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+
+ <li class="toctree-l3"><a href="#kafka-input-operator">KAFKA INPUT OPERATOR</a></li>
+
+ <li><a class="toctree-l4" href="#introduction">Introduction</a></li>
+
+ <li><a class="toctree-l4" href="#kafka-input-operator-for-kafka-08x">Kafka Input Operator for Kafka 0.8.x</a></li>
+
+ <li><a class="toctree-l4" href="#abstractkafkainputoperator">AbstractKafkaInputOperator</a></li>
+
+ <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>
+
+ <li><a class="toctree-l4" href="#concrete-classes">Concrete Classes</a></li>
+
+ <li><a class="toctree-l4" href="#application-example">Application Example</a></li>
+
+ <li><a class="toctree-l4" href="#kafka-input-operator-for-kafka-09x">Kafka Input Operator for Kafka 0.9.x</a></li>
+
+ <li><a class="toctree-l4" href="#abstractkafkainputoperator_1">AbstractKafkaInputOperator</a></li>
+
+ <li><a class="toctree-l4" href="#concrete-classes_1">Concrete Classes</a></li>
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+ <li><a class="toctree-l4" href="#application-example_1">Application Example</a></li>
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+
+ <h1 id="kafka-input-operator">KAFKA INPUT OPERATOR</h1>
+<h3 id="introduction">Introduction</h3>
+<p><a href="http://kafka.apache.org">Apache Kafka</a> is a pull-based and distributed publish subscribe messaging system,
+topics are partitioned and replicated across nodes. </p>
+<p>The Kafka input operator consumes data from the partitions of a Kafka topic for processing in Apex.
+The operator has the ability to automatically scale with the Kafka partitioning for high throughput.
+It is fault-tolerant (consumer offset checkpointing) and guarantees idempotency to allow exactly-once results in the downstream pipeline.</p>
+<p>For more information about the operator design see this <a href="http://www.slideshare.net/ApacheApex/apache-apex-kafka-input-operator">presentation</a>
+and for processing guarantees this <a href="https://www.datatorrent.com/blog/end-to-end-exactly-once-with-apache-apex/">blog</a>.</p>
+<p>There are two separate implementations of the input operator,
+one built against Kafka 0.8 client and a newer version for the
+Kafka 0.9 consumer API that also works with MapR Streams.
+These reside in different packages and are described separately below.</p>
+<h3 id="kafka-input-operator-for-kafka-08x">Kafka Input Operator for Kafka 0.8.x</h3>
+<p>Package: <code>com.datatorrent.contrib.kafka</code></p>
+<p>Maven artifact: <a href="https://mvnrepository.com/artifact/org.apache.apex/malhar-contrib">malhar-contrib</a></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><code>void emitTuple(Message message)</code>: 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 uses the Kafka 0.8.2.1 client consumer API
+and will work with 0.8.x and 0.7.x versions of Kafka broker.</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><code>Map <KafkaPartition, Long> loadInitialOffsets()</code>: Specifies the initial offset for consuming messages; called at the activation stage.</p>
+<p><code>updateOffsets(Map<KafkaPartition, Long> offsetsOfPartitions)</code>: �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 to emit messages through single output port.</p>
+<h4 id="ports">Ports</h4>
+<p><code>outputPort <T></code>: Tuples extracted from Kafka messages are emitted through this port.</p>
+<h4 id="abstract-methods_3">Abstract Methods</h4>
+<p><code>T getTuple(Message msg)</code>: Converts the Kafka message to tuple.</p>
+<h3 id="concrete-classes">Concrete Classes</h3>
+<ol>
+<li>KafkaSinglePortStringInputOperator: extends <code>AbstractSinglePortKafkaInputOperator</code>, extracts string from Kafka message.</li>
+<li>KafkaSinglePortByteArrayInputOperator: extends <code>AbstractSinglePortKafkaInputOperator</code>, extracts byte array from Kafka message.</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>
+
+<h3 id="kafka-input-operator-for-kafka-09x">Kafka Input Operator for Kafka 0.9.x</h3>
+<p>Package: <code>org.apache.apex.malhar.kafka</code></p>
+<p>Maven Artifact: <a href="https://mvnrepository.com/artifact/org.apache.apex/malhar-kafka">malhar-kafka</a></p>
+<p>This version uses the new 0.9 version of consumer API and works with Kafka broker version 0.9 and later.
