Chapter 21 Partitioning

This chapter discusses MySQL's implementation of user-defined partitioning.

Note

As of MySQL 5.7.17, the generic partitioning handler in the MySQL server is deprecated, and is removed in MySQL 8.0, when the storage engine used for a given table is expected to provide its own (native) partitioning handler. Currently, only the InnoDB and NDB storage engines do.

Use of tables with nonnative partitioning results in an ER_WARN_DEPRECATED_SYNTAX warning. Also, the server performs a check at startup to identify tables that use nonnative partitioning; for any found, the server writes a message to its error log. To disable this check, use the --disable-partition-engine-check option.

To prepare for migration to MySQL 8.0, any table with nonnative partitioning should be changed to use an engine that provides native partitioning, or be made nonpartitioned. For example, to change a table to InnoDB, execute this statement:

ALTER TABLE table_name ENGINE = INNODB;

You can determine whether your MySQL Server supports partitioning by checking the output of the SHOW PLUGINS statement, like this:

mysql> SHOW PLUGINS;
+------------+----------+----------------+---------+---------+
| Name       | Status   | Type           | Library | License |
+------------+----------+----------------+---------+---------+
| binlog     | ACTIVE   | STORAGE ENGINE | NULL    | GPL     |
| partition  | ACTIVE   | STORAGE ENGINE | NULL    | GPL     |
| ARCHIVE    | ACTIVE   | STORAGE ENGINE | NULL    | GPL     |
| BLACKHOLE  | ACTIVE   | STORAGE ENGINE | NULL    | GPL     |
| CSV        | ACTIVE   | STORAGE ENGINE | NULL    | GPL     |
| FEDERATED  | DISABLED | STORAGE ENGINE | NULL    | GPL     |
| MEMORY     | ACTIVE   | STORAGE ENGINE | NULL    | GPL     |
| InnoDB     | ACTIVE   | STORAGE ENGINE | NULL    | GPL     |
| MRG_MYISAM | ACTIVE   | STORAGE ENGINE | NULL    | GPL     |
| MyISAM     | ACTIVE   | STORAGE ENGINE | NULL    | GPL     |
| ndbcluster | DISABLED | STORAGE ENGINE | NULL    | GPL     |
+------------+----------+----------------+---------+---------+
11 rows in set (0.00 sec)

You can also check the INFORMATION_SCHEMA.PLUGINS table with a query similar to this one:

mysql> SELECT
    ->     PLUGIN_NAME as Name,
    ->     PLUGIN_VERSION as Version,
    ->     PLUGIN_STATUS as Status
    -> FROM INFORMATION_SCHEMA.PLUGINS
    -> WHERE PLUGIN_TYPE='STORAGE ENGINE';
+--------------------+---------+--------+
| Name               | Version | Status |
+--------------------+---------+--------+
| binlog             | 1.0     | ACTIVE |
| CSV                | 1.0     | ACTIVE |
| MEMORY             | 1.0     | ACTIVE |
| MRG_MYISAM         | 1.0     | ACTIVE |
| MyISAM             | 1.0     | ACTIVE |
| PERFORMANCE_SCHEMA | 0.1     | ACTIVE |
| BLACKHOLE          | 1.0     | ACTIVE |
| ARCHIVE            | 3.0     | ACTIVE |
| InnoDB             | 5.7     | ACTIVE |
| partition          | 1.0     | ACTIVE |
+--------------------+---------+--------+
10 rows in set (0.00 sec)

In either case, if you do not see the partition plugin listed with the value ACTIVE for the Status column in the output (shown in bold text in each of the examples just given), then your version of MySQL was not built with partitioning support.

MySQL 5.7 Community binaries provided by Oracle include partitioning support. For information about partitioning support offered in MySQL Enterprise Edition binaries, see Chapter 28, MySQL Enterprise Edition.

To enable partitioning if you are compiling MySQL 5.7 from source, the build must be configured with the -DWITH_PARTITION_STORAGE_ENGINE option. For more information, see Section 2.9, “Installing MySQL from Source”.

If your MySQL binary is built with partitioning support, nothing further needs to be done to enable it (for example, no special entries are required in your my.cnf file).

If you want to disable partitioning support, you can start the MySQL Server with the --skip-partition option. When partitioning support is disabled, you can see any existing partitioned tables and drop them (although doing this is not advised), but you cannot otherwise manipulate them or access their data.

See Section 21.1, “Overview of Partitioning in MySQL”, for an introduction to partitioning and partitioning concepts.

MySQL supports several types of partitioning as well as subpartitioning; see Section 21.2, “Partitioning Types”, and Section 21.2.6, “Subpartitioning”.

Section 21.3, “Partition Management”, covers methods of adding, removing, and altering partitions in existing partitioned tables.

Section 21.3.4, “Maintenance of Partitions”, discusses table maintenance commands for use with partitioned tables.

The PARTITIONS table in the INFORMATION_SCHEMA database provides information about partitions and partitioned tables. See Section 23.15, “The INFORMATION_SCHEMA PARTITIONS Table”, for more information; for some examples of queries against this table, see Section 21.2.7, “How MySQL Partitioning Handles NULL”.

For known issues with partitioning in MySQL 5.7, see Section 21.6, “Restrictions and Limitations on Partitioning”.

You may also find the following resources to be useful when working with partitioned tables.

Additional Resources.  Other sources of information about user-defined partitioning in MySQL include the following:

  • MySQL Partitioning Forum

    This is the official discussion forum for those interested in or experimenting with MySQL Partitioning technology. It features announcements and updates from MySQL developers and others. It is monitored by members of the Partitioning Development and Documentation Teams.

  • Mikael Ronström's Blog

    MySQL Partitioning Architect and Lead Developer Mikael Ronström frequently posts articles here concerning his work with MySQL Partitioning and MySQL Cluster.

  • PlanetMySQL

    A MySQL news site featuring MySQL-related blogs, which should be of interest to anyone using my MySQL. We encourage you to check here for links to blogs kept by those working with MySQL Partitioning, or to have your own blog added to those covered.

MySQL 5.7 binaries are available from http://dev.mysql.com/downloads/mysql/5.7.html. However, for the latest partitioning bugfixes and feature additions, you can obtain the source from our GitHub repository. To enable partitioning, the build must be configured with the -DWITH_PARTITION_STORAGE_ENGINE option. For more information about building MySQL, see Section 2.9, “Installing MySQL from Source”. If you have problems compiling a partitioning-enabled MySQL 5.7 build, check the MySQL Partitioning Forum and ask for assistance there if you do not find a solution to your problem already posted.

21.1 Overview of Partitioning in MySQL

This section provides a conceptual overview of partitioning in MySQL 5.7.

For information on partitioning restrictions and feature limitations, see Section 21.6, “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 9.12.3, “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 RANGE, LIST, and [LINEAR] HASH 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 returns either NULL or an integer intval such that

-MAXVALUE <= intval <= MAXVALUE

(MAXVALUE is used to represent the least upper bound for the type of integer in question. -MAXVALUE represents the greatest lower bound.)

For [LINEAR] KEY, RANGE COLUMNS, and LIST COLUMNS partitioning, the partitioning expression consists of a list of one or more columns.

For [LINEAR] KEY partitioning, the partitioning function is supplied by MySQL.

For more information about permitted partitioning column types and partitioning functions, see Section 21.2, “Partitioning Types”, as well as Section 14.1.18, “CREATE TABLE Syntax”, which provides partitioning syntax descriptions and additional examples. For information about restrictions on partitioning functions, see Section 21.6.3, “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 5.7 does not support vertical partitioning, in which different columns of a table are assigned to different physical partitions. There are no plans at this time to introduce vertical partitioning into MySQL.

For information about determining whether your MySQL Server binary supports user-defined partitioning, see Chapter 21, 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 5.7, all partitions of the same partitioned table must use the same storage engine; for example, you cannot use MyISAM for one partition and 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 MERGE, CSV, or FEDERATED storage engines.

Partitioning by KEY or LINEAR KEY is possible with NDB, 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 PARTITION BY LINEAR KEY clause of the CREATE TABLE or ALTER TABLE statement used to create or modify a user-partitioned NDB table, then the table is not required to have an explicit primary key. For more information, see Section 20.1.6.1, “Noncompliance with SQL Syntax in NDB Cluster”.

To employ a particular storage engine for a partitioned table, it is necessary only to use the [STORAGE] ENGINE 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 CREATE TABLE statement. This example shows how to create a table that is partitioned by hash into 6 partitions and which uses the 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;

Each PARTITION clause can include a [STORAGE] ENGINE option, but in MySQL 5.7 this has no effect.

Important

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 INDEX DIRECTORY are not supported for individual partitions or subpartitions of MyISAM tables on Windows. They are supported for individual partitions and subpartitions of InnoDB tables (as on all platforms).

All columns used in the table's partitioning expression must be part of every unique key that the table may have, including any primary key. This means that a table such as this one, created by the following SQL statement, cannot be partitioned:

CREATE TABLE tnp (
    id INT NOT NULL AUTO_INCREMENT,
    ref BIGINT NOT NULL,
    name VARCHAR(255),
    PRIMARY KEY pk (id),
    UNIQUE KEY uk (name)
);

Because the keys pk and uk have no columns in common, there are no columns available for use in a partitioning expression. Possible workarounds in this situation include adding the name column to the table's primary key, adding the id column to uk, or simply removing the unique key altogether. See Section 21.6.1, “Partitioning Keys, Primary Keys, and Unique Keys”, for more information.

In addition, MAX_ROWS and MIN_ROWS can be used to determine the maximum and minimum numbers of rows, respectively, that can be stored in each partition. See Section 21.3, “Partition Management”, for more information on these options.

The MAX_ROWS option can also be useful for creating MySQL Cluster tables 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 20.1.2, “NDB Cluster Nodes, Node Groups, Replicas, and Partitions”, for more information.

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 WHERE clause 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 21.4, “Partition Pruning”.

    In addition, MySQL 5.7 supports explicit partition selection for queries. For example, SELECT * FROM t PARTITION (p0,p1) WHERE c < 5 selects only those rows in partitions p0 and p1 that match the WHERE 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 DELETE, INSERT, REPLACE, UPDATE, and LOAD DATA, 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 SUM() and 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 partitions.

  • 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.

21.2 Partitioning Types

This section discusses the types of partitioning which are available in MySQL 5.7. These include the types listed here:

  • RANGE partitioning.  This type of partitioning assigns rows to partitions based on column values falling within a given range. See Section 21.2.1, “RANGE Partitioning”. For information about an extension to this type, RANGE COLUMNS, see Section 21.2.3.1, “RANGE COLUMNS partitioning”.

  • LIST partitioning.  Similar to partitioning by RANGE, except that the partition is selected based on columns matching one of a set of discrete values. See Section 21.2.2, “LIST Partitioning”. For information about an extension to this type, LIST COLUMNS, see Section 21.2.3.2, “LIST COLUMNS partitioning”.

  • HASH partitioning.  With this type of partitioning, a partition is selected based on the value returned by a user-defined expression that operates on column values in rows to be inserted into the table. The function may consist of any expression valid in MySQL that yields a nonnegative integer value. An extension to this type, LINEAR HASH, is also available. See Section 21.2.4, “HASH Partitioning”.

  • KEY partitioning.  This type of partitioning is similar to partitioning by HASH, except that only one or more columns to be evaluated are supplied, and the MySQL server provides its own hashing function. These columns can contain other than integer values, since the hashing function supplied by MySQL guarantees an integer result regardless of the column data type. An extension to this type, LINEAR KEY, is also available. See Section 21.2.5, “KEY Partitioning”.

A very common use of database partitioning is to segregate data by date. Some database systems support explicit date partitioning, which MySQL does not implement in 5.7. However, it is not difficult in MySQL to create partitioning schemes based on DATE, TIME, or DATETIME columns, or based on expressions making use of such columns.

