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+<!--<div class="hero-unit Spark Data Source for Apache CouchDB/Cloudant">
+ <h1></h1>
+</div>
+-->
+
+<div class="row">
+ <div class="col-md-12">
+ <!--
+
+-->
+
+<p>A library for reading data from Cloudant or CouchDB databases using Spark SQL and Spark Streaming.</p>
+
+<p><a href="https://cloudant.com">IBM® Cloudant®</a> is a document-oriented DataBase as a Service (DBaaS). It stores data as documents
+in JSON format. It’s built with scalability, high availability, and durability in mind. It comes with a
+wide variety of indexing options including map-reduce, Cloudant Query, full-text indexing, and
+geospatial indexing. The replication capabilities make it easy to keep data in sync between database
+clusters, desktop PCs, and mobile devices.</p>
+
+<p><a href="http://couchdb.apache.org">Apache CouchDB™</a> is open source database software that focuses on ease of use and having an architecture that “completely embraces the Web”. It has a document-oriented NoSQL database architecture and is implemented in the concurrency-oriented language Erlang; it uses JSON to store data, JavaScript as its query language using MapReduce, and HTTP for an API.</p>
+
+<h2 id="linking">Linking</h2>
+
+<p>Using SBT:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>libraryDependencies += "org.apache.bahir" %% "spark-sql-cloudant" % "2.2.2"
+</code></pre>
+</div>
+
+<p>Using Maven:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code><dependency>
+ <groupId>org.apache.bahir</groupId>
+ <artifactId>spark-sql-cloudant_2.11</artifactId>
+ <version>2.2.2</version>
+</dependency>
+</code></pre>
+</div>
+
+<p>This library can also be added to Spark jobs launched through <code class="highlighter-rouge">spark-shell</code> or <code class="highlighter-rouge">spark-submit</code> by using the <code class="highlighter-rouge">--packages</code> command line option.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>$ bin/spark-shell --packages org.apache.bahir:spark-sql-cloudant_2.11:2.2.2
+</code></pre>
+</div>
+
+<p>Unlike using <code class="highlighter-rouge">--jars</code>, using <code class="highlighter-rouge">--packages</code> ensures that this library and its dependencies will be added to the classpath.
+The <code class="highlighter-rouge">--packages</code> argument can also be used with <code class="highlighter-rouge">bin/spark-submit</code>.</p>
+
+<p>Submit a job in Python:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>spark-submit --master local[4] --packages org.apache.bahir:spark-sql-cloudant_2.11:2.2.2 <path to python script>
+</code></pre>
+</div>
+
+<p>Submit a job in Scala:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>spark-submit --class "<your class>" --master local[4] --packages org.apache.bahir:spark-sql-cloudant_2.11:2.2.2 <path to spark-sql-cloudant jar>
+</code></pre>
+</div>
+
+<p>This library is compiled for Scala 2.11 only, and intends to support Spark 2.0 onwards.</p>
+
+<h2 id="configuration-options">Configuration options</h2>
+<p>The configuration is obtained in the following sequence:</p>
+
+<ol>
+ <li>default in the Config, which is set in the application.conf</li>
+ <li>key in the SparkConf, which is set in SparkConf</li>
+ <li>key in the parameters, which is set in a dataframe or temporaty table options</li>
+ <li>“spark.”+key in the SparkConf (as they are treated as the one passed in through spark-submit using –conf option)</li>
+</ol>
+
+<p>Here each subsequent configuration overrides the previous one. Thus, configuration set using DataFrame option overrides what has beens set in SparkConf. And configuration passed in spark-submit using –conf takes precedence over any setting in the code.</p>
+
+<h3 id="configuration-in-applicationconf">Configuration in application.conf</h3>
+<p>Default values are defined in <a href="src/main/resources/application.conf">here</a>.</p>
+
+<h3 id="configuration-on-sparkconf">Configuration on SparkConf</h3>
+
+<table>
+ <thead>
+ <tr>
+ <th>Name</th>
+ <th style="text-align: center">Default</th>
+ <th>Meaning</th>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td>cloudant.batchInterval</td>
+ <td style="text-align: center">8</td>
+ <td>number of seconds to set for streaming all documents from <code class="highlighter-rouge">_changes</code> endpoint into Spark dataframe. See <a href="https://spark.apache.org/docs/latest/streaming-programming-guide.html#setting-the-right-batch-interval">Setting the right batch interval</a> for tuning this value.</td>
+ </tr>
+ <tr>
+ <td>cloudant.endpoint</td>
+ <td style="text-align: center"><code class="highlighter-rouge">_all_docs</code></td>
+ <td>endpoint for RelationProvider when loading data from Cloudant to DataFrames or SQL temporary tables. Select between the Cloudant <code class="highlighter-rouge">_all_docs</code> or <code class="highlighter-rouge">_changes</code> API endpoint. See <strong>Note</strong> below for differences between endpoints.</td>
+ </tr>
+ <tr>
+ <td>cloudant.protocol</td>
+ <td style="text-align: center">https</td>
