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<a href="pyspark-module.html">Package pyspark</a> ::
<a href="pyspark.sql-module.html">Module sql</a> ::
Class SQLContext
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class SQLContext</h1><p class="nomargin-top"><span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext">source code</a></span></p>
<p>Main entry point for SparkSQL functionality.</p>
<p>A SQLContext can be used create <a
href="pyspark.sql.SchemaRDD-class.html" class="link">SchemaRDD</a>s,
register <a href="pyspark.sql.SchemaRDD-class.html"
class="link">SchemaRDD</a>s as tables, execute SQL over tables, cache
tables, and read parquet files.</p>
<!-- ==================== INSTANCE METHODS ==================== -->
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<td><span class="summary-sig"><a href="pyspark.sql.SQLContext-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">sparkContext</span>,
<span class="summary-sig-arg">sqlContext</span>=<span class="summary-sig-default">None</span>)</span><br />
Create a new SQLContext.</td>
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<td><span class="summary-sig"><a href="pyspark.sql.SQLContext-class.html#inferSchema" class="summary-sig-name">inferSchema</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">rdd</span>)</span><br />
Infer and apply a schema to an RDD of <code
class="link">dict</code>s.</td>
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<span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.inferSchema">source code</a></span>
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<td><span class="summary-sig"><a href="pyspark.sql.SQLContext-class.html#registerRDDAsTable" class="summary-sig-name">registerRDDAsTable</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">rdd</span>,
<span class="summary-sig-arg">tableName</span>)</span><br />
Registers the given RDD as a temporary table in the catalog.</td>
<td align="right" valign="top">
<span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.registerRDDAsTable">source code</a></span>
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<td><span class="summary-sig"><a href="pyspark.sql.SQLContext-class.html#parquetFile" class="summary-sig-name">parquetFile</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">path</span>)</span><br />
Loads a Parquet file, returning the result as a <a
href="pyspark.sql.SchemaRDD-class.html" class="link">SchemaRDD</a>.</td>
<td align="right" valign="top">
<span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.parquetFile">source code</a></span>
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<td><span class="summary-sig"><a href="pyspark.sql.SQLContext-class.html#jsonFile" class="summary-sig-name">jsonFile</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">path</span>)</span><br />
Loads a text file storing one JSON object per line,
returning the result as a L{SchemaRDD}.</td>
<td align="right" valign="top">
<span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.jsonFile">source code</a></span>
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<td><span class="summary-sig"><a href="pyspark.sql.SQLContext-class.html#jsonRDD" class="summary-sig-name">jsonRDD</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">rdd</span>)</span><br />
Loads an RDD storing one JSON object per string, returning the result as a L{SchemaRDD}.</td>
<td align="right" valign="top">
<span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.jsonRDD">source code</a></span>
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<td><span class="summary-sig"><a href="pyspark.sql.SQLContext-class.html#sql" class="summary-sig-name">sql</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">sqlQuery</span>)</span><br />
Return a <a href="pyspark.sql.SchemaRDD-class.html"
class="link">SchemaRDD</a> representing the result of the given
query.</td>
<td align="right" valign="top">
<span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.sql">source code</a></span>
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<td><span class="summary-sig"><a href="pyspark.sql.SQLContext-class.html#table" class="summary-sig-name">table</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">tableName</span>)</span><br />
Returns the specified table as a <a
href="pyspark.sql.SchemaRDD-class.html" class="link">SchemaRDD</a>.</td>
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<span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.table">source code</a></span>
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<td><span class="summary-sig"><a name="cacheTable"></a><span class="summary-sig-name">cacheTable</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">tableName</span>)</span><br />
Caches the specified table in-memory.</td>
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<span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.cacheTable">source code</a></span>
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<td><span class="summary-sig"><a name="uncacheTable"></a><span class="summary-sig-name">uncacheTable</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">tableName</span>)</span><br />
Removes the specified table from the in-memory cache.</td>
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<span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.uncacheTable">source code</a></span>
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<!-- ==================== METHOD DETAILS ==================== -->
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<a name="__init__"></a>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">sparkContext</span>,
<span class="sig-arg">sqlContext</span>=<span class="sig-default">None</span>)</span>
<br /><em class="fname">(Constructor)</em>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.__init__">source code</a></span>
</td>
</tr></table>
<p>Create a new SQLContext.</p>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>sparkContext</code></strong> - The SparkContext to wrap.
