对于大量数据的导入导出,是件挺麻烦的事,需要考虑很多的细节,这类对于需要大量数据导入导出的朋友可以参考下。
昨日中午帮鲤鱼给某大型团购网站转移数据库服务器,这篇文章本该是昨天转移完成后总结发出来的,但由于一些原因没有及时写出来,现在赶快补上。首先大概环境是这样的,旧数据库服务器mysql版本5.0 centos系统 无web,无ftp,新数据库服务器mysql5.1 centos nginx。
看到这样的环境首先想法是通过phpmyadmin导入导出,但原数据库服务器无web环境,另外数据库文件大约300M,仅靠phpmyadmin无法导入。
下来准备停止mysql,打包数据库目录,然后覆盖的新服务器的数据库目录下,但鲤鱼尝试说拷贝过去有表段存在问题,所以这种方法也放弃。
最后先到老服务器使用命令
代码如下:
mysqldump -u用户名 -p 数据库名 > 数据库名.sql
导出为.sql文件,然后zip压缩后,通过另外一台服务器ssh(scp)中转到新数据库服务器,通过下面的语句导入(本可以使用mysqldump -u用户名 -p 数据库名 代码如下:
mysql -u root -p
mysql>use 数据库
mysql>source /data0/tuan/tuan.sql
这样导入ssh的条目会很多很多,建议找windows服务器挂ssh去执行,我使用公司网络带宽跑满4M,占用我的全部网速。
至此数据库转移已经让我唠叨完了,没有太多的技术性东西,只是给自己记录下,避免忘记,温故而知新嘛。

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

MySQL functions can be used for data processing and calculation. 1. Basic usage includes string processing, date calculation and mathematical operations. 2. Advanced usage involves combining multiple functions to implement complex operations. 3. Performance optimization requires avoiding the use of functions in the WHERE clause and using GROUPBY and temporary tables.

Efficient methods for batch inserting data in MySQL include: 1. Using INSERTINTO...VALUES syntax, 2. Using LOADDATAINFILE command, 3. Using transaction processing, 4. Adjust batch size, 5. Disable indexing, 6. Using INSERTIGNORE or INSERT...ONDUPLICATEKEYUPDATE, these methods can significantly improve database operation efficiency.

In MySQL, add fields using ALTERTABLEtable_nameADDCOLUMNnew_columnVARCHAR(255)AFTERexisting_column, delete fields using ALTERTABLEtable_nameDROPCOLUMNcolumn_to_drop. When adding fields, you need to specify a location to optimize query performance and data structure; before deleting fields, you need to confirm that the operation is irreversible; modifying table structure using online DDL, backup data, test environment, and low-load time periods is performance optimization and best practice.


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