found at: <a href="https://github.com/DataTorrent/examples/tree/master/tutorials/enricher">https://github.com/DataTorrent/examples/tree/master/tutorials/enricher</a></p>
+<h2 id="advanced">Advanced</h2>
+<h3 id="file-format-for-json-based-fsloader"><a name="JSONFileFormat"></a> File format for JSON based FSLoader</h3>
+<p>FSLoader expects file to be in specific format:</p>
+<ol>
+<li>Each line makes on record which becomes part of the store</li>
+<li>Each line is a valid JSON Object where <em>key</em> is name of the field name and <em>value</em> is the field value.</li>
+</ol>
+<p>Example for the format look like following:</p>
+<pre><code class="json">{"circleId":0, "circleName":"A"}
+{"circleId":1, "circleName":"B"}
+{"circleId":2, "circleName":"C"}
+{"circleId":3, "circleName":"D"}
+{"circleId":4, "circleName":"E"}
+{"circleId":5, "circleName":"F"}
+{"circleId":6, "circleName":"G"}
+{"circleId":7, "circleName":"H"}
+{"circleId":8, "circleName":"I"}
+{"circleId":9, "circleName":"J"}
+</code></pre>
+
+<h3 id="caching-mechanism-in-pojoenricher">Caching mechanism in POJOEnricher</h3>
+<p>POJOEnricher contains an cache which makes the lookup for keys more efficient. This is specially useful when data in external store is not changing much.
+However, one should carefully tune the <a href="#props">cacheExpirationInterval</a> property for desirable results.</p>
+<p>On every incoming tuple, POJOEnricher first queries the cache. If the cache contains desired record and is within expiration interval, then it uses that to
+enrich the tuple, otherwise does a lookup to configured store and the return value is used to enrich the tuple. The return value is then cached for composite key and composite value.</p>
+<p>POJOEnricher only caches the required fields for enrichment mechanism and not all fields returned by external store. This ensures optimal use of memory.</p>
+<h3 id="partitioning-of-pojoenricher">Partitioning of POJOEnricher</h3>
+<p>Being stateless operator, POJOEnricher will ensure built-in partitioners present in Malhar library can be directly simply by setting few properties as follows:</p>
+<h4 id="stateless-partioning-of-pojoenricher">Stateless partioning of POJOEnricher</h4>
+<p>Stateless partitioning will ensure that POJOEnricher will will be partitioned right at the starting of the application and will remain partitioned throughout the lifetime of the DAG.
+POJOEnricher can be stateless partitioned by adding following lines to properties.xml:</p>
+<pre><code class="xml"> <property>
+ <name>dt.operator.{OperatorName}.attr.PARTITIONER</name>
+ <value>com.datatorrent.common.partitioner.StatelessPartitioner:2</value>
+ </property>
+</code></pre>
+
+<p>where {OperatorName} is the name of the POJOEnricher operator.
+Above lines will partition POJOEnricher statically 2 times. Above value can be changed accordingly to change the number of static partitions.</p>
+<h4 id="dynamic-partitioning-of-pojoenricher">Dynamic Partitioning of POJOEnricher</h4>
+<p>Dynamic partitioning is a feature of Apex platform which changes the partition of the operator based on certain condition.
+POJOEnricher can be dynamically partitioned using 2 out-of-the-box partitioners:</p>
+<h5 id="throughput-based">Throughput based</h5>
+<p>Following code can be added to populateDAG method of application to dynamically partitioning POJOEnricher:</p>
+<pre><code class="java"> StatelessThroughputBasedPartitioner<POJOEnricher> partitioner = new StatelessThroughputBasedPartitioner<>();
+ partitioner.setCooldownMillis(conf.getLong(COOL_DOWN_MILLIS, 10000));
+ partitioner.setMaximumEvents(conf.getLong(MAX_THROUGHPUT, 30000));
+ partitioner.setMinimumEvents(conf.getLong(MIN_THROUGHPUT, 10000));
+ dag.setAttribute(pojoEnricherObj, OperatorContext.STATS_LISTENERS, Arrays.asList(new StatsListener[]{partitioner}));
+ dag.setAttribute(pojoEnricherObj, OperatorContext.PARTITIONER, partitioner);
+</code></pre>
+
+<p>Above code will dynamically partition POJOEnricher when the throughput changes.
+If the overall throughput of POJOEnricher goes beyond 30000 or less than 10000, the platform will repartition POJOEnricher
+to balance throughput of a single partition to be between 10000 and 30000.
+CooldownMillis of 10000 will be used as the threshold time for which the throughout change is observed.</p>
+<h5 id="latency-based">Latency based</h5>
+<p>Following code can be added to populateDAG method of application to dynamically partitioning POJOEnricher:</p>
+<pre><code class="java"> StatelessLatencyBasedPartitioner<POJOEnricher> partitioner = new StatelessLatencyBasedPartitioner<>();
+ partitioner.setCooldownMillis(conf.getLong(COOL_DOWN_MILLIS, 10000));
+ partitioner.setMaximumLatency(conf.getLong(MAX_THROUGHPUT, 10));
+ partitioner.setMinimumLatency(conf.getLong(MIN_THROUGHPUT, 3));
+ dag.setAttribute(pojoEnricherObj, OperatorContext.STATS_LISTENERS, Arrays.asList(new StatsListener[]{partitioner}));
+ dag.setAttribute(pojoEnricherObj, OperatorContext.PARTITIONER, partitioner);
+</code></pre>
+
+<p>Above code will dynamically partition POJOEnricher when the overall latency of POJOEnricher changes.
+If the overall latency of POJOEnricher goes beyond 10 ms or less than 3 ms, the platform will repartition POJOEnricher
+to balance latency of a single partition to be between 3 ms and 10 ms.
+CooldownMillis of 10000 will be used as the threshold time for which the latency change is observed.</p>
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+ <li><a class="toctree-l4" href="#persisting-data-to-files">Persisting data to files</a></li>
+
+ <li><a class="toctree-l4" href="#automatic-rotation">Automatic rotation</a></li>
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+ <h1 id="abstractfileoutputoperator">AbstractFileOutputOperator</h1>
+<p>The abstract file output operator in Apache Apex Malhar library — <a href="https://github.com/apache/incubator-apex-malhar/blob/master/library/src/main/java/com/datatorrent/lib/io/fs/AbstractFileOutputOperator.java"><code>AbstractFileOutputOperator</code></a> writes streaming data to files. The main features of this operator are:</p>
+<ol>
+<li>Persisting data to files.</li>
+<li>Automatic rotation of files based on:<br />
+ a. maximum length of a file.<br />
+ b. time-based rotation where time is specified using a count of application windows.</li>
+<li>Fault-tolerance.</li>
+<li>Compression and encryption of data before it is persisted.</li>
+</ol>
+<p>In this tutorial we will cover the details of the basic structure and implementation of all the above features in <code>AbstractFileOutputOperator</code>. Configuration items related to each feature are discussed as they are introduced in the section of that feature.</p>
+<h2 id="persisting-data-to-files">Persisting data to files</h2>
+<p>The principal function of this operator is to persist tuples to files efficiently. These files are created under a specific directory on the file system. The relevant configuration item is:</p>
+<p><strong>filePath</strong>: path specifying the directory where files are written.</p>
+<p>Different types of file system that are implementations of <code>org.apache.hadoop.fs.FileSystem</code> are supported. The file system instance which is used for creating streams is constructed from the <code>filePath</code> URI.</p>
+<pre><code class="java">FileSystem.newInstance(new Path(filePath).toUri(), new Configuration())
+</code></pre>
+
+<p>Tuples may belong to different files therefore expensive IO operations like creating multiple output streams, flushing of data to disk, and closing streams are handled carefully.</p>
+<h3 id="ports">Ports</h3>
+<ul>
+<li><code>input</code>: the input port on which tuples to be persisted are received.</li>
+</ul>
+<h3 id="streamscache"><code>streamsCache</code></h3>
+<p>This transient state caches output streams per file in memory. The file to which the data is appended may change with incoming tuples. It will be highly inefficient to keep re-opening streams for a file just because tuples for that file are interleaved with tuples for another file. Therefore, the operator maintains a cache of limited size with open output streams.</p>
+<p><code>streamsCache</code> is of type <code>com.google.common.cache.LoadingCache</code>. A <code>LoadingCache</code> has an attached <code>CacheLoader</code> which is responsible to load value of a key when the key is not present in the cache. Details are explained here- <a href="https://github.com/google/guava/wiki/CachesExplained">CachesExplained</a>.</p>
+<p>The operator constructs this cache in <code>setup(...)</code>. It is built with the following configuration items:</p>
+<ul>
+<li><strong>maxOpenFiles</strong>: maximum size of the cache. The cache evicts entries that haven't been used recently when the cache size is approaching this limit. <em>Default</em>: 100</li>
+<li><strong>expireStreamAfterAcessMillis</strong>: expires streams after the specified duration has passed since the stream was last accessed. <em>Default</em>: value of attribute- <code>OperatorContext.SPIN_MILLIS</code>.</li>
+</ul>
+<p>An important point to note here is that the guava cache does not perform cleanup and evict values asynchronously, that is, instantly after a value expires. Instead, it performs small amounts of maintenance during write operations, or during occasional read operations if writes are rare.</p>
+<h4 id="cacheloader">CacheLoader</h4>
+<p><code>streamsCache</code> is created with a <code>CacheLoader</code> that opens an <code>FSDataOutputStream</code> for a file which is not in the cache. The output stream is opened in either <code>append</code> or <code>create</code> mode and the basic logic to determine this is explained by the simple diagram below.</p>
+<p><img alt="Opening an output stream" src="../images/fileoutput/diagram1.png" /></p>
+<p>This process gets complicated when fault-tolerance (writing to temporary files) and rotation is added.</p>
+<p>Following are few configuration items used for opening the streams:</p>
+<ul>
+<li><strong>replication</strong>: specifies the replication factor of the output files. <em>Default</em>: <code>fs.getDefaultReplication(new Path(filePath))</code></li>
+<li><strong>filePermission</strong>: specifies the permission of the output files. The permission is an octal number similar to that used by the Unix chmod command. <em>Default</em>: 0777</li>
+</ul>
+<h4 id="removallistener">RemovalListener</h4>
+<p>A <code>Guava</code> cache also allows specification of removal listener which can perform some operation when an entry is removed from the cache. Since <code>streamsCache</code> is of limited size and also has time-based expiry enabled, it is imperative that when a stream is evicted from the cache it is closed properly. Therefore, we attach a removal listener to <code>streamsCache</code> which closes the stream when it is evicted.</p>
+<h3 id="setupoperatorcontext-context"><code>setup(OperatorContext context)</code></h3>
+<p>During setup the following main tasks are performed:</p>
+<ol>
+<li>FileSystem instance is created.</li>
+<li>The cache of streams is created.</li>
+<li>Files are recovered (see Fault-tolerance section).</li>
+<li>Stray part files are cleaned (see Automatic rotation section).</li>
+</ol>
+<h3 id="processtupleinput-tuple"><a name="processTuple"></a><code>processTuple(INPUT tuple)</code></h3>
+<p>The code snippet below highlights the basic steps of processing a tuple.</p>
+<pre><code class="java">protected void processTuple(INPUT tuple)
+{
+ //which file to write to is derived from the tuple.
+ String fileName = getFileName(tuple);
+
+ //streamsCache is queried for the output stream. If the stream is already opened then it is returned immediately otherwise the cache loader creates one.
+ FilterOutputStream fsOutput = streamsCache.get(fileName).getFilterStream();
+
+ byte[] tupleBytes = getBytesForTuple(tuple);
+
+ fsOutput.write(tupleBytes);
+}
+</code></pre>
+
+<h3 id="endwindow"><a name="endWindow"></a>endWindow()</h3>
+<p>It should be noted that while processing a tuple we do not flush the stream after every write. Since flushing is expensive it is done periodically for all the open streams in the operator's <code>endWindow()</code>.</p>
+<pre><code class="java">Map<String, FSFilterStreamContext> openStreams = streamsCache.asMap();
+for (FSFilterStreamContext streamContext: openStreams.values()) {
+ ...
+ //this flushes the stream
+ streamContext.finalizeContext();
+ ...
+}
+</code></pre>
+
+<p><code>FSFilterStreamContext</code> will be explained with compression and encryption.</p>
+<h3 id="teardown"><a name="teardown"></a>teardown()</h3>
+<p>When any operator in a DAG fails then the application master invokes <code>teardown()</code> for that operator and its downstream operators. In <code>AbstractFileOutputOperator</code> we have a bunch of open streams in the cache and the operator (acting as HDFS client) holds leases for all the corresponding files. It is important to release these leases for clean re-deployment. Therefore, we try to close all the open streams in <code>teardown()</code>.</p>
+<h2 id="automatic-rotation">Automatic rotation</h2>
+<p>In a streaming application where data is being continuously processed, when this output operator is used, data will be continuously written to an output file. The users may want to be able to take the data from time to time to use it, copy it out of Hadoop or do some other processing. Having all the data in a single file makes it difficult as the user needs to keep track of how much data has been read from the file each time so that the same data is not read again. Also users may already have processes and scripts in place that work with full files and not partial data from a file.</p>
+<p>To help solve these problems the operator supports creating many smaller files instead of writing to just one big file. Data is written to a file and when some condition is met the file is finalized and data is written to a new file. This is called file rotation. The user can determine when the file gets rotated. Each of these files is called a part file as they contain portion of the data.</p>
+<h3 id="part-filename">Part filename</h3>
+<p>The filename for a part file is formed by using the original file name and the part number. The part number starts from 0 and is incremented each time a new part file created. The default filename has the format, assuming origfile represents the original filename and partnum represents the part number,</p>
+<p><code>origfile.partnum</code></p>
+<p>This naming scheme can be changed by the user. It can be done so by overriding the following method</p>
+<pre><code class="java">protected String getPartFileName(String fileName, int part)
+</code></pre>
+
+<p>This method is passed the original filename and part number as arguments and should return the part filename.</p>
+<h3 id="mechanisms">Mechanisms</h3>
+<p>The user has a couple of ways to specify when a file gets rotated. First is based on size and second on time. In the first case the files are limited by size and in the second they are rotated by time.</p>
+<h4 id="size-based">Size Based</h4>
+<p>With size based rotation the user specifies a size limit. Once the size of the currently file reaches this limit the file is rotated. The size limit can be specified by setting the following property</p>
+<p><code>maxLength</code></p>
+<p>Like any other property this can be set in Java application code or in the property file.</p>
+<h4 id="time-based">Time Based</h4>
+<p>In time based rotation user specifies a time interval. This interval is specified as number of application windows. The files are rotated periodically once the specified number of application windows have elapsed. Since the interval is application window based it is not always exactly constant time. The interval can be specified using the following property</p>
+<p><code>rotationWindows</code></p>
+<h3 id="setupoperatorcontext-context_1"><code>setup(OperatorContext context)</code></h3>
+<p>When an operator is being started there may be stray part files and they need to be cleaned up. One common scenario, when these could be present, is in the case of failure, where a node running the operator failed and a previous instance of the operator was killed. This cleanup and other initial processing for the part files happens in the operator setup. The following diagram describes this process</p>
+<p><img alt="Rotation setup" src="../images/fileoutput/FileRotation.png" /></p>
+<h2 id="fault-tolerance">Fault-tolerance</h2>
+<p>There are two issues that should be addressed in order to make the operator fault-tolerant:</p>
+<ol>
+<li>
+<p>The operator flushes data to the filesystem every application window. This implies that after a failure when the operator is re-deployed and tuples of a window are replayed, then duplicate data will be saved to the files. This is handled by recording how much the operator has written to each file every window in a state that is checkpointed and truncating files back to the recovery checkpoint after re-deployment.</p>
+</li>
+<li>
+<p>While writing to HDFS, if the operator gets killed and didn't have the opportunity to close a file, then later when it is redeployed it will attempt to truncate/restore that file. Restoring a file may fail because the lease that the previous process (operator instance before failure) had acquired from namenode to write to a file may still linger and therefore there can be exceptions in acquiring the lease again by the new process (operator instance after failure). This is handled by always writing data to temporary files and renaming these files to actual files when a file is finalized (closed) for writing, that is, we are sure that no more data will be written to it. The relevant configuration item is: </p>
+</li>
+<li><strong>alwaysWriteToTmp</strong>: enables/disables writing to a temporary file. <em>Default</em>: true.</li>
+</ol>
+<p>Most of the complexity in the code comes from making this operator fault-tolerant.</p>
+<h3 id="checkpointed-states-needed-for-fault-tolerance">Checkpointed states needed for fault-tolerance</h3>
+<ul>
+<li>
+<p><code>endOffsets</code>: contains the size of each file as it is being updated by the operator. It helps the operator to restore a file during recovery in operator <code>setup(...)</code> and is also used while loading a stream to find out if the operator has seen a file before.</p>
+</li>
+<li>
+<p><code>fileNameToTmpName</code>: contains the name of the temporary file per actual file. It is needed because the name of a temporary file is random. They are named based on the timestamp when the stream is created. During recovery the operator needs to know the temp file which it was writing to and if it needs restoration then it creates a new temp file and updates this mapping.</p>
+</li>
+<li>
+<p><code>finalizedFiles</code>: contains set of files which were requested to be finalized per window id.</p>
+</li>
+<li>
+<p><code>finalizedPart</code>: contains the latest <code>part</code> of each file which was requested to be finalized.</p>
+</li>
+</ul>
+<p>The use of <code>finalizedFiles</code> and <code>finalizedPart</code> are explained in detail under <a href="#requestFinalize"><code>requestFinalize(...)</code></a> method.</p>
+<h3 id="recovering-files">Recovering files</h3>
+<p>When the operator is re-deployed, it checks in its <code>setup(...)</code> method if the state of a file which it has seen before the failure is consistent with the file's state on the file system, that is, the size of the file on the file system should match the size in the <code>endOffsets</code>. When it doesn't the operator truncates the file.</p>
+<p>For example, let's say the operator wrote 100 bytes to test1.txt by the end of window 10. It wrote another 20 bytes by the end of window 12 but failed in window 13. When the operator gets re-deployed it is restored with window 10 (recovery checkpoint) state. In the previous run, by the end of window 10, the size of file on the filesystem was 100 bytes but now it is 120 bytes. Tuples for windows 11 and 12 are going to be replayed. Therefore, in order to avoid writing duplicates to test1.txt, the operator truncates the file to 100 bytes (size at the end of window 10) discarding the last 20 bytes.</p>
+<h3 id="requestfinalizestring-filename"><a name="requestFinalize"></a><code>requestFinalize(String fileName)</code></h3>
+<p>When the operator is always writing to temporary files (in order to avoid HDFS Lease exceptions), then it is necessary to rename the temporary files to the actual files once it has been determined that the files are closed. This is refered to as <em>finalization</em> of files and the method allows the user code to specify when a file is ready for finalization.</p>
+<p>In this method, the requested file (or in the case of rotation — all the file parts including the latest open part which have not yet been requested for finalization) are registered for finalization. Registration is basically adding the file names to <code>finalizedFiles</code> state and updating <code>finalizedPart</code>.</p>
+<p>The process of <em>finalization</em> of all the files which were requested till the window <em>w</em> is deferred till window <em>w</em> is committed. This is because until a window is committed it can be replayed after a failure which means that a file can be open for writing even after it was requested for finalization.</p>
+<p>When rotation is enabled, part files as and when they get completed are requested for finalization. However, when rotation is not enabled user code needs to invoke this method as the knowledge that when a file is closed is unknown to this abstract operator.</p>
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+ <li><span>Operators</span></li>
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+ <a class="current" href="./">File Splitter</a>
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+ <li class="toctree-l3"><a href="#file-splitter">File Splitter</a></li>
+
+ <li><a class="toctree-l4" href="#why-is-it-needed">Why is it needed?</a></li>
+
+ <li><a class="toctree-l4" href="#class-diagram">Class Diagram</a></li>
+
+ <li><a class="toctree-l4" href="#abstractfilesplitter">AbstractFileSplitter</a></li>
+
+ <li><a class="toctree-l4" href="#filesplitterbase">FileSplitterBase</a></li>
+
+ <li><a class="toctree-l4" href="#filesplitterinput">FileSplitterInput</a></li>
+
+ <li><a class="toctree-l4" href="#handling-of-split-records">Handling of split records</a></li>
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+ <a class="" href="../block_reader/">Block Reader</a>
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+
+ <h1 id="file-splitter">File Splitter</h1>
+<p>This is a simple operator whose main function is to split a file virtually and create metadata describing the files and the splits. </p>
+<h2 id="why-is-it-needed">Why is it needed?</h2>
+<p>It is a common operation to read a file and parse it. This operation can be parallelized by having multiple partitions of such operators and each partition operating on different files. However, at times when a file is large then a single partition reading it can become a bottleneck.
