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。

总的来说，这是一个非常微妙的问题，无论如何都不是微不足道的。我建议你阅读mysqlperformanceblog.com和高性能MySQL。我真的认为这个问题没有普遍的答案。

我正在做一个项目，它有一个MySQL数据库，几乎有1TB的数据。最重要的可伸缩性因素是RAM。如果您的表的索引适合内存，并且您的查询得到了高度优化，那么您可以使用普通机器处理合理数量的请求。

记录的数量确实很重要，这取决于表的外观。有很多varchar字段和只有几个int或long类型是有区别的。

数据库的物理大小也很重要:例如，考虑备份。根据你的引擎，你的物理db文件会增长，但不会缩小，例如innodb。因此，删除大量的行，并不有助于缩小您的物理文件。

这个问题有很多，在很多情况下，细节决定成败。

I once was called upon to look at a mysql that had "stopped working". I discovered that the DB files were residing on a Network Appliance filer mounted with NFS2 and with a maximum file size of 2GB. And sure enough, the table that had stopped accepting transactions was exactly 2GB on disk. But with regards to the performance curve I'm told that it was working like a champ right up until it didn't work at all! This experience always serves for me as a nice reminder that there're always dimensions above and below the one you naturally suspect.

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。

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。

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。