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+ <ul class="subnav">
+ <li><span>APIs</span></li>
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+ <a class="" href="../csvParserOperator/">CSV Parser</a>
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+ </li>
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+ <li class="toctree-l1 current">
+ <a class="current" href="./">Deduper</a>
+
+ <ul>
+
+ <li class="toctree-l3"><a href="#deduper-operator-documentation">Deduper - Operator Documentation</a></li>
+
+
+ <li class="toctree-l3"><a href="#introduction">Introduction</a></li>
+
+ <li><a class="toctree-l4" href="#about-this-document">About this document</a></li>
+
+ <li><a class="toctree-l4" href="#terminology">Terminology</a></li>
+
+
+ <li class="toctree-l3"><a href="#overview">Overview</a></li>
+
+ <li><a class="toctree-l4" href="#dedup-what-in-a-nutshell">Dedup - \u201cWhat\u201d in a Nutshell</a></li>
+
+ <li><a class="toctree-l4" href="#dedup-how-in-a-nutshell">Dedup - \u201cHow\u201d in a Nutshell</a></li>
+
+
+ <li class="toctree-l3"><a href="#use-cases-basic-dedup">Use cases - Basic Dedup</a></li>
+
+ <li><a class="toctree-l4" href="#dedup-key">Dedup Key</a></li>
+
+ <li><a class="toctree-l4" href="#use-case-details">Use case Details</a></li>
+
+
+ <li class="toctree-l3"><a href="#use-case-dedup-with-expiry">Use case - Dedup with Expiry</a></li>
+
+ <li><a class="toctree-l4" href="#motivation">Motivation</a></li>
+
+ <li><a class="toctree-l4" href="#expiry-key">Expiry Key</a></li>
+
+ <li><a class="toctree-l4" href="#expiry-period">Expiry Period</a></li>
+
+ <li><a class="toctree-l4" href="#use-case-details_1">Use case Details</a></li>
+
+
+ <li class="toctree-l3"><a href="#use-cases-summary">Use cases - Summary</a></li>
+
+
+ <li class="toctree-l3"><a href="#technical-architecture">Technical Architecture</a></li>
+
+ <li><a class="toctree-l4" href="#class-structure">Class Structure</a></li>
+
+ <li><a class="toctree-l4" href="#architectural-details">Architectural Details</a></li>
+
+ <li><a class="toctree-l4" href="#concepts">Concepts</a></li>
+
+ <li><a class="toctree-l4" href="#assumptions">Assumptions</a></li>
+
+ <li><a class="toctree-l4" href="#flow-of-a-tuple-through-dedup-operator">Flow of a Tuple through Dedup Operator</a></li>
+
+
+ <li class="toctree-l3"><a href="#ports-attributes-and-properties">Ports, Attributes and Properties</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="#properties">Properties</a></li>
+
+
+ <li class="toctree-l3"><a href="#example">Example</a></li>
+
+
+ <li class="toctree-l3"><a href="#partitioning">Partitioning</a></li>
+
+
+ </ul>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../enricher/">Enricher</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../fsInputOperator/">File Input</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../file_output/">File Output</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../file_splitter/">File Splitter</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../filter/">Filter</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../AbstractJdbcTransactionableOutputOperator/">Jdbc Output Operator</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../jdbcPollInputOperator/">JDBC Poller Input</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../jmsInputOperator/">JMS Input</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../jsonFormatter/">JSON Formatter</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../jsonParser/">JSON Parser</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../kafkaInputOperator/">Kafka Input</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../transform/">Transformer</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../windowedOperator/">Windowed Operator</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../xmlParserOperator/">XML Parser</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../jsonParser/">Json Parser</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../jsonFormatter/">Json Formatter</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../transform/">Transform Operator</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../csvformatter/">Csv Formatter</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../s3outputmodule/">S3 Output Module</a>
+
+ </li>
+
+
+ </ul>
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+ <div role="main">
+ <div class="section">
+
+ <h1 id="deduper-operator-documentation">Deduper - Operator Documentation</h1>
+<h1 id="introduction">Introduction</h1>
+<h2 id="about-this-document">About this document</h2>
+<p>This document is intended as a guide for understanding and using
