September 2022
Beginner to intermediate
321 pages
4h 50m
Chinese
Content preview from 语义软件设计
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196
｜
第
8
章
Apache Flink
（
https://flink.apache.org/
）
Flink
是用
Java
和
Scala
编写的，是一种高吞吐量、低延迟的流数据流引擎，
支持并行处理、批量
/
批处理（如数据提取、转换和加载，简称
ETL
）以及实
时事件处理。
Flink
支持只执行一次的语义和容错。它不提供存储系统，但提
供
Cassandra
、
Hadoop
分布式文件系统（
Hadoop Distributed File System
，简称
HDFS
）、
Kinesis
、
Kafka
等数据源和接收器。
所有这些系统都是一个主题的变种：它们是接受无限数据流的数据流水线，用特定
方式表示流向节点的数据，并允许你在节点上对数据进行一些处理、过滤、丰富和
转换。它们必须以数据的来源开头，并以负责转换或处理数据的接收器结束。
流模型必须考虑如下事项：
•
数据源与目标（接收器）。
•
数据更新的时间间隔，或获取快照的时间间隔。
•
近期以及长期的存储需求和限制。
•
持久性需求。
•
可扩展性需求：数据量以及处理时间
/
响应即时性要求该怎么计算？考虑并行和
批量处理服务器的占用空间、成本以及影响管理。你选择的库是否支持横向扩
展？
•
存储层和处理层的容错能力。
•
许多模型都有类似
SQL
的语言，允许开发人员制定匹配规则。考虑创建面向开
发人员的使用指南。
这些工具都比较新，因此正在迅速变化。这些工具的使用和管理也很复杂。你可能
习惯于将数据视为持久存储中由应用程序代码操作的静态被动元素，但是流数据则
完全不同。通过自己的方式设计流架构 ...
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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.