表优化 altering table OPTIMIZE TABLE `sta_addr_copy`

表优化 altering table OPTIMIZE TABLE `sta_addr_copy`

【总结】

1、实际测试的结果是，
在
state sql
altering table OPTIMIZE TABLE `test`
insert成功；

MySQL 8.0 Reference Manual  /  ...  /  OPTIMIZE TABLE Syntax

13.7.3.4 OPTIMIZE TABLE Syntax

OPTIMIZE [NO_WRITE_TO_BINLOG | LOCAL]
    TABLE tbl_name [, tbl_name] ...

OPTIMIZE TABLE reorganizes the physical storage of table data and associated index data, to reduce storage space and improve I/O efficiency when accessing the table. The exact changes made to each table depend on the storage engine used by that table.

Use OPTIMIZE TABLE in these cases, depending on the type of table:

• After doing substantial insert, update, or delete operations on an InnoDB table that has its own .ibd file because it was created with the innodb_file_per_tableoption enabled. The table and indexes are reorganized, and disk space can be reclaimed for use by the operating system.

• After doing substantial insert, update, or delete operations on columns that are part of a FULLTEXT index in an InnoDB table. Set the configuration optioninnodb_optimize_fulltext_only=1 first. To keep the index maintenance period to a reasonable time, set the innodb_ft_num_word_optimize option to specify how many words to update in the search index, and run a sequence of OPTIMIZE TABLE statements until the search index is fully updated.

• After deleting a large part of a MyISAM or ARCHIVE table, or making many changes to a MyISAM or ARCHIVE table with variable-length rows (tables that have VARCHAR,VARBINARY, BLOB, or TEXT columns). Deleted rows are maintained in a linked list and subsequent INSERT operations reuse old row positions. You can use OPTIMIZE TABLE to reclaim the unused space and to defragment the data file. After extensive changes to a table, this statement may also improve performance of statements that use the table, sometimes significantly.

This statement requires SELECT and INSERT privileges for the table.

OPTIMIZE TABLE works for InnoDB, MyISAM, and ARCHIVE tables. OPTIMIZE TABLE is also supported for dynamic columns of in-memory NDB tables. It does not work for fixed-width columns of in-memory tables, nor does it work for Disk Data tables. The performance of OPTIMIZE on NDB Cluster tables can be tuned using --ndb_optimization_delay, which controls the length of time to wait between processing batches of rows by OPTIMIZE TABLE. For more information, see Previous NDB Cluster Issues Resolved in NDB Cluster 7.3.

For NDB Cluster tables, OPTIMIZE TABLE can be interrupted by (for example) killing the SQL thread performing the OPTIMIZE operation.

By default, OPTIMIZE TABLE does not work for tables created using any other storage engine and returns a result indicating this lack of support. You can make OPTIMIZE TABLE work for other storage engines by starting mysqld with the --skip-new option. In this case, OPTIMIZE TABLE is just mapped to ALTER TABLE.

This statement does not work with views.

OPTIMIZE TABLE is supported for partitioned tables. For information about using this statement with partitioned tables and table partitions, see Section 23.3.4, “Maintenance of Partitions”.

By default, the server writes OPTIMIZE TABLE statements to the binary log so that they replicate to replication slaves. To suppress logging, specify the optional NO_WRITE_TO_BINLOG keyword or its alias LOCAL.

• OPTIMIZE TABLE Output

• InnoDB Details

• MyISAM Details

• Other Considerations

OPTIMIZE TABLE Output

OPTIMIZE TABLE returns a result set with the columns shown in the following table.

Column	Value
Table	The table name
Op	Always optimize
Msg_type	status, error, info, note, or warning
Msg_text	An informational message

OPTIMIZE TABLE table catches and throws any errors that occur while copying table statistics from the old file to the newly created file. For example. if the user ID of the owner of the .MYD or .MYI file is different from the user ID of the mysqld process, OPTIMIZE TABLE generates a "cannot change ownership of the file" error unlessmysqld is started by the root user.