When partitioning by KEY or LINEAR KEY, you can use a DATE, TIME, or DATETIME column as the partitioning column without performing any modification of the column value. For example, this table creation statement is perfectly valid in MySQL:

CREATE TABLE members (
    firstname VARCHAR(25) NOT NULL,
    lastname VARCHAR(25) NOT NULL,
    username VARCHAR(16) NOT NULL,
    email VARCHAR(35),
    joined DATE NOT NULL
)
PARTITION BY KEY(joined)
PARTITIONS 6;

In MySQL 5.7, it is also possible to use a DATE or DATETIME column as the partitioning column using RANGE COLUMNS and LIST COLUMNS partitioning.

MySQL's other partitioning types, however, require a partitioning expression that yields an integer value or NULL. If you wish to use date-based partitioning by RANGE, LIST, HASH, or LINEAR HASH, you can simply employ a function that operates on a DATE, TIME, or DATETIME column and returns such a value, as shown here:

CREATE TABLE members (
    firstname VARCHAR(25) NOT NULL,
    lastname VARCHAR(25) NOT NULL,
    username VARCHAR(16) NOT NULL,
    email VARCHAR(35),
    joined DATE NOT NULL
)
PARTITION BY RANGE( YEAR(joined) ) (
    PARTITION p0 VALUES LESS THAN (1960),
    PARTITION p1 VALUES LESS THAN (1970),
    PARTITION p2 VALUES LESS THAN (1980),
    PARTITION p3 VALUES LESS THAN (1990),
    PARTITION p4 VALUES LESS THAN MAXVALUE
);

Additional examples of partitioning using dates may be found in the following sections of this chapter:

For more complex examples of date-based partitioning, see the following sections:

MySQL partitioning is optimized for use with the TO_DAYS(), YEAR(), and TO_SECONDS() functions. However, you can use other date and time functions that return an integer or NULL, such as WEEKDAY(), DAYOFYEAR(), or MONTH(). See Section 13.7, “Date and Time Functions”, for more information about such functions.

It is important to remember—regardless of the type of partitioning that you use—that partitions are always numbered automatically and in sequence when created, starting with 0. When a new row is inserted into a partitioned table, it is these partition numbers that are used in identifying the correct partition. For example, if your table uses 4 partitions, these partitions are numbered 0, 1, 2, and 3. For the RANGE and LIST partitioning types, it is necessary to ensure that there is a partition defined for each partition number. For HASH partitioning, the user-supplied expression must evaluate to an integer value greater than 0. For KEY partitioning, this issue is taken care of automatically by the hashing function which the MySQL server employs internally.

Names of partitions generally follow the rules governing other MySQL identifiers, such as those for tables and databases. However, you should note that partition names are not case-sensitive. For example, the following CREATE TABLE statement fails as shown:

mysql> CREATE TABLE t2 (val INT)
    -> PARTITION BY LIST(val)(
    ->     PARTITION mypart VALUES IN (1,3,5),
    ->     PARTITION MyPart VALUES IN (2,4,6)
    -> );
ERROR 1488 (HY000): Duplicate partition name mypart

Failure occurs because MySQL sees no difference between the partition names mypart and MyPart.

When you specify the number of partitions for the table, this must be expressed as a positive, nonzero integer literal with no leading zeros, and may not be an expression such as 0.8E+01 or 6-2, even if it evaluates to an integer value. Decimal fractions are not permitted.

In the sections that follow, we do not necessarily provide all possible forms for the syntax that can be used for creating each partition type; this information may be found in Section 14.1.18, “CREATE TABLE Syntax”.

21.2.1 RANGE Partitioning

A table that is partitioned by range is partitioned in such a way that each partition contains rows for which the partitioning expression value lies within a given range. Ranges should be contiguous but not overlapping, and are defined using the VALUES LESS THAN operator. For the next few examples, suppose that you are creating a table such as the following to hold personnel records for a chain of 20 video stores, numbered 1 through 20:

CREATE TABLE employees (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30),
    hired DATE NOT NULL DEFAULT '1970-01-01',
    separated DATE NOT NULL DEFAULT '9999-12-31',
    job_code INT NOT NULL,
    store_id INT NOT NULL
);
Note

The employees table used here has no primary or unique keys. While the examples work as shown for purposes of the present discussion, you should keep in mind that tables are extremely likely in practice to have primary keys, unique keys, or both, and that allowable choices for partitioning columns depend on the columns used for these keys, if any are present. For a discussion of these issues, see Section 21.6.1, “Partitioning Keys, Primary Keys, and Unique Keys”.

This table can be partitioned by range in a number of ways, depending on your needs. One way would be to use the store_id column. For instance, you might decide to partition the table 4 ways by adding a PARTITION BY RANGE clause as shown here:

CREATE TABLE employees (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30),
    hired DATE NOT NULL DEFAULT '1970-01-01',
    separated DATE NOT NULL DEFAULT '9999-12-31',
    job_code INT NOT NULL,
    store_id INT NOT NULL
)
PARTITION BY RANGE (store_id) (
    PARTITION p0 VALUES LESS THAN (6),
    PARTITION p1 VALUES LESS THAN (11),
    PARTITION p2 VALUES LESS THAN (16),
    PARTITION p3 VALUES LESS THAN (21)
);

In this partitioning scheme, all rows corresponding to employees working at stores 1 through 5 are stored in partition p0, to those employed at stores 6 through 10 are stored in partition p1, and so on. Note that each partition is defined in order, from lowest to highest. This is a requirement of the PARTITION BY RANGE syntax; you can think of it as being analogous to a series of if ... elseif ... statements in C or Java in this regard.

It is easy to determine that a new row containing the data (72, 'Mitchell', 'Wilson', '1998-06-25', NULL, 13) is inserted into partition p2, but what happens when your chain adds a 21st store? Under this scheme, there is no rule that covers a row whose store_id is greater than 20, so an error results because the server does not know where to place it. You can keep this from occurring by using a catchall VALUES LESS THAN clause in the CREATE TABLE statement that provides for all values greater than the highest value explicitly named:

CREATE TABLE employees (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30),
    hired DATE NOT NULL DEFAULT '1970-01-01',
    separated DATE NOT NULL DEFAULT '9999-12-31',
    job_code INT NOT NULL,
    store_id INT NOT NULL
)
PARTITION BY RANGE (store_id) (
    PARTITION p0 VALUES LESS THAN (6),
    PARTITION p1 VALUES LESS THAN (11),
    PARTITION p2 VALUES LESS THAN (16),
    PARTITION p3 VALUES LESS THAN MAXVALUE
);
Note

Another way to avoid an error when no matching value is found is to use the IGNORE keyword as part of the INSERT statement. For an example, see Section 21.2.2, “LIST Partitioning”. Also see Section 14.2.5, “INSERT Syntax”, for general information about IGNORE.

MAXVALUE represents an integer value that is always greater than the largest possible integer value (in mathematical language, it serves as a least upper bound). Now, any rows whose store_id column value is greater than or equal to 16 (the highest value defined) are stored in partition p3. At some point in the future—when the number of stores has increased to 25, 30, or more—you can use an ALTER TABLE statement to add new partitions for stores 21-25, 26-30, and so on (see Section 21.3, “Partition Management”, for details of how to do this).

In much the same fashion, you could partition the table based on employee job codes—that is, based on ranges of job_code column values. For example—assuming that two-digit job codes are used for regular (in-store) workers, three-digit codes are used for office and support personnel, and four-digit codes are used for management positions—you could create the partitioned table using the following statement:

CREATE TABLE employees (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30),
    hired DATE NOT NULL DEFAULT '1970-01-01',
    separated DATE NOT NULL DEFAULT '9999-12-31',
    job_code INT NOT NULL,
    store_id INT NOT NULL
)
PARTITION BY RANGE (job_code) (
    PARTITION p0 VALUES LESS THAN (100),
    PARTITION p1 VALUES LESS THAN (1000),
    PARTITION p2 VALUES LESS THAN (10000)
);

In this instance, all rows relating to in-store workers would be stored in partition p0, those relating to office and support staff in p1, and those relating to managers in partition p2.

It is also possible to use an expression in VALUES LESS THAN clauses. However, MySQL must be able to evaluate the expression's return value as part of a LESS THAN (<) comparison.

Rather than splitting up the table data according to store number, you can use an expression based on one of the two DATE columns instead. For example, let us suppose that you wish to partition based on the year that each employee left the company; that is, the value of YEAR(separated). An example of a CREATE TABLE statement that implements such a partitioning scheme is shown here:

CREATE TABLE employees (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30),
    hired DATE NOT NULL DEFAULT '1970-01-01',
    separated DATE NOT NULL DEFAULT '9999-12-31',
    job_code INT,
    store_id INT
)
PARTITION BY RANGE ( YEAR(separated) ) (
    PARTITION p0 VALUES LESS THAN (1991),
    PARTITION p1 VALUES LESS THAN (1996),
    PARTITION p2 VALUES LESS THAN (2001),
    PARTITION p3 VALUES LESS THAN MAXVALUE
);

In this scheme, for all employees who left before 1991, the rows are stored in partition p0; for those who left in the years 1991 through 1995, in p1; for those who left in the years 1996 through 2000, in p2; and for any workers who left after the year 2000, in p3.

It is also possible to partition a table by RANGE, based on the value of a TIMESTAMP column, using the UNIX_TIMESTAMP() function, as shown in this example:

CREATE TABLE quarterly_report_status (
    report_id INT NOT NULL,
    report_status VARCHAR(20) NOT NULL,
    report_updated TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
)
PARTITION BY RANGE ( UNIX_TIMESTAMP(report_updated) ) (
    PARTITION p0 VALUES LESS THAN ( UNIX_TIMESTAMP('2008-01-01 00:00:00') ),
    PARTITION p1 VALUES LESS THAN ( UNIX_TIMESTAMP('2008-04-01 00:00:00') ),
    PARTITION p2 VALUES LESS THAN ( UNIX_TIMESTAMP('2008-07-01 00:00:00') ),
    PARTITION p3 VALUES LESS THAN ( UNIX_TIMESTAMP('2008-10-01 00:00:00') ),
    PARTITION p4 VALUES LESS THAN ( UNIX_TIMESTAMP('2009-01-01 00:00:00') ),
    PARTITION p5 VALUES LESS THAN ( UNIX_TIMESTAMP('2009-04-01 00:00:00') ),
    PARTITION p6 VALUES LESS THAN ( UNIX_TIMESTAMP('2009-07-01 00:00:00') ),
    PARTITION p7 VALUES LESS THAN ( UNIX_TIMESTAMP('2009-10-01 00:00:00') ),
    PARTITION p8 VALUES LESS THAN ( UNIX_TIMESTAMP('2010-01-01 00:00:00') ),
    PARTITION p9 VALUES LESS THAN (MAXVALUE)
);

Any other expressions involving TIMESTAMP values are not permitted. (See Bug #42849.)

Range partitioning is particularly useful when one or more of the following conditions is true:

A variant on this type of partitioning is RANGE COLUMNS partitioning. Partitioning by RANGE COLUMNS makes it possible to employ multiple columns for defining partitioning ranges that apply both to placement of rows in partitions and for determining the inclusion or exclusion of specific partitions when performing partition pruning. See Section 21.2.3.1, “RANGE COLUMNS partitioning”, for more information.