+ <td>protocol to use to transfer data: http or https</td>
+ </tr>
+ <tr>
+ <td>cloudant.host</td>
+ <td style="text-align: center"> </td>
+ <td>cloudant host url</td>
+ </tr>
+ <tr>
+ <td>cloudant.username</td>
+ <td style="text-align: center"> </td>
+ <td>cloudant userid</td>
+ </tr>
+ <tr>
+ <td>cloudant.password</td>
+ <td style="text-align: center"> </td>
+ <td>cloudant password</td>
+ </tr>
+ <tr>
+ <td>cloudant.numberOfRetries</td>
+ <td style="text-align: center">3</td>
+ <td>number of times to replay a request that received a 429 <code class="highlighter-rouge">Too Many Requests</code> response</td>
+ </tr>
+ <tr>
+ <td>cloudant.useQuery</td>
+ <td style="text-align: center">false</td>
+ <td>by default, <code class="highlighter-rouge">_all_docs</code> endpoint is used if configuration ‘view’ and ‘index’ (see below) are not set. When useQuery is enabled, <code class="highlighter-rouge">_find</code> endpoint will be used in place of <code class="highlighter-rouge">_all_docs</code> when query condition is not on primary key field (_id), so that query predicates may be driven into datastore.</td>
+ </tr>
+ <tr>
+ <td>cloudant.queryLimit</td>
+ <td style="text-align: center">25</td>
+ <td>the maximum number of results returned when querying the <code class="highlighter-rouge">_find</code> endpoint.</td>
+ </tr>
+ <tr>
+ <td>cloudant.storageLevel</td>
+ <td style="text-align: center">MEMORY_ONLY</td>
+ <td>the storage level for persisting Spark RDDs during load when <code class="highlighter-rouge">cloudant.endpoint</code> is set to <code class="highlighter-rouge">_changes</code>. See <a href="https://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence">RDD Persistence section</a> in Spark’s Progamming Guide for all available storage level options.</td>
+ </tr>
+ <tr>
+ <td>cloudant.timeout</td>
+ <td style="text-align: center">60000</td>
+ <td>stop the response after waiting the defined number of milliseconds for data. Only supported with <code class="highlighter-rouge">changes</code> endpoint.</td>
+ </tr>
+ <tr>
+ <td>jsonstore.rdd.partitions</td>
+ <td style="text-align: center">10</td>
+ <td>the number of partitions intent used to drive JsonStoreRDD loading query result in parallel. The actual number is calculated based on total rows returned and satisfying maxInPartition and minInPartition. Only supported with <code class="highlighter-rouge">_all_docs</code> endpoint.</td>
+ </tr>
+ <tr>
+ <td>jsonstore.rdd.maxInPartition</td>
+ <td style="text-align: center">-1</td>
+ <td>the max rows in a partition. -1 means unlimited</td>
+ </tr>
+ <tr>
+ <td>jsonstore.rdd.minInPartition</td>
+ <td style="text-align: center">10</td>
+ <td>the min rows in a partition.</td>
+ </tr>
+ <tr>
+ <td>jsonstore.rdd.requestTimeout</td>
+ <td style="text-align: center">900000</td>
+ <td>the request timeout in milliseconds</td>
+ </tr>
+ <tr>
+ <td>bulkSize</td>
+ <td style="text-align: center">200</td>
+ <td>the bulk save size</td>
+ </tr>
+ <tr>
+ <td>schemaSampleSize</td>
+ <td style="text-align: center">-1</td>
+ <td>the sample size for RDD schema discovery. 1 means we are using only the first document for schema discovery; -1 means all documents; 0 will be treated as 1; any number N means min(N, total) docs. Only supported with <code class="highlighter-rouge">_all_docs</code> endpoint.</td>
+ </tr>
+ <tr>
+ <td>createDBOnSave</td>
+ <td style="text-align: center">false</td>
+ <td>whether to create a new database during save operation. If false, a database should already exist. If true, a new database will be created. If true, and a database with a provided name already exists, an error will be raised.</td>
+ </tr>
+ </tbody>
+</table>
+
+<p>The <code class="highlighter-rouge">cloudant.endpoint</code> option sets ` _changes<code class="highlighter-rouge"> or </code>_all_docs` API endpoint to be called while loading Cloudant data into Spark DataFrames or SQL Tables.</p>
+
+<p><strong>Note:</strong> When using <code class="highlighter-rouge">_changes</code> API, please consider:
+1. Results are partially ordered and may not be be presented in order in
+which documents were updated.
+2. In case of shards’ unavailability, you may see duplicate results (changes that have been seen already)
+3. Can use <code class="highlighter-rouge">selector</code> option to filter Cloudant docs during load
+4. Supports a real snapshot of the database and represents it in a single point of time.
+5. Only supports a single partition.</p>
+
+<p>When using <code class="highlighter-rouge">_all_docs</code> API:
+1. Supports parallel reads (using offset and range) and partitioning.
+2. Using partitions may not represent the true snapshot of a database. Some docs
+ may be added or deleted in the database between loading data into different
+ Spark partitions.</p>
+
+<p>If loading Cloudant docs from a database greater than 100 MB, set <code class="highlighter-rouge">cloudant.endpoint</code> to <code class="highlighter-rouge">_changes</code> and <code class="highlighter-rouge">spark.streaming.unpersist</code> to <code class="highlighter-rouge">false</code>.