+the Dedup operator.</p>
+<h2 id="terminology">Terminology</h2>
+<p>We will refer to this operator as the Deduper or Dedup operator
+interchangeably.</p>
+<h1 id="overview">Overview</h1>
+<h2 id="dedup-what-in-a-nutshell">Dedup - \u201cWhat\u201d in a Nutshell</h2>
+<p>Dedup is actually short for Deduplication. Duplicates are omnipresent and
+can be found in almost any kind of data. Most of the times it is
+essential to discard, or at the very least separate out the data into
+unique�and duplicate�components. The entire purpose of this
+operator is to de-duplicate data. In other words, when data passes
+through this operator, it will be segregated into two different data
+sets, one of which contains all unique tuples, and the other which are
+occurring more than once in the original data set.</p>
+<p><img alt="" src="../images/deduper/image00.png" /></p>
+<h2 id="dedup-how-in-a-nutshell">Dedup - \u201cHow\u201d in a Nutshell</h2>
+<p>In order to quickly decide whether an incoming tuple is duplicate
+or unique, it has to store each incoming tuple (or a signature, like key,
+for example) to be used for comparison later. A plain in-memory storage
+may work for small datasets, but will not scale for large ones. Deduper employs a large scale distributed persistent hashing mechanism (known as the Managed State) which allows
+it to identify if a particular tuple is duplicate or unique. Managed state is a layer on HDFS which allows all the stored data to be persisted in a distributed fashion.
+Each time it identifies a tuple as a unique tuple, it also
+stores it into the Managed state for future
+lookup.</p>
+<h2 id="_1"><img alt="" src="../images/deduper/image04.png" /></h2>
+<p>Following are the different components of the Deduper</p>
+<ol>
+<li><strong>Dedup Operator</strong> - This is responsible for the overall
+ functionality of the operator. This in turn makes use of other
+ components to establish the end goal of deciding whether a tuple is
+ a duplicate of some earlier tuple, or is a unique tuple.</li>
+<li><strong>Managed State</strong> - Since, all of the data cannot be stored in
+ memory, this component allows us to persist existing unique keys on
+ HDFS in form of buckets. This is also responsible for fetching data as
+ requested by the Deduper. Since, it communicates with the HDFS, data access is slow and so it allows for asynchronous (non-blocking) calls to fetch data. This ensures that the Deduper is not blocked and can continue to process other tuples. It also supports an in-memory cache where it stores the fetched data so that repeated access to the same data is faster. Periodically, based on configuration, this also
+ discards data which is no longer needed.</li>
+</ol>
+<p>This was a very basic introduction to the functioning of the
+Deduper. Following sections will go into more detail on each of the
+components.</p>
+<h1 id="use-cases-basic-dedup">Use cases - Basic Dedup</h1>
+<h2 id="dedup-key">Dedup Key</h2>
+<p>A dedup key is a set of one or more fields in the data tuple which
+acts as the key�for the tuples.
+This is used by the deduper to compare tuples to arrive at the
+conclusion on whether two tuples are duplicates.</p>
+<p>Consider an example schema and two sample tuples</p>
+<p><code>{Name, Phone, Email, Date, State, Zip, Country}</code></p>
+<p>Tuple 1:</p>
+<pre><code>{
+ Austin U. Saunders,
+ +91-319-340-59385,
+ ausaunders@semperegestasurna.com,
+ 2015-11-09 13:38:38,
+ Texas,
+ 73301,
+ United States
+}
+</code></pre>
+
+<p>Tuple 2:</p>
+<pre><code>{
+ Austin U. Saunders,
+ +91-319-340-59385,
+ austin@semperegestasurna.com,
+ 2015-11-09 13:39:38,
+ Texas,
+ 73301,
+ United States
+}
+</code></pre>
+
+<p>Let us assume that the Dedup Key
+is <code>{Name, Phone}</code>. In
+this case, the two tuples are duplicates because the key fields are same
+in both the tuples. However, if the Dedup Key is {Phone,Email},
+the two are unique as the email values differ.</p>
+<h2 id="use-case-details">Use case Details</h2>
+<p>Consider the case of de-duplicating a master data set
+which is stored in a file.�Further also consider the
+following schema for tuples in the data set.</p>
+<p><code>{Name, Phone, Email, Date, City, Zip, Country}</code></p>
+<p>Also consider that we need to identify unique customers from the
+master data set. So, ultimately the output needed for the use case is
+two data sets - Unique Records�and Duplicate Records.</p>
+<p>As part of configuring the operator for this use case, we need to
+set the following parameters:</p>
+<ul>
+<li><strong><em>keyExpression</em></strong>�- This can be set as
+ the primary key which can be used to uniquely identify a Customer.