+The operator is fault-tolerant, scalable and supports input from multiple clusters and multiple topics in a single operator instance.</p>
+<h4 id="pre-requisites_1">Pre-requisites</h4>
+<p>This operator requires version 0.9.0 or later of the Kafka Consumer API.</p>
+<h3 id="abstractkafkainputoperator_1">AbstractKafkaInputOperator</h3>
+<h4 id="ports_1">Ports</h4>
+<hr />
+<p>This abstract class doesn't have any ports.</p>
+<h4 id="configuration-properties">Configuration properties</h4>
+<hr />
+<ul>
+<li>
+<p><strong><em>clusters</em></strong> - String[]</p>
+<ul>
+<li>Mandatory Parameter.</li>
+<li>Specifies the Kafka clusters that you want to consume messages from. To configure multi-cluster support, you need to specify the clusters separated by ";".</li>
+</ul>
+</li>
+<li>
+<p><strong><em>topics</em></strong> - String[]</p>
+<ul>
+<li>Mandatory Parameter.</li>
+<li>Specified the Kafka topics that you want to consume messages from. If you want multi-topic support, then specify the topics separated by ",".</li>
+</ul>
+</li>
+<li>
+<p><strong><em>strategy</em></strong> - PartitionStrategy</p>
+<ul>
+<li>
+<p>Operator supports two types of partitioning strategies, <code>ONE_TO_ONE</code> and <code>ONE_TO_MANY</code>.</p>
+<p><code>ONE_TO_ONE</code>: 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.
+<code>ONE_TO_MANY</code>: 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 = <code>PartitionStrategy.ONE_TO_ONE</code>.</p>
+</li>
+</ul>
+</li>
+<li>
+<p><strong><em>initialPartitionCount</em></strong> - Integer</p>
+<ul>
+<li>When the ONE_TO_MANY partition strategy is enabled, this value indicates the number of Kafka input operator instances.
+ Default Value = 1.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>repartitionInterval</em></strong> - Long</p>
+<ul>
+<li>Interval specified in milliseconds. This value specifies the minimum time required between two repartition actions.
+ Default Value = 30 Seconds.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>repartitionCheckInterval</em></strong> - Long</p>
+<ul>
+<li>Interval specified in milliseconds. This value specifies the minimum interval between two stat checks.
+ Default Value = 5 Seconds.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>maxTuplesPerWindow</em></strong> - Integer</p>
+<ul>
+<li>Controls the maximum number of messages emitted in each streaming window from this operator. Minimum value is 1.
+ Default value = <code>MAX_VALUE</code> </li>
+</ul>
+</li>
+<li>
+<p><strong><em>initialOffset</em></strong> - InitialOffset</p>
+<ul>
+<li>Indicates the type of offset i.e, <code>EARLIEST</code> or <code>LATEST</code> or <code>APPLICATION_OR_EARLIEST</code> or <code>APPLICATION_OR_LATEST</code>.
+ <code>LATEST</code> => Consume new messages from latest offset in the topic.
+ <code>EARLIEST</code> => Consume all messages available in the topic.
+ <code>APPLICATION_OR_EARLIEST</code> => Consume messages from committed position from last run. If there is no committed offset, then start consuming from beginning.
+ <code>APPLICATION_OR_LATEST</code> => Consumes messages from committed position from last run. If a committed offset is unavailable, then start consuming from latest position.
+ Default value = <code>InitialOffset.APPLICATION_OR_LATEST</code></li>
+</ul>
+</li>
+<li>
+<p><strong><em>metricsRefreshInterval</em></strong> - Long</p>
+<ul>
+<li>Interval specified in milliseconds. This value specifies the minimum interval between two metric stat updates.
+ Default value = 5 Seconds.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>consumerTimeout</em></strong> - Long</p>
+<ul>
+<li>Indicates the <a href="http://kafka.apache.org/090/javadoc/org/apache/kafka/clients/consumer/KafkaConsumer.html#poll">time waiting in poll</a> when data is not available.
+ Default value = 5 Seconds.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>holdingBufferSize</em></strong> - Long</p>
+<ul>
+<li>Indicates the maximum number of messages kept in memory for emitting.
