November 2021
Intermediate to advanced
340 pages
10h 46m
Chinese
Content preview from Spark快速大数据分析（第2版）
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Spark SQL
与
DataFrame
：
读写外部数据源
｜
131
right
、
right_outer
、
left_semi
和
left_anti
。若想了解更多信息，可以查阅文档（该文
档同时适用于
Scala
和
Python
）。
以下代码示例展示了如何对
airports_na
和
foo
这两个
DataFrame
进行默认的
inner
连接
操作。
// Scala代码
foo.join(
airports.as('air),
$"air.IATA" === $"origin"
).select("City", "State", "date", "delay", "distance", "destination").show()
# Python代码
# 连接航班延误数据（foo）与机场信息
foo.join(
airports,
airports.IATA == foo.origin
).select("City", "State", "date", "delay", "distance", "destination").show()
-- SQL查询
spark.
sql
("""
SELECT a.City, a.State, f.date, f.delay, f.distance, f.destination
FROM foo f
JOIN airports_na a
ON a.IATA = f.origin
""").
show
()
前面的代码允许你查看
DataFrame
foo
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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.