This section describes some internal implementation details about compression in InnoDB. 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.
InnoDB 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
All user data in InnoDB 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 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 store long
TEXT columns, as
explained in the following sections.
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 InnoDB 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, InnoDB 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, the B-tree nodes are split and the process is repeated until the update or insert succeeds.
Generally, InnoDB requires that each B-tree page 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. Starting with InnoDB storage engine version 1.0.2, and
if InnoDB strict mode is
InnoDB storage engine 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 a clustered index,
that are not part of the primary key may be stored on separately
allocated (“overflow”) pages. We call these
off-page columns whose
values are stored on singly-linked lists of overflow pages.
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, InnoDB 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 previous versions of InnoDB use the Antelope
file format, which supports only
ROW_FORMAT=COMPACT. In these formats, InnoDB
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, InnoDB 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 (but not
previously compressed images).
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. To access the data in a page, InnoDB reads the compressed page from disk if it is not already in the buffer pool, then uncompresses the page to its original 16K byte form. This section describes how 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, InnoDB may “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 may be written to disk, to free space for other data. Thus, at any given time, the buffer pool may contain both the compressed and uncompressed forms of the page, or only the compressed form of the page, or neither.
InnoDB keeps track of which pages to keep in memory and which to evict using a least-recently-used (LRU) list, so that “hot” or frequently accessed data tends to stay in memory. When compressed tables are accessed, InnoDB 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, InnoDB 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 database file, InnoDB 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 will always be usable, even if a future version of InnoDB uses a slightly different compression algorithm. 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 the redo log file format (and the database file format) are different from previous releases when using compression. The MySQL Enterprise Backup product does support this latest Barracuda file format for compressed InnoDB tables. The older InnoDB Hot Backup product can only back up tables using the file format Antelope, and thus does not support InnoDB tables that use compression.