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### What changes were proposed in this pull request? This PR proposes to write the main page of PySpark documentation. The base work is finished at https://github.com/apache/spark/pull/29188. ### Why are the changes needed? For better usability and readability in PySpark documentation. ### Does this PR introduce _any_ user-facing change? Yes, it creates a new main page as below:  ### How was this patch tested? Manually built the PySpark documentation. ```bash cd python make clean html ``` Closes #29320 from HyukjinKwon/SPARK-32507. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
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.. Licensed to the Apache Software Foundation (ASF) under one
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or more contributor license agreements. See the NOTICE file
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distributed with this work for additional information
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regarding copyright ownership. The ASF licenses this file
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to you under the Apache License, Version 2.0 (the
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"License"); you may not use this file except in compliance
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with the License. You may obtain a copy of the License at
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.. http://www.apache.org/licenses/LICENSE-2.0
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.. Unless required by applicable law or agreed to in writing,
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software distributed under the License is distributed on an
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"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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KIND, either express or implied. See the License for the
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specific language governing permissions and limitations
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under the License.
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.. PySpark documentation master file
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=====================
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PySpark Documentation
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=====================
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.. TODO(SPARK-32204): Add Binder integration at Live Notebook.
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PySpark is an interface for Apache Spark in Python. It not only allows you to write
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Spark applications using Python APIs, but also provides the PySpark shell for
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interactively analyzing your data in a distributed environment. PySpark supports most
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of Spark's features such as Spark SQL, DataFrame, Streaming, MLlib
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(Machine Learning) and Spark Core.
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.. image:: ../../../docs/img/pyspark-components.png
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:alt: PySpark Compoenents
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**Spark SQL and DataFrame**
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Spark SQL is a Spark module for structured data processing. It provides
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a programming abstraction called DataFrame and can also act as distributed
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SQL query engine.
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**Streaming**
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Running on top of Spark, the streaming feature in Apache Spark enables powerful
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interactive and analytical applications across both streaming and historical data,
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while inheriting Spark’s ease of use and fault tolerance characteristics.
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**MLlib**
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Built on top of Spark, MLlib is a scalable machine learning library that provides
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a uniform set of high-level APIs that help users create and tune practical machine
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learning pipelines.
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**Spark Core**
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Spark Core is the underlying general execution engine for the Spark platform that all
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other functionality is built on top of. It provides an RDD (Resilient Distributed Dataset)
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and in-memory computing capabilities.
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.. toctree::
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:maxdepth: 2
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:hidden:
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getting_started/index
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user_guide/index
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reference/index
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development/index
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migration_guide/index
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