Partitioning schemes based on time intervals.  If you wish to implement a partitioning scheme based on ranges or intervals of time in MySQL 5.7, you have two options:

  1. Partition the table by RANGE, and for the partitioning expression, employ a function operating on a DATE, TIME, or DATETIME column and returning an integer value, as shown here:

    CREATE TABLE members (
        firstname VARCHAR(25) NOT NULL,
        lastname VARCHAR(25) NOT NULL,
        username VARCHAR(16) NOT NULL,
        email VARCHAR(35),
        joined DATE NOT NULL
    )
    PARTITION BY RANGE( YEAR(joined) ) (
        PARTITION p0 VALUES LESS THAN (1960),
        PARTITION p1 VALUES LESS THAN (1970),
        PARTITION p2 VALUES LESS THAN (1980),
        PARTITION p3 VALUES LESS THAN (1990),
        PARTITION p4 VALUES LESS THAN MAXVALUE
    );
    

    In MySQL 5.7, it is also possible to partition a table by RANGE based on the value of a TIMESTAMP column, using the UNIX_TIMESTAMP() function, as shown in this example:

    CREATE TABLE quarterly_report_status (
        report_id INT NOT NULL,
        report_status VARCHAR(20) NOT NULL,
        report_updated TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
    )
    PARTITION BY RANGE ( UNIX_TIMESTAMP(report_updated) ) (
        PARTITION p0 VALUES LESS THAN ( UNIX_TIMESTAMP('2008-01-01 00:00:00') ),
        PARTITION p1 VALUES LESS THAN ( UNIX_TIMESTAMP('2008-04-01 00:00:00') ),
        PARTITION p2 VALUES LESS THAN ( UNIX_TIMESTAMP('2008-07-01 00:00:00') ),
        PARTITION p3 VALUES LESS THAN ( UNIX_TIMESTAMP('2008-10-01 00:00:00') ),
        PARTITION p4 VALUES LESS THAN ( UNIX_TIMESTAMP('2009-01-01 00:00:00') ),
        PARTITION p5 VALUES LESS THAN ( UNIX_TIMESTAMP('2009-04-01 00:00:00') ),
        PARTITION p6 VALUES LESS THAN ( UNIX_TIMESTAMP('2009-07-01 00:00:00') ),
        PARTITION p7 VALUES LESS THAN ( UNIX_TIMESTAMP('2009-10-01 00:00:00') ),
        PARTITION p8 VALUES LESS THAN ( UNIX_TIMESTAMP('2010-01-01 00:00:00') ),
        PARTITION p9 VALUES LESS THAN (MAXVALUE)
    );
    

    In MySQL 5.7, any other expressions involving TIMESTAMP values are not permitted. (See Bug #42849.)

    Note

    It is also possible in MySQL 5.7 to use UNIX_TIMESTAMP(timestamp_column) as a partitioning expression for tables that are partitioned by LIST. However, it is usually not practical to do so.

  2. Partition the table by RANGE COLUMNS, using a DATE or DATETIME column as the partitioning column. For example, the members table could be defined using the joined column directly, as shown here:

    CREATE TABLE members (
        firstname VARCHAR(25) NOT NULL,
        lastname VARCHAR(25) NOT NULL,
        username VARCHAR(16) NOT NULL,
        email VARCHAR(35),
        joined DATE NOT NULL
    )
    PARTITION BY RANGE COLUMNS(joined) (
        PARTITION p0 VALUES LESS THAN ('1960-01-01'),
        PARTITION p1 VALUES LESS THAN ('1970-01-01'),
        PARTITION p2 VALUES LESS THAN ('1980-01-01'),
        PARTITION p3 VALUES LESS THAN ('1990-01-01'),
        PARTITION p4 VALUES LESS THAN MAXVALUE
    );
    
Note

The use of partitioning columns employing date or time types other than DATE or DATETIME is not supported with RANGE COLUMNS.

21.2.2 LIST Partitioning

List partitioning in MySQL is similar to range partitioning in many ways. As in partitioning by RANGE, each partition must be explicitly defined. The chief difference between the two types of partitioning is that, in list partitioning, each partition is defined and selected based on the membership of a column value in one of a set of value lists, rather than in one of a set of contiguous ranges of values. This is done by using PARTITION BY LIST(expr) where expr is a column value or an expression based on a column value and returning an integer value, and then defining each partition by means of a VALUES IN (value_list), where value_list is a comma-separated list of integers.

Note

In MySQL 5.7, it is possible to match against only a list of integers (and possibly NULL—see Section 21.2.7, “How MySQL Partitioning Handles NULL”) when partitioning by LIST.

However, other column types may be used in value lists when employing LIST COLUMN partitioning, which is described later in this section.

Unlike the case with partitions defined by range, list partitions do not need to be declared in any particular order. For more detailed syntactical information, see Section 14.1.18, “CREATE TABLE Syntax”.

For the examples that follow, we assume that the basic definition of the table to be partitioned is provided by the CREATE TABLE statement shown here:

CREATE TABLE employees (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30),
    hired DATE NOT NULL DEFAULT '1970-01-01',
    separated DATE NOT NULL DEFAULT '9999-12-31',
    job_code INT,
    store_id INT
);

(This is the same table used as a basis for the examples in Section 21.2.1, “RANGE Partitioning”.)

Suppose that there are 20 video stores distributed among 4 franchises as shown in the following table.

RegionStore ID Numbers
North3, 5, 6, 9, 17
East1, 2, 10, 11, 19, 20
West4, 12, 13, 14, 18
Central7, 8, 15, 16

To partition this table in such a way that rows for stores belonging to the same region are stored in the same partition, you could use the CREATE TABLE statement shown here:

CREATE TABLE employees (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30),
    hired DATE NOT NULL DEFAULT '1970-01-01',
    separated DATE NOT NULL DEFAULT '9999-12-31',
    job_code INT,
    store_id INT
)
PARTITION BY LIST(store_id) (
    PARTITION pNorth VALUES IN (3,5,6,9,17),
    PARTITION pEast VALUES IN (1,2,10,11,19,20),
    PARTITION pWest VALUES IN (4,12,13,14,18),
    PARTITION pCentral VALUES IN (7,8,15,16)
);

This makes it easy to add or drop employee records relating to specific regions to or from the table. For instance, suppose that all stores in the West region are sold to another company. In MySQL 5.7, all rows relating to employees working at stores in that region can be deleted with the query ALTER TABLE employees TRUNCATE PARTITION pWest, which can be executed much more efficiently than the equivalent DELETE statement DELETE FROM employees WHERE store_id IN (4,12,13,14,18);. (Using ALTER TABLE employees DROP PARTITION pWest would also delete all of these rows, but would also remove the partition pWest from the definition of the table; you would need to use an ALTER TABLE ... ADD PARTITION statement to restore the table's original partitioning scheme.)

As with RANGE partitioning, it is possible to combine LIST partitioning with partitioning by hash or key to produce a composite partitioning (subpartitioning). See Section 21.2.6, “Subpartitioning”.

Unlike the case with RANGE partitioning, there is no catch-all such as MAXVALUE; all expected values for the partitioning expression should be covered in PARTITION ... VALUES IN (...) clauses. An INSERT statement containing an unmatched partitioning column value fails with an error, as shown in this example:

mysql> CREATE TABLE h2 (
    ->   c1 INT,
    ->   c2 INT
    -> )
    -> PARTITION BY LIST(c1) (
    ->   PARTITION p0 VALUES IN (1, 4, 7),
    ->   PARTITION p1 VALUES IN (2, 5, 8)
    -> );
Query OK, 0 rows affected (0.11 sec)

mysql> INSERT INTO h2 VALUES (3, 5);
ERROR 1525 (HY000): Table has no partition for value 3

When inserting multiple rows using a single INSERT statement the behavior depends on whether the table uses a transactional storage engine. For an InnoDB table, the statement is considered a single transaction, so the presence of any unmatched values causes the statement to fail completely, and no rows are inserted. For a table using a nontransactional storage engine such as MyISAM, any rows coming before the row containing the unmatched value are inserted, but any coming after it are not.

You can cause this type of error to be ignored by using the IGNORE keyword. If you do so, rows containing unmatched partitioning column values are not inserted, but any rows with matching values are inserted, and no errors are reported:

mysql> TRUNCATE h2;
Query OK, 1 row affected (0.00 sec)

mysql> SELECT * FROM h2;
Empty set (0.00 sec)

mysql> INSERT IGNORE INTO h2 VALUES (2, 5), (6, 10), (7, 5), (3, 1), (1, 9);
Query OK, 3 rows affected (0.00 sec)
Records: 5  Duplicates: 2  Warnings: 0

mysql> SELECT * FROM h2;
+------+------+
| c1   | c2   |
+------+------+
|    7 |    5 |
|    1 |    9 |
|    2 |    5 |
+------+------+
3 rows in set (0.00 sec)

MySQL 5.7 provides support for LIST COLUMNS partitioning. This is a variant of LIST partitioning that enables you to use columns of types other than integer types for partitioning columns, as well as to use multiple columns as partitioning keys. For more information, see Section 21.2.3.2, “LIST COLUMNS partitioning”.

21.2.3 COLUMNS Partitioning

The next two sections discuss COLUMNS partitioning, which are variants on RANGE and LIST partitioning. COLUMNS partitioning enables the use of multiple columns in partitioning keys. All of these columns are taken into account both for the purpose of placing rows in partitions and for the determination of which partitions are to be checked for matching rows in partition pruning.

In addition, both RANGE COLUMNS partitioning and LIST COLUMNS partitioning support the use of non-integer columns for defining value ranges or list members. The permitted data types are shown in the following list:

The discussions of RANGE COLUMNS and LIST COLUMNS partitioning in the next two sections assume that you are already familiar with partitioning based on ranges and lists as supported in MySQL 5.1 and later; for more information about these, see Section 21.2.1, “RANGE Partitioning”, and Section 21.2.2, “LIST Partitioning”, respectively.

21.2.3.1 RANGE COLUMNS partitioning

Range columns partitioning is similar to range partitioning, but enables you to define partitions using ranges based on multiple column values. In addition, you can define the ranges using columns of types other than integer types.

RANGE COLUMNS partitioning differs significantly from RANGE partitioning in the following ways:

  • RANGE COLUMNS does not accept expressions, only names of columns.

  • RANGE COLUMNS accepts a list of one or more columns.

    RANGE COLUMNS partitions are based on comparisons between tuples (lists of column values) rather than comparisons between scalar values. Placement of rows in RANGE COLUMNS partitions is also based on comparisons between tuples; this is discussed further later in this section.

  • RANGE COLUMNS partitioning columns are not restricted to integer columns; string, DATE and DATETIME columns can also be used as partitioning columns. (See Section 21.2.3, “COLUMNS Partitioning”, for details.)

The basic syntax for creating a table partitioned by RANGE COLUMNS is shown here:

CREATE TABLE table_name
PARTITIONED BY RANGE COLUMNS(column_list) (
    PARTITION partition_name VALUES LESS THAN (value_list)[,
    PARTITION partition_name VALUES LESS THAN (value_list)][,
    ...]
)

column_list:
    column_name[, column_name][, ...]

value_list:
    value[, value][, ...]
Note

Not all CREATE TABLE options that can be used when creating partitioned tables are shown here. For complete information, see Section 14.1.18, “CREATE TABLE Syntax”.

In the syntax just shown, column_list is a list of one or more columns (sometimes called a partitioning column list), and value_list is a list of values (that is, it is a partition definition value list). A value_list must be supplied for each partition definition, and each value_list must have the same number of values as the column_list has columns. Generally speaking, if you use N columns in the COLUMNS clause, then each VALUES LESS THAN clause must also be supplied with a list of N values.

