测试参数: sysbench--test=/root/sysbench0.5/sysbench/tests/db/insert.lua--mysql-table-engine=innodb--oltp-table-size=1000000--max-requests=0--max-time=300--num-threads=16--oltp-tables-count=10--report-interval=10--mysql-host=10.8.8.100--my
测试参数:
sysbench --test=/root/sysbench0.5/sysbench/tests/db/insert.lua --mysql-table-engine=innodb --oltp-table-size=1000000 --max-requests=0 --max-time=300 --num-threads=16 --oltp-tables-count=10 --report-interval=10 --mysql-host=10.8.8.100 --mysql-port=3312 --mysql-user=admin --mysql-password=123456 --mysql-db=test run
10张100万行表,并发16个线程,纯插入操作。
MySQL参数:
sync_binlog = 0 innodb_flush_log_at_trx_commit = 0 innodb_flush_method = O_DIRECT innodb_flush_neighbors = 1 innodb_buffer_pool_size = 10G innodb_io_capacity = 500 innodb_log_file_size = 512M innodb_log_files_in_group = 3 innodb_log_buffer_size = 16M
普通复制延迟:
Master_Log_File: mysql-bin.000004 Read_Master_Log_Pos: 567748597 Relay_Log_File: host-100-relay-bin.000005 Relay_Log_Pos: 66853042 Relay_Master_Log_File: mysql-bin.000003 Slave_IO_Running: Yes Slave_SQL_Running: Yes Replicate_Do_DB: Replicate_Ignore_DB: Replicate_Do_Table: Replicate_Ignore_Table: Replicate_Wild_Do_Table: Replicate_Wild_Ignore_Table: Last_Errno: 0 Last_Error: Skip_Counter: 0 Exec_Master_Log_Pos: 469504741
还剩1117MB没追完
MariaDB [(none)]> select 1024-469504741/1024/1024+567748597/1024/1024; +----------------------------------------------+ | 1024-469504741/1024/1024+567748597/1024/1024 | +----------------------------------------------+ | 1117.69264221 | +----------------------------------------------+ 1 row in set (0.00 sec)
并行复制(8个并发线程,未开启GTID模式)延迟:
Master_Log_File: mysql-bin.000022 Read_Master_Log_Pos: 333773843 Relay_Log_File: host-100-relay-bin.000073 Relay_Log_Pos: 134217580 Relay_Master_Log_File: mysql-bin.000021 Slave_IO_Running: Yes Slave_SQL_Running: Yes Replicate_Do_DB: Replicate_Ignore_DB: Replicate_Do_Table: Replicate_Ignore_Table: Replicate_Wild_Do_Table: Replicate_Wild_Ignore_Table: Last_Errno: 0 Last_Error: Skip_Counter: 0 Exec_Master_Log_Pos: 722396020
还剩653MB没追完
MariaDB [(none)]> select 1024-722396020/1024/1024+333773843/1024/1024; +----------------------------------------------+ | 1024-722396020/1024/1024+333773843/1024/1024 | +----------------------------------------------+ | 653.38101101 | +----------------------------------------------+ 1 row in set (0.00 sec)
并行复制(8个并发线程,开启GTID模式)延迟:
Master_Log_File: mysql-bin.000002 Read_Master_Log_Pos: 365095829 Relay_Log_File: host-100-relay-bin.000008 Relay_Log_Pos: 134217572 Relay_Master_Log_File: mysql-bin.000001 Slave_IO_Running: Yes Slave_SQL_Running: Yes Replicate_Do_DB: Replicate_Ignore_DB: Replicate_Do_Table: Replicate_Ignore_Table: Replicate_Wild_Do_Table: Replicate_Wild_Ignore_Table: Last_Errno: 0 Last_Error: Skip_Counter: 0 Exec_Master_Log_Pos: 811005100
还剩598MB没追完
MariaDB [(none)]> select 1024-811005100/1024/1024+365095829/1024/1024; +----------------------------------------------+ | 1024-811005100/1024/1024+365095829/1024/1024 | +----------------------------------------------+ | 598.74778080 | +----------------------------------------------+ 1 row in set (0.00 sec)
结论:
在我这个测试中,并行复制的速度要快于普通复制的两倍多。

Stored procedures are precompiled SQL statements in MySQL for improving performance and simplifying complex operations. 1. Improve performance: After the first compilation, subsequent calls do not need to be recompiled. 2. Improve security: Restrict data table access through permission control. 3. Simplify complex operations: combine multiple SQL statements to simplify application layer logic.

The working principle of MySQL query cache is to store the results of SELECT query, and when the same query is executed again, the cached results are directly returned. 1) Query cache improves database reading performance and finds cached results through hash values. 2) Simple configuration, set query_cache_type and query_cache_size in MySQL configuration file. 3) Use the SQL_NO_CACHE keyword to disable the cache of specific queries. 4) In high-frequency update environments, query cache may cause performance bottlenecks and needs to be optimized for use through monitoring and adjustment of parameters.

The reasons why MySQL is widely used in various projects include: 1. High performance and scalability, supporting multiple storage engines; 2. Easy to use and maintain, simple configuration and rich tools; 3. Rich ecosystem, attracting a large number of community and third-party tool support; 4. Cross-platform support, suitable for multiple operating systems.

The steps for upgrading MySQL database include: 1. Backup the database, 2. Stop the current MySQL service, 3. Install the new version of MySQL, 4. Start the new version of MySQL service, 5. Recover the database. Compatibility issues are required during the upgrade process, and advanced tools such as PerconaToolkit can be used for testing and optimization.

MySQL backup policies include logical backup, physical backup, incremental backup, replication-based backup, and cloud backup. 1. Logical backup uses mysqldump to export database structure and data, which is suitable for small databases and version migrations. 2. Physical backups are fast and comprehensive by copying data files, but require database consistency. 3. Incremental backup uses binary logging to record changes, which is suitable for large databases. 4. Replication-based backup reduces the impact on the production system by backing up from the server. 5. Cloud backups such as AmazonRDS provide automation solutions, but costs and control need to be considered. When selecting a policy, database size, downtime tolerance, recovery time, and recovery point goals should be considered.

MySQLclusteringenhancesdatabaserobustnessandscalabilitybydistributingdataacrossmultiplenodes.ItusestheNDBenginefordatareplicationandfaulttolerance,ensuringhighavailability.Setupinvolvesconfiguringmanagement,data,andSQLnodes,withcarefulmonitoringandpe

Optimizing database schema design in MySQL can improve performance through the following steps: 1. Index optimization: Create indexes on common query columns, balancing the overhead of query and inserting updates. 2. Table structure optimization: Reduce data redundancy through normalization or anti-normalization and improve access efficiency. 3. Data type selection: Use appropriate data types, such as INT instead of VARCHAR, to reduce storage space. 4. Partitioning and sub-table: For large data volumes, use partitioning and sub-table to disperse data to improve query and maintenance efficiency.

TooptimizeMySQLperformance,followthesesteps:1)Implementproperindexingtospeedupqueries,2)UseEXPLAINtoanalyzeandoptimizequeryperformance,3)Adjustserverconfigurationsettingslikeinnodb_buffer_pool_sizeandmax_connections,4)Usepartitioningforlargetablestoi


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