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| <h1>PySpark Overview<a class="headerlink" href="#pyspark-overview" title="Permalink to this headline">#</a></h1> |
| <p><strong>Date</strong>: Sep 16, 2024 <strong>Version</strong>: 4.0.0-preview2</p> |
| <p><strong>Useful links</strong>: |
| <a class="reference external" href="https://mybinder.org/v2/gh/apache/spark/f0d465e09b8?filepath=python%2Fdocs%2Fsource%2Fgetting_started%2Fquickstart_df.ipynb">Live Notebook</a> | <a class="reference external" href="https://github.com/apache/spark">GitHub</a> | <a class="reference external" href="https://issues.apache.org/jira/projects/SPARK/issues">Issues</a> | <a class="reference external" href="https://github.com/apache/spark/tree/f0d465e09b8/examples/src/main/python">Examples</a> | <a class="reference external" href="https://spark.apache.org/community.html">Community</a> | <a class="reference external" href="https://stackoverflow.com/questions/tagged/pyspark">Stack Overflow</a> | <a class="reference external" href="https://lists.apache.org/list.html?dev@spark.apache.org">Dev Mailing List</a> | <a class="reference external" href="https://lists.apache.org/list.html?user@spark.apache.org">User Mailing List</a></p> |
| <p>PySpark is the Python API for Apache Spark. It enables you to perform real-time, |
| large-scale data processing in a distributed environment using Python. It also provides a PySpark |
| shell for interactively analyzing your data.</p> |
| <p>PySpark combines Python’s learnability and ease of use with the power of Apache Spark |
| to enable processing and analysis of data at any size for everyone familiar with Python.</p> |
| <p>PySpark supports all of Spark’s features such as Spark SQL, |
| DataFrames, Structured Streaming, Machine Learning (MLlib) and Spark Core.</p> |
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| <td><a class="reference external image-reference" href="reference/pyspark.ss/index.html"><img alt="Streaming" src="_images/pyspark-structured_streaming.png" style="width: 100%;" /></a> |
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| <td><a class="reference external image-reference" href="reference/pyspark.ml.html"><img alt="Machine Learning" src="_images/pyspark-machine_learning.png" style="width: 100%;" /></a> |
| </td> |
| <td></td> |
| </tr> |
| </tbody> |
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| <tbody> |
| <tr class="row-odd"><td></td> |
| <td><a class="reference external image-reference" href="reference/pyspark.html"><img alt="Spark Core and RDDs" src="_images/pyspark-spark_core_and_rdds.png" style="width: 100%;" /></a> |
| </td> |
| <td></td> |
| </tr> |
| </tbody> |
| </table> |
| <p id="index-page-spark-sql-and-dataframes"><strong>Spark SQL and DataFrames</strong></p> |
| <p>Spark SQL is Apache Spark’s module for working with structured data. |
| It allows you to seamlessly mix SQL queries with Spark programs. |
| With PySpark DataFrames you can efficiently read, write, transform, |
| and analyze data using Python and SQL. |
| Whether you use Python or SQL, the same underlying execution |
| engine is used so you will always leverage the full power of Spark.</p> |
| <ul class="simple"> |
| <li><p><a class="reference internal" href="getting_started/quickstart_df.html"><span class="std std-ref">Quickstart: DataFrame</span></a></p></li> |
| <li><p><a class="reference external" href="https://mybinder.org/v2/gh/apache/spark/f0d465e09b8?filepath=python%2Fdocs%2Fsource%2Fgetting_started%2Fquickstart_df.ipynb">Live Notebook: DataFrame</a></p></li> |
| <li><p><a class="reference internal" href="reference/pyspark.sql/index.html"><span class="std std-ref">Spark SQL API Reference</span></a></p></li> |
| </ul> |
| <p><strong>Pandas API on Spark</strong></p> |
| <p>Pandas API on Spark allows you to scale your pandas workload to any size |
| by running it distributed across multiple nodes. If you are already familiar |
| with pandas and want to leverage Spark for big data, pandas API on Spark makes |
| you immediately productive and lets you migrate your applications without modifying the code. |
| You can have a single codebase that works both with pandas (tests, smaller datasets) |
| and with Spark (production, distributed datasets) and you can switch between the |
| pandas API and the Pandas API on Spark easily and without overhead.</p> |
