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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34
3
章
DataFrame
●
DataFrameによる PySpark の高速化
●
DataFrame の生成
●
シンプルな DataFrame のクエリ
●
RDDとのやりとり
●
DataFrame APIでのクエリ
●
Spark SQL でのクエリ
●
DataFrameを使った定刻フライトのパフォーマンス分析
3.1
Python
から
RDD
への通信
PySparkのプログラムがRDDを使って動作する場合、ジョブの実行に際して潜在的に大き
なオーバーヘッドが生ずる可能性があります。次の図に示すとおり、PySparkのドライバでは
SparkContext
が
Py4J
を使ってJVMを起動します。この時には
JavaSparkContext
が使われま
す。RDDに対する変換は、まず Javaの
PythonRDD
オブジェクトに対してマッピングされます。
これらのタスクが Spark のワーカー（群）に送信されると、ワーカーは Python の
subprocess
を起
動し、コードとデータをパイプで送信して Python で処理させます。
ワーカー
ドライバ
Spark
の
ワーカー
パイプ
Spark
の
ワーカー
パイプ
Spark
の
ワーカー
パイプ
こうして PySparkは複数のワーカー上の複数のPythonのサブプロセスにデータ処理を分配します
が、見てわかるとおり PythonとJVMとの間では大量のコンテキストスイッチと通信のオーバーヘッド
が生じます。
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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.