errorsRDD = inputRDD.filter(lambda x: "error" in x)
warningsRDD = inputRDD.filter(lambda x: "warning" in x)
badLinesRDD = errorsRDD.union(warningsRDD)
union()
は、
1
つではなく
2
つの
RDD
に対して操作を行うという点で、
filter()
とは異なって
います。実際には、変換は任意の数の入力
RDD
に対して操作を行うことができます。
リスト3-14と同じ結果を得たい場合、実際には単に
inputRDD
に対し、
error
もしくは
warning
を
検索するフィルタをかける方が良いでしょう。
最終的には、変換によって新しい
RDD
を導出していくたびに、
Spark
はそれぞれの
RDD
間の
依存関係を保持していきます。この依存関係をと呼びます。
Spark
は、必要に応じて
それぞれの
RDD
の計算を行ったり、永続化された
RDD
の一部が失われた場合にそのデータを回
復させたりするために、この情報を利用します。
3-1
は、
3-14
の系統グラフを示してい ...
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