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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6
1
章
Spark
を理解する
されています。
Python
の
pandas
や
R
の
data.frames
に慣れているなら、
Spark
の
DataFrame
も同じよう
な概念です。
DataFrameは、大規模なデータセットの処理をさらに容易にするために設計されました。開発者は、
DataFrameを使うことでデータの構造を定め、高レベルの抽象化が行えるようになります。この点で
は、DataFrameはリレーショナルデータベースの世界におけるテーブルに似ています。DataFrame
は、分散データを操作するためのドメイン固有言語のAPIを提供し、専門的なデータエンジニアのみな
らず、これまで以上に多くの人々が Spark を使えるようにしてくれます。
DataFrameの主な利点の一つは、Sparkのエンジンがまず論理実行プランを構築し、その後にコス
トオプティマイザによって決定された物理プランに基づいて生成されたコードを実行することです。
RDDを使う場合、PythonはJavaやScalaに比べて大きく速度が劣ることがありますが、DataFrame
が登場したことによってすべての言語でパフォーマンスが等しくなりました。
1.2.4
Dataset
Spark 1.6で導入された Spark Dataset が 目 標 と し て い る の は 、頑 健 な Spark SQL の実行エンジンの
パフォーマンスと利点を提供しながら、ドメインオブジェクトに対する変換を容易に表現できるように
することです ...
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.