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MySQL Cluster Performance
(Publish Date: 2009-9-1 3:40pm, Total Visits: 1281, Today: 1, This Week: 1, This Month: 3)

MySQL Cluster has a very different impact on the concepts of response time, throughput, and scalability than a normal single-system, disk-based database, such as MyISAM or InnoDB.


Response time with MySQL Cluster is quite commonly worse than it is with the traditional setup. Yes, response time is quite commonly worse with clustering than with a normal system. If you consider the architecture of MySQL Cluster, this will begin to make more sense. When you do a query with a cluster, it has to first go to the MySQL server, and then it goes to data nodes and sends the data back the same way. When you do a query on a normal system, all access is done within the MySQL server itself. It is clearly faster to access local resources than to read the same thing across a network. As discussed later in this chapter, response time is very much dependant on network latency because of the extra network traffic.
Some queries may be faster than others due to the parallel scanning that is possible, but you cannot expect all queries to have a better response time. So if the response time is worse, why would you use a cluster? First, response time isn’t normally very important. For the vast majority of applications, 10ms versus 15ms isn’t considered a big difference. Where MySQL Cluster shines is in relation to the other two metrics—throughput and scalability.


Throughput and scalability are generally much better with clustering than in a single-system database. If you consider the most common bottlenecks of the different systems, you can see why. With a single-system database, you almost always bottleneck on disk I/O. As you get more users and queries, disk I/O generally becomes slower due to extra seeking overhead. For example, if you are doing 1Mbps worth of access with one client, you might only be able to do 1.5Mbps with two and only 1.75Mbps with three clients. MySQL Cluster is typically bottlenecked on network I/O. Network traffic scales much better with multiple users. For example, if you are transferring 1Mbps with one client, you can do 2Mbps with two clients, 3Mbps with three clients, and so on. You start to have problems only when you near the maximum possible throughput. This allows you to scale the system relatively easily until you start to reach network limits, which are generally are quite high.