The elements in the partitioning column list and in the value list defining each partition must occur in the same order. In addition, each element in the value list must be of the same data type as the corresponding element in the column list. However, the order of the column names in the partitioning column list and the value lists does not have to be the same as the order of the table column definitions in the main part of the CREATE TABLE statement. As with table partitioned by RANGE, you can use MAXVALUE to represent a value such that any legal value inserted into a given column is always less than this value. Here is an example of a CREATE TABLE statement that helps to illustrate all of these points:

mysql> CREATE TABLE rcx (
    ->     a INT,
    ->     b INT,
    ->     c CHAR(3),
    ->     d INT
    -> )
    -> PARTITION BY RANGE COLUMNS(a,d,c) (
    ->     PARTITION p0 VALUES LESS THAN (5,10,'ggg'),
    ->     PARTITION p1 VALUES LESS THAN (10,20,'mmm'),
    ->     PARTITION p2 VALUES LESS THAN (15,30,'sss'),
    ->     PARTITION p3 VALUES LESS THAN (MAXVALUE,MAXVALUE,MAXVALUE)
    -> );
Query OK, 0 rows affected (0.15 sec)

Table rcx contains the columns a, b, c, d. The partitioning column list supplied to the COLUMNS clause uses 3 of these columns, in the order a, d, c. Each value list used to define a partition contains 3 values in the same order; that is, each value list tuple has the form (INT, INT, CHAR(3)), which corresponds to the data types used by columns a, d, and c (in that order).

Placement of rows into partitions is determined by comparing the tuple from a row to be inserted that matches the column list in the COLUMNS clause with the tuples used in the VALUES LESS THAN clauses to define partitions of the table. Because we are comparing tuples (that is, lists or sets of values) rather than scalar values, the semantics of VALUES LESS THAN as used with RANGE COLUMNS partitions differs somewhat from the case with simple RANGE partitions. In RANGE partitioning, a row generating an expression value that is equal to a limiting value in a VALUES LESS THAN is never placed in the corresponding partition; however, when using RANGE COLUMNS partitioning, it is sometimes possible for a row whose partitioning column list's first element is equal in value to the that of the first element in a VALUES LESS THAN value list to be placed in the corresponding partition.

Consider the RANGE partitioned table created by this statement:

CREATE TABLE r1 (
    a INT,
    b INT
)
PARTITION BY RANGE (a)  (
    PARTITION p0 VALUES LESS THAN (5),
    PARTITION p1 VALUES LESS THAN (MAXVALUE)
);

If we insert 3 rows into this table such that the column value for a is 5 for each row, all 3 rows are stored in partition p1 because the a column value is in each case not less than 5, as we can see by executing the proper query against the INFORMATION_SCHEMA.PARTITIONS table:

mysql> INSERT INTO r1 VALUES (5,10), (5,11), (5,12);
Query OK, 3 rows affected (0.00 sec)
Records: 3  Duplicates: 0  Warnings: 0

mysql> SELECT PARTITION_NAME,TABLE_ROWS
    ->     FROM INFORMATION_SCHEMA.PARTITIONS
    ->     WHERE TABLE_NAME = 'r1';
+----------------+------------+
| PARTITION_NAME | TABLE_ROWS |
+----------------+------------+
| p0             |          0 |
| p1             |          3 |
+----------------+------------+
2 rows in set (0.00 sec)

Now consider a similar table rc1 that uses RANGE COLUMNS partitioning with both columns a and b referenced in the COLUMNS clause, created as shown here:

CREATE TABLE rc1 (
    a INT,
    b INT
)
PARTITION BY RANGE COLUMNS(a, b) (
    PARTITION p0 VALUES LESS THAN (5, 12),
    PARTITION p3 VALUES LESS THAN (MAXVALUE, MAXVALUE)
);

If we insert exactly the same rows into rc1 as we just inserted into r1, the distribution of the rows is quite different:

mysql> INSERT INTO rc1 VALUES (5,10), (5,11), (5,12);
Query OK, 3 rows affected (0.00 sec)
Records: 3  Duplicates: 0  Warnings: 0

mysql> SELECT PARTITION_NAME,TABLE_ROWS
    ->     FROM INFORMATION_SCHEMA.PARTITIONS
    ->     WHERE TABLE_NAME = 'rc1';
+--------------+----------------+------------+
| TABLE_SCHEMA | PARTITION_NAME | TABLE_ROWS |
+--------------+----------------+------------+
| p            | p0             |          2 |
| p            | p1             |          1 |
+--------------+----------------+------------+
2 rows in set (0.00 sec)

This is because we are comparing rows rather than scalar values. We can compare the row values inserted with the limiting row value from the VALUES THAN LESS THAN clause used to define partition p0 in table rc1, like this:

mysql> SELECT (5,10) < (5,12), (5,11) < (5,12), (5,12) < (5,12);
+-----------------+-----------------+-----------------+
| (5,10) < (5,12) | (5,11) < (5,12) | (5,12) < (5,12) |
+-----------------+-----------------+-----------------+
|               1 |               1 |               0 |
+-----------------+-----------------+-----------------+
1 row in set (0.00 sec)

The 2 tuples (5,10) and (5,11) evaluate as less than (5,12), so they are stored in partition p0. Since 5 is not less than 5 and 12 is not less than 12, (5,12) is considered not less than (5,12), and is stored in partition p1.

The SELECT statement in the preceding example could also have been written using explicit row constructors, like this:

SELECT ROW(5,10) < ROW(5,12), ROW(5,11) < ROW(5,12), ROW(5,12) < ROW(5,12);

For more information about the use of row constructors in MySQL, see Section 14.2.10.5, “Row Subqueries”.

For a table partitioned by RANGE COLUMNS using only a single partitioning column, the storing of rows in partitions is the same as that of an equivalent table that is partitioned by RANGE. The following CREATE TABLE statement creates a table partitioned by RANGE COLUMNS using 1 partitioning column:

CREATE TABLE rx (
    a INT,
    b INT
)
PARTITION BY RANGE COLUMNS (a)  (
    PARTITION p0 VALUES LESS THAN (5),
    PARTITION p1 VALUES LESS THAN (MAXVALUE)
);

If we insert the rows (5,10), (5,11), and (5,12) into this table, we can see that their placement is the same as it is for the table r we created and populated earlier:

mysql> INSERT INTO rx VALUES (5,10), (5,11), (5,12);
Query OK, 3 rows affected (0.00 sec)
Records: 3  Duplicates: 0  Warnings: 0

mysql> SELECT PARTITION_NAME,TABLE_ROWS
    ->     FROM INFORMATION_SCHEMA.PARTITIONS
    ->     WHERE TABLE_NAME = 'rx';
+--------------+----------------+------------+
| TABLE_SCHEMA | PARTITION_NAME | TABLE_ROWS |
+--------------+----------------+------------+
| p            | p0             |          0 |
| p            | p1             |          3 |
+--------------+----------------+------------+
2 rows in set (0.00 sec)

It is also possible to create tables partitioned by RANGE COLUMNS where limiting values for one or more columns are repeated in successive partition definitions. You can do this as long as the tuples of column values used to define the partitions are strictly increasing. For example, each of the following CREATE TABLE statements is valid:

CREATE TABLE rc2 (
    a INT,
    b INT
)
PARTITION BY RANGE COLUMNS(a,b) (
    PARTITION p0 VALUES LESS THAN (0,10),
    PARTITION p1 VALUES LESS THAN (10,20),
    PARTITION p2 VALUES LESS THAN (10,30),
    PARTITION p3 VALUES LESS THAN (MAXVALUE,MAXVALUE)
 );

CREATE TABLE rc3 (
    a INT,
    b INT
)
PARTITION BY RANGE COLUMNS(a,b) (
    PARTITION p0 VALUES LESS THAN (0,10),
    PARTITION p1 VALUES LESS THAN (10,20),
    PARTITION p2 VALUES LESS THAN (10,30),
    PARTITION p3 VALUES LESS THAN (10,35),
    PARTITION p4 VALUES LESS THAN (20,40),
    PARTITION p5 VALUES LESS THAN (MAXVALUE,MAXVALUE)
 );

The following statement also succeeds, even though it might appear at first glance that it would not, since the limiting value of column b is 25 for partition p0 and 20 for partition p1, and the limiting value of column c is 100 for partition p1 and 50 for partition p2:

CREATE TABLE rc4 (
    a INT,
    b INT,
    c INT
)
PARTITION BY RANGE COLUMNS(a,b,c) (
    PARTITION p0 VALUES LESS THAN (0,25,50),
    PARTITION p1 VALUES LESS THAN (10,20,100),
    PARTITION p2 VALUES LESS THAN (10,30,50)
    PARTITION p3 VALUES LESS THAN (MAXVALUE,MAXVALUE,MAXVALUE)
 );

When designing tables partitioned by RANGE COLUMNS, you can always test successive partition definitions by comparing the desired tuples using the mysql client, like this:

mysql> SELECT (0,25,50) < (10,20,100), (10,20,100) < (10,30,50);
+-------------------------+--------------------------+
| (0,25,50) < (10,20,100) | (10,20,100) < (10,30,50) |
+-------------------------+--------------------------+
|                       1 |                        1 |
+-------------------------+--------------------------+
1 row in set (0.00 sec)

If a CREATE TABLE statement contains partition definitions that are not in strictly increasing order, it fails with an error, as shown in this example:

mysql> CREATE TABLE rcf (
    ->     a INT,
    ->     b INT,
    ->     c INT
    -> )
    -> PARTITION BY RANGE COLUMNS(a,b,c) (
    ->     PARTITION p0 VALUES LESS THAN (0,25,50),
    ->     PARTITION p1 VALUES LESS THAN (20,20,100),
    ->     PARTITION p2 VALUES LESS THAN (10,30,50),
    ->     PARTITION p3 VALUES LESS THAN (MAXVALUE,MAXVALUE,MAXVALUE)
    ->  );
ERROR 1493 (HY000): VALUES LESS THAN value must be strictly increasing for each partition

When you get such an error, you can deduce which partition definitions are invalid by making less than comparisons between their column lists. In this case, the problem is with the definition of partition p2 because the tuple used to define it is not less than the tuple used to define partition p3, as shown here:

mysql> SELECT (0,25,50) < (20,20,100), (20,20,100) < (10,30,50);
+-------------------------+--------------------------+
| (0,25,50) < (20,20,100) | (20,20,100) < (10,30,50) |
+-------------------------+--------------------------+
|                       1 |                        0 |
+-------------------------+--------------------------+
1 row in set (0.00 sec)

It is also possible for MAXVALUE to appear for the same column in more than one VALUES LESS THAN clause when using RANGE COLUMNS. However, the limiting values for individual columns in successive partition definitions should otherwise be increasing, there should be no more than one partition defined where MAXVALUE is used as the upper limit for all column values, and this partition definition should appear last in the list of PARTITION ... VALUES LESS THAN clauses. In addition, you cannot use MAXVALUE as the limiting value for the first column in more than one partition definition.

As stated previously, it is also possible with RANGE COLUMNS partitioning to use non-integer columns as partitioning columns. (See Section 21.2.3, “COLUMNS Partitioning”, for a complete listing of these.) Consider a table named employees (which is not partitioned), created using the following statement:

CREATE TABLE employees (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30),
    hired DATE NOT NULL DEFAULT '1970-01-01',
    separated DATE NOT NULL DEFAULT '9999-12-31',
    job_code INT NOT NULL,
    store_id INT NOT NULL
);

Using RANGE COLUMNS partitioning, you can create a version of this table that stores each row in one of four partitions based on the employee's last name, like this:

CREATE TABLE employees_by_lname (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30),
    hired DATE NOT NULL DEFAULT '1970-01-01',
    separated DATE NOT NULL DEFAULT '9999-12-31',
    job_code INT NOT NULL,
    store_id INT NOT NULL
)
PARTITION BY RANGE COLUMNS (lname)  (
    PARTITION p0 VALUES LESS THAN ('g'),
    PARTITION p1 VALUES LESS THAN ('m'),
    PARTITION p2 VALUES LESS THAN ('t'),
    PARTITION p3 VALUES LESS THAN (MAXVALUE)
);

Alternatively, you could cause the employees table as created previously to be partitioned using this scheme by executing the following ALTER TABLE statement:

ALTER TABLE employees PARTITION BY RANGE COLUMNS (lname)  (
    PARTITION p0 VALUES LESS THAN ('g'),
    PARTITION p1 VALUES LESS THAN ('m'),
    PARTITION p2 VALUES LESS THAN ('t'),
    PARTITION p3 VALUES LESS THAN (MAXVALUE)
);
Note

Because different character sets and collations have different sort orders, the character sets and collations in use may effect which partition of a table partitioned by RANGE COLUMNS a given row is stored in when using string columns as partitioning columns. In addition, changing the character set or collation for a given database, table, or column after such a table is created may cause changes in how rows are distributed. For example, when using a case-sensitive collation, 'and' sorts before 'Andersen', but when using a collation that is case insensitive, the reverse is true.