+ For example, we can set it to <code>Name,Email</code></li>
+</ul>
+<p>The above configuration is sufficient to address this use case.</p>
+<h1 id="use-case-dedup-with-expiry">Use case - Dedup with Expiry</h1>
+<h2 id="motivation">Motivation</h2>
+<p>The Basic Dedup use case is the most straightforward and is
+usually applied when the amount of data to be processed is not huge.
+However, if the incoming data is huge, or even never-ending, it is
+usually not necessary to keep storing all the data. This is because in
+most real world use cases, the duplicates occur only a short distance
+apart. Hence, after a while, it is usually okay to forget part of
+the history and consider only limited history for identifying
+duplicates, in the interest of efficiency. In other words, we expire
+(ignore) some tuples which are (or were supposed to be) delivered long
+back. Doing so, reduces the load on the storage mechanism (managed state) which effectively deletes part of the history, thus making the whole process more
+efficient. We call this use case, Dedup with expiry.</p>
+<h2 id="expiry-key">Expiry Key</h2>
+<p>The easiest way to understand this use case is to consider
+time�as the criterion for expiring
+tuples. Time�is a natural expiry
+key and is in line with the concept of expiry. Formally, an expiry field
+is a field in the input tuple which can be used to discard incoming
+tuples as expired. This expiry key
+usually works with another parameter called Expiry Period defined
+next.</p>
+<h2 id="expiry-period">Expiry Period</h2>
+<p>The expiry period is the value supplied by the user to define the
+extent of history which should be considered while expiring
+tuples.</p>
+<h2 id="use-case-details_1">Use case Details</h2>
+<p>Consider an incoming stream of system logs. The use case requires
+us to identify duplicate log messages and pass on only the unique ones.
+Another relaxation in the use case is that the log messages which are
+older than a day, may not be considered and must be filtered out as
+expired. The expiry must be measured with respect to the time stamp in
+the logs. For example, if the timestamp in the incoming message is
+<code>30-12-2014 00:00:00</code> and the
+latest message that the system has encountered had the time stamp
+<code>31-12-2014 00:00:00</code>, then the
+incoming message must be considered as expired. However, if the incoming
+message had any timestamp like <code>30-12-2014
+00:11:00</code>, it must be accepted into the system and be checked for a possible duplicate.</p>
+<p>The expiry facet in the use case above gives us an advantage in
+that we do not have to compare the incoming record with all�the data to check if it is a duplicate.
+At the same time, all�the
+incoming data need not be stored; just a day worth of data is adequate to address the above use case.</p>
+<p>Configuring the below parameters will solve the problem for this
+use case:</p>
+<ul>
+<li><strong><em>keyExpression</em></strong>�- This is the dedup key for the incoming tuples (similar to the Basic Dedup use case). This can be any key which can uniquely identify a record. For log messages this can be a serial number attached in the log.</li>
+<li><strong><em>timeExpression</em></strong>�- This is the key which can help identify the expired records, as explained above. In this particular use case, it can be a timestamp field which indicates when the log message was generated.</li>
+<li><strong><em>expireBefore</em></strong>�- This is the period of expiry as explained above. In our example use case this will be 24 hour, specified in seconds.</li>
+</ul>
+<p>Configuration of the above parameters is sufficient to address this use
+case.</p>
+<h1 id="use-cases-summary">Use cases - Summary</h1>
+<ol>
+<li><strong>Basic Dedup</strong> - Deduplication of
+ bounded datasets. Data is assumed to be bounded. This use case is
+ not meant for never ending streams of data. For example:
+ Deduplication of master data like customer records, product catalogs
+ etc.</li>
+<li><strong>Time Based Dedup</strong>�- Deduplication of
+ unlimited streams of data. This use case handles unbounded streams
+ of data and can run forever. An expiry key and criterion is expected
+ as part of the input which helps avoid storing all the unique data.
