Most often, the internal optimizations described in Section 126.96.36.199, “ InnoDB Data Storage and Compression ” ensure that the system runs well with compressed data. However, because the efficiency of compression depends on the nature of your data, you can make decisions that affect the performance of compressed tables:
Which tables to compress.
What compressed page size to use.
Whether to adjust the size of the buffer pool based on run-time performance characteristics, such as the amount of time the system spends compressing and uncompressing data. Whether the workload is more like a data warehouse (primarily queries) or an OLTP system (mix of queries and DML).
If the system performs DML operations on compressed tables, and the way the data is distributed leads to expensive compression failures at runtime, you might adjust additional advanced configuration options.
Use the guidelines in this section to help make those architectural and configuration choices. When you are ready to conduct long-term testing and put compressed tables into production, see Section 188.8.131.52, “Monitoring Compression at Runtime” for ways to verify the effectiveness of those choices under real-world conditions.
In general, compression works best on tables that include a reasonable number of character string columns and where the data is read far more often than it is written. Because there are no guaranteed ways to predict whether or not compression benefits a particular situation, always test with a specific workload and data set running on a representative configuration. Consider the following factors when deciding which tables to compress.
A key determinant of the efficiency of compression in reducing the
size of data files is the nature of the data itself. Recall that
compression works by identifying repeated strings of bytes in a
block of data. Completely randomized data is the worst case.
Typical data often has repeated values, and so compresses
effectively. Character strings often compress well, whether
BLOB columns. On the
other hand, tables containing mostly binary data (integers or
floating point numbers) or data that is previously compressed (for
example JPEG or PNG images)
may not generally compress well, significantly or at all.
You choose whether to turn on compression for each InnoDB table. A
table and all of its indexes use the same (compressed)
page size. It might be that
the primary key
(clustered) index, which contains the data for all columns of a
table, compresses more effectively than the secondary indexes. For
those cases where there are long rows, the use of compression
might result in long column values being stored
“off-page”, as discussed in
Section 184.108.40.206, “
COMPRESSED Row Formats”. Those overflow pages
may compress well. Given these considerations, for many
applications, some tables compress more effectively than others,
and you might find that your workload performs best only with a
subset of tables compressed.
To determine whether or not to compress a particular table,
conduct experiments. You can get a rough estimate of how
efficiently your data can be compressed by using a utility that
implements LZ77 compression (such as
WinZip) on a copy of the .ibd
file for an uncompressed table. You can expect less
compression from a MySQL compressed table than from file-based
compression tools, because MySQL compresses data in chunks based
on the page size, 16KB by
default. In addition to user data, the page format includes some
internal system data that is not compressed. File-based
compression utilities can examine much larger chunks of data, and
so might find more repeated strings in a huge file than MySQL can
find in an individual page.
Another way to test compression on a specific table is to copy
some data from your uncompressed table to a similar, compressed
table (having all the same indexes) and look at the size of the
.ibd file. For example:
use test; set global innodb_file_per_table=1; set global innodb_file_format=Barracuda; set global autocommit=0; -- Create an uncompressed table with a million or two rows. create table big_table as select * from information_schema.columns; insert into big_table select * from big_table; insert into big_table select * from big_table; insert into big_table select * from big_table; insert into big_table select * from big_table; insert into big_table select * from big_table; insert into big_table select * from big_table; insert into big_table select * from big_table; insert into big_table select * from big_table; insert into big_table select * from big_table; insert into big_table select * from big_table; commit; alter table big_table add id int unsigned not null primary key auto_increment; show create table big_table\G select count(id) from big_table; -- Check how much space is needed for the uncompressed table. \! ls -l data/test/big_table.ibd create table key_block_size_4 like big_table; alter table key_block_size_4 key_block_size=4 row_format=compressed; insert into key_block_size_4 select * from big_table; commit; -- Check how much space is needed for a compressed table -- with particular compression settings. \! ls -l data/test/key_block_size_4.ibd
This experiment produced the following numbers, which of course could vary considerably depending on your table structure and data:
-rw-rw---- 1 cirrus staff 310378496 Jan 9 13:44 data/test/big_table.ibd -rw-rw---- 1 cirrus staff 83886080 Jan 9 15:10 data/test/key_block_size_4.ibd
To see whether compression is efficient for your particular workload:
For simple tests, use a MySQL instance with no other
compressed tables and run queries against the
For more elaborate tests involving workloads with multiple
compressed tables, run queries against the
table. Because the statistics in the
INNODB_CMP_PER_INDEX table are expensive to
collect, you must enable the configuration option
before querying that table, and you might restrict such
testing to a development server or a non-critical
Run some typical SQL statements against the compressed table you are testing.
