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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3.6
RDD
とのやりとり
45
3.6.2
プログラムからのスキーマの指定
それではこの例で、Spark SQLの デ ー タ 型（
pyspark.sql.types
）を取り入れ、多少の
.csv
デー
タを生成してスキーマをプログラムから指定してみましょう。
#
データ型のインポート
from pyspark.sql.types import *
#
カンマ区切りのデータの生成
stringCSVRDD = sc.parallelize([
(123, 'Katie', 19, 'brown'),
(234, 'Michael', 22, 'green'),
(345, 'Simone', 23, 'blue')
])
まずは次の
schema
変数にあるように
StructType
と
StructField
を使ってスキーマを定義します。
#
スキーマの指定
schema = StructType([
StructField("id", LongType(), True),
StructField("name", StringType(), True),
StructField("age", LongType(), True),
StructField("eyeColor", StringType(), True)
])
StructField
クラスが次の内容からなることに注意してください。
●
name
: フィールドの名前
●
dataType
: フィールドのデータ型
●
nullable
: このフィールドの値が nullに
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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.