InnoDB Details

For InnoDB tables, OPTIMIZE TABLE is mapped to ALTER TABLE ... FORCE, which rebuilds the table to update index statistics and free unused space in the clustered index. This is displayed in the output of OPTIMIZE TABLE when you run it on an InnoDB table, as shown here:

mysql> OPTIMIZE TABLE foo;
+----------+----------+----------+-------------------------------------------------------------------+
| Table    | Op       | Msg_type | Msg_text                                                          |
+----------+----------+----------+-------------------------------------------------------------------+
| test.foo | optimize | note     | Table does not support optimize, doing recreate + analyze instead |
| test.foo | optimize | status   | OK                                                                |
+----------+----------+----------+-------------------------------------------------------------------+

OPTIMIZE TABLE uses online DDL for regular and partitioned InnoDB tables, which reduces downtime for concurrent DML operations. The table rebuild triggered by OPTIMIZE TABLE and performed under the cover by ALTER TABLE ... FORCE is completed in place. An exclusive table lock is only taken briefly during the prepare phase and the commit phase of the operation. During the prepare phase, metadata is updated and an intermediate table is created. During the commit phase, table metadata changes are committed.

OPTIMIZE TABLE rebuilds the table using the table copy method under the following conditions:

• When the old_alter_table system variable is enabled.

• When the mysqld --skip-new option is enabled.

OPTIMIZE TABLE using online DDL is not supported for InnoDB tables that contain FULLTEXT indexes. The table copy method is used instead.

InnoDB stores data using a page-allocation method and does not suffer from fragmentation in the same way that legacy storage engines (such as MyISAM) will. When considering whether or not to run optimize, consider the workload of transactions that your server will process:

• Some level of fragmentation is expected. InnoDB only fills pages 93% full, to leave room for updates without having to split pages.

• Delete operations might leave gaps that leave pages less filled than desired, which could make it worthwhile to optimize the table.

• Updates to rows usually rewrite the data within the same page, depending on the data type and row format, when sufficient space is available. See Section 15.9.1.5, “How Compression Works for InnoDB Tables” and Section 15.10, “InnoDB Row Formats”.

• High-concurrency workloads might leave gaps in indexes over time, as InnoDB retains multiple versions of the same data due through its MVCC mechanism. SeeSection 15.3, “InnoDB Multi-Versioning”.

MyISAM Details

For MyISAM tables, OPTIMIZE TABLE works as follows:

1. If the table has deleted or split rows, repair the table.

2. If the index pages are not sorted, sort them.

3. If the table's statistics are not up to date (and the repair could not be accomplished by sorting the index), update them.

Other Considerations

OPTIMIZE TABLE is performed online for regular and partitioned InnoDB tables. Otherwise, MySQL locks the table during the time OPTIMIZE TABLE is running.

OPTIMIZE TABLE does not sort R-tree indexes, such as spatial indexes on POINT columns. (Bug #23578)

 mysql中OPTIMIZE TABLE的作用及使用 - 煮海焚天 - 博客园 https://www.cnblogs.com/jimmy-muyuan/p/5874410.html

来看看手册中关于 OPTIMIZE 的描述：

OPTIMIZE [LOCAL | NO_WRITE_TO_BINLOG] TABLE tbl_name [, tbl_name] ...

如果您已经删除了表的一大部分，或者如果您已经对含有可变长度行的表（含有VARCHAR, BLOB或TEXT列的表）进行了很多更改，则应使用
OPTIMIZE TABLE。被删除的记录被保持在链接清单中，后续的INSERT操作会重新使用旧的记录位置。您可以使用OPTIMIZE TABLE来重新
利用未使用的空间，并整理数据文件的碎片。

在多数的设置中，您根本不需要运行OPTIMIZE TABLE。即使您对可变长度的行进行了大量的更新，您也不需要经常运行，每周一次或每月一次
即可，只对特定的表运行。

OPTIMIZE TABLE只对MyISAM, BDB和InnoDB表起作用。

注意，在OPTIMIZE TABLE运行过程中，MySQL会锁定表。【注释：不正确】

原始数据

1，数据量

mysql> select count(*) as total from ad_visit_history; 
+---------+ 
| total | 
+---------+ 
| 1187096 | //总共有118万多条数据 
+---------+ 
1 row in set (0.04 sec)