+ This helps speed up performance. Any timestamp field in the incoming
+ tuple can be used as a time based expiry key.<ul>
+<li><em>With respect to system time</em>�- Time progresses with system time. Any expiry criteria are executed with the notion of system time. This is possible if the incoming tuple does not have a time field, or the user does not specify a <code>timeExpression</code>.</li>
+<li><em>With respect to tuple time</em>�- Time progresses based on the time in the incoming tuples. Expiry criteria are executed with the notion of time indicated by the incoming tuple. Specification of the time field (<code>timeExpression</code>) is mandatory for this scenario.</li>
+</ul>
+</li>
+</ol>
+<h1 id="technical-architecture">Technical Architecture</h1>
+<h2 id="class-structure">Class Structure</h2>
+<p><img alt="" src="../images/deduper/image03.png" /></p>
+<hr />
+<h2 id="architectural-details">Architectural Details</h2>
+<p><img alt="" src="../images/deduper/image02.png" /></p>
+<h2 id="concepts">Concepts</h2>
+<h3 id="dedup-key-specified-by-keyexpression-parameter">Dedup Key - Specified by <em>keyExpression</em>�parameter</h3>
+<p>A dedup key is a set of one or more fields in the data tuple which
+acts as the key�for the tuples.
+This is used by the deduper to compare tuples to arrive at the
+conclusion on whether two tuples are duplicates. If Dedup Key of two
+tuples match, then they are duplicates, else they are unique.</p>
+<h3 id="expiry-key-specified-by-timeexpression-parameter">Expiry Key - Specified by <em>timeExpression</em>�parameter</h3>
+<p>A tuple may or may not have an Expiry Key. Dedup operator cannot
+keep storing all the data that is flowing into the operator. At some
+point it becomes essential to discard some of the historical tuples in
+interest of memory and efficiency.</p>
+<p>At the same time, tuples are expected to arrive at the Dedup
+operator within some time after they are generated. After this time, the
+tuples may be considered as stale or obsolete.</p>
+<p>In such cases, the Deduper considers these tuples as
+expired�and takes no action other than
+separating out these tuples on a different port in order to be processed
+by some other operator or stored offline for analysis.</p>
+<p>In order to create a criterion for discarding such tuples, we
+introduce an Expiry Key. Looking at the value of the Expiry Key in each
+tuple, we can decide whether or not to discard this tuple as
+expired.</p>
+<p>The expiry key that we consider in Time Based Dedup is
+time. This usually works with
+another parameter called Expiry Period defined next.</p>
+<h3 id="expiry-period_1">Expiry Period</h3>
+<p>The Expiry Period is the value supplied by the user which decides
+when a particular tuple expires.</p>
+<h3 id="time-points">Time Points</h3>
+<p>For every dataset that the deduper processes, a set of time points is maintained:</p>
+<ol>
+<li><em>Latest Point</em>�- This is the maximum
+ time point observed in all the processed tuples.</li>
+<li><em>Expiry Point</em>�- This is given by:
+ <code>Expiry Point = Latest Point - Expiry Period</code></li>
+</ol>
+<p>These points help the deduper to make decisions related to expiry
+of a tuple.</p>
+<h3 id="example-expiry">Example - Expiry</h3>
+<table>
+<thead>
+<tr>
+<th align="center">Tuple Id</th>
+<th align="center">Expiry Key (Expiry Period = 10)</th>
+<th align="center">Latest Point</th>
+<th align="center">Expiry Point</th>
+<th align="center">Decision for Tuple</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">1</td>
+<td align="center">10</td>
+<td align="center">10</td>
+<td align="center">1</td>
+<td align="center">Not Expired</td>
+</tr>
+<tr>
+<td align="center">2</td>
+<td align="center">20</td>
+<td align="center">20</td>
+<td align="center">11</td>
+<td align="center">Not Expired</td>
+</tr>
+<tr>
+<td align="center">3</td>
+<td align="center">25</td>
+<td align="center">25</td>
+<td align="center">16</td>
+<td align="center">Not Expired</td>
+</tr>
+<tr>
+<td align="center">4</td>
+<td align="center">40</td>
+<td align="center">40</td>
+<td align="center">31</td>
+<td align="center">Not Expired</td>
+</tr>
+<tr>
+<td align="center">5</td>
+<td align="center">21</td>
+<td align="center">40</td>
+<td align="center">31</td>
+<td align="center">Expired</td>
