May 2020
Beginner to intermediate
319 pages
7h 3m
Chinese
Content preview from 数据库系统内幕
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236
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第
13
章
将哈希值映射到节点 ID 的最直接的方法是将哈希值除以集群大小再取其余数（取模）。
如果系统中有 N 个节点，则目标节点 ID 可以通过 hash(v) mod N 算出。该方法的主
要问题是，每当添加或删除节点、集群大小从 N 更改为 N' 时，hash(v) mod N' 算
出的很多值都和原来不同。这意味着大部分数据都要被移动。
一致性哈希
为了缓解该问题，一些数据库（例如 Apache Cassandra 和 Riak）使用一种叫作
一致性哈
希
（consistent hashing）的分区方案。如前所述，路由键的值被输入哈希函数，返回的哈
希值被映射到一个
环
上，以便在超过最大值之后回卷到最小值。每个节点在环上拥有自
己的位置，负责前一个节点到当前节点之间的值
范围
。
使用一致性哈希能减少维持数据均衡所需的移动次数：节点离开或加入只会影响到环上
与该节点
直接相邻
的节点，而不影响整个集群。定义中的
一致性
表示：当哈希表大小发
生变化时，如果我们有 K 个可能的哈希键和 n 个节点，平均而言，我们仅需移动 K/n
个键。也就是说，一致性哈希函数的输出受函数范围变化的影响最小 [KARGER97]。
13.7 Percolator
分布式事务
回到分布式事务上来，由于允许一些读取和写入异常，隔离级别可能很难论证。如果
应用程序不要求可串行化，避免写入异常的一种方法是使用 SQL-92 中描述的
快照隔离
（Snapshot Isolation，SI）事务模型。
快照隔离保证事务内的所有读取结果与数据库的某个快照一致。快照中包含了在事务的 ...
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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.