+In these cases, throughput can be increased if instances of the partitioned operator can read and parse non-overlapping sets of file blocks. This is where file splitter comes in handy. It creates metadata of blocks of file which serves as tasks handed out to downstream operator partitions.
+The downstream partitions can read/parse the block without the need of interacting with other partitions.</p>
+<h2 id="class-diagram">Class Diagram</h2>
+<p><img alt="FileSplitter class dierarchy" src="../images/filesplitter/classdiagram.png" /></p>
+<h2 id="abstractfilesplitter">AbstractFileSplitter</h2>
+<p>The abstract implementation defines the logic of processing <code>FileInfo</code>. This comprises the following tasks - </p>
+<ul>
+<li>
+<p>building <code>FileMetadata</code> per file and emitting it. This metadata contains the file information such as filepath, no. of blocks in it, length of the file, all the block ids, etc.</p>
+</li>
+<li>
+<p>creating <code>BlockMetadataIterator</code> from <code>FileMetadata</code>. The iterator lazy-loads the block metadata when needed. We use an iterator because the no. of blocks in a file can be huge if the block size is small and loading all of them at once in memory may cause out of memory errors.</p>
+</li>
+<li>
+<p>retrieving <code>BlockMetadata.FileBlockMetadata</code> from the block metadata iterator and emitting it. The FileBlockMetadata contains the block id, start offset of the block, length of file in the block, etc. The number of block metadata emitted per window are controlled by <code>blocksThreshold</code> setting which by default is 1. </p>
+</li>
+</ul>
+<p>The main utility method that performs all the above tasks is the <a href="#process_method"><code>process()</code></a> method. Concrete implementations can invoke this method whenever they have data to process.</p>
+<h3 id="ports">Ports</h3>
+<p>Declares only output ports on which file metadata and block metadata are emitted.</p>
+<ul>
+<li>filesMetadataOutput: metadata for each file is emitted on this port. </li>
+<li>blocksMetadataOutput: metadata for each block is emitted on this port. </li>
+</ul>
+<h3 id="process-method"><a name="process_method"></a><code>process()</code> method</h3>
+<p>When process() is invoked, any pending blocks from the current file are emitted on the 'blocksMetadataOutput' port. If the threshold for blocks per window is still not met then a new input file is processed - corresponding metadata is emitted on 'filesMetadataOutput' and more of its blocks are emitted. This operation is repeated until the <code>blocksThreshold</code> is reached or there are no more new files.</p>
+<pre><code class="java"> protected void process()
+ {
+ if (blockMetadataIterator != null && blockCount < blocksThreshold) {
+ emitBlockMetadata();
+ }
+
+ FileInfo fileInfo;
+ while (blockCount < blocksThreshold && (fileInfo = getFileInfo()) != null) {
+ if (!processFileInfo(fileInfo)) {
+ break;
+ }
+ }
+ }
+</code></pre>
+
+<h3 id="abstract-methods">Abstract methods</h3>
+<ul>
+<li>
+<p><code>FileInfo getFileInfo()</code>: called from within the <code>process()</code> and provides the next file to process.</p>
+</li>
+<li>
+<p><code>long getDefaultBlockSize()</code>: provides the block size which is used when user hasn't configured the size.</p>
+</li>
+<li>
+<p><code>FileStatus getFileStatus(Path path)</code>: provides the <code>org.apache.hadoop.fs.FileStatus</code> instance for a path. </p>
+</li>
+</ul>
+<h3 id="configuration">Configuration</h3>
+<ol>
+<li><strong>blockSize</strong>: size of a block.</li>
+<li><strong>blocksThreshold</strong><a name="blocksThreshold"></a>: threshold on the number of blocks emitted by file splitter every window. This setting is used for throttling the work for downstream operators.</li>
+</ol>
+<h2 id="filesplitterbase">FileSplitterBase</h2>
+<p>Simple operator that receives tuples of type <code>FileInfo</code> on its <code>input</code> port. <code>FileInfo</code> contains the information (currently just the file path) about the file which this operator uses to create file metadata and block metadata.</p>
+<h3 id="example-application">Example application</h3>
+<p>This is a simple sub-dag that demonstrates how FileSplitterBase can be plugged into an application.
+<img alt="Application with FileSplitterBase" src="../images/filesplitter/baseexample.png" /></p>
+<p>The upstream operator emits tuples of type <code>FileInfo</code> on its output port which is connected to splitter input port. The downstream receives tuples of type <code>BlockMetadata.FileBlockMetadata</code> from the splitter's block metadata output port.</p>
+<pre><code class="java">public class ApplicationWithBaseSplitter implements StreamingApplication
+{
+ @Override
+ public void populateDAG(DAG dag, Configuration configuration)
+ {
+ JMSInput input = dag.addOperator("Input", new JMSInput());
+ FileSplitterBase splitter = dag.addOperator("Splitter", new FileSplitterBase());
+ FSSliceReader blockReader = dag.addOperator("BlockReader", new FSSliceReader());
+ ...
+ dag.addStream("file-info", input.output, splitter.input);
+ dag.addStream("block-metadata", splitter.blocksMetadataOutput, blockReader.blocksMetadataInput);
+ ...
+ }
+
+ public static class JMSInput extends AbstractJMSInputOperator<AbstractFileSplitter.FileInfo>
+ {
+
+ public final transient DefaultOutputPort<AbstractFileSplitter.FileInfo> output = new DefaultOutputPort<>();
+
+ @Override
+ protected AbstractFileSplitter.FileInfo convert(Message message) throws JMSException
+ {
+ //assuming the message is a text message containing the absolute path of the file.
+ return new AbstractFileSplitter.FileInfo(null, ((TextMessage)message).getText());
+ }
+
+ @Override
+ protected void emit(AbstractFileSplitter.FileInfo payload)
+ {
+ output.emit(payload);
+ }
+ }
+}
+</code></pre>
+
+<h3 id="ports_1">Ports</h3>
+<p>Declares an input port on which it receives tuples from the upstream operator. Output ports are inherited from AbstractFileSplitter.</p>
+<ul>
+<li>input: non optional port on which tuples of type <code>FileInfo</code> are received.</li>
+</ul>
+<h3 id="configuration_1">Configuration</h3>
+<ol>
+<li><strong>file</strong>: path of the file from which the filesystem is inferred. FileSplitter creates an instance of <code>org.apache.hadoop.fs.FileSystem</code> which is why this path is needed. </li>
+</ol>
+<pre><code>FileSystem.newInstance(new Path(file).toUri(), new Configuration());
+</code></pre>
+
+<p>The fs instance is then used to fetch the default block size and <code>org.apache.hadoop.fs.FileStatus</code> for each file path.</p>
+<h2 id="filesplitterinput">FileSplitterInput</h2>
+<p>This is an input operator that discovers files itself. The scanning of the directories for new files is asynchronous which is handled by <code>TimeBasedDirectoryScanner</code>. The function of TimeBasedDirectoryScanner is to periodically scan specified directories and find files which were newly added or modified. The interaction between the operator and the scanner is depicted in the diagram below.</p>
+<p><img alt="Interaction between operator and scanner" src="../images/filesplitter/sequence.png" /></p>
+<h3 id="example-application_1">Example application</h3>
+<p>This is a simple sub-dag that demonstrates how FileSplitterInput can be plugged into an application.</p>
+<p><img alt="Application with FileSplitterInput" src="../images/filesplitter/inputexample.png" /></p>
+<p>Splitter is the input operator here that sends block metadata to the downstream BlockReader.</p>
+<pre><code class="java"> @Override
+ public void populateDAG(DAG dag, Configuration configuration)
+ {
+ FileSplitterInput input = dag.addOperator("Input", new FileSplitterInput());
+ FSSliceReader reader = dag.addOperator("Block Reader", new FSSliceReader());
+ ...
+ dag.addStream("block-metadata", input.blocksMetadataOutput, reader.blocksMetadataInput);
+ ...
+ }
+
+</code></pre>
+
+<h3 id="ports_2">Ports</h3>
+<p>Since it is an input operator there are no input ports and output ports are inherited from AbstractFileSplitter.</p>
+<h3 id="configuration_2">Configuration</h3>
+<ol>
+<li><strong>scanner</strong>: the component that scans directories asynchronously. It is of type <code>com.datatorrent.lib.io.fs.FileSplitter.TimeBasedDirectoryScanner</code>. The basic implementation of TimeBasedDirectoryScanner can be customized by users. </li>
+</ol>
+<p>a. <strong>files</strong>: comma separated list of directories to scan. </p>
+<p>b. <strong>recursive</strong>: flag that controls whether the directories should be scanned recursively. </p>
+<p>c. <strong>scanIntervalMillis</strong>: interval specified in milliseconds after which another scan iteration is triggered. </p>
+<p>d. <strong>filePatternRegularExp</strong>: regular expression for accepted file names. </p>
+<p>e. <strong>trigger</strong>: a flag that triggers a scan iteration instantly. If the scanner thread is idling then it will initiate a scan immediately otherwise if a scan is in progress, then the new iteration will be triggered immediately after the completion of current one.
+2. <strong>idempotentStorageManager</strong>: by default FileSplitterInput is idempotent.
+Idempotency ensures that the operator will process the same set of files/blocks in a window if it has seen that window previously, i.e., before a failure. For example, let's say the operator completed window 10 and failed somewhere between window 11. If the operator gets restored at window 10 then it will process the same file/block again in window 10 which it did in the previous run before the failure. Idempotency is important but comes with higher cost because at the end of each window the operator needs to persist some state with respect to that window. Therefore, if one doesn't care about idempotency then they can set this property to be an instance of <code>com.datatorrent.lib.io.IdempotentStorageManager.NoopIdempotentStorageManager</code>.</p>
+<h2 id="handling-of-split-records">Handling of split records</h2>
+<p>Splitting of files to create tasks for downstream operator needs to be a simple operation that doesn't consume a lot of resources and is fast. This is why the file splitter doesn't open files to read. The downside of that is if the file contains records then a record may split across adjacent blocks. Handling of this is left to the downstream operator.</p>
+<p>We have created Block readers in Apex-malhar library that handle line splits efficiently. The 2 line readers- <code>AbstractFSLineReader</code> and <code>AbstractFSReadAheadLineReader</code> can be found here <a href="https://github.com/apache/incubator-apex-malhar/blob/master/library/src/main/java/com/datatorrent/lib/io/block/AbstractFSBlockReader.java">AbstractFSBlockReader</a>.</p>
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+ {
+ "location": "/",
+ "text": "Apache Apex Malhar\n\n\nApache Apex Malhar is an open source operator and codec library that can be used with the \nApache Apex\n platform to build real-time streaming applications. Enabling users to extract value quickly, Malhar operators help get data in, analyze it in real-time, and get data out of Hadoop. In addition to the operators, the library contains a number of demos applications, demonstrating operator features and capabilities.\n\n\n\n\nCapabilities common across Malhar operators\n\n\nFor most streaming platforms, connectors are afterthoughts and often end up being simple \u2018bolt-ons\u2019 to the platform. As a result they often cause performance issues or data loss when put through failure scenarios and scalability requirements. Malhar operators do not face these issues as they were designed to be integral parts of Apex. Hence, they have following core streaming runtime capabilities\n\n\n\n\nFault tolerance\n \u2013 Malhar operators where appli
cable have fault tolerance built in. They use the checkpoint capability provided by the framework to ensure that there is no data loss under ANY failure scenario.\n\n\nProcessing guarantees\n \u2013 Malhar operators where applicable provide out of the box support for ALL three processing guarantees \u2013 exactly once, at-least once, and at-most once WITHOUT requiring the user to write any additional code. Some operators, like MQTT operator, deal with source systems that can not track processed data and hence need the operators to keep track of the data. Malhar has support for a generic operator that uses alternate storage like HDFS to facilitate this. Finally for databases that support transactions or support any sort of atomic batch operations Malhar operators can do exactly once down to the tuple level.\n\n\nDynamic updates\n \u2013 Based on changing business conditions you often have to tweak several parameters used by the operators in your streaming application without incur
ring any application downtime. You can also change properties of a Malhar operator at runtime without having to bring down the application.\n\n\nEase of extensibility\n \u2013 Malhar operators are based on templates that are easy to extend.\n\n\nPartitioning support\n \u2013 In streaming applications the input data stream often needs to be partitioned based on the contents of the stream. Also for operators that ingest data from external systems partitioning needs to be done based on the capabilities of the external system. For example with Kafka, the operator can automatically scale up or down based on the changes in the number of Kafka partitions.\n\n\n\n\nOperator Library Overview\n\n\nInput/output connectors\n\n\nBelow is a summary of the various sub categories of input and output operators. Input operators also have a corresponding output operator\n\n\n\n\nFile Systems\n \u2013 Most streaming analytics use cases require the data to be stored in HDFS or perhaps S3 if the applica
tion is running in AWS. Users often need to re-run their streaming analytical applications against historical data or consume data from upstream processes that are perhaps writing to some NFS share. Apex supports input \n output operators for HDFS, S3, NFS \n Local Files. There are also File Splitter and Block Reader operators, which can accelecate processing of large files by splitting and paralellizing the work across non-overlapping sets of file blocks.\n\n\nRelational Databases\n \u2013 Most stream processing use cases require some reference data lookups to enrich, tag or filter streaming data. There is also a need to save results of the streaming analytical computation to a database so an operational dashboard can see them. Apex supports a JDBC operator so you can read/write data from any JDBC compliant RDBMS like Oracle, MySQL, Sqlite, etc.\n\n\nNoSQL Databases\n \u2013 NoSQL key-value pair databases like Cassandra \n HBase are a common part of streaming analytics applicati
on architectures to lookup reference data or store results. Malhar has operators for HBase, Cassandra, Accumulo, Aerospike, MongoDB, and CouchDB.\n\n\nMessaging Systems\n \u2013 Kafka, JMS, and similar systems are the workhorses of messaging infrastructure in most enterprises. Malhar has a robust, industry-tested set of operators to read and write Kafka, JMS, ZeroMQ, and RabbitMQ messages.\n\n\nNotification Systems\n \u2013 Malhar includes an operator for sending notifications via SMTP.\n\n\nIn-memory Databases \n Caching platforms\n - Some streaming use cases need instantaneous access to shared state across the application. Caching platforms and in-memory databases serve this purpose really well. To support these use cases, Malhar has operators for memcached and Redis.\n\n\nSocial Media\n - Malhar includes an operator to connect to the popular Twitter stream fire hose.\n\n\nProtocols\n - Malhar provides connectors that can communicate in HTTP, RSS, Socket, WebSocket, FTP, and MQT
T.\n\n\n\n\nParsers\n\n\nThere are many industry vertical specific data formats that a streaming application developer might need to parse. Often there are existing parsers available for these that can be directly plugged into an Apache Apex application. For example in the Telco space, a Java based CDR parser can be directly plugged into Apache Apex operator. To further simplify development experience, Malhar also provides some operators for parsing common formats like XML (DOM \n SAX), JSON (flat map converter), Apache log files, syslog, etc.\n\n\nStream manipulation\n\n\nStreaming data inevitably needs processing to clean, filter, tag, summarize, etc. The goal of Malhar is to enable the application developer to focus on WHAT needs to be done to the stream to get it in the right format and not worry about the HOW. Malhar has several operators to perform the common stream manipulation actions like \u2013 GroupBy, Join, Distinct/Unique, Limit, OrderBy, Split, Sample, Inner join, Out
er join, Select, Update etc.\n\n\nCompute\n\n\nOne of the most important promises of a streaming analytics platform like Apache Apex is the ability to do analytics in real-time. However delivering on the promise becomes really difficult when the platform does not provide out of the box operators to support variety of common compute functions as the user then has to worry about making these scalable, fault tolerant, stateful, etc. Malhar takes this responsibility away from the application developer by providing a variety of out of the box computational operators.\n\n\nBelow is just a snapshot of the compute operators available in Malhar\n\n\n\n\nStatistics and math - Various mathematical and statistical computations over application defined time windows.\n\n\nFiltering and pattern matching\n\n\nSorting, maps, frequency, TopN, BottomN\n\n\nRandom data generators\n\n\n\n\nLanguages Support\n\n\nMigrating to a new platform often requires re-use of the existing code that would be diffic
ult or time-consuming to re-write. With this in mind, Malhar supports invocation of code written in other languages by wrapping them in one of the library operators, and allows execution of software written in:\n\n\n\n\nJavaScript\n\n\nPython\n\n\nR\n\n\nRuby",
+ "title": "Apache Apex Malhar"
+ },
+ {
+ "location": "/#apache-apex-malhar",
+ "text": "Apache Apex Malhar is an open source operator and codec library that can be used with the Apache Apex platform to build real-time streaming applications. Enabling users to extract value quickly, Malhar operators help get data in, analyze it in real-time, and get data out of Hadoop. In addition to the operators, the library contains a number of demos applications, demonstrating operator features and capabilities.",
+ "title": "Apache Apex Malhar"
+ },
+ {
+ "location": "/#capabilities-common-across-malhar-operators",
+ "text": "For most streaming platforms, connectors are afterthoughts and often end up being simple \u2018bolt-ons\u2019 to the platform. As a result they often cause performance issues or data loss when put through failure scenarios and scalability requirements. Malhar operators do not face these issues as they were designed to be integral parts of Apex. Hence, they have following core streaming runtime capabilities Fault tolerance \u2013 Malhar operators where applicable have fault tolerance built in. They use the checkpoint capability provided by the framework to ensure that there is no data loss under ANY failure scenario. Processing guarantees \u2013 Malhar operators where applicable provide out of the box support for ALL three processing guarantees \u2013 exactly once, at-least once, and at-most once WITHOUT requiring the user to write any additional code. Some operators, like MQTT operator, deal with source systems that can not track processed data and hence n
