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MySQL基准测试---sysbench
一、 在对网站整体性能进行benchmark时,可以使用多种工具,比如大名鼎鼎的ab
(Apache bench),http_load等工具。这里我们不关注他们的使用,如果你想了解,可以去
这里http://en.wikipedia.org/wiki/ApacheBench。
重点来说MySQL的基准测试如何进行,也有很多种工具来供我们选择,比如mysqlslap、
sysbench、Super Smack等,其中mysqlslap的使用MySQL官网给出了介绍,Super Smack
是服务器压力测试强有力的工具,那么sysbench便是我们进行MySQL基准测试的很漂亮的工具了。
sysbench是一款开源的多线程性能测试工具,可以执行CPU/内存/线程/IO/数据库等方面的
性能测试。数据库目前支持MySQL/Oracle/PostgreSQL;
在本次试验中,sysbench版本为:0.4.12 MySQL版本为:5.1.61 ubuntu版本为:10.10
sysbench的基本用法通过 man sysbench 或者 sysbench --help可以方便的看到:
sysbench [general-options]... --test=
general-options 暂且不说,test-name 包括fileio/cpu/memory/threads/mutex/oltp 等测试,
command包括prepare/run/cleanup/help/version等
(1)cpu测试
sysbench采用寻找最大素数的方式来测试:命令如下
root@ubuntu:~#sysbench --test=cpu --cpu-max-prime=1500 run
从total time: 一行可以看出在求得1500以内的最大素数时使用了3.2214s
(2)fileio测试
使用fileio测试,首先得生成文件,然后对文件或读或写,测试fileio性能
生成文件:sysbench --test=fileio prepare ,使用fileio命令时,会有很多参数,可以使用
--file-block-size指定单个文件大小,可以使用--file-total-size指定整个文件的大小,但是这里
使用默认参数,单个文件大小为16M,整个文件大小为2G。如下所示:
这里总共生成了128个文件。读写文件sysbench --test=fileio --file-test-mode=rndrw run,
如下所示:
在这份报告中,有两个参数很值得我们关注,即每秒的请求数和总体的吞吐量,比如上面的就
是785.16 Requests/sec 和12.268Mb/sec,这两个参数对于评估磁盘的性能很有帮助。在完成测
试后,需要将测试数据删除: sysbench --test=fileio cleanup
(3)oltp 测试
首先生成数据:sysbench --test=oltp --oltp-table-size=2000000 --oltp-table-name=tt -
-mysql-db=test --mysql-user=root --msyql-password=123 prepare ,这里测试用户名:root
密码:123
生成数据后,进行测试,使用8个线程进行测试,如下所示:
以上数据对于分析多线程事务处理很有帮助,比如事务总统计数,每秒事务的处理量等等,
sysbench对于其他的测试类似。。。
二、对于MySQL的性能分析,在服务器内部有很多很好的命令来供我们使用,比如
show status/show processlist/show innodb status
使用show status 中,“Select%”一般指不同类型的连接执行计划,“Handler%”一般指存储引
擎的相关信息,“Sort%”一般指排序的相关信息,“Created%”一般指新建临时表的相关信息。
一般也可以使用mysqlreport对信息进行统计打印。而show processlist 也打印出了相关参数,
此时可以使用Innotop代替进行方便分析,或者使用mytop来分析。如果是在回话中使用,则需要
添加session 参数,即show session status; 使用show processlist 可以定位到mysql连接和进
程故障,这一功能一般需要和prep netstat psof ps等联合使用。使用tcpdump工具可以抓包分析
流量。innotop 和tcpdump的强大功能以后再分析。
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InnoDB uses redologs and undologs to ensure data consistency and reliability. 1.redologs record data page modification to ensure crash recovery and transaction persistence. 2.undologs records the original data value and supports transaction rollback and MVCC.

Key metrics for EXPLAIN commands include type, key, rows, and Extra. 1) The type reflects the access type of the query. The higher the value, the higher the efficiency, such as const is better than ALL. 2) The key displays the index used, and NULL indicates no index. 3) rows estimates the number of scanned rows, affecting query performance. 4) Extra provides additional information, such as Usingfilesort prompts that it needs to be optimized.

Usingtemporary indicates that the need to create temporary tables in MySQL queries, which are commonly found in ORDERBY using DISTINCT, GROUPBY, or non-indexed columns. You can avoid the occurrence of indexes and rewrite queries and improve query performance. Specifically, when Usingtemporary appears in EXPLAIN output, it means that MySQL needs to create temporary tables to handle queries. This usually occurs when: 1) deduplication or grouping when using DISTINCT or GROUPBY; 2) sort when ORDERBY contains non-index columns; 3) use complex subquery or join operations. Optimization methods include: 1) ORDERBY and GROUPB

MySQL/InnoDB supports four transaction isolation levels: ReadUncommitted, ReadCommitted, RepeatableRead and Serializable. 1.ReadUncommitted allows reading of uncommitted data, which may cause dirty reading. 2. ReadCommitted avoids dirty reading, but non-repeatable reading may occur. 3.RepeatableRead is the default level, avoiding dirty reading and non-repeatable reading, but phantom reading may occur. 4. Serializable avoids all concurrency problems but reduces concurrency. Choosing the appropriate isolation level requires balancing data consistency and performance requirements.

MySQL is suitable for web applications and content management systems and is popular for its open source, high performance and ease of use. 1) Compared with PostgreSQL, MySQL performs better in simple queries and high concurrent read operations. 2) Compared with Oracle, MySQL is more popular among small and medium-sized enterprises because of its open source and low cost. 3) Compared with Microsoft SQL Server, MySQL is more suitable for cross-platform applications. 4) Unlike MongoDB, MySQL is more suitable for structured data and transaction processing.

MySQL index cardinality has a significant impact on query performance: 1. High cardinality index can more effectively narrow the data range and improve query efficiency; 2. Low cardinality index may lead to full table scanning and reduce query performance; 3. In joint index, high cardinality sequences should be placed in front to optimize query.

The MySQL learning path includes basic knowledge, core concepts, usage examples, and optimization techniques. 1) Understand basic concepts such as tables, rows, columns, and SQL queries. 2) Learn the definition, working principles and advantages of MySQL. 3) Master basic CRUD operations and advanced usage, such as indexes and stored procedures. 4) Familiar with common error debugging and performance optimization suggestions, such as rational use of indexes and optimization queries. Through these steps, you will have a full grasp of the use and optimization of MySQL.

MySQL's real-world applications include basic database design and complex query optimization. 1) Basic usage: used to store and manage user data, such as inserting, querying, updating and deleting user information. 2) Advanced usage: Handle complex business logic, such as order and inventory management of e-commerce platforms. 3) Performance optimization: Improve performance by rationally using indexes, partition tables and query caches.


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