2，存放在硬盘中的表文件大小

[root@ www.linuxidc.com test1]# ls |grep visit |xargs -i du {} 
382020 ad_visit_history.MYD //数据文件占了380M 
127116 ad_visit_history.MYI //索引文件占了127M 
12 ad_visit_history.frm //结构文件占了12K

3，查看一下索引信息

mysql> show index from ad_visit_history from test1; //查看一下该表的索引信息 
+------------------+------------+-------------------+--------------+---------------+-----------+-------------+----------+--------+------+------------+---------+ 
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | 
+------------------+------------+-------------------+--------------+---------------+-----------+-------------+----------+--------+------+------------+---------+ 
| ad_visit_history | 0 | PRIMARY | 1 | id | A | 1187096 | NULL | NULL | | BTREE | | 
| ad_visit_history | 1 | ad_code | 1 | ad_code | A | 46 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | unique_id | 1 | unique_id | A | 1187096 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | ad_code_ind | 1 | ad_code | A | 46 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | from_page_url_ind | 1 | from_page_url | A | 30438 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | ip_ind | 1 | ip | A | 593548 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | port_ind | 1 | port | A | 65949 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | session_id_ind | 1 | session_id | A | 1187096 | NULL | NULL | YES | BTREE | | 
+------------------+------------+-------------------+--------------+---------------+-----------+-------------+----------+--------+------+------------+---------+ 
8 rows in set (0.28 sec)

索引信息中的列的信息说明。

Table :表的名称。
Non_unique:如果索引不能包括重复词，则为0。如果可以，则为1。
Key_name:索引的名称。
Seq_in_index:索引中的列序列号，从1开始。
Column_name:列名称。
Collation:列以什么方式存储在索引中。在MySQLSHOW INDEX语法中，有值’A’（升序）或NULL（无分类）。
Cardinality:索引中唯一值的数目的估计值。通过运行ANALYZE TABLE或myisamchk -a可以更新。基数根据被存储为整数的统计数据来计数，所以即使对于小型表，该值也没有必要是精确的。基数越大，当进行联合时，MySQL使用该索引的机会就越大。
Sub_part:如果列只是被部分地编入索引，则为被编入索引的字符的数目。如果整列被编入索引，则为NULL。
Packed:指示关键字如何被压缩。如果没有被压缩，则为NULL。
Null:如果列含有NULL，则含有YES。如果没有，则为空。
Index_type：存储索引数据结构方法（BTREE, FULLTEXT, HASH, RTREE）

二，删除一半数据

mysql> delete from ad_visit_history where id>598000; //删除一半数据 
Query OK, 589096 rows affected (4 min 28.06 sec)

[root@ www.linuxidc.com test1]# ls |grep visit |xargs -i du {} //相对应的MYD，MYI文件大小没有变化 
382020 ad_visit_history.MYD 
127116 ad_visit_history.MYI 
12 ad_visit_history.frm

按常规思想来说，如果在数据库中删除了一半数据后，相对应的.MYD,.MYI文件也应当变为之前的一半。但是删除一半数据后，.MYD.MYI尽然连1KB都没有减少，这是多么的可怕啊。

我们在来看一看，索引信息
mysql> show index from ad_visit_history; 
+------------------+------------+-------------------+--------------+---------------+-----------+-------------+----------+--------+------+------------+---------+ 
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | 
+------------------+------------+-------------------+--------------+---------------+-----------+-------------+----------+--------+------+------------+---------+ 
| ad_visit_history | 0 | PRIMARY | 1 | id | A | 598000 | NULL | NULL | | BTREE | | 
| ad_visit_history | 1 | ad_code | 1 | ad_code | A | 23 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | unique_id | 1 | unique_id | A | 598000 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | ad_code_ind | 1 | ad_code | A | 23 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | from_page_url_ind | 1 | from_page_url | A | 15333 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | ip_ind | 1 | ip | A | 299000 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | port_ind | 1 | port | A | 33222 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | session_id_ind | 1 | session_id | A | 598000 | NULL | NULL | YES | BTREE | | 
+------------------+------------+-------------------+--------------+---------------+-----------+-------------+----------+--------+------+------------+---------+ 
8 rows in set (0.00 sec)