+This will enable RDD persistence during load against <code class="highlighter-rouge">_changes</code> endpoint and allow the persisted RDDs to be accessible after streaming completes.</p>
+
+<p>See <a href="src/test/scala/org/apache/bahir/cloudant/CloudantChangesDFSuite.scala">CloudantChangesDFSuite</a>
+for examples of loading data into a Spark DataFrame with <code class="highlighter-rouge">_changes</code> API.</p>
+
+<h3 id="configuration-on-spark-sql-temporary-table-or-dataframe">Configuration on Spark SQL Temporary Table or DataFrame</h3>
+
+<p>Besides all the configurations passed to a temporary table or dataframe through SparkConf, it is also possible to set the following configurations in temporary table or dataframe using OPTIONS:</p>
+
+<table>
+ <thead>
+ <tr>
+ <th>Name</th>
+ <th style="text-align: center">Default</th>
+ <th>Meaning</th>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td>bulkSize</td>
+ <td style="text-align: center">200</td>
+ <td>the bulk save size</td>
+ </tr>
+ <tr>
+ <td>createDBOnSave</td>
+ <td style="text-align: center">false</td>
+ <td>whether to create a new database during save operation. If false, a database should already exist. If true, a new database will be created. If true, and a database with a provided name already exists, an error will be raised.</td>
+ </tr>
+ <tr>
+ <td>database</td>
+ <td style="text-align: center"> </td>
+ <td>Cloudant database name</td>
+ </tr>
+ <tr>
+ <td>index</td>
+ <td style="text-align: center"> </td>
+ <td>Cloudant Search index without the database name. Search index queries are limited to returning 200 results so can only be used to load data with <= 200 results.</td>
+ </tr>
+ <tr>
+ <td>path</td>
+ <td style="text-align: center"> </td>
+ <td>Cloudant: as database name if database is not present</td>
+ </tr>
+ <tr>
+ <td>schemaSampleSize</td>
+ <td style="text-align: center">-1</td>
+ <td>the sample size used to discover the schema for this temp table. -1 scans all documents</td>
+ </tr>
+ <tr>
+ <td>selector</td>
+ <td style="text-align: center">all documents</td>
+ <td>a selector written in Cloudant Query syntax, specifying conditions for selecting documents when the <code class="highlighter-rouge">cloudant.endpoint</code> option is set to <code class="highlighter-rouge">_changes</code>. Only documents satisfying the selector’s conditions will be retrieved from Cloudant and loaded into Spark.</td>
+ </tr>
+ <tr>
+ <td>view</td>
+ <td style="text-align: center"> </td>
+ <td>Cloudant view w/o the database name. only used for load.</td>
+ </tr>
+ </tbody>
+</table>
+
+<p>For fast loading, views are loaded without include_docs. Thus, a derived schema will always be: <code class="highlighter-rouge"><span class="p">{</span><span class="err">id,</span><span class="w"> </span><span class="err">key,</span><span class="w"> </span><span class="err">value</span><span class="p">}</span></code>, where <code class="highlighter-rouge">value </code>can be a compount field. An example of loading data from a view:</p>
+
+<div class="language-python highlighter-rouge"><pre class="highlight"><code><span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s">" CREATE TEMPORARY TABLE flightTable1 USING org.apache.bahir.cloudant OPTIONS ( database 'n_flight', view '_design/view/_view/AA0')"</span><span class="p">)</span>
+
+</code></pre>
+</div>
+
+<h3 id="configuration-on-cloudant-receiver-for-spark-streaming">Configuration on Cloudant Receiver for Spark Streaming</h3>
+
+<table>
+ <thead>
+ <tr>
+ <th>Name</th>
+ <th style="text-align: center">Default</th>
+ <th>Meaning</th>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td>cloudant.host</td>
+ <td style="text-align: center"> </td>
+ <td>cloudant host url</td>
+ </tr>
+ <tr>
+ <td>cloudant.username</td>
+ <td style="text-align: center"> </td>
+ <td>cloudant userid</td>
+ </tr>
+ <tr>
+ <td>cloudant.password</td>
+ <td style="text-align: center"> </td>
+ <td>cloudant password</td>
+ </tr>
+ <tr>
+ <td>database</td>
+ <td style="text-align: center"> </td>
+ <td>cloudant database name</td>
+ </tr>
+ <tr>
+ <td>selector</td>
+ <td style="text-align: center">all documents</td>
+ <td>a selector written in Cloudant Query syntax, specifying conditions for selecting documents. Only documents satisfying the selector’s conditions will be retrieved from Cloudant and loaded into Spark.</td>
+ </tr>
+ </tbody>
+</table>
+
+<h3 id="configuration-in-spark-submit-using---conf-option">Configuration in spark-submit using –conf option</h3>
+
+<p>The above stated configuration keys can also be set using <code class="highlighter-rouge">spark-submit --conf</code> option. When passing configuration in spark-submit, make sure adding “spark.” as prefix to the keys.</p>
+
+<h2 id="examples">Examples</h2>
+
+<h3 id="python-api">Python API</h3>
+
+<h4 id="using-sql-in-python">Using SQL In Python</h4>
+
+<div class="language-python highlighter-rouge"><pre class="highlight"><code><span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span>\
+ <span class="o">.</span><span class="n">builder</span>\
+ <span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s">"Cloudant Spark SQL Example in Python using temp tables"</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">config</span><span class="p">(</span><span class="s">"cloudant.host"</span><span class="p">,</span><span class="s">"ACCOUNT.cloudant.com"</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">config</span><span class="p">(</span><span class="s">"cloudant.username"</span><span class="p">,</span> <span class="s">"USERNAME"</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">config</span><span class="p">(</span><span class="s">"cloudant.password"</span><span class="p">,</span><span class="s">"PASSWORD"</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span>
+
+
+<span class="c"># Loading temp table from Cloudant db</span>
+<span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s">" CREATE TEMPORARY TABLE airportTable USING org.apache.bahir.cloudant OPTIONS ( database 'n_airportcodemapping')"</span><span class="p">)</span>
+<span class="n">airportData</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s">"SELECT _id, airportName FROM airportTable WHERE _id >= 'CAA' AND _id <= 'GAA' ORDER BY _id"</span><span class="p">)</span>
+<span class="n">airportData</span><span class="o">.</span><span class="n">printSchema</span><span class="p">()</span>
+<span class="k">print</span> <span class="s">'Total # of rows in airportData: '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">airportData</span><span class="o">.</span><span class="n">count</span><span class="p">())</span>
+<span class="k">for</span> <span class="n">code</span> <span class="ow">in</span> <span class="n">airportData</span><span class="o">.</span><span class="n">collect</span><span class="p">():</span>
+ <span class="k">print</span> <span class="n">code</span><span class="o">.</span><span class="n">_id</span>
+</code></pre>
+</div>
+
+<p>See <a href="examples/python/CloudantApp.py">CloudantApp.py</a> for examples.</p>
+
+<p>Submit job example:
+<code class="highlighter-rouge">
+spark-submit --packages org.apache.bahir:spark-sql-cloudant_2.11:2.2.2 --conf spark.cloudant.host=ACCOUNT.cloudant.com --conf spark.cloudant.username=USERNAME --conf spark.cloudant.password=PASSWORD sql-cloudant/examples/python/CloudantApp.py
+</code></p>
+
+<h4 id="using-dataframe-in-python">Using DataFrame In Python</h4>
+
+<div class="language-python highlighter-rouge"><pre class="highlight"><code><span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span>\
+ <span class="o">.</span><span class="n">builder</span>\
+ <span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s">"Cloudant Spark SQL Example in Python using dataframes"</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">config</span><span class="p">(</span><span class="s">"cloudant.host"</span><span class="p">,</span><span class="s">"ACCOUNT.cloudant.com"</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">config</span><span class="p">(</span><span class="s">"cloudant.username"</span><span class="p">,</span> <span class="s">"USERNAME"</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">config</span><span class="p">(</span><span class="s">"cloudant.password"</span><span class="p">,</span><span class="s">"PASSWORD"</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">config</span><span class="p">(</span><span class="s">"jsonstore.rdd.partitions"</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span>
+
+<span class="c"># ***1. Loading dataframe from Cloudant db</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s">"n_airportcodemapping"</span><span class="p">,</span> <span class="s">"org.apache.bahir.cloudant"</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>
+<span class="n">df</span><span class="o">.</span><span class="n">printSchema</span><span class="p">()</span>
+<span class="n">df</span><span class="o">.</span><span class="nb">filter</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">airportName</span> <span class="o">>=</span> <span class="s">'Moscow'</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">"_id"</span><span class="p">,</span><span class="s">'airportName'</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">df</span><span class="o">.</span><span class="nb">filter</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">_id</span> <span class="o">>=</span> <span class="s">'CAA'</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">"_id"</span><span class="p">,</span><span class="s">'airportName'</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</code></pre>
+</div>
+
+<p>See <a href="examples/python/CloudantDF.py">CloudantDF.py</a> for examples.</p>
+
+<p>In case of doing multiple operations on a dataframe (select, filter etc.),
+you should persist a dataframe. Otherwise, every operation on a dataframe will load the same data from Cloudant again.
