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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2.4
変換
27
2.4.6
.leftOuterJoin(...)
変換
.leftOuterJoin(...)
は、SQL の世界の場合と同じく2 つのRDDをどちらのデータセットにも表
れる値に基づいて結合し、2つの RDD がマッチする場合に左側の RDD のレコードに右側のレコードを
付け足して返します。
rdd1 = sc.parallelize([('a', 1), ('b', 4), ('c',10)])
rdd2 = sc.parallelize([('a', 4), ('a', 1), ('b', '6'), ('d', 15)])
rdd3 = rdd1.leftOuterJoin(rdd2)
rdd3
で
.collect(...)
を実行すれば、次の結果が出力されるでしょう。
このメソッドもまた負荷の高いメソッドであり、データをシャッフルすることからパフォーマ
ンスに大きく影響します。本当な場合にのみ慎重に使うようにしてください。
ここで返されているのは、RDDの
rdd1
のすべての要素と、RDDの
rdd2
でそれらに対応する値で
す。見て取れるとおり、
'a'
という値は
rdd3
では 2回現れていますが、
rdd2
でも2回現れています。
b
という値は
rdd1
に一度だけ登場し、
rdd2
の
'6'
という値と結合されています。消えている値も 2つあ
ります。
rdd1
の
'c'
という値は、対応するキーが
rdd2
にないので、返されたタプルの中の値は
None
になっており、ここで行っているのは左外部結合なの ...
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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.