Examine the ratio of successful compression operations to
overall compression operations by querying the
table, and comparing
If a high percentage of compression operations complete successfully, the table might be a good candidate for compression.
If you get a high proportion of
failures, you can adjust
options as described in
Section 220.127.116.11.2, “Compression Enhancements for OLTP Workloads”, and try
Decide whether to compress data in your application or in the table; do not use both types of compression for the same data. When you compress the data in the application and store the results in a compressed table, extra space savings are extremely unlikely, and the double compression just wastes CPU cycles.
When enabled, MySQL table compression is automatic and applies to
all columns and index values. The columns can still be tested with
operators such as
LIKE, and sort operations can
still use indexes even when the index values are compressed.
Because indexes are often a significant fraction of the total size
of a database, compression could result in significant savings in
storage, I/O or processor time. The compression and decompression
operations happen on the database server, which likely is a
powerful system that is sized to handle the expected load.
If you compress data such as text in your application, before it is inserted into the database, You might save overhead for data that does not compress well by compressing some columns and not others. This approach uses CPU cycles for compression and uncompression on the client machine rather than the database server, which might be appropriate for a distributed application with many clients, or where the client machine has spare CPU cycles.
Of course, it is possible to combine these approaches. For some applications, it may be appropriate to use some compressed tables and some uncompressed tables. It may be best to externally compress some data (and store it in uncompressed tables) and allow MySQL to compress (some of) the other tables in the application. As always, up-front design and real-life testing are valuable in reaching the right decision.
In addition to choosing which tables to compress (and the page
size), the workload is another key determinant of performance. If
the application is dominated by reads, rather than updates, fewer
pages need to be reorganized and recompressed after the index page
runs out of room for the per-page “modification log”
that MySQL maintains for compressed data. If the updates
predominantly change non-indexed columns or those containing
BLOBs or large strings that happen to be stored
“off-page”, the overhead of compression may be
acceptable. If the only changes to a table are
INSERTs that use a monotonically increasing
primary key, and there are few secondary indexes, there is little
need to reorganize and recompress index pages. Since MySQL can
“delete-mark” and delete rows on compressed pages
“in place” by modifying uncompressed data,
DELETE operations on a table are relatively
For some environments, the time it takes to load data can be as important as run-time retrieval. Especially in data warehouse environments, many tables may be read-only or read-mostly. In those cases, it might or might not be acceptable to pay the price of compression in terms of increased load time, unless the resulting savings in fewer disk reads or in storage cost is significant.
Fundamentally, compression works best when the CPU time is available for compressing and uncompressing data. Thus, if your workload is I/O bound, rather than CPU-bound, you might find that compression can improve overall performance. When you test your application performance with different compression configurations, test on a platform similar to the planned configuration of the production system.
Reading and writing database pages from and to disk is the slowest aspect of system performance. Compression attempts to reduce I/O by using CPU time to compress and uncompress data, and is most effective when I/O is a relatively scarce resource compared to processor cycles.
This is often especially the case when running in a multi-user environment with fast, multi-core CPUs. When a page of a compressed table is in memory, MySQL often uses additional memory, typically 16KB, in the buffer pool for an uncompressed copy of the page. The adaptive LRU algorithm attempts to balance the use of memory between compressed and uncompressed pages to take into account whether the workload is running in an I/O-bound or CPU-bound manner. Still, a configuration with more memory dedicated to the buffer pool tends to run better when using compressed tables than a configuration where memory is highly constrained.
The optimal setting of the compressed page size depends on the type and distribution of data that the table and its indexes contain. The compressed page size should always be bigger than the maximum record size, or operations may fail as noted in Section 18.104.22.168, “ Compression of B-Tree Pages ”.
Setting the compressed page size too large wastes some space, but the pages do not have to be compressed as often. If the compressed page size is set too small, inserts or updates may require time-consuming recompression, and the B-tree nodes may have to be split more frequently, leading to bigger data files and less efficient indexing.
Typically, you set the compressed page size to 8K or 4K bytes.
Given that the maximum row size for an InnoDB table is around 8K,
KEY_BLOCK_SIZE=8 is usually a safe choice.