Overall application performance, CPU and I/O utilization and the size of disk files are good indicators of how effective compression is for your application. This section builds on the performance tuning advice from Section 14.6.3, “Tuning Compression for InnoDB Tables”, and shows how to find problems that might not turn up during initial testing.
To dig deeper into performance considerations for compressed tables, you can monitor compression performance at runtime using the Information Schema tables described in Example 14.10, “Using the Compression Information Schema Tables”. These tables reflect the internal use of memory and the rates of compression used overall.
INNODB_CMP table reports
information about compression activity for each compressed page
KEY_BLOCK_SIZE) in use. The information
in these tables is system-wide: it summarizes the compression
statistics across all compressed tables in your database. You can
use this data to help decide whether or not to compress a table by
examining these tables when no other compressed tables are being
accessed. It involves relatively low overhead on the server, so
you might query it periodically on a production server to check
the overall efficiency of the compression feature.
reports information about compression activity for individual
tables and indexes. This information is more targeted and more
useful for evaluating compression efficiency and diagnosing
performance issues one table or index at a time. (Because that
InnoDB table is represented as a clustered
index, MySQL does not make a big distinction between tables and
indexes in this context.) The
INNODB_CMP_PER_INDEX table does
involve substantial overhead, so it is more suitable for
development servers, where you can compare the effects of
different workloads, data,
and compression settings in isolation. To guard against imposing
this monitoring overhead by accident, you must enable the
configuration option before you can query the
The key statistics to consider are the number of, and amount of
time spent performing, compression and uncompression operations.
Since MySQL splits B-tree nodes when they are too full to contain
the compressed data following a modification, compare the number
of “successful” compression operations with the
number of such operations overall. Based on the information in the
INNODB_CMP_PER_INDEX tables and
overall application performance and hardware resource utilization,
you might make changes in your hardware configuration, adjust the
size of the buffer pool, choose a different page size, or select a
different set of tables to compress.
If the amount of CPU time required for compressing and uncompressing is high, changing to faster or multi-core CPUs can help improve performance with the same data, application workload and set of compressed tables. Increasing the size of the buffer pool might also help performance, so that more uncompressed pages can stay in memory, reducing the need to uncompress pages that exist in memory only in compressed form.
A large number of compression operations overall (compared to the
DELETE operations in your application and the
size of the database) could indicate that some of your compressed
tables are being updated too heavily for effective compression. If
so, choose a larger page size, or be more selective about which
tables you compress.
If the number of “successful” compression operations
COMPRESS_OPS_OK) is a high percentage of the
total number of compression operations
COMPRESS_OPS), then the system is likely
performing well. If the ratio is low, then MySQL is reorganizing,
recompressing, and splitting B-tree nodes more often than is
desirable. In this case, avoid compressing some tables, or
KEY_BLOCK_SIZE for some of the
compressed tables. You might turn off compression for tables that
cause the number of “compression failures” in your
application to be more than 1% or 2% of the total. (Such a failure
ratio might be acceptable during a temporary operation such as a
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