Hash Tables

哈希索引（Hash Indexes）

除了B+树之外，还有一种常见的是哈希索引。

哈希索引就是采用一定的哈希算法，以计算出键值的哈希值。因此，Hash 索引结构的检索效率非常高，因为索引的检索可以一次定位，不像B+树索引需要从根节点到枝节点，最后才能访问到页节点（这样会进行多次I/O访问），所以 Hash 索引的查询效率要远高于 B+树索引。

The hash table index also has limitations:

The hash table must fit in memory, so if you have a very large number of keys, you’re out of luck. In principle, you could maintain a hash map on disk, but unfortunately it is difficult to make an on-disk hash map perform well. It requires a lot of random access I/O, it is expensive to grow when it becomes full, and hash collisions require fiddly logic.
Range queries are not efficient. For example, you cannot easily scan over all keys between kitty00000 and kitty99999—you’d have to look up each key individually in the hash maps.

Hash indexes have somewhat different characteristics from those just discussed:

They are used only for equality comparisons that use the = or <=> operators (but are very fast). They are not used for comparison operators such as < that find a range of values. Systems that rely on this type of single-value lookup are known as “key-value stores”; to use MySQL for such applications, use hash indexes wherever possible.
The optimizer cannot use a hash index to speed up ORDER BY operations. (This type of index cannot be used to search for the next entry in order.)
MySQL cannot determine approximately how many rows there are between two values (this is used by the range optimizer to decide which index to use). This may affect some queries if you change a MyISAM or InnoDB table to a hash-indexed MEMORY table.
Only whole keys can be used to search for a row. (With a B-tree index, any leftmost prefix of the key can be used to find rows.)

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