<pre class="py-doctest">
<span class="py-prompt">>>> </span>srdd = sqlCtx.inferSchema(rdd)
<span class="py-prompt">>>> </span>sqlCtx.inferSchema(srdd) <span class="py-comment"># doctest: +IGNORE_EXCEPTION_DETAIL</span>
<span class="py-except">Traceback (most recent call last):</span>
<span class="py-except"> ...</span>
<span class="py-except">ValueError:...</span></pre>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>bad_rdd = sc.parallelize([1,2,3])
<span class="py-prompt">>>> </span>sqlCtx.inferSchema(bad_rdd) <span class="py-comment"># doctest: +IGNORE_EXCEPTION_DETAIL</span>
<span class="py-except">Traceback (most recent call last):</span>
<span class="py-except"> ...</span>
<span class="py-except">ValueError:...</span></pre>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>allTypes = sc.parallelize([{<span class="py-string">"int"</span> : 1, <span class="py-string">"string"</span> : <span class="py-string">"string"</span>, <span class="py-string">"double"</span> : 1.0, <span class="py-string">"long"</span>: 1L,
<span class="py-more">... </span><span class="py-string">"boolean"</span> : True}])
<span class="py-prompt">>>> </span>srdd = sqlCtx.inferSchema(allTypes).map(<span class="py-keyword">lambda</span> x: (x.int, x.string, x.double, x.long,
<span class="py-more">... </span>x.boolean))
<span class="py-prompt">>>> </span>srdd.collect()[0]
<span class="py-output">(1, u'string', 1.0, 1, True)</span></pre></li>
</ul></dd>
</dl>
</td></tr></table>
</div>
<a name="inferSchema"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">inferSchema</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">rdd</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.inferSchema">source code</a></span>
</td>
</tr></table>
<p>Infer and apply a schema to an RDD of <code
class="link">dict</code>s.</p>
<p>We peek at the first row of the RDD to determine the fields names and
types, and then use that to extract all the dictionaries. Nested
collections are supported, which include array, dict, list, set, and
tuple.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>srdd = sqlCtx.inferSchema(rdd)
<span class="py-prompt">>>> </span>srdd.collect() == [{<span class="py-string">"field1"</span> : 1, <span class="py-string">"field2"</span> : <span class="py-string">"row1"</span>}, {<span class="py-string">"field1"</span> : 2, <span class="py-string">"field2"</span>: <span class="py-string">"row2"</span>},
<span class="py-more">... </span> {<span class="py-string">"field1"</span> : 3, <span class="py-string">"field2"</span>: <span class="py-string">"row3"</span>}]
<span class="py-output">True</span></pre>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-keyword">from</span> array <span class="py-keyword">import</span> array
<span class="py-prompt">>>> </span>srdd = sqlCtx.inferSchema(nestedRdd1)
<span class="py-prompt">>>> </span>srdd.collect() == [{<span class="py-string">"f1"</span> : array(<span class="py-string">'i'</span>, [1, 2]), <span class="py-string">"f2"</span> : {<span class="py-string">"row1"</span> : 1.0}},
<span class="py-more">... </span> {<span class="py-string">"f1"</span> : array(<span class="py-string">'i'</span>, [2, 3]), <span class="py-string">"f2"</span> : {<span class="py-string">"row2"</span> : 2.0}}]
<span class="py-output">True</span></pre>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>srdd = sqlCtx.inferSchema(nestedRdd2)
<span class="py-prompt">>>> </span>srdd.collect() == [{<span class="py-string">"f1"</span> : [[1, 2], [2, 3]], <span class="py-string">"f2"</span> : set([1, 2]), <span class="py-string">"f3"</span> : (1, 2)},
<span class="py-more">... </span> {<span class="py-string">"f1"</span> : [[2, 3], [3, 4]], <span class="py-string">"f2"</span> : set([2, 3]), <span class="py-string">"f3"</span> : (2, 3)}]
<span class="py-output">True</span></pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="registerRDDAsTable"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">registerRDDAsTable</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">rdd</span>,
<span class="sig-arg">tableName</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.registerRDDAsTable">source code</a></span>
</td>
</tr></table>
<p>Registers the given RDD as a temporary table in the catalog.</p>
<p>Temporary tables exist only during the lifetime of this instance of
SQLContext.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>srdd = sqlCtx.inferSchema(rdd)
<span class="py-prompt">>>> </span>sqlCtx.registerRDDAsTable(srdd, <span class="py-string">"table1"</span>)</pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="parquetFile"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">parquetFile</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">path</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.parquetFile">source code</a></span>
</td>
</tr></table>
<p>Loads a Parquet file, returning the result as a <a
href="pyspark.sql.SchemaRDD-class.html" class="link">SchemaRDD</a>.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span><span class="py-keyword">import</span> tempfile, shutil
<span class="py-prompt">>>> </span>parquetFile = tempfile.mkdtemp()
<span class="py-prompt">>>> </span>shutil.rmtree(parquetFile)
<span class="py-prompt">>>> </span>srdd = sqlCtx.inferSchema(rdd)
<span class="py-prompt">>>> </span>srdd.saveAsParquetFile(parquetFile)
<span class="py-prompt">>>> </span>srdd2 = sqlCtx.parquetFile(parquetFile)
<span class="py-prompt">>>> </span>sorted(srdd.collect()) == sorted(srdd2.collect())
<span class="py-output">True</span></pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="jsonFile"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">jsonFile</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">path</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.jsonFile">source code</a></span>
</td>
</tr></table>
<pre class="literalblock">
Loads a text file storing one JSON object per line,
returning the result as a L{SchemaRDD}.