| <p>Pandas API on Spark aims to make the transition from pandas to Spark easy but |
| if you are new to Spark or deciding which API to use, we recommend using PySpark |
| (see <a class="reference internal" href="#index-page-spark-sql-and-dataframes"><span class="std std-ref">Spark SQL and DataFrames</span></a>).</p> |
| <ul class="simple"> |
| <li><p><a class="reference internal" href="getting_started/quickstart_ps.html"><span class="std std-ref">Quickstart: Pandas API on Spark</span></a></p></li> |
| <li><p><a class="reference external" href="https://mybinder.org/v2/gh/apache/spark/f0d465e09b8?filepath=python%2Fdocs%2Fsource%2Fgetting_started%2Fquickstart_ps.ipynb">Live Notebook: pandas API on Spark</a></p></li> |
| <li><p><a class="reference internal" href="reference/pyspark.pandas/index.html"><span class="std std-ref">Pandas API on Spark Reference</span></a></p></li> |
| </ul> |
| <p id="index-page-structured-streaming"><strong>Structured Streaming</strong></p> |
| <p>Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. |
| You can express your streaming computation the same way you would express a batch computation on static data. |
| The Spark SQL engine will take care of running it incrementally and continuously and updating the final result |
| as streaming data continues to arrive.</p> |
| <ul class="simple"> |
| <li><p><a class="reference external" href="https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html">Structured Streaming Programming Guide</a></p></li> |
| <li><p><a class="reference internal" href="reference/pyspark.ss/index.html"><span class="std std-ref">Structured Streaming API Reference</span></a></p></li> |
| </ul> |
| <p><strong>Machine Learning (MLlib)</strong></p> |
| <p>Built on top of Spark, MLlib is a scalable machine learning library that provides |
| a uniform set of high-level APIs that help users create and tune practical machine |
| learning pipelines.</p> |
| <ul class="simple"> |
| <li><p><a class="reference external" href="https://spark.apache.org/docs/latest/ml-guide.html">Machine Learning Library (MLlib) Programming Guide</a></p></li> |
| <li><p><a class="reference internal" href="reference/pyspark.ml.html"><span class="std std-ref">Machine Learning (MLlib) API Reference</span></a></p></li> |
| </ul> |
| <p><strong>Spark Core and RDDs</strong></p> |
| <p>Spark Core is the underlying general execution engine for the Spark platform that all |
| other functionality is built on top of. It provides RDDs (Resilient Distributed Datasets) |
| and in-memory computing capabilities.</p> |
| <p>Note that the RDD API is a low-level API which can be difficult to use and you do not get |
| the benefit of Spark’s automatic query optimization capabilities. |
| We recommend using DataFrames (see <a class="reference internal" href="#index-page-spark-sql-and-dataframes"><span class="std std-ref">Spark SQL and DataFrames</span></a> above) |
| instead of RDDs as it allows you to express what you want more easily and lets Spark automatically |
| construct the most efficient query for you.</p> |
| <ul class="simple"> |
| <li><p><a class="reference internal" href="reference/pyspark.html"><span class="std std-ref">Spark Core API Reference</span></a></p></li> |
| </ul> |
| <p><strong>Spark Streaming (Legacy)</strong></p> |
| <p>Spark Streaming is an extension of the core Spark API that enables scalable, |
| high-throughput, fault-tolerant stream processing of live data streams.</p> |
| <p>Note that Spark Streaming is the previous generation of Spark’s streaming engine. |
| It is a legacy project and it is no longer being updated. |
| There is a newer and easier to use streaming engine in Spark called |
| <a class="reference internal" href="#index-page-structured-streaming"><span class="std std-ref">Structured Streaming</span></a> which you |
| should use for your streaming applications and pipelines.</p> |
| <ul class="simple"> |
| <li><p><a class="reference external" href="https://spark.apache.org/docs/latest/streaming-programming-guide.html">Spark Streaming Programming Guide (Legacy)</a></p></li> |
| <li><p><a class="reference internal" href="reference/pyspark.streaming.html"><span class="std std-ref">Spark Streaming API Reference (Legacy)</span></a></p></li> |
| </ul> |
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