+Persisting will also speed up computation. This statement will persist an RDD in memory: <code class="highlighter-rouge">df.cache()</code>. Alternatively for large dbs to persist in memory & disk, use:</p>
+
+<div class="language-python highlighter-rouge"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">StorageLevel</span>
+<span class="n">df</span><span class="o">.</span><span class="n">persist</span><span class="p">(</span><span class="n">storageLevel</span> <span class="o">=</span> <span class="n">StorageLevel</span><span class="p">(</span><span class="bp">True</span><span class="p">,</span> <span class="bp">True</span><span class="p">,</span> <span class="bp">False</span><span class="p">,</span> <span class="bp">True</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+</code></pre>
+</div>
+
+<p><a href="examples/python/CloudantDFOption.py">Sample code</a> on using DataFrame option to define cloudant configuration</p>
+
+<h3 id="scala-api">Scala API</h3>
+
+<h4 id="using-sql-in-scala">Using SQL In Scala</h4>
+
+<div class="language-scala highlighter-rouge"><pre class="highlight"><code><span class="k">val</span> <span class="n">spark</span> <span class="k">=</span> <span class="nc">SparkSession</span>
+ <span class="o">.</span><span class="n">builder</span><span class="o">()</span>
+ <span class="o">.</span><span class="n">appName</span><span class="o">(</span><span class="s">"Cloudant Spark SQL Example"</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">config</span><span class="o">(</span><span class="s">"cloudant.host"</span><span class="o">,</span><span class="s">"ACCOUNT.cloudant.com"</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">config</span><span class="o">(</span><span class="s">"cloudant.username"</span><span class="o">,</span> <span class="s">"USERNAME"</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">config</span><span class="o">(</span><span class="s">"cloudant.password"</span><span class="o">,</span><span class="s">"PASSWORD"</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">getOrCreate</span><span class="o">()</span>
+
+<span class="c1">// For implicit conversions of Dataframe to RDDs
+</span><span class="k">import</span> <span class="nn">spark.implicits._</span>
+
+<span class="c1">// create a temp table from Cloudant db and query it using sql syntax
+</span><span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="o">(</span>
+ <span class="n">s</span><span class="s">"""
+ |CREATE TEMPORARY TABLE airportTable
+ |USING org.apache.bahir.cloudant
+ |OPTIONS ( database 'n_airportcodemapping')
+ """</span><span class="o">.</span><span class="n">stripMargin</span><span class="o">)</span>
+<span class="c1">// create a dataframe
+</span><span class="k">val</span> <span class="n">airportData</span> <span class="k">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="o">(</span><span class="s">"SELECT _id, airportName FROM airportTable WHERE _id >= 'CAA' AND _id <= 'GAA' ORDER BY _id"</span><span class="o">)</span>
+<span class="n">airportData</span><span class="o">.</span><span class="n">printSchema</span><span class="o">()</span>
+<span class="n">println</span><span class="o">(</span><span class="n">s</span><span class="s">"Total # of rows in airportData: "</span> <span class="o">+</span> <span class="n">airportData</span><span class="o">.</span><span class="n">count</span><span class="o">())</span>
+<span class="c1">// convert dataframe to array of Rows, and process each row
+</span><span class="n">airportData</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">t</span> <span class="k">=></span> <span class="s">"code: "</span> <span class="o">+</span> <span class="n">t</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span> <span class="o">+</span> <span class="s">",name:"</span> <span class="o">+</span> <span class="n">t</span><span class="o">(</span><span class="mi">1</span><span class="o">)).</span><span class="n">collect</span><span class="o">().</span><span class="n">foreach</span><span class="o">(</span><span class="n">println</span><span class="o">)</span>
+</code></pre>
+</div>
+<p>See <a href="examples/scala/src/main/scala/mytest/spark/CloudantApp.scala">CloudantApp.scala</a> for examples.</p>
+
+<p>Submit job example:
+<code class="highlighter-rouge">
+spark-submit --class org.apache.spark.examples.sql.cloudant.CloudantApp --packages org.apache.bahir:spark-sql-cloudant_2.11:2.2.2 --conf spark.cloudant.host=ACCOUNT.cloudant.com --conf spark.cloudant.username=USERNAME --conf spark.cloudant.password=PASSWORD /path/to/spark-sql-cloudant_2.11-2.2.2-tests.jar
+</code></p>
+
+<h3 id="using-dataframe-in-scala">Using DataFrame In Scala</h3>
+
+<div class="language-scala highlighter-rouge"><pre class="highlight"><code><span class="k">val</span> <span class="n">spark</span> <span class="k">=</span> <span class="nc">SparkSession</span>
+ <span class="o">.</span><span class="n">builder</span><span class="o">()</span>
+ <span class="o">.</span><span class="n">appName</span><span class="o">(</span><span class="s">"Cloudant Spark SQL Example with Dataframe"</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">config</span><span class="o">(</span><span class="s">"cloudant.host"</span><span class="o">,</span><span class="s">"ACCOUNT.cloudant.com"</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">config</span><span class="o">(</span><span class="s">"cloudant.username"</span><span class="o">,</span> <span class="s">"USERNAME"</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">config</span><span class="o">(</span><span class="s">"cloudant.password"</span><span class="o">,</span><span class="s">"PASSWORD"</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">config</span><span class="o">(</span><span class="s">"createDBOnSave"</span><span class="o">,</span><span class="s">"true"</span><span class="o">)</span> <span class="c1">// to create a db on save
+</span> <span class="o">.</span><span class="n">config</span><span class="o">(</span><span class="s">"jsonstore.rdd.partitions"</span><span class="o">,</span> <span class="s">"20"</span><span class="o">)</span> <span class="c1">// using 20 partitions
+</span> <span class="o">.</span><span class="n">getOrCreate</span><span class="o">()</span>
+
+<span class="c1">// 1. Loading data from Cloudant db
