Database performance depends on several factors at the database level, such as tables, queries, andconfiguration 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, youstart by learning the high-level rules and guidelines for the software side, and measuring performanceusing 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
• Optimizing at the Hardware Level
• Balancing Portability and Performance
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 transactional storage engine
such as InnoDB or a nontransactional one such as MyISAM can be very important for performance and
scalability.
Note
InnoDB is the default storage engine for new tables. In practice, the advanced
InnoDB performance features mean that InnoDB tables often outperform the
simpler MyISAM tables, especially for a busy database.
• 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 all kinds of workloads with InnoDB tables, and for read
only MyISAM 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 InnoDB buffer pool, the MyISAM key cache, 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
1319Balancing Portability and Performance
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 10ms, 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 10–20MB/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
To use performance-oriented SQL extensions in a portable MySQL program, you can wrap MySQL
specific keywords in a statement within /*! */ comment delimiters. Other SQL servers ignore the
commented keywords. For information about writing comments, see Section 9.6, “Comment Syntax”.