eed the operators to keep track of the data. Malhar has support for a generic operator that uses alternate storage like HDFS to facilitate this. Finally for databases that support transactions or support any sort of atomic batch operations Malhar operators can do exactly once down to the tuple level. Dynamic updates \u2013 Based on changing business conditions you often have to tweak several parameters used by the operators in your streaming application without incurring any application downtime. You can also change properties of a Malhar operator at runtime without having to bring down the application. Ease of extensibility \u2013 Malhar operators are based on templates that are easy to extend. Partitioning support \u2013 In streaming applications the input data stream often needs to be partitioned based on the contents of the stream. Also for operators that ingest data from external systems partitioning needs to be done based on the capabilities of the external system. Fo
r example with Kafka, the operator can automatically scale up or down based on the changes in the number of Kafka partitions.",
+ "title": "Capabilities common across Malhar operators"
+ },
+ {
+ "location": "/#operator-library-overview",
+ "text": "",
+ "title": "Operator Library Overview"
+ },
+ {
+ "location": "/#inputoutput-connectors",
+ "text": "Below is a summary of the various sub categories of input and output operators. Input operators also have a corresponding output operator File Systems \u2013 Most streaming analytics use cases require the data to be stored in HDFS or perhaps S3 if the application is running in AWS. Users often need to re-run their streaming analytical applications against historical data or consume data from upstream processes that are perhaps writing to some NFS share. Apex supports input output operators for HDFS, S3, NFS Local Files. There are also File Splitter and Block Reader operators, which can accelecate processing of large files by splitting and paralellizing the work across non-overlapping sets of file blocks. Relational Databases \u2013 Most stream processing use cases require some reference data lookups to enrich, tag or filter streaming data. There is also a need to save results of the streaming analytical computation to a database so an operational das
hboard can see them. Apex supports a JDBC operator so you can read/write data from any JDBC compliant RDBMS like Oracle, MySQL, Sqlite, etc. NoSQL Databases \u2013 NoSQL key-value pair databases like Cassandra HBase are a common part of streaming analytics application architectures to lookup reference data or store results. Malhar has operators for HBase, Cassandra, Accumulo, Aerospike, MongoDB, and CouchDB. Messaging Systems \u2013 Kafka, JMS, and similar systems are the workhorses of messaging infrastructure in most enterprises. Malhar has a robust, industry-tested set of operators to read and write Kafka, JMS, ZeroMQ, and RabbitMQ messages. Notification Systems \u2013 Malhar includes an operator for sending notifications via SMTP. In-memory Databases Caching platforms - Some streaming use cases need instantaneous access to shared state across the application. Caching platforms and in-memory databases serve this purpose really well. To support these use cases, Malhar
has operators for memcached and Redis. Social Media - Malhar includes an operator to connect to the popular Twitter stream fire hose. Protocols - Malhar provides connectors that can communicate in HTTP, RSS, Socket, WebSocket, FTP, and MQTT.",
+ "title": "Input/output connectors"
+ },
+ {
+ "location": "/#parsers",
+ "text": "There are many industry vertical specific data formats that a streaming application developer might need to parse. Often there are existing parsers available for these that can be directly plugged into an Apache Apex application. For example in the Telco space, a Java based CDR parser can be directly plugged into Apache Apex operator. To further simplify development experience, Malhar also provides some operators for parsing common formats like XML (DOM SAX), JSON (flat map converter), Apache log files, syslog, etc.",
+ "title": "Parsers"
+ },
+ {
+ "location": "/#stream-manipulation",
+ "text": "Streaming data inevitably needs processing to clean, filter, tag, summarize, etc. The goal of Malhar is to enable the application developer to focus on WHAT needs to be done to the stream to get it in the right format and not worry about the HOW. Malhar has several operators to perform the common stream manipulation actions like \u2013 GroupBy, Join, Distinct/Unique, Limit, OrderBy, Split, Sample, Inner join, Outer join, Select, Update etc.",
+ "title": "Stream manipulation"
+ },
+ {
+ "location": "/#compute",
+ "text": "One of the most important promises of a streaming analytics platform like Apache Apex is the ability to do analytics in real-time. However delivering on the promise becomes really difficult when the platform does not provide out of the box operators to support variety of common compute functions as the user then has to worry about making these scalable, fault tolerant, stateful, etc. Malhar takes this responsibility away from the application developer by providing a variety of out of the box computational operators. Below is just a snapshot of the compute operators available in Malhar Statistics and math - Various mathematical and statistical computations over application defined time windows. Filtering and pattern matching Sorting, maps, frequency, TopN, BottomN Random data generators",
+ "title": "Compute"
+ },
+ {
+ "location": "/#languages-support",
+ "text": "Migrating to a new platform often requires re-use of the existing code that would be difficult or time-consuming to re-write. With this in mind, Malhar supports invocation of code written in other languages by wrapping them in one of the library operators, and allows execution of software written in: JavaScript Python R Ruby",
+ "title": "Languages Support"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/",
+ "text": "KAFKA INPUT OPERATOR\n\n\nIntroduction: About Kafka Input Operator\n\n\nThis is an input operator that consumes data from Kafka messaging system for further processing in Apex. Kafka Input Operator is an fault-tolerant and scalable Malhar Operator.\n\n\nWhy is it needed ?\n\n\nKafka is a pull-based and distributed publish subscribe messaging system, topics are partitioned and replicated across\nnodes. Kafka input operator is needed when you want to read data from multiple\npartitions of a Kafka topic in parallel in an Apex application.\n\n\nAbstractKafkaInputOperator\n\n\nThis is the abstract implementation that serves as base class for consuming messages from Kafka messaging system. This class doesn\u2019t have any ports.\n\n\n\n\nConfiguration Parameters\n\n\n\n\n\n\n\n\n\n\n\n\nParameter\n\n\nDescription\n\n\n\n\n\n\nmaxTuplesPerWindow\n\n\nControls the maximum number of messages emitted in each streaming window from this operator. Minimum value is 1. Default
value = MAX_VALUE \n\n\n\n\n\n\nidempotentStorageManager\n\n\nThis is an instance of IdempotentStorageManager. Idempotency ensures that the operator will process the same set of messages in a window before and after a failure. For example, let's say the operator completed window 10 and failed somewhere between window 11. If the operator gets restored at window 10 then it will process the same messages again in window 10 which it did in the previous run before the failure. Idempotency is important but comes with higher cost because at the end of each window the operator needs to persist some state with respect to that window. Default Value = com.datatorrent.lib.io.IdempotentStorageManager.\nNoopIdempotentStorageManager\n\n\n\n\n\n\nstrategy\n\n\nOperator supports two types of partitioning strategies, ONE_TO_ONE and ONE_TO_MANY.\n\n\nONE_TO_ONE: If this is enabled, the AppMaster creates one input operator instance per Kafka topic partition. So the number of Kafka topic partitions equ
als the number of operator instances.\n\n\nONE_TO_MANY: The AppMaster creates K = min(initialPartitionCount, N) Kafka input operator instances where N is the number of Kafka topic partitions. If K is less than N, the remaining topic partitions are assigned to the K operator instances in round-robin fashion. If K is less than initialPartitionCount, the AppMaster creates one input operator instance per Kafka topic partition. For example, if initialPartitionCount = 5 and number of Kafka partitions(N) = 2 then AppMaster creates 2 Kafka input operator instances.\nDefault Value = ONE_TO_ONE\n\n\n\n\n\n\nmsgRateUpperBound\n\n\nMaximum messages upper bound. Operator repartitions when the \nmsgProcessedPS\n exceeds this bound. \nmsgProcessedPS\n is the average number of messages processed per second by this operator.\n\n\n\n\n\n\nbyteRateUpperBound\n\n\nMaximum bytes upper bound. Operator repartitions when the \nbytesPS\n exceeds this bound. \nbytesPS\n is the average number of bytes process
ed per second by this operator.\n\n\n\n\n\n\n\n\noffsetManager\n\n\nThis is an optional parameter that is useful when the application restarts or start at specific offsets (offsets are explained below)\n\n\n\n\n\n\nrepartitionInterval\n\n\nInterval specified in milliseconds. This value specifies the minimum time required between two repartition actions. Default Value = 30 Seconds\n\n\n\n\n\n\nrepartitionCheckInterval\n\n\nInterval specified in milliseconds. This value specifies the minimum interval between two offset updates. Default Value = 5 Seconds\n\n\n\n\n\n\ninitialPartitionCount\n\n\nWhen the ONE_TO_MANY partition strategy is enabled, this value indicates the number of Kafka input operator instances. Default Value = 1\n\n\n\n\n\n\nconsumer\n\n\nThis is an instance of com.datatorrent.contrib.kafka.KafkaConsumer. Default Value = Instance of SimpleKafkaConsumer.\n\n\n\n\n\n\n\n\nAbstract Methods\n\n\nvoid emitTuple(Message message): Abstract method that emits tuples\nextracted f
rom Kafka message.\n\n\nKafkaConsumer\n\n\nThis is an abstract implementation of Kafka consumer. It sends the fetch\nrequests to the leading brokers of Kafka partitions. For each request,\nit receives the set of messages and stores them into the buffer which is\nArrayBlockingQueue. SimpleKafkaConsumer\u00a0which extends\nKafkaConsumer and serves the functionality of Simple Consumer API and\nHighLevelKafkaConsumer which extends KafkaConsumer and \u00a0serves the\nfunctionality of High Level Consumer API.\n\n\nPre-requisites\n\n\nThis operator referred the Kafka Consumer API of version\n0.8.1.1. So, this operator will work with any 0.8.x and 0.7.x version of Apache Kafka.\n\n\nConfiguration Parameters\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nParameter\n\n\nType\n\n\nDefault\n\n\nDescription\n\n\n\n\n\n\nzookeeper\n\n\nString\n\n\n\n\nSpecifies the zookeeper quorum of Kafka clusters that you want to consume messages from. zookeeper \u00a0is a string in the form of hostname1:port1,hostname2:por
t2,hostname3:port3 \u00a0where hostname1,hostname2,hostname3 are hosts and port1,port2,port3 are ports of zookeeper server. \u00a0If the topic name is the same across the Kafka clusters and want to consume data from these clusters, then configure the zookeeper as follows: c1::hs1:p1,hs2:p2,hs3:p3;c2::hs4:p4,hs5:p5,c3::hs6:p6\n\n\nwhere\n\n\nc1,c2,c3 indicates the cluster names, hs1,hs2,hs3,hs4,hs5,hs6 are zookeeper hosts and p1,p2,p3,p4,p5,p6 are corresponding ports. Here, cluster name is optional in case of single cluster\n\n\n\n\n\n\ncacheSize\n\n\nint\n\n\n1024\n\n\nMaximum of buffered messages hold in memory.\n\n\n\n\n\n\ntopic\n\n\nString\n\n\ndefault_topic\n\n\nIndicates the name of the topic.\n\n\n\n\n\n\ninitialOffset\n\n\nString\n\n\nlatest\n\n\nIndicates the type of offset i.e, \u201cearliest or latest\u201d. If initialOffset is \u201clatest\u201d, then the operator consumes messages from latest point of Kafka queue. If initialOffset is \u201cearliest\u201d, then the opera
tor consumes messages starting from message queue. This can be overridden by OffsetManager.\n\n\n\n\n\n\n\n\n\nAbstract Methods\n\n\n\n\nvoid commitOffset(): Commit the offsets at checkpoint.\n\n\nMap \nKafkaPartition, Long\n getCurrentOffsets(): Return the current\n offset status.\n\n\nresetPartitionsAndOffset(Set \nKafkaPartition\n partitionIds,\n Map \nKafkaPartition, Long\n startOffset): Reset the partitions with\n parittionIds and offsets with startOffset.\n\n\n\n\nConfiguration Parameters\u00a0for SimpleKafkaConsumer\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nParameter\n\n\nType\n\n\nDefault\n\n\nDescription\n\n\n\n\n\n\nbufferSize\n\n\nint\n\n\n1 MB\n\n\nSpecifies the maximum total size of messages for each fetch request.\n\n\n\n\n\n\nmetadataRefreshInterval\n\n\nint\n\n\n30 Seconds\n\n\nInterval in between refresh the metadata change(broker change) in milliseconds. Enabling metadata refresh guarantees an automatic reconnect when a new broker is elected as the host. A value of
-1 disables this feature.\n\n\n\n\n\n\nmetadataRefreshRetryLimit\n\n\nint\n\n\n-1\n\n\nSpecifies the maximum brokers' metadata refresh retry limit. -1 means unlimited retry.\n\n\n\n\n\n\n\n\n\nOffsetManager\n\n\nThis is an interface for offset management and is useful when consuming data\nfrom specified offsets. Updates the offsets for all the Kafka partitions\nperiodically. Below is the code snippet:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\n\npublic interface OffsetManager\n{\n public Map\nKafkaPartition, Long\n loadInitialOffsets();\n public void updateOffsets(Map\nKafkaPartition, Long\n offsetsOfPartitions);\n}\n\n\n\n\nAbstract Methods\n\n\nMap \nKafkaPartition, Long\n loadInitialOffsets(): Specifies the initial offset for consuming messages; called at the activation stage.\n\n\nupdateOffsets(Map \nKafkaPartition, Long\n offsetsOfPartitions): \u00a0This\nmethod is called at every repartitionCheckInterval to update offsets.\n\n\nPartitioning\n\n\nThe logical instance
of the KafkaInputOperator acts as the Partitioner\nas well as a StatsListener. This is because the\nAbstractKafkaInputOperator implements both the\ncom.datatorrent.api.Partitioner and com.datatorrent.api.StatsListener\ninterfaces and provides an implementation of definePartitions(...) and\nprocessStats(...) which makes it auto-scalable.\n\n\nResponse processStats(BatchedOperatorStats stats)\n\n\nThe application master invokes this method on the logical instance with\nthe stats (tuplesProcessedPS, bytesPS, etc.) of each partition.\nRe-partitioning happens based on whether any new Kafka partitions added for\nthe topic or bytesPS and msgPS cross their respective upper bounds.\n\n\nDefinePartitions\n\n\nBased on the repartitionRequired field of the Response object which is\nreturned by processStats(...) method, the application master invokes\ndefinePartitions(...) on the logical instance which is also the\npartitioner instance. Dynamic partition can be disabled by setting the\nparamete
r repartitionInterval value to a negative value.\n\n\nAbstractSinglePortKafkaInputOperator\n\n\nThis class extends AbstractKafkaInputOperator and having single output\nport, will emit the messages through this port.\n\n\nPorts\n\n\noutputPort \nT\n: Tuples extracted from Kafka messages are emitted through\nthis port.\n\n\nAbstract Methods\n\n\nT getTuple(Message msg) : Converts the Kafka message to tuple.\n\n\nConcrete Classes\n\n\n\n\n\n\nKafkaSinglePortStringInputOperator :\nThis class extends AbstractSinglePortKafkaInputOperator and getTuple() method extracts string from Kafka message.\n\n\n\n\n\n\nKafkaSinglePortByteArrayInputOperator:\nThis class extends AbstractSinglePortKafkaInputOperator and getTuple() method extracts byte array from Kafka message.\n\n\n\n\n\n\nApplication Example\n\n\nThis section builds an Apex application using Kafka input operator.\nBelow is the code snippet:\n\n\n@ApplicationAnnotation(name = \nKafkaApp\n)\npublic class ExampleKafkaApplication implement
s StreamingApplication\n{\n@Override\npublic void populateDAG(DAG dag, Configuration entries)\n{\n KafkaSinglePortByteArrayInputOperator input = dag.addOperator(\nMessageReader\n, new KafkaSinglePortByteArrayInputOperator());\n\n ConsoleOutputOperator output = dag.addOperator(\nOutput\n, new ConsoleOutputOperator());\n\n dag.addStream(\nMessageData\n, input.outputPort, output.input);\n}\n}\n\n\n\n\nBelow is the configuration for \u201ctest\u201d Kafka topic name and\n\u201clocalhost:2181\u201d is the zookeeper forum:\n\n\nproperty\n\n\nname\ndt.operator.MessageReader.prop.topic\n/name\n\n\nvalue\ntest\n/value\n\n\n/property\n\n\n\nproperty\n\n\nname\ndt.operator.KafkaInputOperator.prop.zookeeper\n/nam\n\n\nvalue\nlocalhost:2181\n/value\n\n\n/property",
+ "title": "Kafka Input"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#kafka-input-operator",
+ "text": "",
+ "title": "KAFKA INPUT OPERATOR"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#introduction-about-kafka-input-operator",
+ "text": "This is an input operator that consumes data from Kafka messaging system for further processing in Apex. Kafka Input Operator is an fault-tolerant and scalable Malhar Operator.",
+ "title": "Introduction: About Kafka Input Operator"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#why-is-it-needed",
+ "text": "Kafka is a pull-based and distributed publish subscribe messaging system, topics are partitioned and replicated across\nnodes. Kafka input operator is needed when you want to read data from multiple\npartitions of a Kafka topic in parallel in an Apex application.",
+ "title": "Why is it needed ?"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#abstractkafkainputoperator",
+ "text": "This is the abstract implementation that serves as base class for consuming messages from Kafka messaging system. This class doesn\u2019t have any ports.",
+ "title": "AbstractKafkaInputOperator"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#configuration-parameters",
+ "text": "Parameter Description maxTuplesPerWindow Controls the maximum number of messages emitted in each streaming window from this operator. Minimum value is 1. Default value = MAX_VALUE idempotentStorageManager This is an instance of IdempotentStorageManager. Idempotency ensures that the operator will process the same set of messages in a window before and after a failure. For example, let's say the operator completed window 10 and failed somewhere between window 11. If the operator gets restored at window 10 then it will process the same messages again in window 10 which it did in the previous run before the failure. Idempotency is important but comes with higher cost because at the end of each window the operator needs to persist some state with respect to that window. Default Value = com.datatorrent.lib.io.IdempotentStorageManager. NoopIdempotentStorageManager strategy Operator supports two types of partitioning strategies, ONE_TO_ONE and ONE_TO_MANY.
