This section provides a conceptual overview of partitioning in MySQL 5.6.
For information on partitioning restrictions and feature limitations, see Section 19.6, “Restrictions and Limitations on Partitioning”.
The SQL standard does not provide much in the way of guidance
regarding the physical aspects of data storage. The SQL language
itself is intended to work independently of any data structures or
media underlying the schemas, tables, rows, or columns with which
it works. Nonetheless, most advanced database management systems
have evolved some means of determining the physical location to be
used for storing specific pieces of data in terms of the file
system, hardware or even both. In MySQL, the
InnoDB storage engine has long supported the
notion of a tablespace, and the MySQL Server, even prior to the
introduction of partitioning, could be configured to employ
different physical directories for storing different databases
(see Section 22.214.171.124, “Using Symbolic Links”, for an explanation of how
this is done).
Partitioning takes this notion a step further, by enabling you to distribute portions of individual tables across a file system according to rules which you can set largely as needed. In effect, different portions of a table are stored as separate tables in different locations. The user-selected rule by which the division of data is accomplished is known as a partitioning function, which in MySQL can be the modulus, simple matching against a set of ranges or value lists, an internal hashing function, or a linear hashing function. The function is selected according to the partitioning type specified by the user, and takes as its parameter the value of a user-supplied expression. This expression can be a column value, a function acting on one or more column values, or a set of one or more column values, depending on the type of partitioning that is used.
In the case of
partitioning, the value of the partitioning column is passed to
the partitioning function, which returns an integer value
representing the number of the partition in which that particular
record should be stored. This function must be nonconstant and
nonrandom. It may not contain any queries, but may use an SQL
expression that is valid in MySQL, as long as that expression
NULL or an integer
intval such that
MAXVALUE is used to represent the least upper
bound for the type of integer in question.
-MAXVALUE represents the greatest lower bound.)
RANGE COLUMNS, and
COLUMNS partitioning, the partitioning expression
consists of a list of one or more columns.
partitioning, the partitioning function is supplied by MySQL.
For more information about permitted partitioning column types and partitioning functions, see Section 19.2, “Partitioning Types”, as well as Section 13.1.17, “CREATE TABLE Syntax”, which provides partitioning syntax descriptions and additional examples. For information about restrictions on partitioning functions, see Section 19.6.3, “Partitioning Limitations Relating to Functions”.
This is known as horizontal partitioning—that is, different rows of a table may be assigned to different physical partitions. MySQL 5.6 does not support vertical partitioning, in which different columns of a table are assigned to different physical partitions. There are not at this time any plans to introduce vertical partitioning into MySQL 5.6.
For information about determining whether your MySQL Server binary supports user-defined partitioning, see Chapter 19, Partitioning.
For creating partitioned tables, you can use most storage engines
that are supported by your MySQL server; the MySQL partitioning
engine runs in a separate layer and can interact with any of
these. In MySQL 5.6, all partitions of the same
partitioned table must use the same storage engine; for
example, you cannot use
MyISAM for one
InnoDB for another. However,
there is nothing preventing you from using different storage
engines for different partitioned tables on the same MySQL server
or even in the same database.
MySQL partitioning cannot be used with the
FEDERATED storage engines.
KEY is possible with
but other types of user-defined partitioning are not supported for
tables using this storage engine. In addition, an
NDB table that employs user-defined
partitioning must have an explicit primary key, and any columns
referenced in the table's partitioning expression must be
part of the primary key. However, if no columns are listed in the
PARTITION BY KEY or
LINEAR KEY clause of the
TABLE statement used to create or modify a
NDB table, then the
table is not required to have an explicit primary key. For more
Section 126.96.36.199, “Noncompliance with SQL Syntax in MySQL Cluster”.
To employ a particular storage engine for a partitioned table, it
is necessary only to use the
option just as you would for a nonpartitioned table. However, you
should keep in mind that
[STORAGE] ENGINE (and
other table options) need to be listed before
any partitioning options are used in a
TABLE statement. This example shows how to create a
table that is partitioned by hash into 6 partitions and which uses
InnoDB storage engine:
CREATE TABLE ti (id INT, amount DECIMAL(7,2), tr_date DATE) ENGINE=INNODB PARTITION BY HASH( MONTH(tr_date) ) PARTITIONS 6;
PARTITION clause can include a
[STORAGE] ENGINE option, but in MySQL
5.6 this has no effect.
Partitioning applies to all data and indexes of a table; you cannot partition only the data and not the indexes, or vice versa, nor can you partition only a portion of the table.
Data and indexes for each partition can be assigned to a specific
directory using the
DATA DIRECTORY and
INDEX DIRECTORY options for the
PARTITION clause of the
CREATE TABLE statement used to
create the partitioned table.
DATA DIRECTORY and
DIRECTORY are not supported for individual partitions or
MyISAM tables on Windows. They
are supported for individual partitions and subpartitions of
InnoDB tables (as on all platforms).
MIN_ROWS can be used to determine the maximum
and minimum numbers of rows, respectively, that can be stored in
each partition. The
MAX_ROWS option can be
useful for causing MySQL Cluster tables to be created with extra
partitions, thus allowing for greater storage of hash indexes. See
the documentation for the
DataMemory data node
configuration parameter, as well as
Section 18.1.2, “MySQL Cluster Nodes, Node Groups, Replicas, and Partitions”, for more
Some advantages of partitioning are listed here:
Partitioning makes it possible to store more data in one table than can be held on a single disk or file system partition.
Data that loses its usefulness can often be easily removed from a partitioned table by dropping the partition (or partitions) containing only that data. Conversely, the process of adding new data can in some cases be greatly facilitated by adding one or more new partitions for storing specifically that data.
Some queries can be greatly optimized in virtue of the fact
that data satisfying a given
can be stored only on one or more partitions, which
automatically excludes any remaining partitions from the
search. Because partitions can be altered after a partitioned
table has been created, you can reorganize your data to
enhance frequent queries that may not have been often used
when the partitioning scheme was first set up. This ability to
exclude non-matching partitions (and thus any rows they
contain) is often referred to as partition
pruning. For more information, see
Section 19.4, “Partition Pruning”.
In addition, MySQL 5.6 supports explicit
partition selection for queries. For example,
SELECT * FROM t
PARTITION (p0,p1) WHERE c < 5 selects only those
rows in partitions
p1 that match the
condition. In this case, MySQL does not check any other
partitions of table
t; this can greatly
speed up queries when you already know which partition or
partitions you wish to examine. Partition selection is also
supported for the data modification statements
LOAD XML. See the descriptions
of these statements for more information and examples.
Other benefits usually associated with partitioning include those in the following list. These features are not currently implemented in MySQL Partitioning, but are high on our list of priorities.
Queries involving aggregate functions such as
COUNT() can easily be
parallelized. A simple example of such a query might be
SELECT salesperson_id, COUNT(orders) as order_total
FROM sales GROUP BY salesperson_id;. By
“parallelized,” we mean that the query can be run
simultaneously on each partition, and the final result
obtained merely by summing the results obtained for all
Achieving greater query throughput in virtue of spreading data seeks over multiple disks.
Be sure to check this section and chapter frequently for updates as MySQL Partitioning development continues.