March 2019
Intermediate to advanced
201 pages
3h 22m
Chinese
Content preview from Kudu：构建高性能实时数据分析存储系统
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24
｜
第
1
章
为什么会有
Kudu
线就会变得模糊。比如，使用列数据库支持
OLTP
负载的一个大问题是，
OLTP
负载通常会读取一行的大部分字段，这在列存储中就意味着需要做很
多次磁盘寻址，不过使用
SSD
或者可持久的内存基本上可以避免这个问题。
与大数据组件对比——
HDFS
、
HBase
和
Cassandra
我们了解这些以后，
如何将
Kudu
与其他大数据存储系统（比如
HDFS
、
HBase
、
Cassandra
）做比较呢？让我们先来看看这些系统的优势在哪里。
HDFS
非常擅长扫描大量的数据，也即它的“全表扫描”非常出色，这是
分析类负载中很常见的操作。
HBase
和
Cassandra
很擅长随机访问，随机读
取或者修改数据。
HDFS
不擅长随机读，并且严格来说，它并不支持随机写，
不过你可以通过合并的方式来模拟随机写，但是这么做成本很高。
HBase
和
Cassandra
在大规模扫描方面的性能比
HDFS
差得多。
Kudu
的目标是把
扫描的性能做到
HDFS
上的
Parquet
的两倍以内，而随机读的性能则要接
近
HBase
和
Cassandra
。实际的性能目标是在
SSD
上做到随机读
/
写的延
迟在
1 ms
以内。
我们来逐一详细解释为什么
HDFS
、
HBase
、
Cassandra
的性能特性会是
这样子的。
HDFS
是一个纯粹的分布式文件系统，其就是设计来执行快速
的大规模扫描的，毕竟这种设计的原始应用场景就是批量构建
Web
索引。
对于该场景和许多其他场景，你只需要能够高效地扫描整个数据集即可。
HDFS
对数据分区，并将其分散到大量物理磁盘上，以便这些大扫描可以 ...
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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.