+</span><span class="k">val</span> <span class="n">df</span> <span class="k">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">format</span><span class="o">(</span><span class="s">"org.apache.bahir.cloudant"</span><span class="o">).</span><span class="n">load</span><span class="o">(</span><span class="s">"n_flight"</span><span class="o">)</span>
+<span class="c1">// Caching df in memory to speed computations
+// and not to retrieve data from cloudant again
+</span><span class="n">df</span><span class="o">.</span><span class="n">cache</span><span class="o">()</span>
+<span class="n">df</span><span class="o">.</span><span class="n">printSchema</span><span class="o">()</span>
+
+<span class="c1">// 2. Saving dataframe to Cloudant db
+</span><span class="k">val</span> <span class="n">df2</span> <span class="k">=</span> <span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="o">(</span><span class="n">df</span><span class="o">(</span><span class="s">"flightSegmentId"</span><span class="o">)</span> <span class="o">===</span> <span class="s">"AA106"</span><span class="o">)</span>
+ <span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"flightSegmentId"</span><span class="o">,</span><span class="s">"economyClassBaseCost"</span><span class="o">)</span>
+<span class="n">df2</span><span class="o">.</span><span class="n">show</span><span class="o">()</span>
+<span class="n">df2</span><span class="o">.</span><span class="n">write</span><span class="o">.</span><span class="n">format</span><span class="o">(</span><span class="s">"org.apache.bahir.cloudant"</span><span class="o">).</span><span class="n">save</span><span class="o">(</span><span class="s">"n_flight2"</span><span class="o">)</span>
+</code></pre>
+</div>
+
+<p>See <a href="examples/scala/src/main/scala/mytest/spark/CloudantDF.scala">CloudantDF.scala</a> for examples.</p>
+
+<p><a href="examples/scala/src/main/scala/mytest/spark/CloudantDFOption.scala">Sample code</a> on using DataFrame option to define Cloudant configuration.</p>
+
+<h3 id="using-streams-in-scala">Using Streams In Scala</h3>
+
+<div class="language-scala highlighter-rouge"><pre class="highlight"><code><span class="k">val</span> <span class="n">ssc</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">StreamingContext</span><span class="o">(</span><span class="n">sparkConf</span><span class="o">,</span> <span class="nc">Seconds</span><span class="o">(</span><span class="mi">10</span><span class="o">))</span>
+<span class="k">val</span> <span class="n">changes</span> <span class="k">=</span> <span class="n">ssc</span><span class="o">.</span><span class="n">receiverStream</span><span class="o">(</span><span class="k">new</span> <span class="nc">CloudantReceiver</span><span class="o">(</span><span class="nc">Map</span><span class="o">(</span>
+ <span class="s">"cloudant.host"</span> <span class="o">-></span> <span class="s">"ACCOUNT.cloudant.com"</span><span class="o">,</span>
+ <span class="s">"cloudant.username"</span> <span class="o">-></span> <span class="s">"USERNAME"</span><span class="o">,</span>
+ <span class="s">"cloudant.password"</span> <span class="o">-></span> <span class="s">"PASSWORD"</span><span class="o">,</span>
+ <span class="s">"database"</span> <span class="o">-></span> <span class="s">"n_airportcodemapping"</span><span class="o">)))</span>
+
+<span class="n">changes</span><span class="o">.</span><span class="n">foreachRDD</span><span class="o">((</span><span class="n">rdd</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[</span><span class="kt">String</span><span class="o">],</span> <span class="n">time</span><span class="k">:</span> <span class="kt">Time</span><span class="o">)</span> <span class="k">=></span> <span class="o">{</span>
+ <span class="c1">// Get the singleton instance of SparkSession
+</span> <span class="k">val</span> <span class="n">spark</span> <span class="k">=</span> <span class="nc">SparkSessionSingleton</span><span class="o">.</span><span class="n">getInstance</span><span class="o">(</span><span class="n">rdd</span><span class="o">.</span><span class="n">sparkContext</span><span class="o">.</span><span class="n">getConf</span><span class="o">)</span>
+
+ <span class="n">println</span><span class="o">(</span><span class="n">s</span><span class="s">"========= $time ========="</span><span class="o">)</span>
+ <span class="c1">// Convert RDD[String] to DataFrame
+</span> <span class="k">val</span> <span class="n">changesDataFrame</span> <span class="k">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">json</span><span class="o">(</span><span class="n">rdd</span><span class="o">)</span>
+ <span class="k">if</span> <span class="o">(!</span><span class="n">changesDataFrame</span><span class="o">.</span><span class="n">schema</span><span class="o">.</span><span class="n">isEmpty</span><span class="o">)</span> <span class="o">{</span>
+ <span class="n">changesDataFrame</span><span class="o">.</span><span class="n">printSchema</span><span class="o">()</span>
+ <span class="n">changesDataFrame</span><span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"*"</span><span class="o">).</span><span class="n">show</span><span class="o">()</span>
+ <span class="o">....</span>
+ <span class="o">}</span>
+<span class="o">})</span>
+<span class="n">ssc</span><span class="o">.</span><span class="n">start</span><span class="o">()</span>
+<span class="c1">// run streaming for 120 secs
+</span><span class="nc">Thread</span><span class="o">.</span><span class="n">sleep</span><span class="o">(</span><span class="mi">120000L</span><span class="o">)</span>
+<span class="n">ssc</span><span class="o">.</span><span class="n">stop</span><span class="o">(</span><span class="kc">true</span><span class="o">)</span>
+
+</code></pre>
+</div>
+
+<p>See <a href="examples/scala/src/main/scala/mytest/spark/CloudantStreaming.scala">CloudantStreaming.scala</a> for examples.</p>
+
+<p>By default, Spark Streaming will load all documents from a database. If you want to limit the loading to
+specific documents, use <code class="highlighter-rouge">selector</code> option of <code class="highlighter-rouge">CloudantReceiver</code> and specify your conditions
+(See <a href="examples/scala/src/main/scala/mytest/spark/CloudantStreamingSelector.scala">CloudantStreamingSelector.scala</a>
+example for more details):</p>
+