+ Default value = 1024.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>consumerProps</em></strong> - Properties</p>
+<ul>
+<li>Specify the [consumer properties[(http://kafka.apache.org/090/documentation.html#newconsumerconfigs) which are not yet set to the operator.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>windowDataManager</em></strong> - WindowDataManager</p>
+<ul>
+<li>If set to a value other than the default, such as <code>FSWindowDataManager</code>, specifies that the operator will process the same set of messages in a window before and after a failure. This is important but it 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 = <code>WindowDataManager.NoopWindowDataManager</code>.</li>
+</ul>
+</li>
+</ul>
+<h4 id="abstract-methods_4">Abstract Methods</h4>
+<p><code>void emitTuple(String cluster, ConsumerRecord<byte[], byte[]> message)</code>: Abstract method that emits tuples
+extracted from Kafka message.</p>
+<h3 id="concrete-classes_1">Concrete Classes</h3>
+<h4 id="kafkasingleportinputoperator">KafkaSinglePortInputOperator</h4>
+<p>This class extends from AbstractKafkaInputOperator and defines the <code>getTuple()</code> method which extracts byte array from Kafka message.</p>
+<h4 id="ports_2">Ports</h4>
+<p><code>outputPort <byte[]></code>: Tuples extracted from Kafka messages are emitted through this port.</p>
+<h3 id="application-example_1">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)
+ {
+ KafkaSinglePortInputOperator input = dag.addOperator("MessageReader", new KafkaSinglePortInputOperator());
+ 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:9092\u201d is the Broker:</p>
+<pre><code class="xml"><property>
+ <name>dt.operator.MessageReader.prop.topics</name>
+ <value>test</value>
+</property>
+
+<property>
+ <name>dt.operator.KafkaInputOperator.prop.clusters</nam>
+ <value>localhost:9092</value>
+</property>
+</code></pre>
+
+<p>By adding following lines to properties file, Kafka Input Operator supports multi-topic and multi-cluster:</p>
+<pre><code class="xml"><property>
+ <name>dt.operator.MessageReader.prop.topics</name>
+ <value>test1, test2</value>
+</property>
+
+<property>
+ <name>dt.operator.KafkaInputOperator.prop.clusters</nam>
+ <value>localhost:9092; localhost:9093; localhost:9094</value>
+</property>
+</code></pre>
+
+<p>For a full example application project, refer to https://github.com/DataTorrent/examples/tree/master/tutorials/kafka</p>
+
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+ <a class="current" href="./">S3 Output Module</a>
+
+ <ul>
+
+ <li class="toctree-l3"><a href="#s3outputmodule">S3OutputModule</a></li>
+
+ <li><a class="toctree-l4" href="#about-amazon-s3">About Amazon S3</a></li>
+
+ <li><a class="toctree-l4" href="#s3-output-module">S3 Output Module</a></li>
+
+ <li><a class="toctree-l4" href="#overview">Overview</a></li>
+
+ <li><a class="toctree-l4" href="#multipart-upload-feature">Multipart Upload Feature</a></li>
+
+ <li><a class="toctree-l4" href="#module">Module</a></li>
+
+ <li><a class="toctree-l4" href="#operators-in-s3outputmodule">Operators in S3OutputModule</a></li>
+
+ <li><a class="toctree-l4" href="#configuration-parameters">Configuration Parameters</a></li>
+
+ <li><a class="toctree-l4" href="#ports">Ports</a></li>
+
+ <li><a class="toctree-l4" href="#application-example">Application Example</a></li>
+
+ <li><a class="toctree-l4" href="#partitioning">Partitioning</a></li>
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+
+ <h1 id="s3outputmodule">S3OutputModule</h1>
+<h3 id="about-amazon-s3">About Amazon S3</h3>
+<hr />
+<p><strong>Amazon S3 (Simple Storage Service)</strong> is an object storage system with a web service interface to store and retrieve any amount of data at any time from anywhere on the web, offered by Amazon Web Services.</p>
+<h3 id="s3-output-module">S3 Output Module</h3>
+<hr />
+<p>Purpose of S3Output module is to upload files/directories into an Amazon S3 bucket using the multipart upload feature(see below).</p>
+<p>S3Output module is <strong>fault-tolerant</strong>, <strong>statically/dynamically partitionable</strong> and has <strong>exactly once</strong> semantics.</p>
+<p>Module class is <strong>S3OutputModule</strong> located in the package <strong>org.apache.apex.malhar.lib.fs.s3</strong>; please refer to <a href="https://github.com/apache/apex-malhar/blob/master/library/src/main/java/org/apache/apex/malhar/lib/fs/s3/S3OutputModule.java">github URL</a>.</p>
+<h3 id="overview">Overview</h3>
+<hr />
+<p>File upload to S3 can also be done using <strong>AbstractFileOutputOperator</strong> but that operator uploads large files sequentially; the current module in contrast can substantially improve the upload speed of large files by reading and uploading their constituent blocks in parallel. </p>
+<p>The table below lists additional benefits of this module over <strong>AbstractFileOutputOperator</strong>.</p>
+<table>
+<thead>
+<tr>
+<th><strong>S3OutputModule</strong></th>
+<th><strong>AbstractFileOutputOperator</strong></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Maximum upload file size is 5TB.</td>
+<td>Maximum upload file size is 5GB.</td>
+</tr>
+<tr>
+<td>Best fit for both large and small files.</td>
+<td>Best fit for small files.</td>
+</tr>
+<tr>
+<td>Module uses AmazonS3Client API's to upload objects into S3. Large files will upload using multipart feature and small files(single block) will upload using <strong>putObject(...)</strong> API</td>
+<td>Operator uses Hadoop filesystems like <strong>S3AFileSystem</strong>. Consists of couple of steps to upload object into S3: (1) Write the data into the local filesystem. (2) When the stream closes, filesystem uploads the local object into S3.</td>
+</tr>
+<tr>
+<td>If a block fails to upload then you need to re-upload the data for that block only</td>
+<td>If a file fails to upload then you need to re-upload the complete file.</td>
+</tr>
+</tbody>
+</table>
+<h3 id="multipart-upload-feature">Multipart Upload Feature</h3>
+<hr />
+<p>Uploading parts of a file is done via the <a href="http://docs.aws.amazon.com/AmazonS3/latest/dev/mpuoverview.html">multipart feature</a>; using this feature, each part of a file can be uploaded independently.
