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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60
｜
第
3
章
受篇幅限制，这里不会给出完整的示例代码。随书代码包提供了
Python
和
Scala
的在线笔
记本，你可以尝试使用这个数据集编写完整的
DataFrame
示例
。在这些笔记本中，我们可
以使用
DataFrame
API
和
DSL
关系型算子来探索并回答下列常见问题。
• 2018
年的消防报警有哪些类型？
• 2018
年的哪个月有最多的消防报警？
• 2018
年在旧金山的哪个社区消防报警最多？
• 2018
年哪个社区消防报警的响应时间最长？
• 2018
年哪一周的消防报警最多？
•
社区、邮编和报警次数之间是否有关联？
•
如何用
Parquet
文件或
SQL
表来存储这份数据，又如何重新读取？
到目前为止，我们已经比较详尽地了解了
DataFrame
API
，这种结构化数据
API
也延伸到
了
Spark
的
MLlib
和结构化流处理组件中，后续章节对此会有所介绍。
接下来我们将关注点转移到
Dataset
API
上，并探索
DataFrame API
和
Dataset API
如何
为开发人员编写
Spark
程序提供风格一致的结构化接口
。然后，我们将分析
RDD API
和
DataFrame API
，以
及
Dataset API
之间的关系，从而帮你判断什么时候该选用哪种
API
，以
及为什么要选择这种
API
。
3.4
Dataset API
之前说过，
Spark 2.0
用相似的接口统一了
DataFrame API
和
Dataset API
。这样一来，开发人员
只需要学习一套
API
的用法。
Dataset
主要区分两种特性：
有类型
和
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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.