MySQL optimization strategy-related database commands

Source: Internet
Author: User
Tags null null table definition

We discussed the database performance optimization on the other hand, that is, the use of database server built-in tools to analyze and optimize.

▲show

Perform the following command to understand the running state of the server: MySQL >show status;

This command displays a long list of state variables and their corresponding values, including the number of users who are aborted, the number of connections aborted, the number of attempts to connect, the maximum number of concurrent connections, and many other useful information. This information is useful for determining the causes of system problems and inefficiencies.

In addition to displaying the overall state information of the MySQL server, the show command can display valuable information about log files, specified databases, tables, indexes, processes, and permission tables.

▲explain

Explain is able to analyze the process of the Select command. This is useful not only for determining whether to index tables, but also for understanding the process of MySQL handling complex connections.

The following example shows how to progressively optimize a connection query with the information provided by explain. (This example comes from the MySQL document, see http://www.mysql.com/doc/E/X/EXPLAIN.html.) It seems to be a bit of a scrawl here, especially in this case. )

The Select command assumed to be parsed with explain looks like this:

      
       EXPLAIN SELECT TT. Ticketnumber, TT. Timein, 
      TT. Projectreference, TT. Estimatedshipdate, 
      TT. Actualshipdate, TT. ClientID, 
      TT. Servicecodes, TT. Repetitiveid, 
      TT. Currentprocess, TT. Currentdpperson, 
      TT. Recordvolume, TT. Dpprinted, et. COUNTRY, 
      et_1.country, do. CustName from 
    TT, ET, et as et_1, do 
    WHERE tt. Submittime is NULL and 
      TT. ACTUALPC = et. Employid and 
      TT. ASSIGNEDPC = Et_1.employid and 
      tt. ClientID = do. CUSTNMBR;

The table that appears in the Select command is defined as follows:

Table Definition

      
       Table          column           column type  
tt          ACTUALPC      char (a)  
TT          ASSIGNEDPC    char (a)  
TT          ClientID      char (+)  
et          employid      char (          15) do custnmbr      Char

Index

      
       Table  index  
TT  ACTUALPC  
tt  ASSIGNEDPC  
tt  ClientID  
et  Employid (primary key)  
Do  CUSTNMBR (primary key)

Tt. ACTUALPC value distribution is uneven

Before any optimizations are made, the results of explain's analysis of the Select are as follows:

      
       Table type Possible_keys        key Key_len ref rows Extra 
et all  PRIMARY           null  NULL NULL All PRIMARY           null NULL null  2135 
et_1 all PRIMARY           null  NULL NULL ASSIGNEDPC,CLIENTID,ACTUALPC null-null NULL  3872 range checked for each record 
   (key map:35)

The type of each table is all, which indicates that MySQL is fully connected to each table! This operation is time-consuming, because the number of rows to be processed reaches the product of each table row number! That is, the total number of processing lines here is 74 * 2135 * 74 * 3872 = 45,268,558,720.

One of the problems here is that MySQL (also) cannot effectively use the index of a column if the declaration of the database column is different. In this case, varchar and char are the same unless they declare a different length. Due to TT. The ACTUALPC is declared as char (10) and ET. Employid is declared as char (15), so there is a problem with column length mismatch.

To resolve the length mismatch of these two columns, the ALTER Table command extends the ACTUALPC column from 10 characters to 15 characters, as follows: mysql > ALTER table tt MODIFY ACTUALPC VARCHAR (15);

Now TT. ACTUALPC and Et.employid are varchar (15), and the results of performing explain analysis are as follows:

      
       Table type  possible_keys  key   key_len ref     rows  Extra 
tt  all  Assignedpc,clientid , ACTUALPC null null 3872  where used do 
all  PRIMARY     null    NULL null 2135 
   range checked for each record (key map:1) 
et_1 all  PRIMARY     null NULL NULL    74 
   range checked for each record (key map:1) 

et  eq_ref PRIMARY     PRIMARY   tt. ACTUALPC 1

It's not perfect, but it's much better (the product of the number of rows is now less than a factor of 74). This SQL command now takes about a few seconds to execute. To avoid TT. ASSIGNEDPC = Et_1.employid and Tt.clientid = do. The column lengths in the CUSTNMBR comparison do not match, and we can make the following changes:

      
       MySQL > ALTER TABLE tt MODIFY assignedpc VARCHAR, 
           MODIFY ClientID  VARCHAR (15);

Now the results of the explain display are as follows:

      
       Table type  possible_keys  key   key_len ref      rows   Extra 
et  all  PRIMARY     Null  -NULL      - 
null  -ASSIGNEDPC,CLIENTID,ACTUALPC TT-ref-ACTUALPC et. Employid where used 
et_1 eq_ref PRIMARY   tt. ASSIGNEDPC 1 
do  eq_ref PRIMARY     PRIMARY   tt. ClientID  1

The result has been more satisfying. The remaining problem is that, by default, MySQL assumes that the value of the TT.ACTUALPC column is evenly distributed, whereas in fact the TT table is not. Luckily, we can easily let MySQL know this:

      
       Shell > Myisamchk--analyze path_to_mysql_database/tt 
shell > Mysqladmin Refresh

Now that the connection operation is ideal, the results of the explain analysis are as follows:

      
       Table type  possible_keys  key   key_len ref      rows  Extra 
tt  all  assignedpc, CLIENTID,ACTUALPC null null  3872  where used 
et  eq_ref PRIMARY   tt. ACTUALPC  1 
et_1 eq_ref PRIMARY     PRIMARY   tt. ASSIGNEDPC 1 
do  eq_ref PRIMARY     PRIMARY   tt. ClientID  1

▲optimize

Optimize can recover and defragment disk space and data fragmentation, which is necessary once a large number of updates or deletions are made to the table that contains the variable-length rows. Optimize is currently available only for MyISAM and BDB tables.

Conclusion:

Starting from compiling the database server and throughout the management process, there are many factors that can improve MySQL performance, and this article only covers a small part of it. Nevertheless, we hope that the content discussed in this article will help you.

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