+After all parts of a file are uploaded successfully, Amazon S3 combines the parts as a single object.</p>
+<p>Please refer to the <a href="http://docs.aws.amazon.com/AmazonS3/latest/dev/llJavaUploadFile.html">Java code</a> for uploading file into Amazon S3 bucket using multipart feature.</p>
+<h3 id="module">Module</h3>
+<hr />
+<p>A <strong>module</strong> is a group of operators pre-wired together so they work as a single conceptual entity in an application. Typically, a module will contain a set of input ports, output ports and configuration properties. The operators internal to the module will be automatically configured based on the supplied module properties.</p>
+<h3 id="operators-in-s3outputmodule">Operators in S3OutputModule</h3>
+<hr />
+<p>Following diagram illustrates the DAG in this module:</p>
+<p><img alt="" src="../images/s3output/s3outputmodule.png" /></p>
+<ul>
+<li>
+<p><strong><em>S3InitiateFileUploadOperator</em></strong></p>
+<ul>
+<li>Initiate the upload for the file using <strong>AmazonS3Client.initiateMultipartUpload(...)</strong> method only if the number of blocks for a file is greater than 1. By successfully initiating the upload, S3 returns a response of type <strong>InitiateMultipartUploadResult</strong>, which includes the <strong>upload ID</strong>, which is the unique identifier for the multipart upload. This <strong>upload ID</strong> must be included in each operation like block upload and upload completion request.
+If the file has single block then the operator emits an empty string, this is an indication to downstream operators to not use the multi-part feature.</li>
+<li>This operator emits the pair <strong>(filemetadata, uploadId)</strong> to <strong>S3FileMerger</strong> and the triple <strong>(filePath, metadata, uploadId)</strong> to <strong>S3BlockUploadOperator</strong>.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>S3BlockUploadOperator</em></strong></p>
+<ul>
+<li>This operator upload the blocks into S3 using different calls which depend on number of blocks of a file.
+If the file has single block then upload the block using <strong>AmazonS3Client.putObject(...)</strong> call. S3 returns a response of type <strong>PutObjectResult</strong> which includes the <strong>ETag</strong>.
+If the file has more blocks then upload the block using <strong>AmazonS3Client.uploadPart(...)</strong> call. S3 returns a response of type <strong>UploadPartResult</strong> which includes the <strong>ETag</strong>. This <strong>ETag</strong> value must be included in the request to complete multipart upload.</li>
+<li><strong>S3BlockUploadOperator</strong> emits the pair <strong>(path, ETag)</strong> to <strong>s3FileMerger</strong>.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>S3FileMerger</em></strong></p>
+<ul>
+<li>Complete multipart upload request using <strong>AmazonS3Client.completeMultipartUpload(...)</strong>. This call must include the <strong>upload ID</strong> and a list of both part numbers and corresponding <strong>ETag</strong> values. <strong>S3FileMerger</strong> sends the complete multi-part upload request to S3 once it has all the <strong>part ETag's</strong> of a file.