For information about how MySQL handles character sets and collations, see Section 11.1, “Character Set Support”.

Similarly, you can cause the employees table to be partitioned in such a way that each row is stored in one of several partitions based on the decade in which the corresponding employee was hired using the ALTER TABLE statement shown here:

ALTER TABLE employees PARTITION BY RANGE COLUMNS (hired)  (
    PARTITION p0 VALUES LESS THAN ('1970-01-01'),
    PARTITION p1 VALUES LESS THAN ('1980-01-01'),
    PARTITION p2 VALUES LESS THAN ('1990-01-01'),
    PARTITION p3 VALUES LESS THAN ('2000-01-01'),
    PARTITION p4 VALUES LESS THAN ('2010-01-01'),
    PARTITION p5 VALUES LESS THAN (MAXVALUE)
);

See Section 14.1.18, “CREATE TABLE Syntax”, for additional information about PARTITION BY RANGE COLUMNS syntax.

21.2.3.2 LIST COLUMNS partitioning

MySQL 5.7 provides support for LIST COLUMNS partitioning. This is a variant of LIST partitioning that enables the use of multiple columns as partition keys, and for columns of data types other than integer types to be used as partitioning columns; you can use string types, DATE, and DATETIME columns. (For more information about permitted data types for COLUMNS partitioning columns, see Section 21.2.3, “COLUMNS Partitioning”.)

Suppose that you have a business that has customers in 12 cities which, for sales and marketing purposes, you organize into 4 regions of 3 cities each as shown in the following table:

RegionCities
1Oskarshamn, Högsby, Mönsterås
2Vimmerby, Hultsfred, Västervik
3Nässjö, Eksjö, Vetlanda
4Uppvidinge, Alvesta, Växjo

With LIST COLUMNS partitioning, you can create a table for customer data that assigns a row to any of 4 partitions corresponding to these regions based on the name of the city where a customer resides, as shown here:

CREATE TABLE customers_1 (
    first_name VARCHAR(25),
    last_name VARCHAR(25),
    street_1 VARCHAR(30),
    street_2 VARCHAR(30),
    city VARCHAR(15),
    renewal DATE
)
PARTITION BY LIST COLUMNS(city) (
    PARTITION pRegion_1 VALUES IN('Oskarshamn', 'Högsby', 'Mönsterås'),
    PARTITION pRegion_2 VALUES IN('Vimmerby', 'Hultsfred', 'Västervik'),
    PARTITION pRegion_3 VALUES IN('Nässjö', 'Eksjö', 'Vetlanda'),
    PARTITION pRegion_4 VALUES IN('Uppvidinge', 'Alvesta', 'Växjo')
);

As with partitioning by RANGE COLUMNS, you do not need to use expressions in the COLUMNS() clause to convert column values into integers. (In fact, the use of expressions other than column names is not permitted with COLUMNS().)

It is also possible to use DATE and DATETIME columns, as shown in the following example that uses the same name and columns as the customers_1 table shown previously, but employs LIST COLUMNS partitioning based on the renewal column to store rows in one of 4 partitions depending on the week in February 2010 the customer's account is scheduled to renew:

CREATE TABLE customers_2 (
    first_name VARCHAR(25),
    last_name VARCHAR(25),
    street_1 VARCHAR(30),
    street_2 VARCHAR(30),
    city VARCHAR(15),
    renewal DATE
)
PARTITION BY LIST COLUMNS(renewal) (
    PARTITION pWeek_1 VALUES IN('2010-02-01', '2010-02-02', '2010-02-03',
        '2010-02-04', '2010-02-05', '2010-02-06', '2010-02-07'),
    PARTITION pWeek_2 VALUES IN('2010-02-08', '2010-02-09', '2010-02-10',
        '2010-02-11', '2010-02-12', '2010-02-13', '2010-02-14'),
    PARTITION pWeek_3 VALUES IN('2010-02-15', '2010-02-16', '2010-02-17',
        '2010-02-18', '2010-02-19', '2010-02-20', '2010-02-21'),
    PARTITION pWeek_4 VALUES IN('2010-02-22', '2010-02-23', '2010-02-24',
        '2010-02-25', '2010-02-26', '2010-02-27', '2010-02-28')
);

This works, but becomes cumbersome to define and maintain if the number of dates involved grows very large; in such cases, it is usually more practical to employ RANGE or RANGE COLUMNS partitioning instead. In this case, since the column we wish to use as the partitioning key is a DATE column, we use RANGE COLUMNS partitioning, as shown here:

CREATE TABLE customers_3 (
    first_name VARCHAR(25),
    last_name VARCHAR(25),
    street_1 VARCHAR(30),
    street_2 VARCHAR(30),
    city VARCHAR(15),
    renewal DATE
)
PARTITION BY RANGE COLUMNS(renewal) (
    PARTITION pWeek_1 VALUES LESS THAN('2010-02-09'),
    PARTITION pWeek_2 VALUES LESS THAN('2010-02-15'),
    PARTITION pWeek_3 VALUES LESS THAN('2010-02-22'),
    PARTITION pWeek_4 VALUES LESS THAN('2010-03-01')
);

See Section 21.2.3.1, “RANGE COLUMNS partitioning”, for more information.

In addition (as with RANGE COLUMNS partitioning), you can use multiple columns in the COLUMNS() clause.

See Section 14.1.18, “CREATE TABLE Syntax”, for additional information about PARTITION BY LIST COLUMNS() syntax.

21.2.4 HASH Partitioning

Partitioning by HASH is used primarily to ensure an even distribution of data among a predetermined number of partitions. With range or list partitioning, you must specify explicitly into which partition a given column value or set of column values is to be stored; with hash partitioning, MySQL takes care of this for you, and you need only specify a column value or expression based on a column value to be hashed and the number of partitions into which the partitioned table is to be divided.

To partition a table using HASH partitioning, it is necessary to append to the CREATE TABLE statement a PARTITION BY HASH (expr) clause, where expr is an expression that returns an integer. This can simply be the name of a column whose type is one of MySQL's integer types. In addition, you most likely want to follow this with PARTITIONS num, where num is a positive integer representing the number of partitions into which the table is to be divided.

Note

For simplicity, the tables in the examples that follow do not use any keys. You should be aware that, if a table has any unique keys, every column used in the partitioning expression for this table must be part of every unique key, including the primary key. See Section 21.6.1, “Partitioning Keys, Primary Keys, and Unique Keys”, for more information.

The following statement creates a table that uses hashing on the store_id column and is divided into 4 partitions:

CREATE TABLE employees (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30),
    hired DATE NOT NULL DEFAULT '1970-01-01',
    separated DATE NOT NULL DEFAULT '9999-12-31',
    job_code INT,
    store_id INT
)
PARTITION BY HASH(store_id)
PARTITIONS 4;

If you do not include a PARTITIONS clause, the number of partitions defaults to 1.

Using the PARTITIONS keyword without a number following it results in a syntax error.

You can also use an SQL expression that returns an integer for expr. For instance, you might want to partition based on the year in which an employee was hired. This can be done as shown here:

CREATE TABLE employees (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30),
    hired DATE NOT NULL DEFAULT '1970-01-01',
    separated DATE NOT NULL DEFAULT '9999-12-31',
    job_code INT,
    store_id INT
)
PARTITION BY HASH( YEAR(hired) )
PARTITIONS 4;

expr must return a nonconstant, nonrandom integer value (in other words, it should be varying but deterministic), and must not contain any prohibited constructs as described in Section 21.6, “Restrictions and Limitations on Partitioning”. You should also keep in mind that this expression is evaluated each time a row is inserted or updated (or possibly deleted); this means that very complex expressions may give rise to performance issues, particularly when performing operations (such as batch inserts) that affect a great many rows at one time.

The most efficient hashing function is one which operates upon a single table column and whose value increases or decreases consistently with the column value, as this allows for pruning on ranges of partitions. That is, the more closely that the expression varies with the value of the column on which it is based, the more efficiently MySQL can use the expression for hash partitioning.

For example, where date_col is a column of type DATE, then the expression TO_DAYS(date_col) is said to vary directly with the value of date_col, because for every change in the value of date_col, the value of the expression changes in a consistent manner. The variance of the expression YEAR(date_col) with respect to date_col is not quite as direct as that of TO_DAYS(date_col), because not every possible change in date_col produces an equivalent change in YEAR(date_col). Even so, YEAR(date_col) is a good candidate for a hashing function, because it varies directly with a portion of date_col and there is no possible change in date_col that produces a disproportionate change in YEAR(date_col).

By way of contrast, suppose that you have a column named int_col whose type is INT. Now consider the expression POW(5-int_col,3) + 6. This would be a poor choice for a hashing function because a change in the value of int_col is not guaranteed to produce a proportional change in the value of the expression. Changing the value of int_col by a given amount can produce widely differing changes in the value of the expression. For example, changing int_col from 5 to 6 produces a change of -1 in the value of the expression, but changing the value of int_col from 6 to 7 produces a change of -7 in the expression value.

In other words, the more closely the graph of the column value versus the value of the expression follows a straight line as traced by the equation y=cx where c is some nonzero constant, the better the expression is suited to hashing. This has to do with the fact that the more nonlinear an expression is, the more uneven the distribution of data among the partitions it tends to produce.

In theory, pruning is also possible for expressions involving more than one column value, but determining which of such expressions are suitable can be quite difficult and time-consuming. For this reason, the use of hashing expressions involving multiple columns is not particularly recommended.

When PARTITION BY HASH is used, MySQL determines which partition of num partitions to use based on the modulus of the result of the expression. In other words, for a given expression expr, the partition in which the record is stored is partition number N, where N = MOD(expr, num). Suppose that table t1 is defined as follows, so that it has 4 partitions:

CREATE TABLE t1 (col1 INT, col2 CHAR(5), col3 DATE)
    PARTITION BY HASH( YEAR(col3) )
    PARTITIONS 4;

If you insert a record into t1 whose col3 value is '2005-09-15', then the partition in which it is stored is determined as follows:

MOD(YEAR('2005-09-01'),4)
=  MOD(2005,4)
=  1

MySQL 5.7 also supports a variant of HASH partitioning known as linear hashing which employs a more complex algorithm for determining the placement of new rows inserted into the partitioned table. See Section 21.2.4.1, “LINEAR HASH Partitioning”, for a description of this algorithm.

The user-supplied expression is evaluated each time a record is inserted or updated. It may also—depending on the circumstances—be evaluated when records are deleted.

21.2.4.1 LINEAR HASH Partitioning

MySQL also supports linear hashing, which differs from regular hashing in that linear hashing utilizes a linear powers-of-two algorithm whereas regular hashing employs the modulus of the hashing function's value.

Syntactically, the only difference between linear-hash partitioning and regular hashing is the addition of the LINEAR keyword in the PARTITION BY clause, as shown here:

CREATE TABLE employees (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30),
    hired DATE NOT NULL DEFAULT '1970-01-01',
    separated DATE NOT NULL DEFAULT '9999-12-31',
    job_code INT,
    store_id INT
)
PARTITION BY LINEAR HASH( YEAR(hired) )
PARTITIONS 4;

Given an expression expr, the partition in which the record is stored when linear hashing is used is partition number N from among num partitions, where N is derived according to the following algorithm:

  1. Find the next power of 2 greater than num. We call this value V; it can be calculated as:

    V = POWER(2, CEILING(LOG(2, num)))
    

    (Suppose that num is 13. Then LOG(2,13) is 3.7004397181411. CEILING(3.7004397181411) is 4, and V = POWER(2,4), which is 16.)