ONE_TO_ONE: If this is enabled, the AppMaster creates one input operator instance per Kafka topic partition. So the number of Kafka topic partitions equals the number of operator instances. ONE_TO_MANY: The AppMaster creates K = min(initialPartitionCount, N) Kafka input operator instances where N is the number of Kafka topic partitions. If K is less than N, the remaining topic partitions are assigned to the K operator instances in round-robin fashion. If K is less than initialPartitionCount, the AppMaster creates one input operator instance per Kafka topic partition. For example, if initialPartitionCount = 5 and number of Kafka partitions(N) = 2 then AppMaster creates 2 Kafka input operator instances.\nDefault Value = ONE_TO_ONE msgRateUpperBound Maximum messages upper bound. Operator repartitions when the msgProcessedPS exceeds this bound. msgProcessedPS is the average number of messages processed per second by this operator. byteRateUpperBound Maximum bytes upper bo
und. Operator repartitions when the bytesPS exceeds this bound. bytesPS is the average number of bytes processed per second by this operator. offsetManager This is an optional parameter that is useful when the application restarts or start at specific offsets (offsets are explained below) repartitionInterval Interval specified in milliseconds. This value specifies the minimum time required between two repartition actions. Default Value = 30 Seconds repartitionCheckInterval Interval specified in milliseconds. This value specifies the minimum interval between two offset updates. Default Value = 5 Seconds initialPartitionCount When the ONE_TO_MANY partition strategy is enabled, this value indicates the number of Kafka input operator instances. Default Value = 1 consumer This is an instance of com.datatorrent.contrib.kafka.KafkaConsumer. Default Value = Instance of SimpleKafkaConsumer.",
+ "title": "Configuration Parameters"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#abstract-methods",
+ "text": "void emitTuple(Message message): Abstract method that emits tuples\nextracted from Kafka message.",
+ "title": "Abstract Methods"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#kafkaconsumer",
+ "text": "This is an abstract implementation of Kafka consumer. It sends the fetch\nrequests to the leading brokers of Kafka partitions. For each request,\nit receives the set of messages and stores them into the buffer which is\nArrayBlockingQueue. SimpleKafkaConsumer\u00a0which extends\nKafkaConsumer and serves the functionality of Simple Consumer API and\nHighLevelKafkaConsumer which extends KafkaConsumer and \u00a0serves the\nfunctionality of High Level Consumer API.",
+ "title": "KafkaConsumer"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#pre-requisites",
+ "text": "This operator referred the Kafka Consumer API of version\n0.8.1.1. So, this operator will work with any 0.8.x and 0.7.x version of Apache Kafka.",
+ "title": "Pre-requisites"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#configuration-parameters_1",
+ "text": "Parameter Type Default Description zookeeper String Specifies the zookeeper quorum of Kafka clusters that you want to consume messages from. zookeeper \u00a0is a string in the form of hostname1:port1,hostname2:port2,hostname3:port3 \u00a0where hostname1,hostname2,hostname3 are hosts and port1,port2,port3 are ports of zookeeper server. \u00a0If the topic name is the same across the Kafka clusters and want to consume data from these clusters, then configure the zookeeper as follows: c1::hs1:p1,hs2:p2,hs3:p3;c2::hs4:p4,hs5:p5,c3::hs6:p6 where c1,c2,c3 indicates the cluster names, hs1,hs2,hs3,hs4,hs5,hs6 are zookeeper hosts and p1,p2,p3,p4,p5,p6 are corresponding ports. Here, cluster name is optional in case of single cluster cacheSize int 1024 Maximum of buffered messages hold in memory. topic String default_topic Indicates the name of the topic. initialOffset String latest Indicates the type of offset i.e, \u201cearliest or latest\u201
d. If initialOffset is \u201clatest\u201d, then the operator consumes messages from latest point of Kafka queue. If initialOffset is \u201cearliest\u201d, then the operator consumes messages starting from message queue. This can be overridden by OffsetManager.",
+ "title": "Configuration Parameters"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#abstract-methods_1",
+ "text": "void commitOffset(): Commit the offsets at checkpoint. Map KafkaPartition, Long getCurrentOffsets(): Return the current\n offset status. resetPartitionsAndOffset(Set KafkaPartition partitionIds,\n Map KafkaPartition, Long startOffset): Reset the partitions with\n parittionIds and offsets with startOffset.",
+ "title": "Abstract Methods"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#configuration-parameters-for-simplekafkaconsumer",
+ "text": "Parameter Type Default Description bufferSize int 1 MB Specifies the maximum total size of messages for each fetch request. metadataRefreshInterval int 30 Seconds Interval in between refresh the metadata change(broker change) in milliseconds. Enabling metadata refresh guarantees an automatic reconnect when a new broker is elected as the host. A value of -1 disables this feature. metadataRefreshRetryLimit int -1 Specifies the maximum brokers' metadata refresh retry limit. -1 means unlimited retry.",
+ "title": "Configuration Parameters\u00a0for SimpleKafkaConsumer"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#offsetmanager",
+ "text": "This is an interface for offset management and is useful when consuming data\nfrom specified offsets. Updates the offsets for all the Kafka partitions\nperiodically. Below is the code snippet:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 public interface OffsetManager\n{\n public Map KafkaPartition, Long loadInitialOffsets();\n public void updateOffsets(Map KafkaPartition, Long offsetsOfPartitions);\n}",
+ "title": "OffsetManager"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#abstract-methods_2",
+ "text": "Map KafkaPartition, Long loadInitialOffsets(): Specifies the initial offset for consuming messages; called at the activation stage. updateOffsets(Map KafkaPartition, Long offsetsOfPartitions): \u00a0This\nmethod is called at every repartitionCheckInterval to update offsets.",
+ "title": "Abstract Methods"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#partitioning",
+ "text": "The logical instance of the KafkaInputOperator acts as the Partitioner\nas well as a StatsListener. This is because the\nAbstractKafkaInputOperator implements both the\ncom.datatorrent.api.Partitioner and com.datatorrent.api.StatsListener\ninterfaces and provides an implementation of definePartitions(...) and\nprocessStats(...) which makes it auto-scalable.",
+ "title": "Partitioning"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#response-processstatsbatchedoperatorstats-stats",
+ "text": "The application master invokes this method on the logical instance with\nthe stats (tuplesProcessedPS, bytesPS, etc.) of each partition.\nRe-partitioning happens based on whether any new Kafka partitions added for\nthe topic or bytesPS and msgPS cross their respective upper bounds.",
+ "title": "Response processStats(BatchedOperatorStats stats)"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#definepartitions",
+ "text": "Based on the repartitionRequired field of the Response object which is\nreturned by processStats(...) method, the application master invokes\ndefinePartitions(...) on the logical instance which is also the\npartitioner instance. Dynamic partition can be disabled by setting the\nparameter repartitionInterval value to a negative value.",
+ "title": "DefinePartitions"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#abstractsingleportkafkainputoperator",
+ "text": "This class extends AbstractKafkaInputOperator and having single output\nport, will emit the messages through this port.",
+ "title": "AbstractSinglePortKafkaInputOperator"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#ports",
+ "text": "outputPort T : Tuples extracted from Kafka messages are emitted through\nthis port.",
+ "title": "Ports"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#abstract-methods_3",
+ "text": "T getTuple(Message msg) : Converts the Kafka message to tuple.",
+ "title": "Abstract Methods"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#concrete-classes",
+ "text": "KafkaSinglePortStringInputOperator :\nThis class extends AbstractSinglePortKafkaInputOperator and getTuple() method extracts string from Kafka message. KafkaSinglePortByteArrayInputOperator:\nThis class extends AbstractSinglePortKafkaInputOperator and getTuple() method extracts byte array from Kafka message.",
+ "title": "Concrete Classes"
+ },
+ {
+ "location": "/operators/kafkaInputOperator/#application-example",
+ "text": "This section builds an Apex application using Kafka input operator.\nBelow is the code snippet: @ApplicationAnnotation(name = KafkaApp )\npublic class ExampleKafkaApplication implements StreamingApplication\n{\n@Override\npublic void populateDAG(DAG dag, Configuration entries)\n{\n KafkaSinglePortByteArrayInputOperator input = dag.addOperator( MessageReader , new KafkaSinglePortByteArrayInputOperator());\n\n ConsoleOutputOperator output = dag.addOperator( Output , new ConsoleOutputOperator());\n\n dag.addStream( MessageData , input.outputPort, output.input);\n}\n} Below is the configuration for \u201ctest\u201d Kafka topic name and\n\u201clocalhost:2181\u201d is the zookeeper forum: property name dt.operator.MessageReader.prop.topic /name value test /value /property property name dt.operator.KafkaInputOperator.prop.zookeeper /nam value localhost:2181 /value /property",
+ "title": "Application Example"
+ },
+ {
+ "location": "/operators/file_splitter/",
+ "text": "File Splitter\n\n\nThis is a simple operator whose main function is to split a file virtually and create metadata describing the files and the splits. \n\n\nWhy is it needed?\n\n\nIt is a common operation to read a file and parse it. This operation can be parallelized by having multiple partitions of such operators and each partition operating on different files. However, at times when a file is large then a single partition reading it can become a bottleneck.\nIn these cases, throughput can be increased if instances of the partitioned operator can read and parse non-overlapping sets of file blocks. This is where file splitter comes in handy. It creates metadata of blocks of file which serves as tasks handed out to downstream operator partitions. \nThe downstream partitions can read/parse the block without the need of interacting with other partitions.\n\n\nClass Diagram\n\n\n\n\nAbstractFileSplitter\n\n\nThe abstract implementation defines the logic of processi
ng \nFileInfo\n. This comprises the following tasks - \n\n\n\n\n\n\nbuilding \nFileMetadata\n per file and emitting it. This metadata contains the file information such as filepath, no. of blocks in it, length of the file, all the block ids, etc.\n\n\n\n\n\n\ncreating \nBlockMetadataIterator\n from \nFileMetadata\n. The iterator lazy-loads the block metadata when needed. We use an iterator because the no. of blocks in a file can be huge if the block size is small and loading all of them at once in memory may cause out of memory errors.\n\n\n\n\n\n\nretrieving \nBlockMetadata.FileBlockMetadata\n from the block metadata iterator and emitting it. The FileBlockMetadata contains the block id, start offset of the block, length of file in the block, etc. The number of block metadata emitted per window are controlled by \nblocksThreshold\n setting which by default is 1. \n\n\n\n\n\n\nThe main utility method that performs all the above tasks is the \nprocess()\n method. Concrete implementa
tions can invoke this method whenever they have data to process.\n\n\nPorts\n\n\nDeclares only output ports on which file metadata and block metadata are emitted.\n\n\n\n\nfilesMetadataOutput: metadata for each file is emitted on this port. \n\n\nblocksMetadataOutput: metadata for each block is emitted on this port. \n\n\n\n\nprocess()\n method\n\n\nWhen process() is invoked, any pending blocks from the current file are emitted on the 'blocksMetadataOutput' port. If the threshold for blocks per window is still not met then a new input file is processed - corresponding metadata is emitted on 'filesMetadataOutput' and more of its blocks are emitted. This operation is repeated until the \nblocksThreshold\n is reached or there are no more new files.\n\n\n protected void process()\n {\n if (blockMetadataIterator != null \n blockCount \n blocksThreshold) {\n emitBlockMetadata();\n }\n\n FileInfo fileInfo;\n while (blockCount \n blocksThreshold \n (fileInfo = getFileInfo
()) != null) {\n if (!processFileInfo(fileInfo)) {\n break;\n }\n }\n }\n\n\n\n\nAbstract methods\n\n\n\n\n\n\nFileInfo getFileInfo()\n: called from within the \nprocess()\n and provides the next file to process.\n\n\n\n\n\n\nlong getDefaultBlockSize()\n: provides the block size which is used when user hasn't configured the size.\n\n\n\n\n\n\nFileStatus getFileStatus(Path path)\n: provides the \norg.apache.hadoop.fs.FileStatus\n instance for a path. \n\n\n\n\n\n\nConfiguration\n\n\n\n\nblockSize\n: size of a block.\n\n\nblocksThreshold\n: threshold on the number of blocks emitted by file splitter every window. This setting is used for throttling the work for downstream operators.\n\n\n\n\nFileSplitterBase\n\n\nSimple operator that receives tuples of type \nFileInfo\n on its \ninput\n port. \nFileInfo\n contains the information (currently just the file path) about the file which this operator uses to create file metadata and block metadata.\n\n\nExample applica
tion\n\n\nThis is a simple sub-dag that demonstrates how FileSplitterBase can be plugged into an application.\n\n\n\nThe upstream operator emits tuples of type \nFileInfo\n on its output port which is connected to splitter input port. The downstream receives tuples of type \nBlockMetadata.FileBlockMetadata\n from the splitter's block metadata output port.\n\n\npublic class ApplicationWithBaseSplitter implements StreamingApplication\n{\n @Override\n public void populateDAG(DAG dag, Configuration configuration)\n {\n JMSInput input = dag.addOperator(\nInput\n, new JMSInput());\n FileSplitterBase splitter = dag.addOperator(\nSplitter\n, new FileSplitterBase());\n FSSliceReader blockReader = dag.addOperator(\nBlockReader\n, new FSSliceReader());\n ...\n dag.addStream(\nfile-info\n, input.output, splitter.input);\n dag.addStream(\nblock-metadata\n, splitter.blocksMetadataOutput, blockReader.blocksMetadataInput);\n ...\n }\n\n public static class JMSInput extends
AbstractJMSInputOperator\nAbstractFileSplitter.FileInfo\n\n {\n\n public final transient DefaultOutputPort\nAbstractFileSplitter.FileInfo\n output = new DefaultOutputPort\n();\n\n @Override\n protected AbstractFileSplitter.FileInfo convert(Message message) throws JMSException\n {\n //assuming the message is a text message containing the absolute path of the file.\n return new AbstractFileSplitter.FileInfo(null, ((TextMessage)message).getText());\n }\n\n @Override\n protected void emit(AbstractFileSplitter.FileInfo payload)\n {\n output.emit(payload);\n }\n }\n}\n\n\n\n\nPorts\n\n\nDeclares an input port on which it receives tuples from the upstream operator. Output ports are inherited from AbstractFileSplitter.\n\n\n\n\ninput: non optional port on which tuples of type \nFileInfo\n are received.\n\n\n\n\nConfiguration\n\n\n\n\nfile\n: path of the file from which the filesystem is inferred. FileSplitter creates an instance of \norg.apache.h
adoop.fs.FileSystem\n which is why this path is needed. \n\n\n\n\nFileSystem.newInstance(new Path(file).toUri(), new Configuration());\n\n\n\n\nThe fs instance is then used to fetch the default block size and \norg.apache.hadoop.fs.FileStatus\n for each file path.\n\n\nFileSplitterInput\n\n\nThis is an input operator that discovers files itself. The scanning of the directories for new files is asynchronous which is handled by \nTimeBasedDirectoryScanner\n. The function of TimeBasedDirectoryScanner is to periodically scan specified directories and find files which were newly added or modified. The interaction between the operator and the scanner is depicted in the diagram below.\n\n\n\n\nExample application\n\n\nThis is a simple sub-dag that demonstrates how FileSplitterInput can be plugged into an application.\n\n\n\n\nSplitter is the input operator here that sends block metadata to the downstream BlockReader.\n\n\n @Override\n public void populateDAG(DAG dag, Configuration confi
guration)\n {\n FileSplitterInput input = dag.addOperator(\nInput\n, new FileSplitterInput());\n FSSliceReader reader = dag.addOperator(\nBlock Reader\n, new FSSliceReader());\n ...\n dag.addStream(\nblock-metadata\n, input.blocksMetadataOutput, reader.blocksMetadataInput);\n ...\n }\n\n\n\n\n\nPorts\n\n\nSince it is an input operator there are no input ports and output ports are inherited from AbstractFileSplitter.\n\n\nConfiguration\n\n\n\n\nscanner\n: the component that scans directories asynchronously. It is of type \ncom.datatorrent.lib.io.fs.FileSplitter.TimeBasedDirectoryScanner\n. The basic implementation of TimeBasedDirectoryScanner can be customized by users. \n\n\n\n\na. \nfiles\n: comma separated list of directories to scan. \n\n\nb. \nrecursive\n: flag that controls whether the directories should be scanned recursively. \n\n\nc. \nscanIntervalMillis\n: interval specified in milliseconds after which another scan iteration is triggered. \n\n\nd. \nfil
ePatternRegularExp\n: regular expression for accepted file names. \n\n\ne. \ntrigger\n: a flag that triggers a scan iteration instantly. If the scanner thread is idling then it will initiate a scan immediately otherwise if a scan is in progress, then the new iteration will be triggered immediately after the completion of current one.\n2. \nidempotentStorageManager\n: by default FileSplitterInput is idempotent. \nIdempotency ensures that the operator will process the same set of files/blocks in a window if it has seen that window previously, i.e., before a failure. For example, let's say the operator completed window 10 and failed somewhere between window 11. If the operator gets restored at window 10 then it will process the same file/block again in window 10 which it did in the previous run before the failure. Idempotency is important but comes with higher cost because at the end of each window the operator needs to persist some state with respect to that window. Therefore, if one
doesn't care about idempotency then they can set this property to be an instance of \ncom.datatorrent.lib.io.IdempotentStorageManager.NoopIdempotentStorageManager\n.\n\n\nHandling of split records\n\n\nSplitting of files to create tasks for downstream operator needs to be a simple operation that doesn't consume a lot of resources and is fast. This is why the file splitter doesn't open files to read. The downside of that is if the file contains records then a record may split across adjacent blocks. Handling of this is left to the downstream operator.\n\n\nWe have created Block readers in Apex-malhar library that handle line splits efficiently. The 2 line readers- \nAbstractFSLineReader\n and \nAbstractFSReadAheadLineReader\n can be found here \nAbstractFSBlockReader\n.",
+ "title": "File Splitter"
+ },
+ {
+ "location": "/operators/file_splitter/#file-splitter",
+ "text": "This is a simple operator whose main function is to split a file virtually and create metadata describing the files and the splits.",
+ "title": "File Splitter"
+ },
+ {
+ "location": "/operators/file_splitter/#why-is-it-needed",
+ "text": "It is a common operation to read a file and parse it. This operation can be parallelized by having multiple partitions of such operators and each partition operating on different files. However, at times when a file is large then a single partition reading it can become a bottleneck.\nIn these cases, throughput can be increased if instances of the partitioned operator can read and parse non-overlapping sets of file blocks. This is where file splitter comes in handy. It creates metadata of blocks of file which serves as tasks handed out to downstream operator partitions. \nThe downstream partitions can read/parse the block without the need of interacting with other partitions.",
+ "title": "Why is it needed?"