+<strong>Amazon S3</strong> creates an object by concatenating the parts in ascending order based on part number. After a successful upload request, the parts no longer exist and S3 response includes an <strong>ETag</strong> which uniquely identifies the combined object data. </li>
+</ul>
+</li>
+</ul>
+<h3 id="configuration-parameters">Configuration Parameters</h3>
+<hr />
+<ul>
+<li>
+<p><strong><em>accessKey</em></strong> - String</p>
+<ul>
+<li>Mandatory Parameter </li>
+<li>Specifies the AWS access key to access Amazon S3 and has permissions to access the specified bucket.</li>
+<li>Example value = AKIAJVAGFANC2LSZCJ4Q</li>
+</ul>
+</li>
+<li>
+<p><strong><em>secretAccessKey</em></strong> - String</p>
+<ul>
+<li>Mandatory Parameter</li>
+<li>Specifies the AWS secret access key to access Amazon S3 and has permissions to access the specified bucket.</li>
+<li>Example value = wpVr3U82RmCKJoY007YfkaawT7CenhTcK1B8clue</li>
+</ul>
+</li>
+<li>
+<p><strong><em>endPoint</em></strong> - String</p>
+<ul>
+<li>Endpoint is the URL for the entry point for a web service. Specify the valid endpoint to access S3 bucket.</li>
+<li>This is an optional parameter. If the bucket is accessed only from specific end point then the user has to specify this parameter.</li>
+<li>Please refer to <a href="http://docs.aws.amazon.com/general/latest/gr/rande.html#s3_region">endPoint</a> table about the endpoints supported by S3. </li>
+<li>Example value = s3.amazonaws.com </li>
+</ul>
+</li>
+<li>
+<p><strong><em>bucketName</em></strong> - String</p>
+<ul>
+<li>Mandatory Parameter</li>
+<li>S3 buckets are used to store objects which consists of data and metadata that describes the data. Specify the name of the bucket.</li>
+<li>Example value = apex.app.test.s3</li>
+</ul>
+</li>
+<li>
+<p><strong><em>outputDirectoryPath</em></strong> - String</p>
+<ul>
+<li>Mandatory Parameter</li>
+<li>Specifies the path of the output directory. </li>
+<li>Example value = dt/app/output</li>
+</ul>
+</li>
+<li>
+<p><strong><em>mergerCount</em></strong> - int</p>
+<ul>
+<li>Specify the number of instances of S3FileMerger operator.</li>
+<li>Default value = 1</li>
+</ul>
+</li>
+<li>
+<p><strong><em>timeOutWIndowCount</em></strong> - int</p>
+<ul>
+<li>This property maps to the <a href="https://github.com/apache/apex-core/blob/master/api/src/main/java/com/datatorrent/api/Context.java">OperatorContext.TIMEOUT_WINDOW_COUNT</a> attribute and is a count of streaming windows. If specified, it will be set on all the operators of this module. Since these operators interact with S3, there may be additional latencies that cause the platform to kill them because they are considered stalled. Increasing this value prevents this and allows the application to proceed despite the latencies.</li>
+<li>Default value = 6000</li>
+</ul>
+</li>
+</ul>
+<h3 id="ports">Ports</h3>
+<hr />
+<ul>
+<li>
+<p><strong><em>filesMetadataInput</em></strong> - AbstractFileSplitter.FileMetadata</p>
+<ul>
+<li>Input port for files metadata.</li>
+<li>Mandatory</li>
+</ul>
+</li>
+<li>
+<p><strong><em>blocksMetadataInput</em></strong> - BlockMetadata.FileBlockMetadata</p>
+<ul>
+<li>Input port for blocks metadata.</li>
+<li>Mandatory</li>
+</ul>
+</li>
+<li>
+<p><strong><em>blockData</em></strong> - AbstractBlockReader.ReaderRecord<Slice></p>
+<ul>
+<li>Input port for blocks data.</li>
+<li>Mandatory</li>
+</ul>
+</li>
+</ul>
+<h3 id="application-example">Application Example</h3>
+<hr />
+<p>Please refer to <a href="https://github.com/DataTorrent/examples/tree/master/tutorials/s3output">Example</a> for S3OutputModule sample application.</p>
+<h3 id="partitioning">Partitioning</h3>
+<hr />
+<p>Partitioning the module means that the operators in the module can be partitioned.</p>
+<h4 id="stateless-partitioning">Stateless Partitioning</h4>
+<hr />
+<p>Partitioning the operator in module can be achieved as follows:</p>
+<h5 id="s3initiatefileuploadoperator">S3InitiateFileUploadOperator</h5>
+<hr />
+<p>Partition of this operator is achieved indirectly as follows:</p>
+<pre><code class="xml"><property>
+ <name>dt.operator.{ModuleName}#InitiateUpload.attr.PARTITIONER</name>
+ <value>com.datatorrent.common.partitioner.StatelessPartitioner:{N}</value>
+</property>
+</code></pre>
+
+<p>where {ModuleName} is the name of the S3OutputModule and
+ {N} is the number of static partitions.