+<div class="language-scala highlighter-rouge"><pre class="highlight"><code><span class="k">val</span> <span class="n">changes</span> <span class="k">=</span> <span class="n">ssc</span><span class="o">.</span><span class="n">receiverStream</span><span class="o">(</span><span class="k">new</span> <span class="nc">CloudantReceiver</span><span class="o">(</span><span class="nc">Map</span><span class="o">(</span>
+ <span class="s">"cloudant.host"</span> <span class="o">-></span> <span class="s">"ACCOUNT.cloudant.com"</span><span class="o">,</span>
+ <span class="s">"cloudant.username"</span> <span class="o">-></span> <span class="s">"USERNAME"</span><span class="o">,</span>
+ <span class="s">"cloudant.password"</span> <span class="o">-></span> <span class="s">"PASSWORD"</span><span class="o">,</span>
+ <span class="s">"database"</span> <span class="o">-></span> <span class="s">"sales"</span><span class="o">,</span>
+ <span class="s">"selector"</span> <span class="o">-></span> <span class="s">"{\"month\":\"May\", \"rep\":\"John\"}"</span><span class="o">)))</span>
+</code></pre>
+</div>
+
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+<!--<div class="hero-unit Spark Structured Streaming Akka">
+ <h1></h1>
+</div>
+-->
+
+<div class="row">
+ <div class="col-md-12">
+ <!--
+
+-->
+
+<p>A library for reading data from Akka Actors using Spark SQL Streaming ( or Structured streaming.).</p>
+
+<h2 id="linking">Linking</h2>
+
+<p>Using SBT:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>libraryDependencies += "org.apache.bahir" %% "spark-sql-streaming-akka" % "2.2.2"
+</code></pre>
+</div>
+
+<p>Using Maven:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code><dependency>
+ <groupId>org.apache.bahir</groupId>
+ <artifactId>spark-sql-streaming-akka_2.11</artifactId>
+ <version>2.2.2</version>
+</dependency>
+</code></pre>
+</div>
+
+<p>This library can also be added to Spark jobs launched through <code class="highlighter-rouge">spark-shell</code> or <code class="highlighter-rouge">spark-submit</code> by using the <code class="highlighter-rouge">--packages</code> command line option.
+For example, to include it when starting the spark shell:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>$ bin/spark-shell --packages org.apache.bahir:spark-sql-streaming-akka_2.11:2.2.2
+</code></pre>
+</div>
+
+<p>Unlike using <code class="highlighter-rouge">--jars</code>, using <code class="highlighter-rouge">--packages</code> ensures that this library and its dependencies will be added to the classpath.
+The <code class="highlighter-rouge">--packages</code> argument can also be used with <code class="highlighter-rouge">bin/spark-submit</code>.</p>
+
+<p>This library is compiled for Scala 2.11 only, and intends to support Spark 2.0 onwards.</p>
+
+<h2 id="examples">Examples</h2>
+
+<p>A SQL Stream can be created with data streams received from Akka Feeder actor using,</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code> sqlContext.readStream
+ .format("org.apache.bahir.sql.streaming.akka.AkkaStreamSourceProvider")
+ .option("urlOfPublisher", "feederActorUri")
+ .load()
+</code></pre>
+</div>
+
+<h2 id="enable-recovering-from-failures">Enable recovering from failures.</h2>
+
+<p>Setting values for option <code class="highlighter-rouge">persistenceDirPath</code> helps in recovering in case of a restart, by restoring the state where it left off before the shutdown.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code> sqlContext.readStream
+ .format("org.apache.bahir.sql.streaming.akka.AkkaStreamSourceProvider")
+ .option("urlOfPublisher", "feederActorUri")
+ .option("persistenceDirPath", "/path/to/localdir")
+ .load()
+</code></pre>
+</div>
+
+<h2 id="configuration-options">Configuration options.</h2>
+
+<p>This source uses <a href="http://doc.akka.io/api/akka/2.4/akka/actor/Actor.html">Akka Actor api</a>.</p>
+
+<ul>
+ <li><code class="highlighter-rouge">urlOfPublisher</code> The url of Publisher or Feeder actor that the Receiver actor connects to. Set this as the tcp url of the Publisher or Feeder actor.</li>
+ <li><code class="highlighter-rouge">persistenceDirPath</code> By default it is used for storing incoming messages on disk.</li>
+</ul>
+
+<h3 id="scala-api">Scala API</h3>
+
+<p>An example, for scala API to count words from incoming message stream.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code> // Create DataFrame representing the stream of input lines from connection
+ // to publisher or feeder actor
+ val lines = spark.readStream
+ .format("org.apache.bahir.sql.streaming.akka.AkkaStreamSourceProvider")
+ .option("urlOfPublisher", urlOfPublisher)
+ .load().as[(String, Timestamp)]
+
+ // Split the lines into words
+ val words = lines.map(_._1).flatMap(_.split(" "))
+
+ // Generate running word count
+ val wordCounts = words.groupBy("value").count()
+
+ // Start running the query that prints the running counts to the console
+ val query = wordCounts.writeStream
+ .outputMode("complete")
+ .format("console")
+ .start()
+
+ query.awaitTermination()
+</code></pre>
+</div>
+
+<p>Please see <code class="highlighter-rouge">AkkaStreamWordCount.scala</code> for full example.</p>
+
+<h3 id="java-api">Java API</h3>
+
+<p>An example, for Java API to count words from incoming message stream.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code> // Create DataFrame representing the stream of input lines from connection
+ // to publisher or feeder actor
+ Dataset<String> lines = spark
+ .readStream()
+ .format("org.apache.bahir.sql.streaming.akka.AkkaStreamSourceProvider")
+ .option("urlOfPublisher", urlOfPublisher)
+ .load().select("value").as(Encoders.STRING());
+
+ // Split the lines into words
+ Dataset<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
+ @Override
+ public Iterator<String> call(String s) throws Exception {
+ return Arrays.asList(s.split(" ")).iterator();
+ }
+ }, Encoders.STRING());
+
+ // Generate running word count
+ Dataset<Row> wordCounts = words.groupBy("value").count();
+
+ // Start running the query that prints the running counts to the console
+ StreamingQuery query = wordCounts.writeStream()
+ .outputMode("complete")
+ .format("console")
+ .start();
+
+ query.awaitTermination();
+</code></pre>
+</div>
+
+<p>Please see <code class="highlighter-rouge">JavaAkkaStreamWordCount.java</code> for full example.</p>
+
+ </div>
+</div>
+