It goes through the entire dataset once to determine the schema.
>>> import tempfile, shutil
>>> jsonFile = tempfile.mkdtemp()
>>> shutil.rmtree(jsonFile)
>>> ofn = open(jsonFile, 'w')
>>> for json in jsonStrings:
... print>>ofn, json
>>> ofn.close()
>>> srdd = sqlCtx.jsonFile(jsonFile)
>>> sqlCtx.registerRDDAsTable(srdd, "table1")
>>> srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2, field3 as f3 from table1")
>>> srdd2.collect() == [{"f1": 1, "f2": "row1", "f3":{"field4":11}},
... {"f1": 2, "f2": "row2", "f3":{"field4":22}},
... {"f1": 3, "f2": "row3", "f3":{"field4":33}}]
True
</pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="jsonRDD"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">jsonRDD</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">rdd</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.jsonRDD">source code</a></span>
</td>
</tr></table>
<pre class="literalblock">
Loads an RDD storing one JSON object per string, returning the result as a L{SchemaRDD}.
It goes through the entire dataset once to determine the schema.
>>> srdd = sqlCtx.jsonRDD(json)
>>> sqlCtx.registerRDDAsTable(srdd, "table1")
>>> srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2, field3 as f3 from table1")
>>> srdd2.collect() == [{"f1": 1, "f2": "row1", "f3":{"field4":11}},
... {"f1": 2, "f2": "row2", "f3":{"field4":22}},
... {"f1": 3, "f2": "row3", "f3":{"field4":33}}]
True
</pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="sql"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">sql</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">sqlQuery</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.sql">source code</a></span>
</td>
</tr></table>
<p>Return a <a href="pyspark.sql.SchemaRDD-class.html"
class="link">SchemaRDD</a> representing the result of the given
query.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>srdd = sqlCtx.inferSchema(rdd)
<span class="py-prompt">>>> </span>sqlCtx.registerRDDAsTable(srdd, <span class="py-string">"table1"</span>)
<span class="py-prompt">>>> </span>srdd2 = sqlCtx.sql(<span class="py-string">"SELECT field1 AS f1, field2 as f2 from table1"</span>)
<span class="py-prompt">>>> </span>srdd2.collect() == [{<span class="py-string">"f1"</span> : 1, <span class="py-string">"f2"</span> : <span class="py-string">"row1"</span>}, {<span class="py-string">"f1"</span> : 2, <span class="py-string">"f2"</span>: <span class="py-string">"row2"</span>},
<span class="py-more">... </span> {<span class="py-string">"f1"</span> : 3, <span class="py-string">"f2"</span>: <span class="py-string">"row3"</span>}]
<span class="py-output">True</span></pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="table"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">table</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">tableName</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="pyspark.sql-pysrc.html#SQLContext.table">source code</a></span>
</td>
</tr></table>
<p>Returns the specified table as a <a
href="pyspark.sql.SchemaRDD-class.html" class="link">SchemaRDD</a>.</p>
<pre class="py-doctest">
<span class="py-prompt">>>> </span>srdd = sqlCtx.inferSchema(rdd)
<span class="py-prompt">>>> </span>sqlCtx.registerRDDAsTable(srdd, <span class="py-string">"table1"</span>)
<span class="py-prompt">>>> </span>srdd2 = sqlCtx.table(<span class="py-string">"table1"</span>)
<span class="py-prompt">>>> </span>sorted(srdd.collect()) == sorted(srdd2.collect())
<span class="py-output">True</span></pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
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