+Above lines will partition S3InitiateFileUploadOperator statically {N} times.</p>
+<h5 id="s3blockuploadoperator">S3BlockUploadOperator</h5>
+<hr />
+<p>Locality of S3BlockUploadOperator with upstream operator (FSInputModule/BlockReader) must set to PARTITION_PARALLEL for performance benefits by avoiding serialization/deserialization of objects. So, partitioning of this operator depends on upstream operator which is of type FSInputModule/BlockReader.</p>
+<h5 id="s3filemerger">S3FileMerger</h5>
+<hr />
+<p>By setting the parameter "mergerCount", <strong>S3FileMerger</strong> be statically partitioned. This can be achieved by two ways:</p>
+<p>(a) Following code can be added to populateDAG(DAG dag, Configuration conf) method of application to statically partitioning <strong>S3FileMerger</strong> {N} times:</p>
+<pre><code class="java"> FSInputModule inputModule = dag.addModule("HDFSInputModule", new FSInputModule());
+ S3OutputModule outputModule = dag.addModule("S3OutputModule", new S3OutputModule());
+ outputModule.setMergerCount({N});
+</code></pre>
+
+<p>(b) By setting the parameter in properties file as follows</p>
+<pre><code class="xml"> <property>
+ <name>dt.operator.{ModuleName}.prop.mergerCount</name>
+ <value>{N}</value>
+ </property>
+</code></pre>
+
+<p>where {ModuleName} is the name of the S3OutputModule and {N} is the number of static partitions.
+Above lines will partition <strong>S3FileMerger</strong> statically {N} times. </p>
+<h4 id="dynamic-partitioning">Dynamic Partitioning</h4>
+<hr />
+<p>Dynamic partitioning is a feature of Apex platform which changes the number of partitions of an operator at run time.
+Locality of <strong>S3BlockUploadOperator</strong> with upstream operator(FSInputModule/BlockReader) must set to PARTITION_PARALLEL for performance benefits by avoiding serialization/deserialization of objects. So, dynamic partitioning of this operator depends on upstream operator which is of type FSInputModule/BlockReader.</p>
+<p>From the example application, by setting the maxReaders and minReaders value to FSInputModule, <strong>S3BlockUploadOperator</strong> dynamically partitioned between minReaders and maxReaders. This can be achieved by two ways:
+(a) Following code can be added to <strong>populateDAG(DAG dag, Configuration conf)</strong> method of application to dynamically partitioned <strong>S3BlockUploadOperator</strong> between {N1} and {N2} times:</p>
+<pre><code class="java">FSInputModule inputModule = dag.addModule("HDFSInputModule", new FSInputModule());
+inputModule.setMinReaders({N1});
+inputModule.setMaxReaders({N2});
+S3OutputModule outputModule = dag.addModule("S3OutputModule", new S3OutputModule());
+</code></pre>
+
+<p>(b) By setting the parameter in properties file as follows:</p>
+<pre><code class="xml"><property>
+ <name>dt.operator.HDFSInputModule.prop.minReaders</name>
+ <value>{N1}</value>
+</property>
+<property>
+ <name>dt.operator.HDFSInputModule.prop.maxReaders</name>
+ <value>{N2}</value>
+</property>
+</code></pre>
+
+<p>{N1} and {N2} represents the number of minimum and maximum partitions of BlockReader.
+Above lines will dynamically partitioned the <strong>S3BlockUploadOperator</strong> between {N1} and {N2} times. </p>
+
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+
+ <ul>
+
+ <li class="toctree-l3"><a href="#transform-operator-documentation">Transform - Operator Documentation</a></li>
+
+ <li><a class="toctree-l4" href="#about-transform-operator">About Transform operator</a></li>
+
+ <li><a class="toctree-l4" href="#use-case">Use Case</a></li>
+
+ <li><a class="toctree-l4" href="#configuration-parameters">Configuration Parameters</a></li>
+
+ <li><a class="toctree-l4" href="#configuration-example">Configuration Example</a></li>
+
+ <li><a class="toctree-l4" href="#ports">Ports</a></li>
+
+ <li><a class="toctree-l4" href="#attributes">Attributes</a></li>
+
+ <li><a class="toctree-l4" href="#application-example">Application Example</a></li>
+
+ <li><a class="toctree-l4" href="#partitioning">Partitioning</a></li>
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+
+ <h1 id="transform-operator-documentation">Transform - Operator Documentation</h1>
+<h3 id="about-transform-operator">About Transform operator</h3>
+<hr />
+<p>Transform means mapping of field expression from input to output or conversion of fields from one type to another.
