This section describes some internal implementation details about compression for InnoDB tables. The information presented here may be helpful in tuning for performance, but is not necessary to know for basic use of compression.
Some operating systems implement compression at the file system level. Files are typically divided into fixed-size blocks that are compressed into variable-size blocks, which easily leads into fragmentation. Every time something inside a block is modified, the whole block is recompressed before it is written to disk. These properties make this compression technique unsuitable for use in an update-intensive database system.
MySQL implements compression with the help of the well-known zlib library, which implements the LZ77 compression algorithm. This compression algorithm is mature, robust, and efficient in both CPU utilization and in reduction of data size. The algorithm is “lossless”, so that the original uncompressed data can always be reconstructed from the compressed form. LZ77 compression works by finding sequences of data that are repeated within the data to be compressed. The patterns of values in your data determine how well it compresses, but typical user data often compresses by 50% or more.
Unlike compression performed by an application, or compression
features of some other database management systems, InnoDB
compression applies both to user data and to indexes. In many
cases, indexes can constitute 40-50% or more of the total database
size, so this difference is significant. When compression is
working well for a data set, the size of the InnoDB data files
.idb files) is 25% to 50% of the
uncompressed size or possibly smaller. Depending on the
workload, this smaller
database can in turn lead to a reduction in I/O, and an increase
in throughput, at a modest cost in terms of increased CPU
utilization. You can adjust the balance between compression level
and CPU overhead by modifying the
All user data in InnoDB tables is stored in pages comprising a B-tree index (the clustered index). In some other database systems, this type of index is called an “index-organized table”. Each row in the index node contains the values of the (user-specified or system-generated) primary key and all the other columns of the table.
Secondary indexes in InnoDB tables are also B-trees, containing pairs of values: the index key and a pointer to a row in the clustered index. The pointer is in fact the value of the primary key of the table, which is used to access the clustered index if columns other than the index key and primary key are required. Secondary index records must always fit on a single B-tree page.
The compression of B-tree nodes (of both clustered and secondary
indexes) is handled differently from compression of
overflow pages used to
TEXT columns, as explained in the following
Because they are frequently updated, B-tree pages require special treatment. It is important to minimize the number of times B-tree nodes are split, as well as to minimize the need to uncompress and recompress their content.
One technique MySQL uses is to maintain some system information in the B-tree node in uncompressed form, thus facilitating certain in-place updates. For example, this allows rows to be delete-marked and deleted without any compression operation.
In addition, MySQL attempts to avoid unnecessary uncompression and recompression of index pages when they are changed. Within each B-tree page, the system keeps an uncompressed “modification log” to record changes made to the page. Updates and inserts of small records may be written to this modification log without requiring the entire page to be completely reconstructed.
When the space for the modification log runs out, InnoDB uncompresses the page, applies the changes and recompresses the page. If recompression fails (a situation known as a compression failure), the B-tree nodes are split and the process is repeated until the update or insert succeeds.
To avoid frequent compression failures in write-intensive
workloads, such as for OLTP
applications, MySQL sometimes reserves some empty space (padding)
in the page, so that the modification log fills up sooner and the
page is recompressed while there is still enough room to avoid
splitting it. The amount of padding space left in each page varies
as the system keeps track of the frequency of page splits. On a
busy server doing frequent writes to compressed tables, you can
configuration options to fine-tune this mechanism.
Generally, MySQL requires that each B-tree page in an InnoDB table
can accommodate at least two records. For compressed tables, this
requirement has been relaxed. Leaf pages of B-tree nodes (whether
of the primary key or secondary indexes) only need to accommodate
one record, but that record must fit, in uncompressed form, in the
per-page modification log. If
ON, MySQL checks the maximum row size during
CREATE TABLE or
CREATE INDEX. If the row does not
fit, the following error message is issued:
Too big row.
If you create a table when
innodb_strict_mode is OFF, and a
statement attempts to create an index entry that does not fit in
the size of the compressed page, the operation fails with
ERROR 42000: Row size too large. (This error
message does not name the index for which the record is too large,
or mention the length of the index record or the maximum record
size on that particular index page.) To solve this problem,
rebuild the table with
and select a larger compressed page size
KEY_BLOCK_SIZE), shorten any column prefix
indexes, or disable compression entirely with
In an InnoDB table,
TEXT columns that are not part of
the primary key may be stored on separately allocated
overflow pages. We refer
to these columns as off-page
columns. Their values are stored on singly-linked lists of
For tables created in
ROW_FORMAT=COMPRESSED, the values of
VARCHAR columns may be stored fully
off-page, depending on their length and the length of the entire
row. For columns that are stored off-page, the clustered index
record only contains 20-byte pointers to the overflow pages, one
per column. Whether any columns are stored off-page depends on the
page size and the total size of the row. When the row is too long
to fit entirely within the page of the clustered index, MySQL
chooses the longest columns for off-page storage until the row
fits on the clustered index page. As noted above, if a row does
not fit by itself on a compressed page, an error occurs.