+ },
+ {
+ "location": "/operators/file_splitter/#class-diagram",
+ "text": "",
+ "title": "Class Diagram"
+ },
+ {
+ "location": "/operators/file_splitter/#abstractfilesplitter",
+ "text": "The abstract implementation defines the logic of processing FileInfo . This comprises the following tasks - building FileMetadata per file and emitting it. This metadata contains the file information such as filepath, no. of blocks in it, length of the file, all the block ids, etc. creating BlockMetadataIterator from FileMetadata . The iterator lazy-loads the block metadata when needed. We use an iterator because the no. of blocks in a file can be huge if the block size is small and loading all of them at once in memory may cause out of memory errors. retrieving BlockMetadata.FileBlockMetadata from the block metadata iterator and emitting it. The FileBlockMetadata contains the block id, start offset of the block, length of file in the block, etc. The number of block metadata emitted per window are controlled by blocksThreshold setting which by default is 1. The main utility method that performs all the above tasks is the process() met
hod. Concrete implementations can invoke this method whenever they have data to process.",
+ "title": "AbstractFileSplitter"
+ },
+ {
+ "location": "/operators/file_splitter/#ports",
+ "text": "Declares only output ports on which file metadata and block metadata are emitted. filesMetadataOutput: metadata for each file is emitted on this port. blocksMetadataOutput: metadata for each block is emitted on this port.",
+ "title": "Ports"
+ },
+ {
+ "location": "/operators/file_splitter/#abstract-methods",
+ "text": "FileInfo getFileInfo() : called from within the process() and provides the next file to process. long getDefaultBlockSize() : provides the block size which is used when user hasn't configured the size. FileStatus getFileStatus(Path path) : provides the org.apache.hadoop.fs.FileStatus instance for a path.",
+ "title": "Abstract methods"
+ },
+ {
+ "location": "/operators/file_splitter/#configuration",
+ "text": "blockSize : size of a block. blocksThreshold : threshold on the number of blocks emitted by file splitter every window. This setting is used for throttling the work for downstream operators.",
+ "title": "Configuration"
+ },
+ {
+ "location": "/operators/file_splitter/#filesplitterbase",
+ "text": "Simple operator that receives tuples of type FileInfo on its input port. FileInfo contains the information (currently just the file path) about the file which this operator uses to create file metadata and block metadata.",
+ "title": "FileSplitterBase"
+ },
+ {
+ "location": "/operators/file_splitter/#example-application",
+ "text": "This is a simple sub-dag that demonstrates how FileSplitterBase can be plugged into an application. The upstream operator emits tuples of type FileInfo on its output port which is connected to splitter input port. The downstream receives tuples of type BlockMetadata.FileBlockMetadata from the splitter's block metadata output port. public class ApplicationWithBaseSplitter implements StreamingApplication\n{\n @Override\n public void populateDAG(DAG dag, Configuration configuration)\n {\n JMSInput input = dag.addOperator( Input , new JMSInput());\n FileSplitterBase splitter = dag.addOperator( Splitter , new FileSplitterBase());\n FSSliceReader blockReader = dag.addOperator( BlockReader , new FSSliceReader());\n ...\n dag.addStream( file-info , input.output, splitter.input);\n dag.addStream( block-metadata , splitter.blocksMetadataOutput, blockReader.blocksMetadataInput);\n ...\n }\n\n public static class JMSInput extends AbstractJMSI
nputOperator AbstractFileSplitter.FileInfo \n {\n\n public final transient DefaultOutputPort AbstractFileSplitter.FileInfo output = new DefaultOutputPort ();\n\n @Override\n protected AbstractFileSplitter.FileInfo convert(Message message) throws JMSException\n {\n //assuming the message is a text message containing the absolute path of the file.\n return new AbstractFileSplitter.FileInfo(null, ((TextMessage)message).getText());\n }\n\n @Override\n protected void emit(AbstractFileSplitter.FileInfo payload)\n {\n output.emit(payload);\n }\n }\n}",
+ "title": "Example application"
+ },
+ {
+ "location": "/operators/file_splitter/#ports_1",
+ "text": "Declares an input port on which it receives tuples from the upstream operator. Output ports are inherited from AbstractFileSplitter. input: non optional port on which tuples of type FileInfo are received.",
+ "title": "Ports"
+ },
+ {
+ "location": "/operators/file_splitter/#configuration_1",
+ "text": "file : path of the file from which the filesystem is inferred. FileSplitter creates an instance of org.apache.hadoop.fs.FileSystem which is why this path is needed. FileSystem.newInstance(new Path(file).toUri(), new Configuration()); The fs instance is then used to fetch the default block size and org.apache.hadoop.fs.FileStatus for each file path.",
+ "title": "Configuration"
+ },
+ {
+ "location": "/operators/file_splitter/#filesplitterinput",
+ "text": "This is an input operator that discovers files itself. The scanning of the directories for new files is asynchronous which is handled by TimeBasedDirectoryScanner . The function of TimeBasedDirectoryScanner is to periodically scan specified directories and find files which were newly added or modified. The interaction between the operator and the scanner is depicted in the diagram below.",
+ "title": "FileSplitterInput"
+ },
+ {
+ "location": "/operators/file_splitter/#example-application_1",
+ "text": "This is a simple sub-dag that demonstrates how FileSplitterInput can be plugged into an application. Splitter is the input operator here that sends block metadata to the downstream BlockReader. @Override\n public void populateDAG(DAG dag, Configuration configuration)\n {\n FileSplitterInput input = dag.addOperator( Input , new FileSplitterInput());\n FSSliceReader reader = dag.addOperator( Block Reader , new FSSliceReader());\n ...\n dag.addStream( block-metadata , input.blocksMetadataOutput, reader.blocksMetadataInput);\n ...\n }",
+ "title": "Example application"
+ },
+ {
+ "location": "/operators/file_splitter/#ports_2",
+ "text": "Since it is an input operator there are no input ports and output ports are inherited from AbstractFileSplitter.",
+ "title": "Ports"
+ },
+ {
+ "location": "/operators/file_splitter/#configuration_2",
+ "text": "scanner : the component that scans directories asynchronously. It is of type com.datatorrent.lib.io.fs.FileSplitter.TimeBasedDirectoryScanner . The basic implementation of TimeBasedDirectoryScanner can be customized by users. a. files : comma separated list of directories to scan. b. recursive : flag that controls whether the directories should be scanned recursively. c. scanIntervalMillis : interval specified in milliseconds after which another scan iteration is triggered. d. filePatternRegularExp : regular expression for accepted file names. e. trigger : a flag that triggers a scan iteration instantly. If the scanner thread is idling then it will initiate a scan immediately otherwise if a scan is in progress, then the new iteration will be triggered immediately after the completion of current one.\n2. idempotentStorageManager : by default FileSplitterInput is idempotent. \nIdempotency ensures that the operator will process the same set o
f files/blocks in a window if it has seen that window previously, i.e., before a failure. For example, let's say the operator completed window 10 and failed somewhere between window 11. If the operator gets restored at window 10 then it will process the same file/block again in window 10 which it did in the previous run before the failure. Idempotency is important but comes with higher cost because at the end of each window the operator needs to persist some state with respect to that window. Therefore, if one doesn't care about idempotency then they can set this property to be an instance of com.datatorrent.lib.io.IdempotentStorageManager.NoopIdempotentStorageManager .",
+ "title": "Configuration"
+ },
+ {
+ "location": "/operators/file_splitter/#handling-of-split-records",
+ "text": "Splitting of files to create tasks for downstream operator needs to be a simple operation that doesn't consume a lot of resources and is fast. This is why the file splitter doesn't open files to read. The downside of that is if the file contains records then a record may split across adjacent blocks. Handling of this is left to the downstream operator. We have created Block readers in Apex-malhar library that handle line splits efficiently. The 2 line readers- AbstractFSLineReader and AbstractFSReadAheadLineReader can be found here AbstractFSBlockReader .",
+ "title": "Handling of split records"
+ },
+ {
+ "location": "/operators/block_reader/",
+ "text": "Block Reader\n\n\nThis is a scalable operator that reads and parses blocks of data sources into records. A data source can be a file or a message bus that contains records and a block defines a chunk of data in the source by specifying the block offset and the length of the source belonging to the block. \n\n\nWhy is it needed?\n\n\nA Block Reader is needed to parallelize reading and parsing of a single data source, for example a file. Simple parallelism of reading data sources can be achieved by multiple partitions reading different source of same type (for files see \nAbstractFileInputOperator\n) but Block Reader partitions can read blocks of same source in parallel and parse them for records ensuring that no record is duplicated or missed.\n\n\nClass Diagram\n\n\n\n\nAbstractBlockReader\n\n\nThis is the abstract implementation that serves as the base for different types of data sources. It defines how a block metadata is processed. The flow diagram below desc
ribes the processing of a block metadata.\n\n\n\n\nPorts\n\n\n\n\n\n\nblocksMetadataInput: input port on which block metadata are received.\n\n\n\n\n\n\nblocksMetadataOutput: output port on which block metadata are emitted if the port is connected. This port is useful when a downstream operator that receives records from block reader may also be interested to know the details of the corresponding blocks.\n\n\n\n\n\n\nmessages: output port on which tuples of type \ncom.datatorrent.lib.io.block.AbstractBlockReader.ReaderRecord\n are emitted. This class encapsulates a \nrecord\n and the \nblockId\n of the corresponding block.\n\n\n\n\n\n\nreaderContext\n\n\nThis is one of the most important fields in the block reader. It is of type \ncom.datatorrent.lib.io.block.ReaderContext\n and is responsible for fetching bytes that make a record. It also lets the reader know how many total bytes were consumed which may not be equal to the total bytes in a record because consumed bytes also include
bytes for the record delimiter which may not be a part of the actual record.\n\n\nOnce the reader creates an input stream for the block (or uses the previous opened stream if the current block is successor of the previous block) it initializes the reader context by invoking \nreaderContext.initialize(stream, blockMetadata, consecutiveBlock);\n. Initialize method is where any implementation of \nReaderContext\n can perform all the operations which have to be executed just before reading the block or create states which are used during the lifetime of reading the block.\n\n\nOnce the initialization is done, \nreaderContext.next()\n is called repeatedly until it returns \nnull\n. It is left to the \nReaderContext\n implementations to decide when a block is completely processed. In cases when a record is split across adjacent blocks, reader context may decide to read ahead of the current block boundary to completely fetch the split record (examples- \nLineReaderContext\n and \nReadAhea
dLineReaderContext\n). In other cases when there isn't a possibility of split record (example- \nFixedBytesReaderContext\n), it returns \nnull\n immediately when the block boundary is reached. The return type of \nreaderContext.next()\n is of type \ncom.datatorrent.lib.io.block.ReaderContext.Entity\n which is just a wrapper for a \nbyte[]\n that represents the record and total bytes used in fetching the record.\n\n\nAbstract methods\n\n\n\n\n\n\nSTREAM setupStream(B block)\n: creating a stream for a block is dependent on the type of source which is not known to AbstractBlockReader. Sub-classes which deal with a specific data source provide this implementation.\n\n\n\n\n\n\nR convertToRecord(byte[] bytes)\n: this converts the array of bytes into the actual instance of record type.\n\n\n\n\n\n\nAuto-scalability\n\n\nBlock reader can auto-scale, that is, depending on the backlog (total number of all the blocks which are waiting in the \nblocksMetadataInput\n port queue of all partition
s) it can create more partitions or reduce them. Details are discussed in the last section which covers the \npartitioner and stats-listener\n.\n\n\nConfiguration\n\n\n\n\nmaxReaders\n: when auto-scaling is enabled, this controls the maximum number of block reader partitions that can be created.\n\n\nminReaders\n: when auto-scaling is enabled, this controls the minimum number of block reader partitions that should always exist.\n\n\ncollectStats\n: this enables or disables auto-scaling. When it is set to \ntrue\n the stats (number of blocks in the queue) are collected and this triggers partitioning; otherwise auto-scaling is disabled.\n\n\nintervalMillis\n: when auto-scaling is enabled, this specifies the interval at which the reader will trigger the logic of computing the backlog and auto-scale.\n\n\n\n\n AbstractFSBlockReader\n\n\nThis abstract implementation deals with files. Different types of file systems that are implementations of \norg.apache.hadoop.fs.FileSystem\n are suppo
rted. The user can override \ngetFSInstance()\n method to create an instance of a specific \nFileSystem\n. By default, filesystem instance is created from the filesytem URI that comes from the default hadoop configuration.\n\n\nprotected FileSystem getFSInstance() throws IOException\n{\n return FileSystem.newInstance(configuration);\n}\n\n\n\n\nIt uses this filesystem instance to setup a stream of type \norg.apache.hadoop.fs.FSDataInputStream\n to read the block.\n\n\n@Override\nprotected FSDataInputStream setupStream(BlockMetadata.FileBlockMetadata block) throws IOException\n{\n return fs.open(new Path(block.getFilePath()));\n}\n\n\n\n\nAll the ports and configurations are derived from the super class. It doesn't provide an implementation of \nconvertToRecord(byte[] bytes)\n method which is delegated to concrete sub-classes.\n\n\nExample Application\n\n\nThis simple dag demonstrates how any concrete implementation of \nAbstractFSBlockReader\n can be plugged into an application. \
n\n\n\n\nIn the above application, file splitter creates block metadata for files which are sent to block reader. Partitions of the block reader parses the file blocks for records which are filtered, transformed and then persisted to a file (created per block). Therefore block reader is parallel partitioned with the 2 downstream operators - filter/converter and record output operator. The code which implements this dag is below.\n\n\npublic class ExampleApplication implements StreamingApplication\n{\n @Override\n public void populateDAG(DAG dag, Configuration configuration)\n {\n FileSplitterInput input = dag.addOperator(\nFile-splitter\n, new FileSplitterInput());\n //any concrete implementation of AbstractFSBlockReader based on the use-case can be added here.\n LineReader blockReader = dag.addOperator(\nBlock-reader\n, new LineReader());\n Filter filter = dag.addOperator(\nFilter\n, new Filter());\n RecordOutputOperator recordOutputOperator = dag.addOperator(\nRe
cord-writer\n, new RecordOutputOperator());\n\n dag.addStream(\nfile-block metadata\n, input.blocksMetadataOutput, blockReader.blocksMetadataInput);\n dag.addStream(\nrecords\n, blockReader.messages, filter.input);\n dag.addStream(\nfiltered-records\n, filter.output, recordOutputOperator.input);\n }\n\n /**\n * Concrete implementation of {@link AbstractFSBlockReader} for which a record is a line in the file.\n */\n public static class LineReader extends AbstractFSBlockReader.AbstractFSReadAheadLineReader\nString\n\n {\n\n @Override\n protected String convertToRecord(byte[] bytes)\n {\n return new String(bytes);\n }\n }\n\n /**\n * Considers any line starting with a '.' as invalid. Emits the valid records.\n */\n public static class Filter extends BaseOperator\n {\n public final transient DefaultOutputPort\nAbstractBlockReader.ReaderRecord\nString\n output = new DefaultOutputPort\n();\n public final transient DefaultInputPort\nAbstractB
lockReader.ReaderRecord\nString\n input = new DefaultInputPort\nAbstractBlockReader.ReaderRecord\nString\n()\n {\n @Override\n public void process(AbstractBlockReader.ReaderRecord\nString\n stringRecord)\n {\n //filter records and transform\n //if the string starts with a '.' ignore the string.\n if (!StringUtils.startsWith(stringRecord.getRecord(), \n.\n)) {\n output.emit(stringRecord);\n }\n }\n };\n }\n\n /**\n * Persists the valid records to corresponding block files.\n */\n public static class RecordOutputOperator extends AbstractFileOutputOperator\nAbstractBlockReader.ReaderRecord\nString\n\n {\n @Override\n protected String getFileName(AbstractBlockReader.ReaderRecord\nString\n tuple)\n {\n return Long.toHexString(tuple.getBlockId());\n }\n\n @Override\n protected byte[] getBytesForTuple(AbstractBlockReader.ReaderRecord\nString\n tuple)\n {\n return tuple.getRecord().getBytes
();\n }\n }\n}\n\n\n\n\nConfiguration to parallel partition block reader with its downstream operators.\n\n\n \nproperty\n\n \nname\ndt.operator.Filter.port.input.attr.PARTITION_PARALLEL\n/name\n\n \nvalue\ntrue\n/value\n\n \n/property\n\n \nproperty\n\n \nname\ndt.operator.Record-writer.port.input.attr.PARTITION_PARALLEL\n/name\n\n \nvalue\ntrue\n/value\n\n \n/property\n\n\n\n\n\nAbstractFSReadAheadLineReader\n\n\nThis extension of \nAbstractFSBlockReader\n parses lines from a block and binds the \nreaderContext\n field to an instance of \nReaderContext.ReadAheadLineReaderContext\n.\n\n\nIt is abstract because it doesn't provide an implementation of \nconvertToRecord(byte[] bytes)\n since the user may want to convert the bytes that make a line into some other type. \n\n\nReadAheadLineReaderContext\n\n\nIn order to handle a line split across adjacent blocks, ReadAheadLineReaderContext always reads beyond the block boundary and ignores the bytes till the first end
-of-line character of all the blocks except the first block of the file. This ensures that no line is missed or incomplete.\n\n\nThis is one of the most common ways of handling a split record. It doesn't require any further information to decide if a line is complete. However, the cost of this consistent way to handle a line split is that it always reads from the next block.\n\n\nAbstractFSLineReader\n\n\nSimilar to \nAbstractFSReadAheadLineReader\n, even this parses lines from a block. However, it binds the \nreaderContext\n field to an instance of \nReaderContext.LineReaderContext\n.\n\n\nLineReaderContext\n\n\nThis handles the line split differently from \nReadAheadLineReaderContext\n. It doesn't always read from the next block. If the end of the last line is aligned with the block boundary then it stops processing the block. It does read from the next block when the boundaries are not aligned, that is, last line extends beyond the block boundary. The result of this is an inconsi
stency in reading the next block.\n\n\nWhen the boundary of the last line of the previous block was aligned with its block, then the first line of the current block is a valid line. However, in the other case the bytes from the block start offset to the first end-of-line character should be ignored. Therefore, this means that any record formed by this reader context has to be validated. For example, if the lines are of fixed size then size of each record can be validated or if each line begins with a special field then that knowledge can be used to check if a record is complete.\n\n\nIf the validations of completeness fails for a line then \nconvertToRecord(byte[] bytes)\n should return null.\n\n\nFSSliceReader\n\n\nA concrete extension of \nAbstractFSBlockReader\n that reads fixed-size \nbyte[]\n from a block and emits the byte array wrapped in \ncom.datatorrent.netlet.util.Slice\n.\n\n\nThis operator binds the \nreaderContext\n to an instance of \nReaderContext.FixedBytesReaderCon
text\n.\n\n\nFixedBytesReaderContext\n\n\nThis implementation of \nReaderContext\n never reads beyond a block boundary which can result in the last \nbyte[]\n of a block to be of a shorter length than the rest of the records.\n\n\nConfiguration\n\n\nreaderContext.length\n: length of each record. By default, this is initialized to the default hdfs block size.\n\n\nPartitioner and StatsListener\n\n\nThe logical instance of the block reader acts as the Partitioner (unless a custom partitioner is set using the operator attribute - \nPARTITIONER\n) as well as a StatsListener. This is because the \n\nAbstractBlockReader\n implements both the \ncom.datatorrent.api.Partitioner\n and \ncom.datatorrent.api.StatsListener\n interfaces and provides an implementation of \ndefinePartitions(...)\n and \nprocessStats(...)\n which make it auto-scalable.\n\n\nprocessStats \n\n\nThe application master invokes \nResponse processStats(BatchedOperatorStats stats)\n method on the logical instance with the
stats (\ntuplesProcessedPSMA\n, \ntuplesEmittedPSMA\n, \nlatencyMA\n, etc.) of each partition. The data which this operator is interested in is the \nqueueSize\n of the input port \nblocksMetadataInput\n.\n\n\nUsually the \nqueueSize\n of an input port gives the count of waiting control tuples plus data tuples. However, if a stats listener is interested only in the count of data tuples then that can be expressed by annotating the class with \n@DataQueueSize\n. In this case \nAbstractBlockReader\n itself is the \nStatsListener\n which is why it is annotated with \n@DataQueueSize\n.\n\n\nThe logical instance caches the queue size per partition and at regular intervals (configured by \nintervalMillis\n) sums these values to find the total backlog which is then used to decide whether re-partitioning is needed. The flow-diagram below describes this logic.\n\n\n\n\nThe goal of this logic is to create as many partitions within bounds (see \nmaxReaders\n and \nminReaders\n above) to quickly
reduce this backlog or if the backlog is small then remove any idle partitions.\n\n\ndefinePartitions\n\n\nBased on the \nrepartitionRequired\n field of the \nResponse\n object which is returned by \nprocessStats\n method, the application master invokes \n\n\nCollection\nPartition\nAbstractBlockReader\n...\n definePartitions(Collection\nPartition\nAbstractBlockReader\n...\n partitions, PartitioningContext context)\n\n\n\n\non the logical instance which is also the partitioner instance. The implementation calculates the difference between required partitions and the existing count of partitions. If this difference is negative, then equivalent number of partitions are removed otherwise new partitions are created. \n\n\nPlease note auto-scaling can be disabled by setting \ncollectStats\n to \nfalse\n. If the use-case requires only static partitioning, then that can be achieved by setting \nStatelessPartitioner\n as the operator attribute- \nPARTITIONER\n on the block reader.",
+ "title": "Block Reader"
+ },
+ {
+ "location": "/operators/block_reader/#block-reader",
+ "text": "This is a scalable operator that reads and parses blocks of data sources into records. A data source can be a file or a message bus that contains records and a block defines a chunk of data in the source by specifying the block offset and the length of the source belonging to the block.",
+ "title": "Block Reader"
+ },
+ {
+ "location": "/operators/block_reader/#why-is-it-needed",
+ "text": "A Block Reader is needed to parallelize reading and parsing of a single data source, for example a file. Simple parallelism of reading data sources can be achieved by multiple partitions reading different source of same type (for files see AbstractFileInputOperator ) but Block Reader partitions can read blocks of same source in parallel and parse them for records ensuring that no record is duplicated or missed.",
+ "title": "Why is it needed?"
