2GB和约15M条记录是一个非常小的数据库-我在奔腾III上运行过更大的数据库(!)，一切仍然运行得非常快。如果你的慢，那是数据库/应用程序设计的问题，而不是mysql的问题。

数据库大小确实与字节数和表的行数有关。您将注意到light数据库和blob填充数据库之间的巨大性能差异。有一次我的应用程序卡住了，因为我把二进制图像放在字段中，而不是把图像保存在磁盘上的文件中，只把文件名放在数据库中。另一方面，迭代大量的行并不是免费的。

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，可能是时候进行主/从配置了。

这是真的。另一个通常有效的方法是减少重复处理的数据量。如果你有“旧数据”和“新数据”，并且99%的查询都使用新数据，只需将所有旧数据移动到另一个表中-并且不要查看它;)

->看看分区。

还要注意复杂连接。除了交易量之外，交易复杂性也是一个很大的因素。

重构繁重的查询有时会大大提高性能。

I'm currently managing a MySQL database on Amazon's cloud infrastructure that has grown to 160 GB. Query performance is fine. What has become a nightmare is backups, restores, adding slaves, or anything else that deals with the whole dataset, or even DDL on large tables. Getting a clean import of a dump file has become problematic. In order to make the process stable enough to automate, various choices needed to be made to prioritize stability over performance. If we ever had to recover from a disaster using a SQL backup, we'd be down for days.

Horizontally scaling SQL is also pretty painful, and in most cases leads to using it in ways you probably did not intend when you chose to put your data in SQL in the first place. Shards, read slaves, multi-master, et al, they are all really shitty solutions that add complexity to everything you ever do with the DB, and not one of them solves the problem; only mitigates it in some ways. I would strongly suggest looking at moving some of your data out of MySQL (or really any SQL) when you start approaching a dataset of a size where these types of things become an issue.

更新:几年后，我们的数据集已经增长到大约800 GiB。此外，我们还有一个200+ GiB的表和其他一些50-100 GiB的表。我之前说的都成立。它的性能仍然很好，但运行完整数据集操作的问题变得更糟了。

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.