+</tr>
+<tr>
+<td align="center">6</td>
+<td align="center">35</td>
+<td align="center">40</td>
+<td align="center">31</td>
+<td align="center">Not Expired</td>
+</tr>
+<tr>
+<td align="center">7</td>
+<td align="center">45</td>
+<td align="center">45</td>
+<td align="center">36</td>
+<td align="center">Not Expired</td>
+</tr>
+<tr>
+<td align="center">8</td>
+<td align="center">57</td>
+<td align="center">57</td>
+<td align="center">48</td>
+<td align="center">Not Expired</td>
+</tr>
+</tbody>
+</table>
+<h3 id="time-buckets-a-component-of-managed-state">Time Buckets (A component of Managed State)</h3>
+<p>One of the requirements of the Deduper is to store all the unique
+tuples (actually, just the keys of tuples). Keeping an ever growing
+cache in memory is not scalable. So what we need is a limited cache
+backed by a persistent store. When data is requested to be fetched from managed
+state, it is also cached in an in-memory cache. Buckets help
+narrow down the search of duplicates for incoming tuples. A Bucket is an
+abstraction for a collection of tuples all of which share a common hash
+value based on some hash function or a range of time, for example: a
+bucket of data for 5 contiguous minutes. A Bucket�has a span property called Bucket Span.</p>
+<h3 id="bucket-span">Bucket Span</h3>
+<p>Bucket span is simply the range of the domain
+that is covered by the Bucket. This span is specified in
+the domain of the Expiry key. If the Expiry
+Key is time, �then the Bucket span
+will be specified in seconds. It is
+only defined in case tuples have an Expiry Key.</p>
+<h3 id="number-of-buckets">Number of Buckets</h3>
+<p>The number of buckets can be given by - <code>Num Buckets = Expiry
+Period / Bucket Span</code></p>
+<p>This is because at any point of time, we need only store Expiry
+Period worth of data.</p>
+<h3 id="example-buckets">Example - Buckets</h3>
+<p><img alt="" src="../images/deduper/image01.png" /></p>
+<h2 id="assumptions">Assumptions</h2>
+<h3 id="assumption-1">Assumption 1 <a name="Assumption1"></a></h3>
+<p>This assumption is only applicable in case of Dedup with
+Expiry.</p>
+<p>For any two tuples, t1 and t2 having dedup keys d1 and d2, and
+expiry keys e1 and e2, respectively, the following holds:</p>
+<pre><code>If d1 = d2,
+ then e1 = e2
+</code></pre>
+
+<p>In other words, there may never
+be�two tuples t1 and t2 such that:</p>
+<pre><code>Tuple 1: d1, e1
+Tuple 2: d2, e2
+d1 = d2 and e1 != e2
+</code></pre>
+
+<p>In other words, any two tuples with the same dedup key are assumed to have the
+same expiry key as well.
+This assumption was made with respect to certain use cases. These
+use cases follow this assumption in that the records which are
+duplicates are exactly identical. An example use case is when log
+messages are replayed erroneously, and we want to identify the duplicate
+log messages. In such cases, we need not worry about two different log
+messages having the same identifier but different timestamps. Since its
+a replay of the same data, the duplicate records are assumed to be
+exactly identical.</p>
+<p>In case the duplicate tuple has a different value for expiry key, the behavior of
+the deduper can be non-deterministic.</p>
+<h2 id="flow-of-a-tuple-through-dedup-operator">Flow of a Tuple through Dedup Operator</h2>
+<p>Tuples flow through the Dedup operator one by one. Deduper may process a tuple immediately, or store it in some data
+structure for later processing.</p>
+<p>When a tuple always arrives at the input
+port�of the Dedup operator, it does
+the following tasks.</p>
+<h4 id="check-if-tuple-is-expired">Check if tuple is Expired</h4>
+<p>This is only done in case of Dedup with expiry. The
+following condition is used to check if the tuple is expired.</p>
+<pre><code>if ( Latest Point - Expiry Key < Expiry Point )
+ then Expired
+</code></pre>
+
+<p>If the tuple is expired, then send it to the expired port.</p>
+<h4 id="check-if-tuple-is-a-duplicate-or-unique">Check if tuple is a Duplicate or Unique</h4>
+<p>Once a tuple passes the check of expiry, we proceed to check if
+the tuple is a duplicate of some earlier tuple. Note that
+if the tuple in question is not expired, the duplicate will also not
+have expired due to the assumption listed <a href="#Assumption1">here</a>.