  2. Set N = F(column_list) & (V - 1).

  3. While N >= num:

    • Set V = V / 2

    • Set N = N & (V - 1)

Suppose that the table t1, using linear hash partitioning and having 6 partitions, is created using this statement:

CREATE TABLE t1 (col1 INT, col2 CHAR(5), col3 DATE)
    PARTITION BY LINEAR HASH( YEAR(col3) )
    PARTITIONS 6;

Now assume that you want to insert two records into t1 having the col3 column values '2003-04-14' and '1998-10-19'. The partition number for the first of these is determined as follows:

V = POWER(2, CEILING( LOG(2,6) )) = 8
N = YEAR('2003-04-14') & (8 - 1)
   = 2003 & 7
   = 3

(3 >= 6 is FALSE: record stored in partition #3)

The number of the partition where the second record is stored is calculated as shown here:

V = 8
N = YEAR('1998-10-19') & (8 - 1)
  = 1998 & 7
  = 6

(6 >= 6 is TRUE: additional step required)

N = 6 & ((8 / 2) - 1)
  = 6 & 3
  = 2

(2 >= 6 is FALSE: record stored in partition #2)

The advantage in partitioning by linear hash is that the adding, dropping, merging, and splitting of partitions is made much faster, which can be beneficial when dealing with tables containing extremely large amounts (terabytes) of data. The disadvantage is that data is less likely to be evenly distributed between partitions as compared with the distribution obtained using regular hash partitioning.

21.2.5 KEY Partitioning

Partitioning by key is similar to partitioning by hash, except that where hash partitioning employs a user-defined expression, the hashing function for key partitioning is supplied by the MySQL server. MySQL Cluster uses MD5() for this purpose; for tables using other storage engines, the server employs its own internal hashing function which is based on the same algorithm as PASSWORD().

The syntax rules for CREATE TABLE ... PARTITION BY KEY are similar to those for creating a table that is partitioned by hash. The major differences are listed here:

  • KEY is used rather than HASH.

  • KEY takes only a list of zero or more column names. Any columns used as the partitioning key must comprise part or all of the table's primary key, if the table has one. Where no column name is specified as the partitioning key, the table's primary key is used, if there is one. For example, the following CREATE TABLE statement is valid in MySQL 5.7:

    CREATE TABLE k1 (
        id INT NOT NULL PRIMARY KEY,
        name VARCHAR(20)
    )
    PARTITION BY KEY()
    PARTITIONS 2;
    

    If there is no primary key but there is a unique key, then the unique key is used for the partitioning key:

    CREATE TABLE k1 (
        id INT NOT NULL,
        name VARCHAR(20),
        UNIQUE KEY (id)
    )
    PARTITION BY KEY()
    PARTITIONS 2;
    

    However, if the unique key column were not defined as NOT NULL, then the previous statement would fail.

    In both of these cases, the partitioning key is the id column, even though it is not shown in the output of SHOW CREATE TABLE or in the PARTITION_EXPRESSION column of the INFORMATION_SCHEMA.PARTITIONS table.

    Unlike the case with other partitioning types, columns used for partitioning by KEY are not restricted to integer or NULL values. For example, the following CREATE TABLE statement is valid:

    CREATE TABLE tm1 (
        s1 CHAR(32) PRIMARY KEY
    )
    PARTITION BY KEY(s1)
    PARTITIONS 10;
    

    The preceding statement would not be valid, were a different partitioning type to be specified. (In this case, simply using PARTITION BY KEY() would also be valid and have the same effect as PARTITION BY KEY(s1), since s1 is the table's primary key.)

    For additional information about this issue, see Section 21.6, “Restrictions and Limitations on Partitioning”.

    Note

    Tables using the NDB storage engine are implicitly partitioned by KEY, again using the table's primary key as the partitioning key. In the event that the MySQL Cluster table has no explicit primary key, the hidden primary key generated by the NDB storage engine for each MySQL Cluster table is used as the partitioning key.

    If you define an explicit partitioning scheme for an NDB table, the table must have an explicit primary key, and any columns used in the partitioning expression must be part of this key. However, if the table uses an empty partitioning expression—that is, PARTITION BY KEY() with no column references—then no explicit primary key is required.

    You can observe this partitioning using the ndb_desc utility (with the -p option).

    Important

    For a key-partitioned table, you cannot execute an ALTER TABLE DROP PRIMARY KEY, as doing so generates the error ERROR 1466 (HY000): Field in list of fields for partition function not found in table. This is not an issue for MySQL Cluster tables which are partitioned by KEY; in such cases, the table is reorganized using the hidden primary key as the table's new partitioning key. See Chapter 20, MySQL NDB Cluster 7.5.

It is also possible to partition a table by linear key. Here is a simple example:

CREATE TABLE tk (
    col1 INT NOT NULL,
    col2 CHAR(5),
    col3 DATE
)
PARTITION BY LINEAR KEY (col1)
PARTITIONS 3;

Using LINEAR has the same effect on KEY partitioning as it does on HASH partitioning, with the partition number being derived using a powers-of-two algorithm rather than modulo arithmetic. See Section 21.2.4.1, “LINEAR HASH Partitioning”, for a description of this algorithm and its implications.

21.2.6 Subpartitioning

Subpartitioning—also known as composite partitioning—is the further division of each partition in a partitioned table. Consider the following CREATE TABLE statement:

CREATE TABLE ts (id INT, purchased DATE)
    PARTITION BY RANGE( YEAR(purchased) )
    SUBPARTITION BY HASH( TO_DAYS(purchased) )
    SUBPARTITIONS 2 (
        PARTITION p0 VALUES LESS THAN (1990),
        PARTITION p1 VALUES LESS THAN (2000),
        PARTITION p2 VALUES LESS THAN MAXVALUE
    );

Table ts has 3 RANGE partitions. Each of these partitions—p0, p1, and p2—is further divided into 2 subpartitions. In effect, the entire table is divided into 3 * 2 = 6 partitions. However, due to the action of the PARTITION BY RANGE clause, the first 2 of these store only those records with a value less than 1990 in the purchased column.

In MySQL 5.7, it is possible to subpartition tables that are partitioned by RANGE or LIST. Subpartitions may use either HASH or KEY partitioning. This is also known as composite partitioning.

Note

SUBPARTITION BY HASH and SUBPARTITION BY KEY generally follow the same syntax rules as PARTITION BY HASH and PARTITION BY KEY, respectively. An exception to this is that SUBPARTITION BY KEY (unlike PARTITION BY KEY) does not currently support a default column, so the column used for this purpose must be specified, even if the table has an explicit primary key. This is a known issue which we are working to address; see Issues with subpartitions, for more information and an example.

It is also possible to define subpartitions explicitly using SUBPARTITION clauses to specify options for individual subpartitions. For example, a more verbose fashion of creating the same table ts as shown in the previous example would be:

CREATE TABLE ts (id INT, purchased DATE)
    PARTITION BY RANGE( YEAR(purchased) )
    SUBPARTITION BY HASH( TO_DAYS(purchased) ) (
        PARTITION p0 VALUES LESS THAN (1990) (
            SUBPARTITION s0,
            SUBPARTITION s1
        ),
        PARTITION p1 VALUES LESS THAN (2000) (
            SUBPARTITION s2,
            SUBPARTITION s3
        ),
        PARTITION p2 VALUES LESS THAN MAXVALUE (
            SUBPARTITION s4,
            SUBPARTITION s5
        )
    );

Some syntactical items of note are listed here:

  • Each partition must have the same number of subpartitions.

  • If you explicitly define any subpartitions using SUBPARTITION on any partition of a partitioned table, you must define them all. In other words, the following statement will fail:

    CREATE TABLE ts (id INT, purchased DATE)
        PARTITION BY RANGE( YEAR(purchased) )
        SUBPARTITION BY HASH( TO_DAYS(purchased) ) (
            PARTITION p0 VALUES LESS THAN (1990) (
                SUBPARTITION s0,
                SUBPARTITION s1
            ),
            PARTITION p1 VALUES LESS THAN (2000),
            PARTITION p2 VALUES LESS THAN MAXVALUE (
                SUBPARTITION s2,
                SUBPARTITION s3
            )
        );
    

    This statement would still fail even if it included a SUBPARTITIONS 2 clause.

  • Each SUBPARTITION clause must include (at a minimum) a name for the subpartition. Otherwise, you may set any desired option for the subpartition or allow it to assume its default setting for that option.

  • Subpartition names must be unique across the entire table. For example, the following CREATE TABLE statement is valid in MySQL 5.7:

    CREATE TABLE ts (id INT, purchased DATE)
        PARTITION BY RANGE( YEAR(purchased) )
        SUBPARTITION BY HASH( TO_DAYS(purchased) ) (
            PARTITION p0 VALUES LESS THAN (1990) (
                SUBPARTITION s0,
                SUBPARTITION s1
            ),
            PARTITION p1 VALUES LESS THAN (2000) (
                SUBPARTITION s2,
                SUBPARTITION s3
            ),
            PARTITION p2 VALUES LESS THAN MAXVALUE (
                SUBPARTITION s4,
                SUBPARTITION s5
            )
        );
    

Subpartitions can be used with especially large MyISAM tables to distribute data and indexes across many disks. Suppose that you have 6 disks mounted as /disk0, /disk1, /disk2, and so on. Now consider the following example:

CREATE TABLE ts (id INT, purchased DATE)
    ENGINE = MYISAM
    PARTITION BY RANGE( YEAR(purchased) )
    SUBPARTITION BY HASH( TO_DAYS(purchased) ) (
        PARTITION p0 VALUES LESS THAN (1990) (
            SUBPARTITION s0
                DATA DIRECTORY = '/disk0/data'
                INDEX DIRECTORY = '/disk0/idx',
            SUBPARTITION s1
                DATA DIRECTORY = '/disk1/data'
                INDEX DIRECTORY = '/disk1/idx'
        ),
        PARTITION p1 VALUES LESS THAN (2000) (
            SUBPARTITION s2
                DATA DIRECTORY = '/disk2/data'
                INDEX DIRECTORY = '/disk2/idx',
            SUBPARTITION s3
                DATA DIRECTORY = '/disk3/data'
                INDEX DIRECTORY = '/disk3/idx'
        ),
        PARTITION p2 VALUES LESS THAN MAXVALUE (
            SUBPARTITION s4
                DATA DIRECTORY = '/disk4/data'
                INDEX DIRECTORY = '/disk4/idx',
            SUBPARTITION s5
                DATA DIRECTORY = '/disk5/data'
                INDEX DIRECTORY = '/disk5/idx'
        )
    );

In this case, a separate disk is used for the data and for the indexes of each RANGE. Many other variations are possible; another example might be:

CREATE TABLE ts (id INT, purchased DATE)
    ENGINE = MYISAM
    PARTITION BY RANGE(YEAR(purchased))
    SUBPARTITION BY HASH( TO_DAYS(purchased) ) (
        PARTITION p0 VALUES LESS THAN (1990) (
            SUBPARTITION s0a
                DATA DIRECTORY = '/disk0'
                INDEX DIRECTORY = '/disk1',
            SUBPARTITION s0b
                DATA DIRECTORY = '/disk2'
                INDEX DIRECTORY = '/disk3'
        ),
        PARTITION p1 VALUES LESS THAN (2000) (
            SUBPARTITION s1a
                DATA DIRECTORY = '/disk4/data'
                INDEX DIRECTORY = '/disk4/idx',
            SUBPARTITION s1b
                DATA DIRECTORY = '/disk5/data'
                INDEX DIRECTORY = '/disk5/idx'
        ),
        PARTITION p2 VALUES LESS THAN MAXVALUE (
            SUBPARTITION s2a,
            SUBPARTITION s2b
        )
    );

Here, the storage is as follows:

  • Rows with purchased dates from before 1990 take up a vast amount of space, so are split up 4 ways, with a separate disk dedicated to the data and to the indexes for each of the two subpartitions (s0a and s0b) making up partition p0. In other words:

    • The data for subpartition s0a is stored on /disk0.