对比一下，这次索引查询和上次索引查询，里面的数据信息基本上是上次一次的一本，这点还是合乎常理。

三，用optimize table来优化一下

??mysql> optimize table ad_visit_history; //删除数据后的优化 
+------------------------+----------+----------+----------+ 
| Table | Op | Msg_type | Msg_text | 
+------------------------+----------+----------+----------+ 
| test1.ad_visit_history | optimize | status | OK | 
+------------------------+----------+----------+----------+ 
1 row in set (1 min 21.05 sec)

1，查看一下.MYD,.MYI文件的大小

??[root@ www.linuxidc.com test1]# ls |grep visit |xargs -i du {} 
182080 ad_visit_history.MYD //数据文件差不多为优化前的一半 
66024 ad_visit_history.MYI //索引文件也一样，差不多是优化前的一半 
12 ad_visit_history.frm

2，查看一下索引信息
??mysql> show index from ad_visit_history; 
+------------------+------------+-------------------+--------------+---------------+-----------+-------------+----------+--------+------+------------+---------+ 
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | 
+------------------+------------+-------------------+--------------+---------------+-----------+-------------+----------+--------+------+------------+---------+ 
| ad_visit_history | 0 | PRIMARY | 1 | id | A | 598000 | NULL | NULL | | BTREE | | 
| ad_visit_history | 1 | ad_code | 1 | ad_code | A | 42 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | unique_id | 1 | unique_id | A | 598000 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | ad_code_ind | 1 | ad_code | A | 42 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | from_page_url_ind | 1 | from_page_url | A | 24916 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | ip_ind | 1 | ip | A | 598000 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | port_ind | 1 | port | A | 59800 | NULL | NULL | YES | BTREE | | 
| ad_visit_history | 1 | session_id_ind | 1 | session_id | A | 598000 | NULL | NULL | YES | BTREE | | 
+------------------+------------+-------------------+--------------+---------------+-----------+-------------+----------+--------+------+------------+---------+ 
8 rows in set (0.00 sec)

从以上数据我们可以得出，ad_code，ad_code_ind，from_page_url_ind等索引机会差不多都提高了85％，这样效率提高了好多。

四，小结

结合mysql官方网站的信息，个人是这样理解的。当你删除数据时，mysql并不会回收，被已删除数据的占据的存储空间，以及索引位。而是空在那里，而是等待新的数据来弥补这个空缺，这样就有一个缺少，如果一时半会，没有数据来填补这个空缺，那这样就太浪费资源了。所以对于写比较频烦的表，要定期进行optimize，一个月一次，看实际情况而定了。

 

MySQL中optimize优化表 - hsd2012的专栏 - CSDN博客 https://blog.csdn.net/hsd2012/article/details/51485250

2016年05月23日 22:09:17 

前言

当对MySQL进行大量的增删改操作的时候，很容易产生一些碎片，这些碎片占据着空间，所以可能会出现删除很多数据后，数据文件大小变化不大的现象。当然新插入的数据仍然会利用这些碎片。但过多的碎片，对数据的插入操作是有一定影响的，此时，我们可以通过optimize来对表的优化。 
为了更加直观的看到数据碎片，Mysql可以使用如下命令查看 
show table status [like table_name] 
如下图

data_free选项代表数据碎片。
针对MySQL的不同数据库存储引擎，在optimize使用清除碎片，回收闲置的数据库空间，把分散存储（fragmented）的数据和索引重新挪到一起(defragmentation)，对I/O速度有好处。
当然optimize在对表进行操作的时候，会加锁，所以不宜经常在程序中调用。

MyISAM存储引擎

针对MyISAM表，直接使用如下命令进行优化
optimize table table1[,table2][,table3]
如果同时优化多个表可以使用逗号分隔。
下面优化dede_member_vhistory表，可以看出，优化后data_free值为0。

#InnoDB存储引擎
InnoDB引擎的表分为独享表空间和同享表空间的表，我们可以通过show variables like ‘innodb_file_per_table’;来查看是否开启独享表空间。

我本地是开启了独享表空间的。此时是无法对表进行optimize操作的，如果操作，会返回如图信息，最后的一条Table does not support optimize, doing recreate + analyze instead。因为该结构下删除了大量的行，此时索引会重组并且会释放相应的空间因此不必优化。