+The Deduper queries the Managed state to fetch the value for the tuple key.
+This request is processed by the Managed state in a separate asynchronous thread.
+Once this request is submitted, the Deduper moves on to process other
+tuples. Additionally the Deduper also inserts the tuple being processed
+into a waiting events�queue for later processing.</p>
+<h4 id="process-pending-tuples">Process pending tuples</h4>
+<p>Once the Deduper has looked at the all the tuples in the current window,
+it starts to process the tuples in the waiting queue to finalize the decision
+(unique or duplicate) for these tuples.
+Once the request to Managed state is completed for a tuple and the value is
+fetched from persistent storage, the Deduper can decide if the tuple in
+question is a duplicate or a unique.
+Depending on whether there is enough time left in the current window,
+it can do one of the following:</p>
+<ul>
+<li>Process only the tuples for which the managed state has completed processing.
+The tuples which are still being processed by managed state are skipped only to come back to them when it can no longer postpone it. This is typically done when the operator
+has idle time as there are no tuples on the input ports and the current window
+has still not ended.</li>
+<li>Block on them to complete their processing. This will happen when the current
+window has no time left, and the decision cannot be postponed. Note: An operator can end its window, only when all the tuples have been completely processed. </li>
+</ul>
+<h1 id="ports-attributes-and-properties">Ports, Attributes and Properties</h1>
+<h2 id="ports">Ports</h2>
+<p>The deduper has a single input port and multiple output
+ports.</p>
+<ul>
+<li><strong><em>input</em></strong> - This is the input port through
+ which the tuples arrive at the Deduper.</li>
+<li><strong><em>unique</em></strong>�- This is the output port on
+ which unique tuples are sent out by the Deduper.</li>
+<li><strong><em>duplicate</em></strong>�- This is the output port on
+ which duplicate tuples are sent out by the Deduper.</li>
+<li><strong><em>expired</em></strong>�- This is the output port on
+ which expired tuples are sent out by the Deduper.</li>
+</ul>
+<p>The user can choose which output ports to connect the down stream operators.
+All the output ports are optional and can be used as required by the use case.</p>
+<h2 id="attributes">Attributes</h2>
+<ul>
+<li><strong><em>Input port Attribute - input.TUPLE_CLASS</em></strong>�- Class or the fully
+qualified class name.<ul>
+<li>Mandatory attribute</li>
+<li>Tells the operator about the type of the incoming
+tuple.</li>
+</ul>
+</li>
+</ul>
+<h2 id="properties">Properties</h2>
+<ul>
+<li>
+<p><strong><em>keyExpression</em></strong>�- String</p>
+<ul>
+<li>Mandatory parameter.</li>
+<li>The java expression to extract the key fields in the incoming tuple (POJO)</li>
+</ul>
+</li>
+<li>
+<p><strong><em>timeExpression</em></strong>�- String - (Time Based Deduper only)</p>
+<ul>
+<li>The java expression to extract the time field in the incoming tuple (POJO).</li>
+</ul>
+</li>
+<li>
+<p><strong><em>expireBefore</em></strong>�- Long (Seconds) - (Time Based Deduper only)</p>
+<ul>
+<li>This is the total time period during which a tuple stays in the system and blocks any other tuple with the same key.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>bucketSpan</em></strong>�- Long (Seconds) - (Time Based Deduper only)</p>
+<ul>
+<li>Mandatory parameter</li>
+<li>This is the unit which describes how large a bucket can be. Typically this should be defined depending on the use case. For example, if we have expireBefore set to 1 hour, then typically we would be clubbing data in the order of minutes, so a <code>bucketSpan</code> of a few minutes would make sense. Note that in this case, the entire data worth the <code>bucketSpan</code> will expire as a whole. Setting it to 1 minute would make the number of time buckets in the system to be 1 hour / 1 minute = 60 buckets. Similarly setting bucketSpan to 5 minutes would make number of buckets to be 12.</li>
+<li>Note that having too many or too few buckets could have a performance impact. If unsure, set the bucketSpan to the square root of <code>expireBefore</code>. This way the number of buckets and bucket span are balanced.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>referenceInstant</em></strong>�- �Long (Seconds) - (Time Based Deduper only)</p>
