MySQL Optimization Part 1

Database performance depends on several factors at the database level, such as tables, queries, and configuration settings. These software constructs result in CPU and I/O operations at the hardware level, which you must minimize and make as efficient as possible.

As you work on database performance, you start by learning the high-level rules and guidelines for the software side, and measuring performance using wall-clock time.

As you become an expert, you learn more about what happens internally, and start measuring things such as CPU cycles and I/O operations.

Typical users aim to get the best database performance out of their existing software and hardware configurations.

Advanced users look for opportunities to improve the MySQL software itself, or develop their own storage engines and hardware appliances to expand the MySQL ecosystem.

Optimizing at the Database Level

The most important factor in making a database application fast is its basic design:

  • Are the tables structured properly? In particular, do the columns have the right data types, and does each table have the appropriate columns for the type of work? For example, applications that perform frequent updates often have many tables with few columns, while applications that analyze large amounts of data often have few tables with many columns.
  • Are the right indexes in place to make queries efficient?
  • Are you using the appropriate storage engine for each table, and taking advantage of the strengths and features of each storage engine you use? In particular, the choice of a nontransactional storage engine such as MyISAM or a transactional one such as InnoDB can be very important for performance and scalability.
  • Does each table use an appropriate row format? This choice also depends on the storage engine used for the table. In particular, compressed tables use less disk space and so require less disk I/O to read and write the data. Compression is available for read-only MyISAM tables, and for all kinds of workloads with InnoDB tables.
  • Does the application use an appropriate locking strategy? For example, by allowing shared access when possible so that database operations can run concurrently, and requesting exclusive access when appropriate so that critical operations get top priority. Again, the choice of storage engine is significant. The InnoDB storage engine handles most locking issues without involvement from you, allowing for better concurrency in the database and reducing the amount of experimentation and tuning for your code.
  • Are all memory areas used for caching sized correctly? That is, large enough to hold frequently accessed data, but not so large that they overload physical memory and cause paging. The main memory areas to configure are the MyISAM key cache, the InnoDB buffer pool, and the MySQL query cache.

Optimizing at the Hardware Level

Any database application eventually hits hardware limits as the database becomes more and more busy. A DBA must evaluate whether it is possible to tune the application or reconfigure the server to avoid these bottlenecks, or whether more hardware resources are required. System bottlenecks typically arise from these sources:

  • Disk seeks : It takes time for the disk to find a piece of data. With modern disks, the mean time for this is usually lower than 10 ms, so we can in theory do about 100 seeks a second. This time improves slowly with new disks and is very hard to optimize for a single table. The way to optimize seek time is to distribute the data onto more than one disk.
  • Disk reading and writing : When the disk is at the correct position, we need to read or write the data. With modern disks, one disk delivers at least 10–20 MB/s throughput. This is easier to optimize than seeks because you can read in parallel from multiple disks.
  • CPU cycles : When the data is in main memory, we must process it to get our result. Having large tables compared to the amount of memory is the most common limiting factor. But with small tables, speed is usually not the problem.
  • Memory bandwidth : When the CPU needs more data than can fit in the CPU cache, main memory bandwidth becomes a bottleneck. This is an uncommon bottleneck for most systems, but one to be aware of.

Balancing Portability and Performance

Because all SQL servers implement different parts of standard SQL, it takes work to write portable database applications. It is very easy to achieve portability for very simple selects and inserts, but becomes more difficult the more capabilities you require. If you want an application that is fast with many database systems, it becomes even more difficult.

All database systems have some weak points. That is, they have different design compromises that lead to different behavior.

  • To make a complex application portable, you need to determine which SQL servers it must work with, and then determine what features those servers support. You can use the MySQL crash-me program to find functions, types, and limits that you can use with a selection of database servers. crash-me does not check for every possible feature, but it is still reasonably comprehensive, performing about 450 tests. An example of the type of information crash-me can provide is that you should not use column names that are longer than 18 characters if you want to be able to use Informix or DB2.
  • If you strive for database independence, you need to get a good feeling for each SQL server’s bottlenecks. For example, MySQL is very fast in retrieving and updating rows for MyISAM tables, but has a problem in mixing slow readers and writers on the same table. Transactional database systems in general are not very good at generating summary tables from log tables, because in this case row locking is almost useless.
  • If high performance is more important than exactness, as for some Web applications, it is possible to create an application layer that caches all results to give you even higher performance. By letting old results expire after a while, you can keep the cache reasonably fresh. This provides a method to handle high load spikes, in which case you can dynamically increase the cache size and set the expiration timeout higher until things get back to normal.
  • In this case of higher performance, the table creation information should contain information about the initial cache size and how often the table should normally be refreshed.
  • An attractive alternative to implementing an application cache is to use the MySQL query cache. By enabling the query cache, the server handles the details of determining whether a query result can be reused. This simplifies your application.

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