+ },
+ {
+ "location": "/operators/block_reader/#class-diagram",
+ "text": "",
+ "title": "Class Diagram"
+ },
+ {
+ "location": "/operators/block_reader/#abstractblockreader",
+ "text": "This is the abstract implementation that serves as the base for different types of data sources. It defines how a block metadata is processed. The flow diagram below describes the processing of a block metadata.",
+ "title": "AbstractBlockReader"
+ },
+ {
+ "location": "/operators/block_reader/#ports",
+ "text": "blocksMetadataInput: input port on which block metadata are received. blocksMetadataOutput: output port on which block metadata are emitted if the port is connected. This port is useful when a downstream operator that receives records from block reader may also be interested to know the details of the corresponding blocks. messages: output port on which tuples of type com.datatorrent.lib.io.block.AbstractBlockReader.ReaderRecord are emitted. This class encapsulates a record and the blockId of the corresponding block.",
+ "title": "Ports"
+ },
+ {
+ "location": "/operators/block_reader/#readercontext",
+ "text": "This is one of the most important fields in the block reader. It is of type com.datatorrent.lib.io.block.ReaderContext and is responsible for fetching bytes that make a record. It also lets the reader know how many total bytes were consumed which may not be equal to the total bytes in a record because consumed bytes also include bytes for the record delimiter which may not be a part of the actual record. Once the reader creates an input stream for the block (or uses the previous opened stream if the current block is successor of the previous block) it initializes the reader context by invoking readerContext.initialize(stream, blockMetadata, consecutiveBlock); . Initialize method is where any implementation of ReaderContext can perform all the operations which have to be executed just before reading the block or create states which are used during the lifetime of reading the block. Once the initialization is done, readerContext.next() is called repeatedl
y until it returns null . It is left to the ReaderContext implementations to decide when a block is completely processed. In cases when a record is split across adjacent blocks, reader context may decide to read ahead of the current block boundary to completely fetch the split record (examples- LineReaderContext and ReadAheadLineReaderContext ). In other cases when there isn't a possibility of split record (example- FixedBytesReaderContext ), it returns null immediately when the block boundary is reached. The return type of readerContext.next() is of type com.datatorrent.lib.io.block.ReaderContext.Entity which is just a wrapper for a byte[] that represents the record and total bytes used in fetching the record.",
+ "title": "readerContext"
+ },
+ {
+ "location": "/operators/block_reader/#abstract-methods",
+ "text": "STREAM setupStream(B block) : creating a stream for a block is dependent on the type of source which is not known to AbstractBlockReader. Sub-classes which deal with a specific data source provide this implementation. R convertToRecord(byte[] bytes) : this converts the array of bytes into the actual instance of record type.",
+ "title": "Abstract methods"
+ },
+ {
+ "location": "/operators/block_reader/#auto-scalability",
+ "text": "Block reader can auto-scale, that is, depending on the backlog (total number of all the blocks which are waiting in the blocksMetadataInput port queue of all partitions) it can create more partitions or reduce them. Details are discussed in the last section which covers the partitioner and stats-listener .",
+ "title": "Auto-scalability"
+ },
+ {
+ "location": "/operators/block_reader/#configuration",
+ "text": "maxReaders : when auto-scaling is enabled, this controls the maximum number of block reader partitions that can be created. minReaders : when auto-scaling is enabled, this controls the minimum number of block reader partitions that should always exist. collectStats : this enables or disables auto-scaling. When it is set to true the stats (number of blocks in the queue) are collected and this triggers partitioning; otherwise auto-scaling is disabled. intervalMillis : when auto-scaling is enabled, this specifies the interval at which the reader will trigger the logic of computing the backlog and auto-scale.",
+ "title": "Configuration"
+ },
+ {
+ "location": "/operators/block_reader/#example-application",
+ "text": "This simple dag demonstrates how any concrete implementation of AbstractFSBlockReader can be plugged into an application. In the above application, file splitter creates block metadata for files which are sent to block reader. Partitions of the block reader parses the file blocks for records which are filtered, transformed and then persisted to a file (created per block). Therefore block reader is parallel partitioned with the 2 downstream operators - filter/converter and record output operator. The code which implements this dag is below. public class ExampleApplication implements StreamingApplication\n{\n @Override\n public void populateDAG(DAG dag, Configuration configuration)\n {\n FileSplitterInput input = dag.addOperator( File-splitter , new FileSplitterInput());\n //any concrete implementation of AbstractFSBlockReader based on the use-case can be added here.\n LineReader blockReader = dag.addOperator( Block-reader , new LineReader());\n
Filter filter = dag.addOperator( Filter , new Filter());\n RecordOutputOperator recordOutputOperator = dag.addOperator( Record-writer , new RecordOutputOperator());\n\n dag.addStream( file-block metadata , input.blocksMetadataOutput, blockReader.blocksMetadataInput);\n dag.addStream( records , blockReader.messages, filter.input);\n dag.addStream( filtered-records , filter.output, recordOutputOperator.input);\n }\n\n /**\n * Concrete implementation of {@link AbstractFSBlockReader} for which a record is a line in the file.\n */\n public static class LineReader extends AbstractFSBlockReader.AbstractFSReadAheadLineReader String \n {\n\n @Override\n protected String convertToRecord(byte[] bytes)\n {\n return new String(bytes);\n }\n }\n\n /**\n * Considers any line starting with a '.' as invalid. Emits the valid records.\n */\n public static class Filter extends BaseOperator\n {\n public final transient DefaultOutputPort AbstractBlockRea
der.ReaderRecord String output = new DefaultOutputPort ();\n public final transient DefaultInputPort AbstractBlockReader.ReaderRecord String input = new DefaultInputPort AbstractBlockReader.ReaderRecord String ()\n {\n @Override\n public void process(AbstractBlockReader.ReaderRecord String stringRecord)\n {\n //filter records and transform\n //if the string starts with a '.' ignore the string.\n if (!StringUtils.startsWith(stringRecord.getRecord(), . )) {\n output.emit(stringRecord);\n }\n }\n };\n }\n\n /**\n * Persists the valid records to corresponding block files.\n */\n public static class RecordOutputOperator extends AbstractFileOutputOperator AbstractBlockReader.ReaderRecord String \n {\n @Override\n protected String getFileName(AbstractBlockReader.ReaderRecord String tuple)\n {\n return Long.toHexString(tuple.getBlockId());\n }\n\n @Override\n protected byte[] getBytesForTup
le(AbstractBlockReader.ReaderRecord String tuple)\n {\n return tuple.getRecord().getBytes();\n }\n }\n} Configuration to parallel partition block reader with its downstream operators. property \n name dt.operator.Filter.port.input.attr.PARTITION_PARALLEL /name \n value true /value \n /property \n property \n name dt.operator.Record-writer.port.input.attr.PARTITION_PARALLEL /name \n value true /value \n /property",
+ "title": "Example Application"
+ },
+ {
+ "location": "/operators/block_reader/#abstractfsreadaheadlinereader",
+ "text": "This extension of AbstractFSBlockReader parses lines from a block and binds the readerContext field to an instance of ReaderContext.ReadAheadLineReaderContext . It is abstract because it doesn't provide an implementation of convertToRecord(byte[] bytes) since the user may want to convert the bytes that make a line into some other type.",
+ "title": "AbstractFSReadAheadLineReader"
+ },
+ {
+ "location": "/operators/block_reader/#readaheadlinereadercontext",
+ "text": "In order to handle a line split across adjacent blocks, ReadAheadLineReaderContext always reads beyond the block boundary and ignores the bytes till the first end-of-line character of all the blocks except the first block of the file. This ensures that no line is missed or incomplete. This is one of the most common ways of handling a split record. It doesn't require any further information to decide if a line is complete. However, the cost of this consistent way to handle a line split is that it always reads from the next block.",
+ "title": "ReadAheadLineReaderContext"
+ },
+ {
+ "location": "/operators/block_reader/#abstractfslinereader",
+ "text": "Similar to AbstractFSReadAheadLineReader , even this parses lines from a block. However, it binds the readerContext field to an instance of ReaderContext.LineReaderContext .",
+ "title": "AbstractFSLineReader"
+ },
+ {
+ "location": "/operators/block_reader/#linereadercontext",
+ "text": "This handles the line split differently from ReadAheadLineReaderContext . It doesn't always read from the next block. If the end of the last line is aligned with the block boundary then it stops processing the block. It does read from the next block when the boundaries are not aligned, that is, last line extends beyond the block boundary. The result of this is an inconsistency in reading the next block. When the boundary of the last line of the previous block was aligned with its block, then the first line of the current block is a valid line. However, in the other case the bytes from the block start offset to the first end-of-line character should be ignored. Therefore, this means that any record formed by this reader context has to be validated. For example, if the lines are of fixed size then size of each record can be validated or if each line begins with a special field then that knowledge can be used to check if a record is complete. If the validations
of completeness fails for a line then convertToRecord(byte[] bytes) should return null.",
+ "title": "LineReaderContext"
+ },
+ {
+ "location": "/operators/block_reader/#fsslicereader",
+ "text": "A concrete extension of AbstractFSBlockReader that reads fixed-size byte[] from a block and emits the byte array wrapped in com.datatorrent.netlet.util.Slice . This operator binds the readerContext to an instance of ReaderContext.FixedBytesReaderContext .",
+ "title": "FSSliceReader"
+ },
+ {
+ "location": "/operators/block_reader/#fixedbytesreadercontext",
+ "text": "This implementation of ReaderContext never reads beyond a block boundary which can result in the last byte[] of a block to be of a shorter length than the rest of the records.",
+ "title": "FixedBytesReaderContext"
+ },
+ {
+ "location": "/operators/block_reader/#configuration_1",
+ "text": "readerContext.length : length of each record. By default, this is initialized to the default hdfs block size.",
+ "title": "Configuration"
+ },
+ {
+ "location": "/operators/block_reader/#partitioner-and-statslistener",
+ "text": "The logical instance of the block reader acts as the Partitioner (unless a custom partitioner is set using the operator attribute - PARTITIONER ) as well as a StatsListener. This is because the AbstractBlockReader implements both the com.datatorrent.api.Partitioner and com.datatorrent.api.StatsListener interfaces and provides an implementation of definePartitions(...) and processStats(...) which make it auto-scalable.",
+ "title": "Partitioner and StatsListener"
+ },
+
<TRUNCATED>
[08/12] apex-site git commit: Adding malhar-3.5.0 documentation
Posted by th...@apache.org.
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+ <a class="current" href=".">Apache Apex Malhar</a>
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+ <ul>
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+ <li class="toctree-l3"><a href="#apache-apex-malhar">Apache Apex Malhar</a></li>
+
+
+ <li class="toctree-l3"><a href="#capabilities-common-across-malhar-operators">Capabilities common across Malhar operators</a></li>
+
+
+ <li class="toctree-l3"><a href="#operator-library-overview">Operator Library Overview</a></li>
+
+ <li><a class="toctree-l4" href="#inputoutput-connectors">Input/output connectors</a></li>
+
+ <li><a class="toctree-l4" href="#parsers">Parsers</a></li>
+
+ <li><a class="toctree-l4" href="#stream-manipulation">Stream manipulation</a></li>
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+ <li><a class="toctree-l4" href="#compute">Compute</a></li>
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+ <li><a class="toctree-l4" href="#languages-support">Languages Support</a></li>
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+<li>
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+ <ul class="subnav">
+ <li><span>Operators</span></li>
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+ <li class="toctree-l1 ">
+ <a class="" href="operators/kafkaInputOperator/">Kafka Input</a>
+
+ </li>
+
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+ <a class="" href="operators/file_splitter/">File Splitter</a>
+
+ </li>
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+ <li class="toctree-l1 ">
+ <a class="" href="operators/block_reader/">Block Reader</a>
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+ </li>
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+ <a class="" href="operators/file_output/">File Output</a>
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+ </li>
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+ <a class="" href="operators/enricher/">Enricher</a>
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+ <h1 id="apache-apex-malhar">Apache Apex Malhar</h1>
+<p>Apache Apex Malhar is an open source operator and codec library that can be used with the <a href="http://apex.apache.org/">Apache Apex</a> platform to build real-time streaming applications. Enabling users to extract value quickly, Malhar operators help get data in, analyze it in real-time, and get data out of Hadoop. In addition to the operators, the library contains a number of demos applications, demonstrating operator features and capabilities.</p>
+<p><img alt="MalharDiagram" src="./images/malhar-operators.png" /></p>
+<h1 id="capabilities-common-across-malhar-operators">Capabilities common across Malhar operators</h1>
+<p>For most streaming platforms, connectors are afterthoughts and often end up being simple \u2018bolt-ons\u2019 to the platform. As a result they often cause performance issues or data loss when put through failure scenarios and scalability requirements. Malhar operators do not face these issues as they were designed to be integral parts of Apex. Hence, they have following core streaming runtime capabilities</p>
+<ol>
+<li><strong>Fault tolerance</strong> \u2013 Malhar operators where applicable have fault tolerance built in. They use the checkpoint capability provided by the framework to ensure that there is no data loss under ANY failure scenario.</li>
+<li><strong>Processing guarantees</strong> \u2013 Malhar operators where applicable provide out of the box support for ALL three processing guarantees \u2013 exactly once, at-least once, and at-most once WITHOUT requiring the user to write any additional code. Some operators, like MQTT operator, deal with source systems that can not track processed data and hence need the operators to keep track of the data. Malhar has support for a generic operator that uses alternate storage like HDFS to facilitate this. Finally for databases that support transactions or support any sort of atomic batch operations Malhar operators can do exactly once down to the tuple level.</li>
+<li><strong>Dynamic updates</strong> \u2013 Based on changing business conditions you often have to tweak several parameters used by the operators in your streaming application without incurring any application downtime. You can also change properties of a Malhar operator at runtime without having to bring down the application.</li>
+<li><strong>Ease of extensibility</strong> \u2013 Malhar operators are based on templates that are easy to extend.</li>
+<li><strong>Partitioning support</strong> \u2013 In streaming applications the input data stream often needs to be partitioned based on the contents of the stream. Also for operators that ingest data from external systems partitioning needs to be done based on the capabilities of the external system. For example with Kafka, the operator can automatically scale up or down based on the changes in the number of Kafka partitions.</li>
+</ol>
+<h1 id="operator-library-overview">Operator Library Overview</h1>
+<h2 id="inputoutput-connectors">Input/output connectors</h2>
+<p>Below is a summary of the various sub categories of input and output operators. Input operators also have a corresponding output operator</p>
+<ul>
+<li><strong>File Systems</strong> \u2013 Most streaming analytics use cases require the data to be stored in HDFS or perhaps S3 if the application is running in AWS. Users often need to re-run their streaming analytical applications against historical data or consume data from upstream processes that are perhaps writing to some NFS share. Apex supports input & output operators for HDFS, S3, NFS & Local Files. There are also File Splitter and Block Reader operators, which can accelecate processing of large files by splitting and paralellizing the work across non-overlapping sets of file blocks.</li>
+<li><strong>Relational Databases</strong> \u2013 Most stream processing use cases require some reference data lookups to enrich, tag or filter streaming data. There is also a need to save results of the streaming analytical computation to a database so an operational dashboard can see them. Apex supports a JDBC operator so you can read/write data from any JDBC compliant RDBMS like Oracle, MySQL, Sqlite, etc.</li>
+<li><strong>NoSQL Databases</strong> \u2013 NoSQL key-value pair databases like Cassandra & HBase are a common part of streaming analytics application architectures to lookup reference data or store results. Malhar has operators for HBase, Cassandra, Accumulo, Aerospike, MongoDB, and CouchDB.</li>
+<li><strong>Messaging Systems</strong> \u2013 Kafka, JMS, and similar systems are the workhorses of messaging infrastructure in most enterprises. Malhar has a robust, industry-tested set of operators to read and write Kafka, JMS, ZeroMQ, and RabbitMQ messages.</li>
+<li><strong>Notification Systems</strong> \u2013 Malhar includes an operator for sending notifications via SMTP.</li>
+<li><strong>In-memory Databases & Caching platforms</strong> - Some streaming use cases need instantaneous access to shared state across the application. Caching platforms and in-memory databases serve this purpose really well. To support these use cases, Malhar has operators for memcached and Redis.</li>
+<li><strong>Social Media</strong> - Malhar includes an operator to connect to the popular Twitter stream fire hose.</li>
+<li><strong>Protocols</strong> - Malhar provides connectors that can communicate in HTTP, RSS, Socket, WebSocket, FTP, and MQTT.</li>
+</ul>
+<h2 id="parsers">Parsers</h2>
+<p>There are many industry vertical specific data formats that a streaming application developer might need to parse. Often there are existing parsers available for these that can be directly plugged into an Apache Apex application. For example in the Telco space, a Java based CDR parser can be directly plugged into Apache Apex operator. To further simplify development experience, Malhar also provides some operators for parsing common formats like XML (DOM & SAX), JSON (flat map converter), Apache log files, syslog, etc.</p>
+<h2 id="stream-manipulation">Stream manipulation</h2>
+<p>Streaming data inevitably needs processing to clean, filter, tag, summarize, etc. The goal of Malhar is to enable the application developer to focus on WHAT needs to be done to the stream to get it in the right format and not worry about the HOW. Malhar has several operators to perform the common stream manipulation actions like \u2013 GroupBy, Join, Distinct/Unique, Limit, OrderBy, Split, Sample, Inner join, Outer join, Select, Update etc.</p>
+<h2 id="compute">Compute</h2>
+<p>One of the most important promises of a streaming analytics platform like Apache Apex is the ability to do analytics in real-time. However delivering on the promise becomes really difficult when the platform does not provide out of the box operators to support variety of common compute functions as the user then has to worry about making these scalable, fault tolerant, stateful, etc. Malhar takes this responsibility away from the application developer by providing a variety of out of the box computational operators.</p>
+<p>Below is just a snapshot of the compute operators available in Malhar</p>
+<ul>
+<li>Statistics and math - Various mathematical and statistical computations over application defined time windows.</li>
+<li>Filtering and pattern matching</li>
+<li>Sorting, maps, frequency, TopN, BottomN</li>
+<li>Random data generators</li>
+</ul>
+<h2 id="languages-support">Languages Support</h2>
+<p>Migrating to a new platform often requires re-use of the existing code that would be difficult or time-consuming to re-write. With this in mind, Malhar supports invocation of code written in other languages by wrapping them in one of the library operators, and allows execution of software written in:</p>
+<ul>
+<li>JavaScript</li>
+<li>Python</li>
+<li>R</li>
+<li>Ruby</li>
+</ul>
+
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[06/12] apex-site git commit: Adding malhar-3.5.0 documentation
Posted by th...@apache.org.
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/js/jquery-2.1.1.min.js
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diff --git a/docs/malhar-3.5/js/jquery-2.1.1.min.js b/docs/malhar-3.5/js/jquery-2.1.1.min.js
new file mode 100644
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[05/12] apex-site git commit: Adding malhar-3.5.0 documentation
Posted by th...@apache.org.
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/js/modernizr-2.8.3.min.js
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/js/modernizr-2.8.3.min.js b/docs/malhar-3.5/js/modernizr-2.8.3.min.js
new file mode 100644
index 0000000..40dd2a9
--- /dev/null
+++ b/docs/malhar-3.5/js/modernizr-2.8.3.min.js
@@ -0,0 +1 @@
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http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/js/theme.js
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/js/theme.js b/docs/malhar-3.5/js/theme.js
new file mode 100644
index 0000000..6396162
--- /dev/null
+++ b/docs/malhar-3.5/js/theme.js
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+$( document ).ready(function() {
+
+ // Shift nav in mobile when clicking the menu.
+ $(document).on('click', "[data-toggle='wy-nav-top']", function() {
+ $("[data-toggle='wy-nav-shift']").toggleClass("shift");
+ $("[data-toggle='rst-versions']").toggleClass("shift");
+ });
+
+ // Close menu when you click a link.
+ $(document).on('click', ".wy-menu-vertical .current ul li a", function() {
+ $("[data-toggle='wy-nav-shift']").removeClass("shift");
+ $("[data-toggle='rst-versions']").toggleClass("shift");
+ });
+
+ $(document).on('click', "[data-toggle='rst-current-version']", function() {
+ $("[data-toggle='rst-versions']").toggleClass("shift-up");
+ });
+
+ // Make tables responsive
+ $("table.docutils:not(.field-list)").wrap("<div class='wy-table-responsive'></div>");
+
+ hljs.initHighlightingOnLoad();
+
+ $('table').addClass('docutils');
+});
+
+window.SphinxRtdTheme = (function (jquery) {
+ var stickyNav = (function () {
+ var navBar,
+ win,
+ stickyNavCssClass = 'stickynav',
+ applyStickNav = function () {
+ if (navBar.height() <= win.height()) {
+ navBar.addClass(stickyNavCssClass);
+ } else {
+ navBar.removeClass(stickyNavCssClass);
+ }
+ },
+ enable = function () {
+ applyStickNav();
+ win.on('resize', applyStickNav);
+ },
+ init = function () {
+ navBar = jquery('nav.wy-nav-side:first');
+ win = jquery(window);
+ };
+ jquery(init);
+ return {
+ enable : enable
+ };
+ }());
+ return {
+ StickyNav : stickyNav
+ };
+}($));
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/main.html
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/main.html b/docs/malhar-3.5/main.html
new file mode 100644
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--- /dev/null
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+{% extends "base.html" %}
+
+{#
+The entry point for the ReadTheDocs Theme.