+<ul>
+<li>The reference point from which to start the time which is use for expiry. Setting the referenceInstant to say, r seconds from the epoch, would initialize the start of expiry to be from that <code>instant = r</code>. The start and end of the expiry window periodically move by the span of a single bucket.</li>
+</ul>
+</li>
+<li>
+<p><strong><em>numBuckets</em></strong>�- �Integer - (Bounded Deduper only)</p>
+<ul>
+<li>Optional parameter, but recommended to be provided by the user.</li>
+<li>This is the number of buckets that need to be used for storing the keys of the incoming tuples.</li>
+<li>Users can decide upon the proper value for this parameter by guessing the number of distinct keys in the application. A reasonable value is the square root of N, where N is the number of distinct keys. If omitted, the Java MAX_VALUE for integer is used for N.</li>
+</ul>
+</li>
+</ul>
+<h1 id="example">Example</h1>
+<p>Please refer to <a href="https://github.com/DataTorrent/examples/tree/master/tutorials/dedup">https://github.com/DataTorrent/examples/tree/master/tutorials/dedup</a>�for
+an example on how to use Deduper.</p>
+<h1 id="partitioning">Partitioning</h1>
+<p>Deduper can be statically partitioned using the operator
+attribute: PARTITIONER</p>
+<p>Add the following property to the properties.xml file:</p>
+<pre><code><property>
+��������<name>dt.operator.{OperatorName}.attr.PARTITIONER</name>
+��������<value>com.datatorrent.common.partitioner.StatelessPartitioner:2</value>
+</property>
+</code></pre>
+
+<p>This will partition the Dedup operator into 2 static partitions. Change the number
+to the required number of partitions.</p>
+<p>Dynamic partitioning is currently not supported in the Deduper.</p>
+
+ </div>
+ </div>
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+
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+ <a class="" href="../csvParserOperator/">CSV Parser</a>
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+ </li>
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+ <a class="" href="../deduper/">Deduper</a>
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+ </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>
+
+ </li>
+
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+ <li class="toctree-l1 ">
+ <a class="" href="../fsInputOperator/">File Input</a>
+
+ </li>
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+ <a class="" href="../file_output/">File Output</a>
+
+ </li>
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+ <a class="" href="../file_splitter/">File Splitter</a>
+
+ </li>
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+ <a class="" href="../filter/">Filter</a>
+
+ </li>
+
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+ <li class="toctree-l1 ">
+ <a class="" href="../AbstractJdbcTransactionableOutputOperator/">Jdbc Output Operator</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../jdbcPollInputOperator/">JDBC Poller Input</a>
+
+ </li>
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+ <li class="toctree-l1 ">
+ <a class="" href="../jmsInputOperator/">JMS Input</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../jsonFormatter/">JSON Formatter</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../jsonParser/">JSON Parser</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../kafkaInputOperator/">Kafka Input</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../transform/">Transformer</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../windowedOperator/">Windowed Operator</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../xmlParserOperator/">XML Parser</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../jsonParser/">Json Parser</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../jsonFormatter/">Json Formatter</a>
+
+ </li>
+
+
+
+ <li class="toctree-l1 ">
+ <a class="" href="../transform/">Transform Operator</a>
+
+ </li>
+
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+ <a class="" href="../csvformatter/">Csv Formatter</a>
+
+ </li>
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+ <a class="" href="../s3outputmodule/">S3 Output Module</a>
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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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+ <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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