    • The indexes for subpartition s0a are stored on /disk1.

    • The data for subpartition s0b is stored on /disk2.

    • The indexes for subpartition s0b are stored on /disk3.

  • Rows containing dates ranging from 1990 to 1999 (partition p1) do not require as much room as those from before 1990. These are split between 2 disks (/disk4 and /disk5) rather than 4 disks as with the legacy records stored in p0:

    • Data and indexes belonging to p1's first subpartition (s1a) are stored on /disk4—the data in /disk4/data, and the indexes in /disk4/idx.

    • Data and indexes belonging to p1's second subpartition (s1b) are stored on /disk5—the data in /disk5/data, and the indexes in /disk5/idx.

  • Rows reflecting dates from the year 2000 to the present (partition p2) do not take up as much space as required by either of the two previous ranges. Currently, it is sufficient to store all of these in the default location.

    In future, when the number of purchases for the decade beginning with the year 2000 grows to a point where the default location no longer provides sufficient space, the corresponding rows can be moved using an ALTER TABLE ... REORGANIZE PARTITION statement. See Section 21.3, “Partition Management”, for an explanation of how this can be done.

The DATA DIRECTORY and INDEX DIRECTORY options are not permitted in partition definitions when the NO_DIR_IN_CREATE server SQL mode is in effect. In MySQL 5.7, these options are also not permitted when defining subpartitions (Bug #42954).

21.2.7 How MySQL Partitioning Handles NULL

Partitioning in MySQL does nothing to disallow NULL as the value of a partitioning expression, whether it is a column value or the value of a user-supplied expression. Even though it is permitted to use NULL as the value of an expression that must otherwise yield an integer, it is important to keep in mind that NULL is not a number. MySQL's partitioning implementation treats NULL as being less than any non-NULL value, just as ORDER BY does.

This means that treatment of NULL varies between partitioning of different types, and may produce behavior which you do not expect if you are not prepared for it. This being the case, we discuss in this section how each MySQL partitioning type handles NULL values when determining the partition in which a row should be stored, and provide examples for each.

Handling of NULL with RANGE partitioning.  If you insert a row into a table partitioned by RANGE such that the column value used to determine the partition is NULL, the row is inserted into the lowest partition. Consider these two tables in a database named p, created as follows:

mysql> CREATE TABLE t1 (
    ->     c1 INT,
    ->     c2 VARCHAR(20)
    -> )
    -> PARTITION BY RANGE(c1) (
    ->     PARTITION p0 VALUES LESS THAN (0),
    ->     PARTITION p1 VALUES LESS THAN (10),
    ->     PARTITION p2 VALUES LESS THAN MAXVALUE
    -> );
Query OK, 0 rows affected (0.09 sec)

mysql> CREATE TABLE t2 (
    ->     c1 INT,
    ->     c2 VARCHAR(20)
    -> )
    -> PARTITION BY RANGE(c1) (
    ->     PARTITION p0 VALUES LESS THAN (-5),
    ->     PARTITION p1 VALUES LESS THAN (0),
    ->     PARTITION p2 VALUES LESS THAN (10),
    ->     PARTITION p3 VALUES LESS THAN MAXVALUE
    -> );
Query OK, 0 rows affected (0.09 sec)

You can see the partitions created by these two CREATE TABLE statements using the following query against the PARTITIONS table in the INFORMATION_SCHEMA database:

mysql> SELECT TABLE_NAME, PARTITION_NAME, TABLE_ROWS, AVG_ROW_LENGTH, DATA_LENGTH
     >   FROM INFORMATION_SCHEMA.PARTITIONS
     >   WHERE TABLE_SCHEMA = 'p' AND TABLE_NAME LIKE 't_';
+------------+----------------+------------+----------------+-------------+
| TABLE_NAME | PARTITION_NAME | TABLE_ROWS | AVG_ROW_LENGTH | DATA_LENGTH |
+------------+----------------+------------+----------------+-------------+
| t1         | p0             |          0 |              0 |           0 |
| t1         | p1             |          0 |              0 |           0 |
| t1         | p2             |          0 |              0 |           0 |
| t2         | p0             |          0 |              0 |           0 |
| t2         | p1             |          0 |              0 |           0 |
| t2         | p2             |          0 |              0 |           0 |
| t2         | p3             |          0 |              0 |           0 |
+------------+----------------+------------+----------------+-------------+
7 rows in set (0.00 sec)

(For more information about this table, see Section 23.15, “The INFORMATION_SCHEMA PARTITIONS Table”.) Now let us populate each of these tables with a single row containing a NULL in the column used as the partitioning key, and verify that the rows were inserted using a pair of SELECT statements:

mysql> INSERT INTO t1 VALUES (NULL, 'mothra');
Query OK, 1 row affected (0.00 sec)

mysql> INSERT INTO t2 VALUES (NULL, 'mothra');
Query OK, 1 row affected (0.00 sec)

mysql> SELECT * FROM t1;
+------+--------+
| id   | name   |
+------+--------+
| NULL | mothra |
+------+--------+
1 row in set (0.00 sec)

mysql> SELECT * FROM t2;
+------+--------+
| id   | name   |
+------+--------+
| NULL | mothra |
+------+--------+
1 row in set (0.00 sec)

You can see which partitions are used to store the inserted rows by rerunning the previous query against INFORMATION_SCHEMA.PARTITIONS and inspecting the output:

mysql> SELECT TABLE_NAME, PARTITION_NAME, TABLE_ROWS, AVG_ROW_LENGTH, DATA_LENGTH
     >   FROM INFORMATION_SCHEMA.PARTITIONS
     >   WHERE TABLE_SCHEMA = 'p' AND TABLE_NAME LIKE 't_';
+------------+----------------+------------+----------------+-------------+
| TABLE_NAME | PARTITION_NAME | TABLE_ROWS | AVG_ROW_LENGTH | DATA_LENGTH |
+------------+----------------+------------+----------------+-------------+
| t1         | p0             |          1 |             20 |          20 |
| t1         | p1             |          0 |              0 |           0 |
| t1         | p2             |          0 |              0 |           0 |
| t2         | p0             |          1 |             20 |          20 |
| t2         | p1             |          0 |              0 |           0 |
| t2         | p2             |          0 |              0 |           0 |
| t2         | p3             |          0 |              0 |           0 |
+------------+----------------+------------+----------------+-------------+
7 rows in set (0.01 sec)

You can also demonstrate that these rows were stored in the lowest partition of each table by dropping these partitions, and then re-running the SELECT statements:

mysql> ALTER TABLE t1 DROP PARTITION p0;
Query OK, 0 rows affected (0.16 sec)

mysql> ALTER TABLE t2 DROP PARTITION p0;
Query OK, 0 rows affected (0.16 sec)

mysql> SELECT * FROM t1;
Empty set (0.00 sec)

mysql> SELECT * FROM t2;
Empty set (0.00 sec)

(For more information on ALTER TABLE ... DROP PARTITION, see Section 14.1.8, “ALTER TABLE Syntax”.)

NULL is also treated in this way for partitioning expressions that use SQL functions. Suppose that we define a table using a CREATE TABLE statement such as this one:

CREATE TABLE tndate (
    id INT,
    dt DATE
)
PARTITION BY RANGE( YEAR(dt) ) (
    PARTITION p0 VALUES LESS THAN (1990),
    PARTITION p1 VALUES LESS THAN (2000),
    PARTITION p2 VALUES LESS THAN MAXVALUE
);

As with other MySQL functions, YEAR(NULL) returns NULL. A row with a dt column value of NULL is treated as though the partitioning expression evaluated to a value less than any other value, and so is inserted into partition p0.

Handling of NULL with LIST partitioning.  A table that is partitioned by LIST admits NULL values if and only if one of its partitions is defined using that value-list that contains NULL. The converse of this is that a table partitioned by LIST which does not explicitly use NULL in a value list rejects rows resulting in a NULL value for the partitioning expression, as shown in this example:

mysql> CREATE TABLE ts1 (
    ->     c1 INT,
    ->     c2 VARCHAR(20)
    -> )
    -> PARTITION BY LIST(c1) (
    ->     PARTITION p0 VALUES IN (0, 3, 6),
    ->     PARTITION p1 VALUES IN (1, 4, 7),
    ->     PARTITION p2 VALUES IN (2, 5, 8)
    -> );
Query OK, 0 rows affected (0.01 sec)

mysql> INSERT INTO ts1 VALUES (9, 'mothra');
ERROR 1504 (HY000): Table has no partition for value 9

mysql> INSERT INTO ts1 VALUES (NULL, 'mothra');
ERROR 1504 (HY000): Table has no partition for value NULL

Only rows having a c1 value between 0 and 8 inclusive can be inserted into ts1. NULL falls outside this range, just like the number 9. We can create tables ts2 and ts3 having value lists containing NULL, as shown here:

mysql> CREATE TABLE ts2 (
    ->     c1 INT,
    ->     c2 VARCHAR(20)
    -> )
    -> PARTITION BY LIST(c1) (
    ->     PARTITION p0 VALUES IN (0, 3, 6),
    ->     PARTITION p1 VALUES IN (1, 4, 7),
    ->     PARTITION p2 VALUES IN (2, 5, 8),
    ->     PARTITION p3 VALUES IN (NULL)
    -> );
Query OK, 0 rows affected (0.01 sec)

mysql> CREATE TABLE ts3 (
    ->     c1 INT,
    ->     c2 VARCHAR(20)
    -> )
    -> PARTITION BY LIST(c1) (
    ->     PARTITION p0 VALUES IN (0, 3, 6),
    ->     PARTITION p1 VALUES IN (1, 4, 7, NULL),
    ->     PARTITION p2 VALUES IN (2, 5, 8)
    -> );
Query OK, 0 rows affected (0.01 sec)

When defining value lists for partitioning, you can (and should) treat NULL just as you would any other value. For example, both VALUES IN (NULL) and VALUES IN (1, 4, 7, NULL) are valid, as are VALUES IN (1, NULL, 4, 7), VALUES IN (NULL, 1, 4, 7), and so on. You can insert a row having NULL for column c1 into each of the tables ts2 and ts3:

mysql> INSERT INTO ts2 VALUES (NULL, 'mothra');
Query OK, 1 row affected (0.00 sec)

mysql> INSERT INTO ts3 VALUES (NULL, 'mothra');
Query OK, 1 row affected (0.00 sec)

By issuing the appropriate query against INFORMATION_SCHEMA.PARTITIONS, you can determine which partitions were used to store the rows just inserted (we assume, as in the previous examples, that the partitioned tables were created in the p database):

mysql> SELECT TABLE_NAME, PARTITION_NAME, TABLE_ROWS, AVG_ROW_LENGTH, DATA_LENGTH
     >   FROM INFORMATION_SCHEMA.PARTITIONS
     >   WHERE TABLE_SCHEMA = 'p' AND TABLE_NAME LIKE 'ts_';
+------------+----------------+------------+----------------+-------------+
| TABLE_NAME | PARTITION_NAME | TABLE_ROWS | AVG_ROW_LENGTH | DATA_LENGTH |
+------------+----------------+------------+----------------+-------------+
| ts2        | p0             |          0 |              0 |           0 |
| ts2        | p1             |          0 |              0 |           0 |
| ts2        | p2             |          0 |              0 |           0 |
| ts2        | p3             |          1 |             20 |          20 |
| ts3        | p0             |          0 |              0 |           0 |
| ts3        | p1             |          1 |             20 |          20 |
| ts3        | p2             |          0 |              0 |           0 |
+------------+----------------+------------+----------------+-------------+
7 rows in set (0.01 sec)

As shown earlier in this section, you can also verify which partitions were used for storing the rows by deleting these partitions and then performing a SELECT.

