不同DBMS之间的可扩展性比较
当将一台机器添加到运行以下任一者的机器集群时，性能（读取查询/秒）会提高多少倍：
- 类似 Bigtable 的数据库
- MySQL？
Google 关于 Bigtable 的研究论文表明，使用 Bigtable 可以实现“近线性”缩放。 此页面采用 MySQL 的营销术语表明 MySQL 具有扩展能力线性地。
真相在哪里？
By what factor does the performance (read queries/sec) increase when a machine is added to a cluster of machines running either:
- a Bigtable-like database
- MySQL?
Google's research paper on Bigtable suggests that "near-linear" scaling is achieved can be achieved with Bigtable. This page here featuring MySQL's marketing jargon suggests that MySQL is capable of scaling linearly.
Where is the truth?
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使用 VoltDB 构建多个应用程序并对其进行基准测试后，我始终测量每个新应用程序的额外事务吞吐量在 90% 到 95% 之间服务器已添加到集群中。因此，如果应用程序在一台服务器上每秒执行 100,000 个事务 (TPS)，我会在 2 台服务器上测量 190,000 TPS，在 3 台服务器上测量 280,000 TPS，依此类推。在某些时候，我们预计服务器到服务器的网络将成为瓶颈，但我们最大的集群（30 台服务器）仍然高于 90%。
Having built and benchmarked several applications using VoltDB I consistently measure between 90% and 95% of additional transactional throughput as each new server is added to the cluster. So if an application is performing 100,000 transaction per second (TPS) on a single server, I measure 190,000 TPS on 2 servers, 280,000 TPS on 3 servers, and so on. At some point we expect the server to server networking to become a bottleneck but our largest cluster (30 servers) is still above 90%.
如果您不对数据库执行那么多写入，
MySQL可能是一个很好且简单的解决方案，特别是如果按顺序与memcached结合使用以提高读取速度。OTOH，如果您的数据不断变化，您可能应该看看其他地方：
这些系统被设计为随着添加的计算机数量线性扩展到系统。
完整列表可在此处获取。
If you don't do that many writes to the database,
MySQLmay be a good and easy solution, especially if coupled withmemcachedin order to increase the read speed.OTOH if you data is constantly changing, you should probably look somewhere else:
These systems have been designed to scale linearly with the number of computers added to the system.
A full list is available here.