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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44
3
章
DataFrame
3.6
RDD
とのやりとり
既存の RDDをDataFrame（あるいは Datasets［T］） に 変 換 す る 方 法 は 2つあります。1つはリフレ
クションを使ってスキーマを推測させる方法であり、もう1つはプログラムからスキーマを指定する方
法です。前者の方法を使えば（作成するSparkのアプリケーションがスキーマを把握できているなら）
コンパクトなコードを書くことができますが、後者の方法を使えば、列と列のデータ型が実行時にしか
わからないような場合にも DataFrameを構築できるようになります。ここでいうリフレクションは、
Pythonの
reflection
ではなく、スキーマのリフレクションを指していることに注意してください。
3.6.1
リフレクションによるスキーマの推定
これまでは、DataFrameを構築してクエリを実行する際に自動的にDataFrameのスキーマが定
義されているということには触れませんでした。行のオブジェクトは、キー／値のペアのリストを
**kwargs
として行のクラスに渡すことによって構築されます。そして Spark SQLがこの行のオブ
ジェクトを DataFrameに変換することになりますが、その際にキーを列とし、値のデータをサンプリン
グしてデータ型を推定します。
**kwargs
という要素を使うと、実行時にメソッドに対して可変長で複数のパラメータを渡す
ことができます。
コードに戻りましょう。スキーマを指定せず
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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.