+This operator is stateless. This operator receives objects on its input port; for each such input object, it creates a new output object whose fields are computed as expressions involving fields of the input object.
+The types of the input and output objects are configurable as are the expressions used to compute the output fields. </p>
+<p>The operator class is <code>TransformOperator</code> located in the package <code>com.datatorrent.lib.transform</code>.
+Please refer to <a href="https://github.com/apache/apex-malhar/blob/master/library/src/main/java/com/datatorrent/lib/transform/TransformOperator.java">github URL</a> for <code>TransformOperator</code>.</p>
+<h3 id="use-case">Use Case</h3>
+<hr />
+<p>Consider the data that needs to be transformed as per output schema.</p>
+<p>Consider input objects with these fields:</p>
+<table>
+<thead>
+<tr>
+<th>Name</th>
+<th>Type</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>FirstName</td>
+<td>String</td>
+</tr>
+<tr>
+<td>LastName</td>
+<td>String</td>
+</tr>
+<tr>
+<td>Phone</td>
+<td>String</td>
+</tr>
+<tr>
+<td>DateOfBirth</td>
+<td>java.util.Date</td>
+</tr>
+<tr>
+<td>Address</td>
+<td>String</td>
+</tr>
+</tbody>
+</table>
+<p>and output objects with fields: </p>
+<table>
+<thead>
+<tr>
+<th>Name</th>
+<th>Type</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Name</td>
+<td>String</td>
+</tr>
+<tr>
+<td>Phone</td>
+<td>String</td>
+</tr>
+<tr>
+<td>Age</td>
+<td>Integer</td>
+</tr>
+<tr>
+<td>Address</td>
+<td>String</td>
+</tr>
+</tbody>
+</table>
+<p>Suppose <code>Name</code> is a concatenation of <code>FirstName</code> and <code>LastName</code> and
+ <code>Age</code> is computed by subtracting the <code>DateOfBirth</code> from the current year.</p>
+<p>These simple computations can be expressed as Java expressions where the input object is
+represented by $ and provided as configuration parameters as follows:</p>
+<pre><code>Name => {$.FirstName}.concat(\" \").concat({$.LastName})
+Age => (new java.util.Date()).getYear() - {$.dateOfBirth}.getYear()
+</code></pre>
+
+<h3 id="configuration-parameters">Configuration Parameters</h3>
+<hr />
+<ul>
+<li>
+<p><strong><em>expressionMap</em></strong> - Map<String, String></p>
+<ul>
+<li>Mandatory Parameter</li>
+<li>Specifies the map between the output field (key) and the expression used to compute it (value) using fields of the input Java object.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>expressionFunctions</em></strong> - List<String></p>
+<ul>
+<li>List of imported classes or methods should be made available to expression to use. It overrides the default list.</li>
+<li>Default Value = {java.lang.Math.<em>, org.apache.commons.lang3.StringUtils.</em>, org.apache.commons.lang3.StringEscapeUtils.<em>, org.apache.commons.lang3.time.DurationFormatUtils.</em>, org.apache.commons.lang3.time.DateFormatUtils.*}</li>
+</ul>
+</li>
+<li>
+<p><strong><em>copyMatchingFields</em></strong> - boolean</p>
+<ul>
+<li>Specifies whether matching fields should be copied; here matching means the name and type of an input field is the same as the name and type of an output field.
