November 2017
Intermediate to advanced
328 pages
6h 51m
Japanese
Content preview from 入門 PySpark ―PythonとJupyterで活用するSpark 2エコシステム
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36
3
章
DataFrame
Project Tungstenの一部では、データの各行を解釈するのではなく、バイトコードを生成する（コー
ド生成あるいは
codegen
と呼ばれます）ことによってさらにパフォーマンスを向上させようとしていま
す。Tungstenの詳細については「1 章 Spark を理解する」の Project Tungstenのセクションを参照し
てください。
すでに述べたとおり、このオプティマイザは関数型プログラミングの構成要素を基盤としており、2 つ
の目標を念頭に置いて設計されています。1つは新しい最適化の手法や機能をSpark SQLに追加しや
す く す る こ と 、も う 1つは外部の開発者がオプティマイザを拡張できるようにする（たとえばデータソー
スに固有のルールの追加や新しいデータ型のサポートなど）ことです。
さらに詳しい情報については、
Michael Armbrust
の素晴らしいプレゼンテーション
Structuring Spark: SQL DataFrames, Datasets, and Streaming（
http://bit.ly/2cJ508x
）を
参照してください。Catalyst Optimizer を深く理解したいなら、Deep Dive into Spark SQL's
Catalyst Optimizer（
http://bit.ly/2bDVB1T
）を参照してください。また、Project Tungsten
に関する詳しい情報については、 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.