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http://git-wip-us.apache.org/repos/asf/bahir-website/blob/c8b0d7a6/content/docs/spark/2.2.2/spark-sql-streaming-mqtt/index.html
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+ <div class="container">
+
+
+
+<!--<div class="hero-unit Spark Structured Streaming MQTT">
+ <h1></h1>
+</div>
+-->
+
+<div class="row">
+ <div class="col-md-12">
+ <!--
+
+-->
+
+<p>A library for reading data from MQTT Servers using Spark SQL Streaming ( or Structured streaming.).</p>
+
+<h2 id="linking">Linking</h2>
+
+<p>Using SBT:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>libraryDependencies += "org.apache.bahir" %% "spark-sql-streaming-mqtt" % "2.2.2"
+</code></pre>
+</div>
+
+<p>Using Maven:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code><dependency>
+ <groupId>org.apache.bahir</groupId>
+ <artifactId>spark-sql-streaming-mqtt_2.11</artifactId>
+ <version>2.2.2</version>
+</dependency>
+</code></pre>
+</div>
+
+<p>This library can also be added to Spark jobs launched through <code class="highlighter-rouge">spark-shell</code> or <code class="highlighter-rouge">spark-submit</code> by using the <code class="highlighter-rouge">--packages</code> command line option.
+For example, to include it when starting the spark shell:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>$ bin/spark-shell --packages org.apache.bahir:spark-sql-streaming-mqtt_2.11:2.2.2
+</code></pre>
+</div>
+
+<p>Unlike using <code class="highlighter-rouge">--jars</code>, using <code class="highlighter-rouge">--packages</code> ensures that this library and its dependencies will be added to the classpath.
+The <code class="highlighter-rouge">--packages</code> argument can also be used with <code class="highlighter-rouge">bin/spark-submit</code>.</p>
+
+<p>This library is compiled for Scala 2.11 only, and intends to support Spark 2.0 onwards.</p>
+
+<h2 id="examples">Examples</h2>
+
+<p>A SQL Stream can be created with data streams received through MQTT Server using,</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>sqlContext.readStream
+ .format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
+ .option("topic", "mytopic")
+ .load("tcp://localhost:1883")
+</code></pre>
+</div>
+
+<h2 id="enable-recovering-from-failures">Enable recovering from failures.</h2>
+
+<p>Setting values for option <code class="highlighter-rouge">localStorage</code> and <code class="highlighter-rouge">clientId</code> helps in recovering in case of a restart, by restoring the state where it left off before the shutdown.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>sqlContext.readStream
+ .format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
+ .option("topic", "mytopic")
+ .option("localStorage", "/path/to/localdir")
+ .option("clientId", "some-client-id")
+ .load("tcp://localhost:1883")
+</code></pre>
+</div>
+
+<h2 id="configuration-options">Configuration options.</h2>
+
+<p>This source uses <a href="https://eclipse.org/paho/clients/java/">Eclipse Paho Java Client</a>. Client API documentation is located <a href="http://www.eclipse.org/paho/files/javadoc/index.html">here</a>.</p>
+
+<ul>
+ <li><code class="highlighter-rouge">brokerUrl</code> A url MqttClient connects to. Set this or <code class="highlighter-rouge">path</code> as the url of the Mqtt Server. e.g. tcp://localhost:1883.</li>
+ <li><code class="highlighter-rouge">persistence</code> By default it is used for storing incoming messages on disk. If <code class="highlighter-rouge">memory</code> is provided as value for this option, then recovery on restart is not supported.</li>
+ <li><code class="highlighter-rouge">topic</code> Topic MqttClient subscribes to.</li>
+ <li><code class="highlighter-rouge">clientId</code> clientId, this client is assoicated with. Provide the same value to recover a stopped client.</li>
+ <li><code class="highlighter-rouge">QoS</code> The maximum quality of service to subscribe each topic at. Messages published at a lower quality of service will be received at the published QoS. Messages published at a higher quality of service will be received using the QoS specified on the subscribe.</li>
+ <li><code class="highlighter-rouge">username</code> Sets the user name to use for the connection to Mqtt Server. Do not set it, if server does not need this. Setting it empty will lead to errors.</li>
+ <li><code class="highlighter-rouge">password</code> Sets the password to use for the connection.</li>
+ <li><code class="highlighter-rouge">cleanSession</code> Setting it true starts a clean session, removes all checkpointed messages by a previous run of this source. This is set to false by default.</li>
+ <li><code class="highlighter-rouge">connectionTimeout</code> Sets the connection timeout, a value of 0 is interpretted as wait until client connects. See <code class="highlighter-rouge">MqttConnectOptions.setConnectionTimeout</code> for more information.</li>
+ <li><code class="highlighter-rouge">keepAlive</code> Same as <code class="highlighter-rouge">MqttConnectOptions.setKeepAliveInterval</code>.</li>
+ <li><code class="highlighter-rouge">mqttVersion</code> Same as <code class="highlighter-rouge">MqttConnectOptions.setMqttVersion</code>.</li>
+</ul>
+
+<h3 id="scala-api">Scala API</h3>
+
+<p>An example, for scala API to count words from incoming message stream.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>// Create DataFrame representing the stream of input lines from connection to mqtt server
+val lines = spark.readStream
+ .format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
+ .option("topic", topic)
+ .load(brokerUrl).as[(String, Timestamp)]
+
+// Split the lines into words
+val words = lines.map(_._1).flatMap(_.split(" "))
+
+// Generate running word count
+val wordCounts = words.groupBy("value").count()
+
+// Start running the query that prints the running counts to the console
+val query = wordCounts.writeStream
+ .outputMode("complete")
+ .format("console")
+ .start()
+
+query.awaitTermination()
+</code></pre>
+</div>
+
+<p>Please see <code class="highlighter-rouge">MQTTStreamWordCount.scala</code> for full example.</p>
+
+<h3 id="java-api">Java API</h3>
+
+<p>An example, for Java API to count words from incoming message stream.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>// Create DataFrame representing the stream of input lines from connection to mqtt server.