+ If the matching field appears in <code>expressionMap</code> then it ignores copy to output object.</li>
+<li>Default Value = true.</li>
+</ul>
+</li>
+</ul>
+<h3 id="configuration-example">Configuration Example</h3>
+<hr />
+<p>Consider input object with fields:</p>
+<table>
+<thead>
+<tr>
+<th>Name</th>
+<th>Type</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>FirstName</td>
+<td>String</td>
+</tr>
+<tr>
+<td>LastName</td>
+<td>String</td>
+</tr>
+<tr>
+<td>StartDate</td>
+<td>org.joda.time.DateTime</td>
+</tr>
+</tbody>
+</table>
+<p>and output objects with fields:</p>
+<table>
+<thead>
+<tr>
+<th>Name</th>
+<th>Type</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Name</td>
+<td>String</td>
+</tr>
+<tr>
+<td>isLeapYear</td>
+<td>Boolean</td>
+</tr>
+</tbody>
+</table>
+<p>Note: <code>org.joda.time.DateTime</code> class is not present in the default list. So, we need to add this library to <code>expressionFunctions</code> as below in populateDAG method:</p>
+<pre><code class="java">TransformOperator operator = dag.addOperator("transform", new TransformOperator());
+operator.setExpressionFunctions(Arrays.asList("org.joda.time.DateTime", org.apache.commons.lang3.StringUtils));
+Map<String,String> expressionMap = new HashMap<>();
+expressionMap.put(isLeapYear, {$.StartDate}.year().isLeap());
+expressionMap.put(Name, org.apache.commons.lang3.StringUtils.joinWith(\" \", {$.FirstName},{$.LastName});
+operator.setExpressionMap(expressionMap);
+</code></pre>
+
+<p>Above Properties also can be set in properties file as follows:</p>
+<pre><code class="xml"><property>
+ <name>dt.operator.transform.expressionFunctions[0]</name>
+ <value>org.joda.time.DateTime</value>
+</property>
+<property>
+ <name>dt.operator.transform.expressionFunctions[1]</name>
+ <value>org.apache.commons.lang3.StringUtils</value>
+</property>
+<property>
+ <name>dt.operator.transform.expressionMap(isLeapYear)</name>
+ <value>{$.StartDate}.year().isLeap()</value>
+</property>
+<property>
+ <name>dt.operator.transform.expressionMap(Name)</name>
+ <value>org.apache.commons.lang3.StringUtils.joinWith(\" \", {$.FirstName}, {$.LastName})</value>
+</property>
+</code></pre>
+
+<h3 id="ports">Ports</h3>
+<hr />
+<ul>
+<li>
+<p><strong><em>input</em></strong> - Port for input tuples.</p>
+<ul>
+<li>Mandatory input port</li>
+</ul>
+</li>
+<li>
+<p><strong><em>output</em></strong> - Port for transformed output tuples.</p>
+<ul>
+<li>Mandatory output port</li>
+</ul>
+</li>
+</ul>
+<h3 id="attributes">Attributes</h3>
+<hr />
+<ul>
+<li>
+<p><strong><em>Input port Attribute - input.TUPLE_CLASS</em></strong>�- Fully qualified class name and class should be Kryo serializable.</p>
+<ul>
+<li>Mandatory attribute</li>
+<li>Type of input tuple.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>Output port Attribute - output.TUPLE_CLASS</em></strong>�- Fully qualified class name and class should be Kryo serializable.</p>
+<ul>
+<li>Mandatory attribute</li>
+<li>Type of output tuple.</li>
+</ul>
+</li>
+</ul>
+<h3 id="application-example">Application Example</h3>
+<hr />
+<p>Please refer <a href="https://github.com/DataTorrent/examples/tree/master/tutorials/transform">Example</a> for transform sample application.</p>
+<h3 id="partitioning">Partitioning</h3>
+<hr />
+<p>Being stateless, this operator can be partitioned using any of the built-in partitioners present in the Malhar library by setting a few properties as follows:</p>
+<h4 id="stateless-partitioning">Stateless partitioning</h4>
+<p>Stateless partitioning will ensure that TransformOperator will be partitioned right at the starting of the application and will remain partitioned throughout the lifetime of the DAG.
+TransformOperator 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:{N}/value>
+ </property>
+</code></pre>
+
+<p>where {OperatorName} is the name of the TransformOperator operator and
+ {N} is the number of static partitions.
+Above lines will partition TransformOperator statically {N} times. </p>
+<h4 id="dynamic-partitioning">Dynamic Partitioning</h4>
+<p>Dynamic partitioning is a feature of Apex platform which changes the partition of the operator based on certain condition.
+TransformOperator can be dynamically partitioned using the below two partitioners:</p>
+<h5 id="throughput-based">Throughput based</h5>
+<p>Following code can be added to populateDAG(DAG dag, Configuration conf) method of application to dynamically partitioning TransformOperator:</p>
+<pre><code class="java">StatelessThroughputBasedPartitioner<TransformOperator> partitioner = new StatelessThroughputBasedPartitioner<>();
+partitioner.setCooldownMillis(10000);
+partitioner.setMaximumEvents(30000);
+partitioner.setMinimumEvents(10000);
+dag.setAttribute(transform, OperatorContext.STATS_LISTENERS, Arrays.asList(new StatsListener[]{partitioner}));
+dag.setAttribute(transform, OperatorContext.PARTITIONER, partitioner);
+</code></pre>
+
+<p>Above code will dynamically partition TransformOperator when the throughput changes.
+If the overall throughput of TransformOperator goes beyond 30000 or less than 10000, the platform will repartition TransformOperator
+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>
+<p>Source code for this dynamic application can be found <a href="https://github.com/DataTorrent/examples/blob/master/tutorials/transform/src/main/java/com/example/transform/DynamicTransformApplication.java">here</a>.</p>
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