上篇文章中说道，Mysql中的Btree索引和Hash索引的区别，没做展开描述，今天有空，上Mysql官方文档找到了相关答案，看完之后，针对两者的区别做如下总结：

　　引用维基百科上的描述，来解释一下这两种数据结构，这些知识在《数据结构与算法》这门课程中也有讲述：

　　在计算机科学中，B树（英语：B-tree）是一种自平衡的树，能够保持数据有序。这种数据结构能够让查找数据、顺序访问、插入数据及删除的动作，都在对数时间内完成。B树，概括来说是一个一般化的二叉查找树（binary search tree）一个节点可以拥有最少2个子节点。与自平衡二叉查找树不同，B树适用于读写相对大的数据块的存储系统，例如磁盘。B树减少定位记录时所经历的中间过程，从而加快存取速度。B树这种数据结构可以用来描述外部存储。这种数据结构常被应用在数据库和文件系统的实现上。

　　什么是HASH数据结构：

　　散列表（Hash table，也叫哈希表），是根据键（Key）而直接访问在内存存储位置的数据结构。也就是说，它通过计算一个关于键值的函数，将所需查询的数据映射到表中一个位置来访问记录，这加快了查找速度。这个映射函数称做散列函数，存放记录的数组称做散列表。

一个通俗的例子是，为了查找电话簿中某人的号码，可以创建一个按照人名首字母顺序排列的表（即建立人名{\displaystyle x}到首字母{\displaystyle F(x)}的一个函数关系），在首字母为W的表中查找“王”姓的电话号码，显然比直接查找就要快得多。这里使用人名作为关键字，“取首字母”是这个例子中散列函数的函数法则{\displaystyle F()}，存放首字母的表对应散列表。关键字和函数法则理论上可以任意确定。

　　总言之：

1. HASH这种数据结构，数据是无序的，是key-value型，被用于精确匹配非常高效。所以在mysql中使用这种索引类型，将不支持模糊匹配，比如like ‘aaa%’。
2. B-Tree这种数据结构数据是有序的，在Mysql中默认的索引类型是 B-Tree。B-Tree这种索引类型，决定了mysql能够基于这种类型做数据区间匹配，可以实现=, >, >=, <, <=, or BETWEEN 这些语法。且支持模糊搜索，但是不支持 类似 like '%xxx%'这种前后模糊匹配的语句，仅支持后半段模糊匹配。
3. 其中，mysql并不是有索引就一定会使用，查询优化阶段会判断扫描行进行预估，可能表扫描更快，这个时候就不走索引，解决方法就是加上limit。

　　以下是mysql的官方文档说明，简单明了，还配有两个案例，原文链接：https://dev.mysql.com/doc/refman/5.7/en/index-btree-hash.html

8.3.8 Comparison of B-Tree and Hash Indexes

Understanding the B-tree and hash data structures can help predict how different queries perform on different storage engines that use these data structures in their indexes, particularly for the MEMORY storage engine that lets you choose B-tree or hash indexes.

B-Tree Index Characteristics

A B-tree index can be used for column comparisons in expressions that use the =, >, >=, <, <=, or BETWEEN operators. The index also can be used for LIKE comparisons if the argument to LIKE is a constant string that does not start with a wildcard character. For example, the following SELECT statements use indexes:

SELECT * FROM tbl_name WHERE key_col LIKE 'Patrick%'; SELECT * FROM tbl_name WHERE key_col LIKE 'Pat%_ck%';

In the first statement, only rows with 'Patrick' <= key_col < 'Patricl' are considered. In the second statement, only rows with 'Pat' <= key_col < 'Pau' are considered.

The following SELECT statements do not use indexes:

SELECT * FROM tbl_name WHERE key_col LIKE '%Patrick%'; SELECT * FROM tbl_name WHERE key_col LIKE other_col;

In the first statement, the LIKE value begins with a wildcard character. In the second statement, the LIKE value is not a constant.

If you use ... LIKE '%string%' and string is longer than three characters, MySQL uses the Turbo Boyer-Moore algorithm to initialize the pattern for the string and then uses this pattern to perform the search more quickly.

A search using col_name IS NULL employs indexes if col_name is indexed.

Any index that does not span all AND levels in the WHERE clause is not used to optimize the query. In other words, to be able to use an index, a prefix of the index must be used in every AND group.

The following WHERE clauses use indexes:

... WHERE index_part1=1 AND index_part2=2 AND other_column=3 /* index = 1 OR index = 2 */ ... WHERE index=1 OR A=10 AND index=2 /* optimized like "index_part1='hello'" */ ... WHERE index_part1='hello' AND index_part3=5 /* Can use index on index1 but not on index2 or index3 */ ... WHERE index1=1 AND index2=2 OR index1=3 AND index3=3;

These WHERE clauses do not use indexes:

    /* index_part1 is not used */
... WHERE index_part2=1 AND index_part3=2 /* Index is not used in both parts of the WHERE clause */ ... WHERE index=1 OR A=10 /* No index spans all rows */ ... WHERE index_part1=1 OR index_part2=10

Sometimes MySQL does not use an index, even if one is available. One circumstance under which this occurs is when the optimizer estimates that using the index would require MySQL to access a very large percentage of the rows in the table. (In this case, a table scan is likely to be much faster because it requires fewer seeks.) However, if such a query uses LIMIT to retrieve only some of the rows, MySQL uses an index anyway, because it can much more quickly find the few rows to return in the result.

Hash Index Characteristics

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.)