+Dataset<String> lines = spark
+ .readStream()
+ .format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
+ .option("topic", topic)
+ .load(brokerUrl).select("value").as(Encoders.STRING());
+
+// Split the lines into words
+Dataset<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
+ @Override
+ public Iterator<String> call(String x) {
+ return Arrays.asList(x.split(" ")).iterator();
+ }
+}, Encoders.STRING());
+
+// Generate running word count
+Dataset<Row> wordCounts = words.groupBy("value").count();
+
+// Start running the query that prints the running counts to the console
+StreamingQuery query = wordCounts.writeStream()
+ .outputMode("complete")
+ .format("console")
+ .start();
+
+query.awaitTermination();
+</code></pre>
+</div>
+
+<p>Please see <code class="highlighter-rouge">JavaMQTTStreamWordCount.java</code> for full example.</p>
+
+
+ </div>
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+ <h1></h1>
+</div>
+-->
+
+<div class="row">
+ <div class="col-md-12">
+ <!--
+
+-->
+
+<p>A library for reading data from Akka Actors using Spark Streaming.</p>
+
+<h2 id="linking">Linking</h2>
+
+<p>Using SBT:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>libraryDependencies += "org.apache.bahir" %% "spark-streaming-akka" % "2.2.2"
+</code></pre>
+</div>
+
+<p>Using Maven:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code><dependency>
+ <groupId>org.apache.bahir</groupId>
+ <artifactId>spark-streaming-akka_2.11</artifactId>
+ <version>2.2.2</version>
+</dependency>
+</code></pre>
+</div>
+
+<p>This library can also be added to Spark jobs launched through <code class="highlighter-rouge">spark-shell</code> or <code class="highlighter-rouge">spark-submit</code> by using the <code class="highlighter-rouge">--packages</code> command line option.
+For example, to include it when starting the spark shell:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>$ bin/spark-shell --packages org.apache.bahir:spark-streaming-akka_2.11:2.2.2
+</code></pre>
+</div>
+
+<p>Unlike using <code class="highlighter-rouge">--jars</code>, using <code class="highlighter-rouge">--packages</code> ensures that this library and its dependencies will be added to the classpath.
+The <code class="highlighter-rouge">--packages</code> argument can also be used with <code class="highlighter-rouge">bin/spark-submit</code>.</p>
+
+<p>This library is cross-published for Scala 2.10 and Scala 2.11, so users should replace the proper Scala version (2.10 or 2.11) in the commands listed above.</p>
+
+<h2 id="examples">Examples</h2>
+
+<p>DStreams can be created with data streams received through Akka actors by using <code class="highlighter-rouge">AkkaUtils.createStream(ssc, actorProps, actor-name)</code>.</p>
+
+<h3 id="scala-api">Scala API</h3>
+
+<p>You need to extend <code class="highlighter-rouge">ActorReceiver</code> so as to store received data into Spark using <code class="highlighter-rouge">store(...)</code> methods. The supervisor strategy of
+this actor can be configured to handle failures, etc.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>class CustomActor extends ActorReceiver {
+ def receive = {
+ case data: String => store(data)
+ }
+}
+
+// A new input stream can be created with this custom actor as
+val ssc: StreamingContext = ...
+val lines = AkkaUtils.createStream[String](ssc, Props[CustomActor](), "CustomReceiver")
+</code></pre>
+</div>
+
+<h3 id="java-api">Java API</h3>
+
+<p>You need to extend <code class="highlighter-rouge">JavaActorReceiver</code> so as to store received data into Spark using <code class="highlighter-rouge">store(...)</code> methods. The supervisor strategy of
+this actor can be configured to handle failures, etc.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>class CustomActor extends JavaActorReceiver {
+ @Override
+ public void onReceive(Object msg) throws Exception {
+ store((String) msg);
+ }
+}
+
+// A new input stream can be created with this custom actor as
+JavaStreamingContext jssc = ...;
+JavaDStream<String> lines = AkkaUtils.<String>createStream(jssc, Props.create(CustomActor.class), "CustomReceiver");
+</code></pre>
+</div>
+
+<p>See end-to-end examples at <a href="https://github.com/apache/bahir/tree/master/streaming-akka/examples">Akka Examples</a></p>
+
+ </div>
+</div>
+
+
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