+
+Any theme customisations should override this file to redefine blocks defined in
+the various templates. The custom theme should only need to define a main.html
+which `{% extends "base.html" %}` and defines various blocks which will replace
+the blocks defined in base.html and its included child templates.
+#}
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/mkdocs/js/lunr-0.5.7.min.js
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/mkdocs/js/lunr-0.5.7.min.js b/docs/malhar-3.5/mkdocs/js/lunr-0.5.7.min.js
new file mode 100644
index 0000000..b72449a
--- /dev/null
+++ b/docs/malhar-3.5/mkdocs/js/lunr-0.5.7.min.js
@@ -0,0 +1,7 @@
+/**
+ * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 0.5.7
+ * Copyright (C) 2014 Oliver Nightingale
+ * MIT Licensed
+ * @license
+ */
+!function(){var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.5.7",t.utils={},t.utils.warn=function(t){return function(e){t.console&&console.warn&&console.warn(e)}}(this),t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var t=Array.prototype.slice.call(arguments),e=t.pop(),n=t;if("function"!=typeof e)throw new TypeError("last argument must be a function");n.forEach(function(t){this.hasHandler(t)||(this.events[t]=[]),this.events[t].push(e)},this)},t.EventEmitter.prototype.removeListener=function(t,e){if(this.hasHandler(t)){var n=this.events[t].indexOf(e);this.events[t].splice(n,1),this.events[t].length||delete this.events[t]}},t.EventEmitter.prototype.emit=function(t){if(this.hasHandler(t)){var e=Array.prototype.slice.call(arguments,1);this.events[t].forEach(function(t){t.apply(void 0,e)})}},t.EventEmitter.prototype.hasHandler=function(t){return t in this.events},t.to
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ace(a,"$1$2")),s=m,a=g,s.test(n)){var $=s.exec(n);s=l,s.test($[1])&&(s=y,n=n.replace(s,""))}else if(a.test(n)){var $=a.exec(n);i=$[1],a=f,a.test(i)&&(n=i,a=S,h=w,u=x,a.test(n)?n+="e":h.test(n)?(s=y,n=n.replace(s,"")):u.test(n)&&(n+="e"))}if(s=k,s.test(n)){var $=s.exec(n);i=$[1],n=i+"i"}if(s=b,s.test(n)){var $=s.exec(n);i=$[1],o=$[2],s=l,s.test(i)&&(n=i+t[o])}if(s=E,s.test(n)){var $=s.exec(n);i=$[1],o=$[2],s=l,s.test(i)&&(n=i+e[o])}if(s=_,a=O,s.test(n)){var $=s.exec(n);i=$[1],s=c,s.test(i)&&(n=i)}else if(a.test(n)){var $=a.exec(n);i=$[1]+$[2],a=c,a.test(i)&&(n=i)}if(s=F,s.test(n)){var $=s.exec(n);i=$[1],s=c,a=p,h=T,(s.test(i)||a.test(i)&&!h.test(i))&&(n=i)}return s=P,a=c,s.test(n)&&a.test(n)&&(s=y,n=n.replace(s,"")),"y"==r&&(n=r.toLowerCase()+n.substr(1)),n};return $}(),t.Pipeline.registerFunction(t.stemmer,"stemmer"),t.stopWordFilter=function(e){return-1===t.stopWordFilter.stopWords.indexOf(e)?e:void 0},t.stopWordFilter.stopWords=new t.SortedSet,t.stopWordFilter.stopWords.length=119
,t.stopWordFilter.stopWords.elements=["","a","able","about","across","after","all","almost","also","am","among","an","and","any","are","as","at","be","because","been","but","by","can","cannot","could","dear","did","do","does","either","else","ever","every","for","from","get","got","had","has","have","he","her","hers","him","his","how","however","i","if","in","into","is","it","its","just","least","let","like","likely","may","me","might","most","must","my","neither","no","nor","not","of","off","often","on","only","or","other","our","own","rather","said","say","says","she","should","since","so","some","than","that","the","their","them","then","there","these","they","this","tis","to","too","twas","us","wants","was","we","were","what","when","where","which","while","who","whom","why","will","with","would","yet","you","your"],t.Pipeline.registerFunction(t.stopWordFilter,"stopWordFilter"),t.trimmer=function(t){return t.replace(/^\W+/,"").replace(/\W+$/,"")},t.Pipeline.registerFunction(t.tr
immer,"trimmer"),t.TokenStore=function(){this.root={docs:{}},this.length=0},t.TokenStore.load=function(t){var e=new this;return e.root=t.root,e.length=t.length,e},t.TokenStore.prototype.add=function(t,e,n){var n=n||this.root,i=t[0],o=t.slice(1);return i in n||(n[i]={docs:{}}),0===o.length?(n[i].docs[e.ref]=e,void(this.length+=1)):this.add(o,e,n[i])},t.TokenStore.prototype.has=function(t){if(!t)return!1;for(var e=this.root,n=0;n<t.length;n++){if(!e[t[n]])return!1;e=e[t[n]]}return!0},t.TokenStore.prototype.getNode=function(t){if(!t)return{};for(var e=this.root,n=0;n<t.length;n++){if(!e[t[n]])return{};e=e[t[n]]}return e},t.TokenStore.prototype.get=function(t,e){return this.getNode(t,e).docs||{}},t.TokenStore.prototype.count=function(t,e){return Object.keys(this.get(t,e)).length},t.TokenStore.prototype.remove=function(t,e){if(t){for(var n=this.root,i=0;i<t.length;i++){if(!(t[i]in n))return;n=n[t[i]]}delete n.docs[e]}},t.TokenStore.prototype.expand=function(t,e){var n=this.getNode(t),i=n
.docs||{},e=e||[];return Object.keys(i).length&&e.push(t),Object.keys(n).forEach(function(n){"docs"!==n&&e.concat(this.expand(t+n,e))},this),e},t.TokenStore.prototype.toJSON=function(){return{root:this.root,length:this.length}},function(t,e){"function"==typeof define&&define.amd?define(e):"object"==typeof exports?module.exports=e():t.lunr=e()}(this,function(){return t})}();
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/mkdocs/js/lunr.min.js
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/mkdocs/js/lunr.min.js b/docs/malhar-3.5/mkdocs/js/lunr.min.js
new file mode 100644
index 0000000..b0198df
--- /dev/null
+++ b/docs/malhar-3.5/mkdocs/js/lunr.min.js
@@ -0,0 +1,7 @@
+/**
+ * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 0.7.0
+ * Copyright (C) 2016 Oliver Nightingale
+ * MIT Licensed
+ * @license
+ */
+!function(){var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.7.0",t.utils={},t.utils.warn=function(t){return function(e){t.console&&console.warn&&console.warn(e)}}(this),t.utils.asString=function(t){return void 0===t||null===t?"":t.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var t=Array.prototype.slice.call(arguments),e=t.pop(),n=t;if("function"!=typeof e)throw new TypeError("last argument must be a function");n.forEach(function(t){this.hasHandler(t)||(this.events[t]=[]),this.events[t].push(e)},this)},t.EventEmitter.prototype.removeListener=function(t,e){if(this.hasHandler(t)){var n=this.events[t].indexOf(e);this.events[t].splice(n,1),this.events[t].length||delete this.events[t]}},t.EventEmitter.prototype.emit=function(t){if(this.hasHandler(t)){var e=Array.prototype.slice.call(arguments,1);this.events[t].forEach(function(t){t.apply(void 0,e)})}},t.E
ventEmitter.prototype.hasHandler=function(t){return t in this.events},t.tokenizer=function(e){return arguments.length&&null!=e&&void 0!=e?Array.isArray(e)?e.map(function(e){return t.utils.asString(e).toLowerCase()}):e.toString().trim().toLowerCase().split(t.tokenizer.seperator):[]},t.tokenizer.seperator=/[\s\-]+/,t.tokenizer.load=function(t){var e=this.registeredFunctions[t];if(!e)throw new Error("Cannot load un-registered function: "+t);return e},t.tokenizer.label="default",t.tokenizer.registeredFunctions={"default":t.tokenizer},t.tokenizer.registerFunction=function(e,n){n in this.registeredFunctions&&t.utils.warn("Overwriting existing tokenizer: "+n),e.label=n,this.registeredFunctions[n]=e},t.Pipeline=function(){this._stack=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in this.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[e.label]=e},t.Pipeline.warnIfFunctionNotRegister
ed=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. This may cause problems when serialising the index.\n",e)},t.Pipeline.load=function(e){var n=new t.Pipeline;return e.forEach(function(e){var i=t.Pipeline.registeredFunctions[e];if(!i)throw new Error("Cannot load un-registered function: "+e);n.add(i)}),n},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(e){t.Pipeline.warnIfFunctionNotRegistered(e),this._stack.push(e)},this)},t.Pipeline.prototype.after=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._stack.indexOf(e);if(-1==i)throw new Error("Cannot find existingFn");i+=1,this._stack.splice(i,0,n)},t.Pipeline.prototype.before=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._stack.indexOf(e);if(-1==i)throw new Error("Cannot find existingFn");this._stack.splice(i,0,n)},t.Pipeline.prototype.remove=function(t){var e=this._stack.i
ndexOf(t);-1!=e&&this._stack.splice(e,1)},t.Pipeline.prototype.run=function(t){for(var e=[],n=t.length,i=this._stack.length,r=0;n>r;r++){for(var o=t[r],s=0;i>s&&(o=this._stack[s](o,r,t),void 0!==o&&""!==o);s++);void 0!==o&&""!==o&&e.push(o)}return e},t.Pipeline.prototype.reset=function(){this._stack=[]},t.Pipeline.prototype.toJSON=function(){return this._stack.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Vector=function(){this._magnitude=null,this.list=void 0,this.length=0},t.Vector.Node=function(t,e,n){this.idx=t,this.val=e,this.next=n},t.Vector.prototype.insert=function(e,n){this._magnitude=void 0;var i=this.list;if(!i)return this.list=new t.Vector.Node(e,n,i),this.length++;if(e<i.idx)return this.list=new t.Vector.Node(e,n,i),this.length++;for(var r=i,o=i.next;void 0!=o;){if(e<o.idx)return r.next=new t.Vector.Node(e,n,o),this.length++;r=o,o=o.next}return r.next=new t.Vector.Node(e,n,o),this.length++},t.Vector.prototype.magnitude=function(){if(this.
_magnitude)return this._magnitude;for(var t,e=this.list,n=0;e;)t=e.val,n+=t*t,e=e.next;return this._magnitude=Math.sqrt(n)},t.Vector.prototype.dot=function(t){for(var e=this.list,n=t.list,i=0;e&&n;)e.idx<n.idx?e=e.next:e.idx>n.idx?n=n.next:(i+=e.val*n.val,e=e.next,n=n.next);return i},t.Vector.prototype.similarity=function(t){return this.dot(t)/(this.magnitude()*t.magnitude())},t.SortedSet=function(){this.length=0,this.elements=[]},t.SortedSet.load=function(t){var e=new this;return e.elements=t,e.length=t.length,e},t.SortedSet.prototype.add=function(){var t,e;for(t=0;t<arguments.length;t++)e=arguments[t],~this.indexOf(e)||this.elements.splice(this.locationFor(e),0,e);this.length=this.elements.length},t.SortedSet.prototype.toArray=function(){return this.elements.slice()},t.SortedSet.prototype.map=function(t,e){return this.elements.map(t,e)},t.SortedSet.prototype.forEach=function(t,e){return this.elements.forEach(t,e)},t.SortedSet.prototype.indexOf=function(t){for(var e=0,n=this.elemen
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={name:t,boost:e.boost||1};return this._fields.push(n),this},t.Index.prototype.ref=function(t){return this._ref=t,this},t.Index.prototype.tokenizer=function(e){var n=e.label&&e.label in t.tokenizer.registeredFunctions;return n||t.utils.warn("Function is not a registered tokenizer. This may cause problems when serialising the index"),this.tokenizerFn=e,this},t.Index.prototype.add=function(e,n){var i={},r=new t.SortedSet,o=e[this._ref],n=void 0===n?!0:n;this._fields.forEach(function(t){var n=this.pipeline.run(this.tokenizerFn(e[t.name]));i[t.name]=n;for(var o=0;o<n.length;o++){var s=n[o];r.add(s),this.corpusTokens.add(s)}},this),this.documentStore.set(o,r);for(var s=0;s<r.length;s++){for(var a=r.elements[s],h=0,u=0;u<this._fields.length;u++){var l=this._fields[u],c=i[l.name],f=c.length;if(f){for(var d=0,p=0;f>p;p++)c[p]===a&&d++;h+=d/f*l.boost}}this.tokenStore.add(a,{ref:o,tf:h})}n&&this.eventEmitter.emit("add",e,this)},t.Index.prototype.remove=function(t,e){var n=t[this._ref],e=void
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e.call(arguments,1);e.unshift(this),t.apply(this,e)},t.Store=function(){this.store={},this.length=0},t.Store.load=function(e){var n=new this;return n.length=e.length,n.store=Object.keys(e.store).reduce(function(n,i){return n[i]=t.SortedSet.load(e.store[i]),n},{}),n},t.Store.prototype.set=function(t,e){this.has(t)||this.length++,this.store[t]=e},t.Store.prototype.get=function(t){return this.store[t]},t.Store.prototype.has=function(t){return t in this.store},t.Store.prototype.remove=function(t){this.has(t)&&(delete this.store[t],this.length--)},t.Store.prototype.toJSON=function(){return{store:this.store,length:this.length}},t.stemmer=function(){var t={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},e={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i=
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http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/mkdocs/js/mustache.min.js
----------------------------------------------------------------------
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http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/mkdocs/js/require.js
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diff --git a/docs/malhar-3.5/mkdocs/js/require.js b/docs/malhar-3.5/mkdocs/js/require.js
new file mode 100644
index 0000000..8638a31
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+registry:h,defined:r,urlFetched:S,defQueue:A,Module:Z,makeModuleMap:p,nextTick:g.nextTick,onError:w,configure:function(a){a.baseUrl&&"/"!==a.baseUrl.charAt(a.baseUrl.length-1)&&(a.baseUrl+="/");var b=j.shim,c={paths:!0,bundles:!0,config:!0,map:!0};B(a,function(a,b){c[b]?(j[b]||(j[b]={}),U(j[b],a,!0,!0)):j[b]=a});a.bundles&&B(a.bundles,function(a,b){v(a,function(a){a!==b&&(aa[a]=b)})});a.shim&&(B(a.shim,function(a,c){H(a)&&(a={deps:a});if((a.exports||a.init)&&!a.exportsFn)a.exportsFn=i.makeShimExports(a);
+b[c]=a}),j.shim=b);a.packages&&v(a.packages,function(a){var b,a="string"===typeof a?{name:a}:a;b=a.name;a.location&&(j.paths[b]=a.location);j.pkgs[b]=a.name+"/"+(a.main||"main").replace(ia,"").replace(Q,"")});B(h,function(a,b){!a.inited&&!a.map.unnormalized&&(a.map=p(b))});if(a.deps||a.callback)i.require(a.deps||[],a.callback)},makeShimExports:function(a){return function(){var b;a.init&&(b=a.init.apply(ba,arguments));return b||a.exports&&da(a.exports)}},makeRequire:function(a,e){function j(c,d,m){var n,
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+s(a).enable()},completeLoad:function(a){var b,c,d=m(j.shim,a)||{},g=d.exports;for(x();A.length;){c=A.shift();if(null===c[0]){c[0]=a;if(b)break;b=!0}else c[0]===a&&(b=!0);E(c)}c=m(h,a);if(!b&&!t(r,a)&&c&&!c.inited){if(j.enforceDefine&&(!g||!da(g)))return e(a)?void 0:w(C("nodefine","No define call for "+a,null,[a]));E([a,d.deps||[],d.exportsFn])}D()},nameToUrl:function(a,b,c){var d,e,h;(d=m(j.pkgs,a))&&(a=d);if(d=m(aa,a))return i.nameToUrl(d,b,c);if(g.jsExtRegExp.test(a))d=a+(b||"");else{d=j.paths;
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+onScriptError:function(a){var b=I(a);if(!e(b.id))return w(C("scripterror","Script error for: "+b.id,a,[b.id]))}};i.require=i.makeRequire();return i}var g,x,y,D,I,E,N,J,s,O,ka=/(\/\*([\s\S]*?)\*\/|([^:]|^)\/\/(.*)$)/mg,la=/[^.]\s*require\s*\(\s*["']([^'"\s]+)["']\s*\)/g,Q=/\.js$/,ia=/^\.\//;x=Object.prototype;var K=x.toString,fa=x.hasOwnProperty,ha=Array.prototype.splice,z=!!("undefined"!==typeof window&&"undefined"!==typeof navigator&&window.document),ea=!z&&"undefined"!==typeof importScripts,ja=
+z&&"PLAYSTATION 3"===navigator.platform?/^complete$/:/^(complete|loaded)$/,Y="undefined"!==typeof opera&&"[object Opera]"===opera.toString(),F={},q={},R=[],M=!1;if("undefined"===typeof define){if("undefined"!==typeof requirejs){if(G(requirejs))return;q=requirejs;requirejs=void 0}"undefined"!==typeof require&&!G(require)&&(q=require,require=void 0);g=requirejs=function(b,c,d,e){var n,p="_";!H(b)&&"string"!==typeof b&&(n=b,H(c)?(b=c,c=d,d=e):b=[]);n&&n.context&&(p=n.context);(e=m(F,p))||(e=F[p]=g.s.newContext(p));
+n&&e.configure(n);return e.require(b,c,d)};g.config=function(b){return g(b)};g.nextTick="undefined"!==typeof setTimeout?function(b){setTimeout(b,4)}:function(b){b()};require||(require=g);g.version="2.1.16";g.jsExtRegExp=/^\/|:|\?|\.js$/;g.isBrowser=z;x=g.s={contexts:F,newContext:ga};g({});v(["toUrl","undef","defined","specified"],function(b){g[b]=function(){var c=F._;return c.require[b].apply(c,arguments)}});if(z&&(y=x.head=document.getElementsByTagName("head")[0],D=document.getElementsByTagName("base")[0]))y=
+x.head=D.parentNode;g.onError=ca;g.createNode=function(b){var c=b.xhtml?document.createElementNS("http://www.w3.org/1999/xhtml","html:script"):document.createElement("script");c.type=b.scriptType||"text/javascript";c.charset="utf-8";c.async=!0;return c};g.load=function(b,c,d){var e=b&&b.config||{};if(z)return e=g.createNode(e,c,d),e.setAttribute("data-requirecontext",b.contextName),e.setAttribute("data-requiremodule",c),e.attachEvent&&!(e.attachEvent.toString&&0>e.attachEvent.toString().indexOf("[native code"))&&
+!Y?(M=!0,e.attachEvent("onreadystatechange",b.onScriptLoad)):(e.addEventListener("load",b.onScriptLoad,!1),e.addEventListener("error",b.onScriptError,!1)),e.src=d,J=e,D?y.insertBefore(e,D):y.appendChild(e),J=null,e;if(ea)try{importScripts(d),b.completeLoad(c)}catch(m){b.onError(C("importscripts","importScripts failed for "+c+" at "+d,m,[c]))}};z&&!q.skipDataMain&&T(document.getElementsByTagName("script"),function(b){y||(y=b.parentNode);if(I=b.getAttribute("data-main"))return s=I,q.baseUrl||(E=s.split("/"),
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+b.readyState)return N=b}),e=N;e&&(b||(b=e.getAttribute("data-requiremodule")),g=F[e.getAttribute("data-requirecontext")])}(g?g.defQueue:R).push([b,c,d])};define.amd={jQuery:!0};g.exec=function(b){return eval(b)};g(q)}})(this);
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/mkdocs/js/search-results-template.mustache
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/mkdocs/js/search-results-template.mustache b/docs/malhar-3.5/mkdocs/js/search-results-template.mustache
new file mode 100644
index 0000000..a8b3862
--- /dev/null
+++ b/docs/malhar-3.5/mkdocs/js/search-results-template.mustache
@@ -0,0 +1,4 @@
+<article>
+ <h3><a href="{{location}}">{{title}}</a></h3>
+ <p>{{summary}}</p>
+</article>
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/mkdocs/js/search.js
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/mkdocs/js/search.js b/docs/malhar-3.5/mkdocs/js/search.js
new file mode 100644
index 0000000..88d563a
--- /dev/null
+++ b/docs/malhar-3.5/mkdocs/js/search.js
@@ -0,0 +1,88 @@
+require([
+ base_url + '/mkdocs/js/mustache.min.js',
+ base_url + '/mkdocs/js/lunr.min.js',
+ 'text!search-results-template.mustache',
+ 'text!../search_index.json',
+], function (Mustache, lunr, results_template, data) {
+ "use strict";
+
+ function getSearchTerm()
+ {
+ var sPageURL = window.location.search.substring(1);
+ var sURLVariables = sPageURL.split('&');
+ for (var i = 0; i < sURLVariables.length; i++)
+ {
+ var sParameterName = sURLVariables[i].split('=');
+ if (sParameterName[0] == 'q')
+ {
+ return decodeURIComponent(sParameterName[1].replace(/\+/g, '%20'));
+ }
+ }
+ }
+
+ var index = lunr(function () {
+ this.field('title', {boost: 10});
+ this.field('text');
+ this.ref('location');
+ });
+
+ data = JSON.parse(data);
+ var documents = {};
+
+ for (var i=0; i < data.docs.length; i++){
+ var doc = data.docs[i];
+ doc.location = base_url + doc.location;
+ index.add(doc);
+ documents[doc.location] = doc;
+ }
+
+ var search = function(){
+
+ var query = document.getElementById('mkdocs-search-query').value;
+ var search_results = document.getElementById("mkdocs-search-results");
+ while (search_results.firstChild) {
+ search_results.removeChild(search_results.firstChild);
+ }
+
+ if(query === ''){
+ return;
+ }
+
+ var results = index.search(query);
+
+ if (results.length > 0){
+ for (var i=0; i < results.length; i++){
+ var result = results[i];
+ doc = documents[result.ref];
+ doc.base_url = base_url;
+ doc.summary = doc.text.substring(0, 200);
+ var html = Mustache.to_html(results_template, doc);
+ search_results.insertAdjacentHTML('beforeend', html);
+ }
+ } else {
+ search_results.insertAdjacentHTML('beforeend', "<p>No results found</p>");
+ }
+
+ if(jQuery){
+ /*
+ * We currently only automatically hide bootstrap models. This
+ * requires jQuery to work.
