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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3
章
DataFrame
33
3
章
DataFrame
DataFrameは、名前付きの列で構成されたイミュータブルなデータの分散コレクションで、リ
レーショナルデータベースにおけるテーブルに似ています。DataFrameはApache Spark 1.0にお
いて
SchemaRDD
という名前で experimentalな機能として導入され、Apache Spark 1.3リリースで
DataFrame
という名前に変更されました。Pythonのpandas の
DataFrame
やRの
data.frame
に
慣れているなら、構造化データを簡単に扱えるようにしてくれるという点で SparkのDataFrameも似
た概念です。とはいえ異なっている点もあるので、期待しすぎは禁物です。
データの分散コレクションに構造を持ち込むことによって、Sparkのユーザーは Spark SQLを使っ
て構造化データに対してクエリを実行したり、（ラムダ式ではなく）式のメソッドを使ったりすることが
できるようになります。本章では、コードサンプルの中でどちらの方法も使っていきます。データを構
造化することで、Apache Spark のエンジン、中でも Catalyst OptimizerはSpark のクエリのパフォー
マンスを大きく改善できます。以前の Spark のAPI（すなわち RDD API）で は、JavaのVMとPy4Jの
間の通信時に生ずるオーバーヘッドのため、Pythonで実行するクエリはかなり低速に
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.