Tables created in older versions of MySQL use the
Antelope file format, which
ROW_FORMAT=COMPACT. In these formats, MySQL
stores the first 768 bytes of
TEXT columns in the clustered index
record along with the primary key. The 768-byte prefix is followed
by a 20-byte pointer to the overflow pages that contain the rest
of the column value.
When a table is in
COMPRESSED format, all data
written to overflow pages is compressed “as is”; that
is, MySQL applies the zlib compression algorithm to the entire
data item. Other than the data, compressed overflow pages contain
an uncompressed header and trailer comprising a page checksum and
a link to the next overflow page, among other things. Therefore,
very significant storage savings can be obtained for longer
VARCHAR columns if the data is highly
compressible, as is often the case with text data. Image data,
JPEG, is typically already compressed
and so does not benefit much from being stored in a compressed
table; the double compression can waste CPU cycles for little or
no space savings.
The overflow pages are of the same size as other pages. A row containing ten columns stored off-page occupies ten overflow pages, even if the total length of the columns is only 8K bytes. In an uncompressed table, ten uncompressed overflow pages occupy 160K bytes. In a compressed table with an 8K page size, they occupy only 80K bytes. Thus, it is often more efficient to use compressed table format for tables with long column values.
Using a 16K compressed page size can reduce storage and I/O costs
TEXT columns, because such data
often compress well, and might therefore require fewer overflow
pages, even though the B-tree nodes themselves take as many pages
as in the uncompressed form.
In a compressed
InnoDB table, every compressed
page (whether 1K, 2K, 4K or 8K) corresponds to an uncompressed
page of 16K bytes (or a smaller size if
innodb_page_size is set). To
access the data in a page, MySQL reads the compressed page from
disk if it is not already in the
buffer pool, then
uncompresses the page to its original form. This section describes
InnoDB manages the buffer pool with respect
to pages of compressed tables.
To minimize I/O and to reduce the need to uncompress a page, at times the buffer pool contains both the compressed and uncompressed form of a database page. To make room for other required database pages, MySQL can evict from the buffer pool an uncompressed page, while leaving the compressed page in memory. Or, if a page has not been accessed in a while, the compressed form of the page might be written to disk, to free space for other data. Thus, at any given time, the buffer pool might contain both the compressed and uncompressed forms of the page, or only the compressed form of the page, or neither.
MySQL keeps track of which pages to keep in memory and which to evict using a least-recently-used (LRU) list, so that hot (frequently accessed) data tends to stay in memory. When compressed tables are accessed, MySQL uses an adaptive LRU algorithm to achieve an appropriate balance of compressed and uncompressed pages in memory. This adaptive algorithm is sensitive to whether the system is running in an I/O-bound or CPU-bound manner. The goal is to avoid spending too much processing time uncompressing pages when the CPU is busy, and to avoid doing excess I/O when the CPU has spare cycles that can be used for uncompressing compressed pages (that may already be in memory). When the system is I/O-bound, the algorithm prefers to evict the uncompressed copy of a page rather than both copies, to make more room for other disk pages to become memory resident. When the system is CPU-bound, MySQL prefers to evict both the compressed and uncompressed page, so that more memory can be used for “hot” pages and reducing the need to uncompress data in memory only in compressed form.
Before a compressed page is written to a
data file, MySQL writes a
copy of the page to the redo log (if it has been recompressed
since the last time it was written to the database). This is done
to ensure that redo logs are usable for
crash recovery, even in
the unlikely case that the
zlib library is
upgraded and that change introduces a compatibility problem with
the compressed data. Therefore, some increase in the size of
log files, or a need for more
frequent checkpoints, can
be expected when using compression. The amount of increase in the
log file size or checkpoint frequency depends on the number of
times compressed pages are modified in a way that requires
reorganization and recompression.
Note that compressed tables use a different file format for the redo log and the per-table tablespaces than in MySQL 5.1 and earlier. The MySQL Enterprise Backup product supports this latest Barracuda file format for compressed InnoDB tables.