+ */
+ jQuery('#mkdocs_search_modal a').click(function(){
+ jQuery('#mkdocs_search_modal').modal('hide');
+ })
+ }
+
+ };
+
+ var search_input = document.getElementById('mkdocs-search-query');
+
+ var term = getSearchTerm();
+ if (term){
+ search_input.value = term;
+ search();
+ }
+
+ search_input.addEventListener("keyup", search);
+
+});
[04/12] apex-site git commit: Adding malhar-3.5.0 documentation
Posted by th...@apache.org.
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/mkdocs/js/text.js
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/mkdocs/js/text.js b/docs/malhar-3.5/mkdocs/js/text.js
new file mode 100644
index 0000000..17921b6
--- /dev/null
+++ b/docs/malhar-3.5/mkdocs/js/text.js
@@ -0,0 +1,390 @@
+/**
+ * @license RequireJS text 2.0.12 Copyright (c) 2010-2014, The Dojo Foundation All Rights Reserved.
+ * Available via the MIT or new BSD license.
+ * see: http://github.com/requirejs/text for details
+ */
+/*jslint regexp: true */
+/*global require, XMLHttpRequest, ActiveXObject,
+ define, window, process, Packages,
+ java, location, Components, FileUtils */
+
+define(['module'], function (module) {
+ 'use strict';
+
+ var text, fs, Cc, Ci, xpcIsWindows,
+ progIds = ['Msxml2.XMLHTTP', 'Microsoft.XMLHTTP', 'Msxml2.XMLHTTP.4.0'],
+ xmlRegExp = /^\s*<\?xml(\s)+version=[\'\"](\d)*.(\d)*[\'\"](\s)*\?>/im,
+ bodyRegExp = /<body[^>]*>\s*([\s\S]+)\s*<\/body>/im,
+ hasLocation = typeof location !== 'undefined' && location.href,
+ defaultProtocol = hasLocation && location.protocol && location.protocol.replace(/\:/, ''),
+ defaultHostName = hasLocation && location.hostname,
+ defaultPort = hasLocation && (location.port || undefined),
+ buildMap = {},
+ masterConfig = (module.config && module.config()) || {};
+
+ text = {
+ version: '2.0.12',
+
+ strip: function (content) {
+ //Strips <?xml ...?> declarations so that external SVG and XML
+ //documents can be added to a document without worry. Also, if the string
+ //is an HTML document, only the part inside the body tag is returned.
+ if (content) {
+ content = content.replace(xmlRegExp, "");
+ var matches = content.match(bodyRegExp);
+ if (matches) {
+ content = matches[1];
+ }
+ } else {
+ content = "";
+ }
+ return content;
+ },
+
+ jsEscape: function (content) {
+ return content.replace(/(['\\])/g, '\\$1')
+ .replace(/[\f]/g, "\\f")
+ .replace(/[\b]/g, "\\b")
+ .replace(/[\n]/g, "\\n")
+ .replace(/[\t]/g, "\\t")
+ .replace(/[\r]/g, "\\r")
+ .replace(/[\u2028]/g, "\\u2028")
+ .replace(/[\u2029]/g, "\\u2029");
+ },
+
+ createXhr: masterConfig.createXhr || function () {
+ //Would love to dump the ActiveX crap in here. Need IE 6 to die first.
+ var xhr, i, progId;
+ if (typeof XMLHttpRequest !== "undefined") {
+ return new XMLHttpRequest();
+ } else if (typeof ActiveXObject !== "undefined") {
+ for (i = 0; i < 3; i += 1) {
+ progId = progIds[i];
+ try {
+ xhr = new ActiveXObject(progId);
+ } catch (e) {}
+
+ if (xhr) {
+ progIds = [progId]; // so faster next time
+ break;
+ }
+ }
+ }
+
+ return xhr;
+ },
+
+ /**
+ * Parses a resource name into its component parts. Resource names
+ * look like: module/name.ext!strip, where the !strip part is
+ * optional.
+ * @param {String} name the resource name
+ * @returns {Object} with properties "moduleName", "ext" and "strip"
+ * where strip is a boolean.
+ */
+ parseName: function (name) {
+ var modName, ext, temp,
+ strip = false,
+ index = name.indexOf("."),
+ isRelative = name.indexOf('./') === 0 ||
+ name.indexOf('../') === 0;
+
+ if (index !== -1 && (!isRelative || index > 1)) {
+ modName = name.substring(0, index);
+ ext = name.substring(index + 1, name.length);
+ } else {
+ modName = name;
+ }
+
+ temp = ext || modName;
+ index = temp.indexOf("!");
+ if (index !== -1) {
+ //Pull off the strip arg.
+ strip = temp.substring(index + 1) === "strip";
+ temp = temp.substring(0, index);
+ if (ext) {
+ ext = temp;
+ } else {
+ modName = temp;
+ }
+ }
+
+ return {
+ moduleName: modName,
+ ext: ext,
+ strip: strip
+ };
+ },
+
+ xdRegExp: /^((\w+)\:)?\/\/([^\/\\]+)/,
+
+ /**
+ * Is an URL on another domain. Only works for browser use, returns
+ * false in non-browser environments. Only used to know if an
+ * optimized .js version of a text resource should be loaded
+ * instead.
+ * @param {String} url
+ * @returns Boolean
+ */
+ useXhr: function (url, protocol, hostname, port) {
+ var uProtocol, uHostName, uPort,
+ match = text.xdRegExp.exec(url);
+ if (!match) {
+ return true;
+ }
+ uProtocol = match[2];
+ uHostName = match[3];
+
+ uHostName = uHostName.split(':');
+ uPort = uHostName[1];
+ uHostName = uHostName[0];
+
+ return (!uProtocol || uProtocol === protocol) &&
+ (!uHostName || uHostName.toLowerCase() === hostname.toLowerCase()) &&
+ ((!uPort && !uHostName) || uPort === port);
+ },
+
+ finishLoad: function (name, strip, content, onLoad) {
+ content = strip ? text.strip(content) : content;
+ if (masterConfig.isBuild) {
+ buildMap[name] = content;
+ }
+ onLoad(content);
+ },
+
+ load: function (name, req, onLoad, config) {
+ //Name has format: some.module.filext!strip
+ //The strip part is optional.
+ //if strip is present, then that means only get the string contents
+ //inside a body tag in an HTML string. For XML/SVG content it means
+ //removing the <?xml ...?> declarations so the content can be inserted
+ //into the current doc without problems.
+
+ // Do not bother with the work if a build and text will
+ // not be inlined.
+ if (config && config.isBuild && !config.inlineText) {
+ onLoad();
+ return;
+ }
+
+ masterConfig.isBuild = config && config.isBuild;
+
+ var parsed = text.parseName(name),
+ nonStripName = parsed.moduleName +
+ (parsed.ext ? '.' + parsed.ext : ''),
+ url = req.toUrl(nonStripName),
+ useXhr = (masterConfig.useXhr) ||
+ text.useXhr;
+
+ // Do not load if it is an empty: url
+ if (url.indexOf('empty:') === 0) {
+ onLoad();
+ return;
+ }
+
+ //Load the text. Use XHR if possible and in a browser.
+ if (!hasLocation || useXhr(url, defaultProtocol, defaultHostName, defaultPort)) {
+ text.get(url, function (content) {
+ text.finishLoad(name, parsed.strip, content, onLoad);
+ }, function (err) {
+ if (onLoad.error) {
+ onLoad.error(err);
+ }
+ });
+ } else {
+ //Need to fetch the resource across domains. Assume
+ //the resource has been optimized into a JS module. Fetch
+ //by the module name + extension, but do not include the
+ //!strip part to avoid file system issues.
+ req([nonStripName], function (content) {
+ text.finishLoad(parsed.moduleName + '.' + parsed.ext,
+ parsed.strip, content, onLoad);
+ });
+ }
+ },
+
+ write: function (pluginName, moduleName, write, config) {
+ if (buildMap.hasOwnProperty(moduleName)) {
+ var content = text.jsEscape(buildMap[moduleName]);
+ write.asModule(pluginName + "!" + moduleName,
+ "define(function () { return '" +
+ content +
+ "';});\n");
+ }
+ },
+
+ writeFile: function (pluginName, moduleName, req, write, config) {
+ var parsed = text.parseName(moduleName),
+ extPart = parsed.ext ? '.' + parsed.ext : '',
+ nonStripName = parsed.moduleName + extPart,
+ //Use a '.js' file name so that it indicates it is a
+ //script that can be loaded across domains.
+ fileName = req.toUrl(parsed.moduleName + extPart) + '.js';
+
+ //Leverage own load() method to load plugin value, but only
+ //write out values that do not have the strip argument,
+ //to avoid any potential issues with ! in file names.
+ text.load(nonStripName, req, function (value) {
+ //Use own write() method to construct full module value.
+ //But need to create shell that translates writeFile's
+ //write() to the right interface.
+ var textWrite = function (contents) {
+ return write(fileName, contents);
+ };
+ textWrite.asModule = function (moduleName, contents) {
+ return write.asModule(moduleName, fileName, contents);
+ };
+
+ text.write(pluginName, nonStripName, textWrite, config);
+ }, config);
+ }
+ };
+
+ if (masterConfig.env === 'node' || (!masterConfig.env &&
+ typeof process !== "undefined" &&
+ process.versions &&
+ !!process.versions.node &&
+ !process.versions['node-webkit'])) {
+ //Using special require.nodeRequire, something added by r.js.
+ fs = require.nodeRequire('fs');
+
+ text.get = function (url, callback, errback) {
+ try {
+ var file = fs.readFileSync(url, 'utf8');
+ //Remove BOM (Byte Mark Order) from utf8 files if it is there.
+ if (file.indexOf('\uFEFF') === 0) {
+ file = file.substring(1);
+ }
+ callback(file);
+ } catch (e) {
+ if (errback) {
+ errback(e);
+ }
+ }
+ };
+ } else if (masterConfig.env === 'xhr' || (!masterConfig.env &&
+ text.createXhr())) {
+ text.get = function (url, callback, errback, headers) {
+ var xhr = text.createXhr(), header;
+ xhr.open('GET', url, true);
+
+ //Allow plugins direct access to xhr headers
+ if (headers) {
+ for (header in headers) {
+ if (headers.hasOwnProperty(header)) {
+ xhr.setRequestHeader(header.toLowerCase(), headers[header]);
+ }
+ }
+ }
+
+ //Allow overrides specified in config
+ if (masterConfig.onXhr) {
+ masterConfig.onXhr(xhr, url);
+ }
+
+ xhr.onreadystatechange = function (evt) {
+ var status, err;
+ //Do not explicitly handle errors, those should be
+ //visible via console output in the browser.
+ if (xhr.readyState === 4) {
+ status = xhr.status || 0;
+ if (status > 399 && status < 600) {
+ //An http 4xx or 5xx error. Signal an error.
+ err = new Error(url + ' HTTP status: ' + status);
+ err.xhr = xhr;
+ if (errback) {
+ errback(err);
+ }
+ } else {
+ callback(xhr.responseText);
+ }
+
+ if (masterConfig.onXhrComplete) {
+ masterConfig.onXhrComplete(xhr, url);
+ }
+ }
+ };
+ xhr.send(null);
+ };
+ } else if (masterConfig.env === 'rhino' || (!masterConfig.env &&
+ typeof Packages !== 'undefined' && typeof java !== 'undefined')) {
+ //Why Java, why is this so awkward?
+ text.get = function (url, callback) {
+ var stringBuffer, line,
+ encoding = "utf-8",
+ file = new java.io.File(url),
+ lineSeparator = java.lang.System.getProperty("line.separator"),
+ input = new java.io.BufferedReader(new java.io.InputStreamReader(new java.io.FileInputStream(file), encoding)),
+ content = '';
+ try {
+ stringBuffer = new java.lang.StringBuffer();
+ line = input.readLine();
+
+ // Byte Order Mark (BOM) - The Unicode Standard, version 3.0, page 324
+ // http://www.unicode.org/faq/utf_bom.html
+
+ // Note that when we use utf-8, the BOM should appear as "EF BB BF", but it doesn't due to this bug in the JDK:
+ // http://bugs.sun.com/bugdatabase/view_bug.do?bug_id=4508058
+ if (line && line.length() && line.charAt(0) === 0xfeff) {
+ // Eat the BOM, since we've already found the encoding on this file,
+ // and we plan to concatenating this buffer with others; the BOM should
+ // only appear at the top of a file.
+ line = line.substring(1);
+ }
+
+ if (line !== null) {
+ stringBuffer.append(line);
+ }
+
+ while ((line = input.readLine()) !== null) {
+ stringBuffer.append(lineSeparator);
+ stringBuffer.append(line);
+ }
+ //Make sure we return a JavaScript string and not a Java string.
+ content = String(stringBuffer.toString()); //String
+ } finally {
+ input.close();
+ }
+ callback(content);
+ };
+ } else if (masterConfig.env === 'xpconnect' || (!masterConfig.env &&
+ typeof Components !== 'undefined' && Components.classes &&
+ Components.interfaces)) {
+ //Avert your gaze!
+ Cc = Components.classes;
+ Ci = Components.interfaces;
+ Components.utils['import']('resource://gre/modules/FileUtils.jsm');
+ xpcIsWindows = ('@mozilla.org/windows-registry-key;1' in Cc);
+
+ text.get = function (url, callback) {
+ var inStream, convertStream, fileObj,
+ readData = {};
+
+ if (xpcIsWindows) {
+ url = url.replace(/\//g, '\\');
+ }
+
+ fileObj = new FileUtils.File(url);
+
+ //XPCOM, you so crazy
+ try {
+ inStream = Cc['@mozilla.org/network/file-input-stream;1']
+ .createInstance(Ci.nsIFileInputStream);
+ inStream.init(fileObj, 1, 0, false);
+
+ convertStream = Cc['@mozilla.org/intl/converter-input-stream;1']
+ .createInstance(Ci.nsIConverterInputStream);
+ convertStream.init(inStream, "utf-8", inStream.available(),
+ Ci.nsIConverterInputStream.DEFAULT_REPLACEMENT_CHARACTER);
+
+ convertStream.readString(inStream.available(), readData);
+ convertStream.close();
+ inStream.close();
+ callback(readData.value);
+ } catch (e) {
+ throw new Error((fileObj && fileObj.path || '') + ': ' + e);
+ }
+ };
+ }
+ return text;
+});
[09/12] apex-site git commit: Adding malhar-3.5.0 documentation
Posted by th...@apache.org.
http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/fonts/fontawesome-webfont.svg
----------------------------------------------------------------------
diff --git a/docs/malhar-3.5/fonts/fontawesome-webfont.svg b/docs/malhar-3.5/fonts/fontawesome-webfont.svg
new file mode 100755
index 0000000..2edb4ec
--- /dev/null
+++ b/docs/malhar-3.5/fonts/fontawesome-webfont.svg
@@ -0,0 +1,399 @@
+<?xml version="1.0" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
+<svg xmlns="http://www.w3.org/2000/svg">
+<metadata></metadata>
+<defs>
+<font id="fontawesomeregular" horiz-adv-x="1536" >
+<font-face units-per-em="1792" ascent="1536" descent="-256" />
+<missing-glyph horiz-adv-x="448" />
+<glyph unicode=" " horiz-adv-x="448" />
+<glyph unicode="	" horiz-adv-x="448" />
+<glyph unicode=" " horiz-adv-x="448" />
+<glyph unicode="¨" horiz-adv-x="1792" />
+<glyph unicode="©" horiz-adv-x="1792" />
+<glyph unicode="®" horiz-adv-x="1792" />
+<glyph unicode="´" horiz-adv-x="1792" />
+<glyph unicode="Æ" horiz-adv-x="1792" />
+<glyph unicode=" " horiz-adv-x="768" />
+<glyph unicode=" " />
+<glyph unicode=" " horiz-adv-x="768" />
+<glyph unicode=" " />
+<glyph unicode=" " horiz-adv-x="512" />
+<glyph unicode=" " horiz-adv-x="384" />
+<glyph unicode=" " horiz-adv-x="256" />
+<glyph unicode=" " horiz-adv-x="256" />
+<glyph unicode=" " horiz-adv-x="192" />
+<glyph unicode=" " horiz-adv-x="307" />
+<glyph unicode=" " horiz-adv-x="85" />
+<glyph unicode=" " horiz-adv-x="307" />
+<glyph unicode=" " horiz-adv-x="384" />
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+<glyph unicode="∞" horiz-adv-x="1792" />
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http://git-wip-us.apache.org/repos/asf/apex-site/blob/37ae8b76/docs/malhar-3.5/fonts/fontawesome-webfont.ttf
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