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| <h1 class="title">Spark SQL, DataFrames and Datasets Guide</h1> |
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| <p>Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided |
| by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Internally, |
| Spark SQL uses this extra information to perform extra optimizations. There are several ways to |
| interact with Spark SQL including SQL and the Dataset API. When computing a result, |
| the same execution engine is used, independent of which API/language you are using to express the |
| computation. This unification means that developers can easily switch back and forth between |
| different APIs based on which provides the most natural way to express a given transformation.</p> |
| |
| <p>All of the examples on this page use sample data included in the Spark distribution and can be run in |
| the <code class="language-plaintext highlighter-rouge">spark-shell</code>, <code class="language-plaintext highlighter-rouge">pyspark</code> shell, or <code class="language-plaintext highlighter-rouge">sparkR</code> shell.</p> |
| |
| <h2 id="sql">SQL</h2> |
| |
| <p>One use of Spark SQL is to execute SQL queries. |
| Spark SQL can also be used to read data from an existing Hive installation. For more on how to |
| configure this feature, please refer to the <a href="sql-data-sources-hive-tables.html">Hive Tables</a> section. When running |
| SQL from within another programming language the results will be returned as a <a href="#datasets-and-dataframes">Dataset/DataFrame</a>. |
| You can also interact with the SQL interface using the <a href="sql-distributed-sql-engine.html#running-the-spark-sql-cli">command-line</a> |
| or over <a href="sql-distributed-sql-engine.html#running-the-thrift-jdbcodbc-server">JDBC/ODBC</a>.</p> |
| |
| <h2 id="datasets-and-dataframes">Datasets and DataFrames</h2> |
| |
| <p>A Dataset is a distributed collection of data. |
| Dataset is a new interface added in Spark 1.6 that provides the benefits of RDDs (strong |
| typing, ability to use powerful lambda functions) with the benefits of Spark SQL’s optimized |
| execution engine. A Dataset can be <a href="sql-getting-started.html#creating-datasets">constructed</a> from JVM objects and then |
| manipulated using functional transformations (<code class="language-plaintext highlighter-rouge">map</code>, <code class="language-plaintext highlighter-rouge">flatMap</code>, <code class="language-plaintext highlighter-rouge">filter</code>, etc.). |
| The Dataset API is available in <a href="api/scala/org/apache/spark/sql/Dataset.html">Scala</a> and |
| <a href="api/java/index.html?org/apache/spark/sql/Dataset.html">Java</a>. Python does not have the support for the Dataset API. But due to Python’s dynamic nature, |
| many of the benefits of the Dataset API are already available (i.e. you can access the field of a row by name naturally |
| <code class="language-plaintext highlighter-rouge">row.columnName</code>). The case for R is similar.</p> |
| |
| <p>A DataFrame is a <em>Dataset</em> organized into named columns. It is conceptually |
| equivalent to a table in a relational database or a data frame in R/Python, but with richer |
| optimizations under the hood. DataFrames can be constructed from a wide array of <a href="sql-data-sources.html">sources</a> such |
| as: structured data files, tables in Hive, external databases, or existing RDDs. |
| The DataFrame API is available in |
| <a href="api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame">Python</a>, Scala, |
| Java and <a href="api/R/index.html">R</a>. |
| In Scala and Java, a DataFrame is represented by a Dataset of <code class="language-plaintext highlighter-rouge">Row</code>s. |
| In <a href="api/scala/org/apache/spark/sql/Dataset.html">the Scala API</a>, <code class="language-plaintext highlighter-rouge">DataFrame</code> is simply a type alias of <code class="language-plaintext highlighter-rouge">Dataset[Row]</code>. |
| While, in <a href="api/java/index.html?org/apache/spark/sql/Dataset.html">Java API</a>, users need to use <code class="language-plaintext highlighter-rouge">Dataset<Row></code> to represent a <code class="language-plaintext highlighter-rouge">DataFrame</code>.</p> |
| |
| <p>Throughout this document, we will often refer to Scala/Java Datasets of <code class="language-plaintext highlighter-rouge">Row</code>s as DataFrames.</p> |
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