这取决于您的查询和验证。

例如，我处理过一个包含10万种药物的表格，表格中每个药物都有一个超过15个字符的列通用名。我输入了一个查询来比较两个表格之间药物的通用名。查询需要更多的时间来运行。同样，如果使用药物索引，使用id列(如上所述)比较药物，只需要几秒钟。

The database size does matter. If you have more than one table with more than a million records, then performance starts indeed to degrade. The number of records does of course affect the performance: MySQL can be slow with large tables. If you hit one million records you will get performance problems if the indices are not set right (for example no indices for fields in "WHERE statements" or "ON conditions" in joins). If you hit 10 million records, you will start to get performance problems even if you have all your indices right. Hardware upgrades - adding more memory and more processor power, especially memory - often help to reduce the most severe problems by increasing the performance again, at least to a certain degree. For example 37 signals went from 32 GB RAM to 128GB of RAM for the Basecamp database server.

这取决于您的查询和验证。

例如，我处理过一个包含10万种药物的表格，表格中每个药物都有一个超过15个字符的列通用名。我输入了一个查询来比较两个表格之间药物的通用名。查询需要更多的时间来运行。同样，如果使用药物索引，使用id列(如上所述)比较药物，只需要几秒钟。

物理数据库大小无关紧要。记录的数量并不重要。

In my experience the biggest problem that you are going to run in to is not size, but the number of queries you can handle at a time. Most likely you are going to have to move to a master/slave configuration so that the read queries can run against the slaves and the write queries run against the master. However if you are not ready for this yet, you can always tweak your indexes for the queries you are running to speed up the response times. Also there is a lot of tweaking you can do to the network stack and kernel in Linux that will help.

我的内存达到了10GB，只有中等数量的连接，它处理请求还不错。

我将首先关注您的索引，然后让服务器管理员查看您的操作系统，如果所有这些都没有帮助，那么可能是时候实现主/从配置了。

还有一点需要考虑的是系统和数据在日常生活中的用途。

例如，对于一个用GPS监控汽车的系统来说，查询汽车前几个月的位置数据是不相关的。

因此，可以将数据传递给其他历史表，以便进行可能的查询，并减少日常查询的执行次数。

如果数据库设计不当，性能可能会在几千行中下降。

如果你有合适的索引，使用合适的引擎(不要使用MyISAM，因为需要多个dml)，使用分区，根据使用情况分配正确的内存，当然还有良好的服务器配置，MySQL可以处理tb级的数据!

总有办法提高数据库性能。