Handling of NULL with HASH and KEY partitioning.  NULL is handled somewhat differently for tables partitioned by HASH or KEY. In these cases, any partition expression that yields a NULL value is treated as though its return value were zero. We can verify this behavior by examining the effects on the file system of creating a table partitioned by HASH and populating it with a record containing appropriate values. Suppose that you have a table th (also in the p database) created using the following statement:

mysql> CREATE TABLE th (
    ->     c1 INT,
    ->     c2 VARCHAR(20)
    -> )
    -> PARTITION BY HASH(c1)
    -> PARTITIONS 2;
Query OK, 0 rows affected (0.00 sec)

The partitions belonging to this table can be viewed using the query shown here:

mysql> SELECT TABLE_NAME,PARTITION_NAME,TABLE_ROWS,AVG_ROW_LENGTH,DATA_LENGTH
     >   FROM INFORMATION_SCHEMA.PARTITIONS
     >   WHERE TABLE_SCHEMA = 'p' AND TABLE_NAME ='th';
+------------+----------------+------------+----------------+-------------+
| TABLE_NAME | PARTITION_NAME | TABLE_ROWS | AVG_ROW_LENGTH | DATA_LENGTH |
+------------+----------------+------------+----------------+-------------+
| th         | p0             |          0 |              0 |           0 |
| th         | p1             |          0 |              0 |           0 |
+------------+----------------+------------+----------------+-------------+
2 rows in set (0.00 sec)

Note that TABLE_ROWS for each partition is 0. Now insert two rows into th whose c1 column values are NULL and 0, and verify that these rows were inserted, as shown here:

mysql> INSERT INTO th VALUES (NULL, 'mothra'), (0, 'gigan');
Query OK, 1 row affected (0.00 sec)

mysql> SELECT * FROM th;
+------+---------+
| c1   | c2      |
+------+---------+
| NULL | mothra  |
+------+---------+
|    0 | gigan   |
+------+---------+
2 rows in set (0.01 sec)

Recall that for any integer N, the value of NULL MOD N is always NULL. For tables that are partitioned by HASH or KEY, this result is treated for determining the correct partition as 0. Checking the INFORMATION_SCHEMA.PARTITIONS table once again, we can see that both rows were inserted into partition p0:

mysql> SELECT TABLE_NAME, PARTITION_NAME, TABLE_ROWS, AVG_ROW_LENGTH, DATA_LENGTH
     >   FROM INFORMATION_SCHEMA.PARTITIONS
     >   WHERE TABLE_SCHEMA = 'p' AND TABLE_NAME ='th';
+------------+----------------+------------+----------------+-------------+
| TABLE_NAME | PARTITION_NAME | TABLE_ROWS | AVG_ROW_LENGTH | DATA_LENGTH |
+------------+----------------+------------+----------------+-------------+
| th         | p0             |          2 |             20 |          20 |
| th         | p1             |          0 |              0 |           0 |
+------------+----------------+------------+----------------+-------------+
2 rows in set (0.00 sec)

If you repeat this example using PARTITION BY KEY in place of PARTITION BY HASH in the definition of the table, you can verify easily that NULL is also treated like 0 for this type of partitioning.

21.3 Partition Management

MySQL 5.7 provides a number of ways to modify partitioned tables. It is possible to add, drop, redefine, merge, or split existing partitions. All of these actions can be carried out using the partitioning extensions to the ALTER TABLE statement. There are also ways to obtain information about partitioned tables and partitions. We discuss these topics in the sections that follow.

Note

In MySQL 5.7, all partitions of a partitioned table must have the same number of subpartitions, and it is not possible to change the subpartitioning once the table has been created.

To change a table's partitioning scheme, it is necessary only to use the ALTER TABLE statement with a partition_options clause. This clause has the same syntax as that as used with CREATE TABLE for creating a partitioned table, and always begins with the keywords PARTITION BY. Suppose that you have a table partitioned by range using the following CREATE TABLE statement:

CREATE TABLE trb3 (id INT, name VARCHAR(50), purchased DATE)
    PARTITION BY RANGE( YEAR(purchased) ) (
        PARTITION p0 VALUES LESS THAN (1990),
        PARTITION p1 VALUES LESS THAN (1995),
        PARTITION p2 VALUES LESS THAN (2000),
        PARTITION p3 VALUES LESS THAN (2005)
    );

To repartition this table so that it is partitioned by key into two partitions using the id column value as the basis for the key, you can use this statement:

ALTER TABLE trb3 PARTITION BY KEY(id) PARTITIONS 2;

This has the same effect on the structure of the table as dropping the table and re-creating it using CREATE TABLE trb3 PARTITION BY KEY(id) PARTITIONS 2;.

ALTER TABLE ... ENGINE = ... changes only the storage engine used by the table, and leaves the table's partitioning scheme intact. Use ALTER TABLE ... REMOVE PARTITIONING to remove a table's partitioning. See Section 14.1.8, “ALTER TABLE Syntax”.

Important

Only a single PARTITION BY, ADD PARTITION, DROP PARTITION, REORGANIZE PARTITION, or COALESCE PARTITION clause can be used in a given ALTER TABLE statement. If you (for example) wish to drop a partition and reorganize a table's remaining partitions, you must do so in two separate ALTER TABLE statements (one using DROP PARTITION and then a second one using REORGANIZE PARTITION).

In MySQL 5.7, it is possible to delete all rows from one or more selected partitions using ALTER TABLE ... TRUNCATE PARTITION.

21.3.1 Management of RANGE and LIST Partitions

Range and list partitions are very similar with regard to how the adding and dropping of partitions are handled. For this reason we discuss the management of both sorts of partitioning in this section. For information about working with tables that are partitioned by hash or key, see Section 21.3.2, “Management of HASH and KEY Partitions”. Dropping a RANGE or LIST partition is more straightforward than adding one, so we discuss this first.

Dropping a partition from a table that is partitioned by either RANGE or by LIST can be accomplished using the ALTER TABLE statement with a DROP PARTITION clause. Here is a very basic example, which supposes that you have already created a table which is partitioned by range and then populated with 10 records using the following CREATE TABLE and INSERT statements:

mysql> CREATE TABLE tr (id INT, name VARCHAR(50), purchased DATE)
    ->     PARTITION BY RANGE( YEAR(purchased) ) (
    ->         PARTITION p0 VALUES LESS THAN (1990),
    ->         PARTITION p1 VALUES LESS THAN (1995),
    ->         PARTITION p2 VALUES LESS THAN (2000),
    ->         PARTITION p3 VALUES LESS THAN (2005)
    ->     );
Query OK, 0 rows affected (0.01 sec)

mysql> INSERT INTO tr VALUES
    ->     (1, 'desk organiser', '2003-10-15'),
    ->     (2, 'CD player', '1993-11-05'),
    ->     (3, 'TV set', '1996-03-10'),
    ->     (4, 'bookcase', '1982-01-10'),
    ->     (5, 'exercise bike', '2004-05-09'),
    ->     (6, 'sofa', '1987-06-05'),
    ->     (7, 'popcorn maker', '2001-11-22'),
    ->     (8, 'aquarium', '1992-08-04'),
    ->     (9, 'study desk', '1984-09-16'),
    ->     (10, 'lava lamp', '1998-12-25');
Query OK, 10 rows affected (0.01 sec)

You can see which items should have been inserted into partition p2 as shown here:

mysql> SELECT * FROM tr
    -> WHERE purchased BETWEEN '1995-01-01' AND '1999-12-31';
+------+-----------+------------+
| id   | name      | purchased  |
+------+-----------+------------+
|    3 | TV set    | 1996-03-10 |
|   10 | lava lamp | 1998-12-25 |
+------+-----------+------------+
2 rows in set (0.00 sec)

To drop the partition named p2, execute the following command:

mysql> ALTER TABLE tr DROP PARTITION p2;
Query OK, 0 rows affected (0.03 sec)
Note

The NDB storage engine does not support ALTER TABLE ... DROP PARTITION. It does, however, support the other partitioning-related extensions to ALTER TABLE that are described in this chapter.

It is very important to remember that, when you drop a partition, you also delete all the data that was stored in that partition. You can see that this is the case by re-running the previous SELECT query:

mysql> SELECT * FROM tr WHERE purchased
    -> BETWEEN '1995-01-01' AND '1999-12-31';
Empty set (0.00 sec)

Because of this, you must have the DROP privilege for a table before you can execute ALTER TABLE ... DROP PARTITION on that table.

If you wish to drop all data from all partitions while preserving the table definition and its partitioning scheme, use the TRUNCATE TABLE statement. (See Section 14.1.34, “TRUNCATE TABLE Syntax”.)

If you intend to change the partitioning of a table without losing data, use ALTER TABLE ... REORGANIZE PARTITION instead. See later in this section, or Section 14.1.8.1, “ALTER TABLE Partition Operations”, for information about REORGANIZE PARTITION.

If you now execute a SHOW CREATE TABLE statement, you can see how the partitioning makeup of the table has been changed:

mysql> SHOW CREATE TABLE tr\G
*************************** 1. row ***************************
       Table: tr
Create Table: CREATE TABLE `tr` (
  `id` int(11) default NULL,
  `name` varchar(50) default NULL,
  `purchased` date default NULL
) ENGINE=MyISAM DEFAULT CHARSET=latin1
PARTITION BY RANGE ( YEAR(purchased) ) (
  PARTITION p0 VALUES LESS THAN (1990) ENGINE = MyISAM,
  PARTITION p1 VALUES LESS THAN (1995) ENGINE = MyISAM,
  PARTITION p3 VALUES LESS THAN (2005) ENGINE = MyISAM
)
1 row in set (0.01 sec)

When you insert new rows into the changed table with purchased column values between '1995-01-01' and '2004-12-31' inclusive, those rows will be stored in partition p3. You can verify this as follows:

mysql> INSERT INTO tr VALUES (11, 'pencil holder', '1995-07-12');
Query OK, 1 row affected (0.00 sec)

mysql> SELECT * FROM tr WHERE purchased
    -> BETWEEN '1995-01-01' AND '2004-12-31';
+------+----------------+------------+
| id   | name           | purchased  |
+------+----------------+------------+
|   11 | pencil holder  | 1995-07-12 |
|    1 | desk organiser | 2003-10-15 |
|    5 | exercise bike  | 2004-05-09 |
|    7 | popcorn maker  | 2001-11-22 |
+------+----------------+------------+
4 rows in set (0.00 sec)

mysql> ALTER TABLE tr DROP PARTITION p3;
Query OK, 0 rows affected (0.03 sec)

mysql> SELECT * FROM tr WHERE purchased
    -> BETWEEN '1995-01-01' AND '2004-12-31';
Empty set (0.00 sec)

Note that the number of rows dropped from the table as a result of ALTER TABLE ... DROP PARTITION is not reported by the server as it would be by the equivalent DELETE query.

Dropping LIST partitions uses exactly the same ALTER TABLE ... DROP PARTITION syntax as used for dropping RANGE partitions. However, there is one important difference in the effect this has on your use of the table afterward: You can no longer insert into the table any rows having any of the values that were included in the value list defining the deleted partition. (See Section 21.2.2, “LIST Partitioning”, for an example.)

To add a new range or list partition to a previously partitioned table, use the ALTER TABLE ... ADD PARTITION statement. For tables which are partitioned by RANGE, this can be used to add a new range to the end of the list of existing partitions. Suppose that you have a partitioned table containing membership data for your organization, which is defined as follows:

CREATE TABLE members (
    id INT,
    fname VARCHAR(25),
    lname VARCHAR(25),
    dob DATE
)
PARTITION BY RANGE( YEAR(dob) ) (
    PARTITION p0 VALUES LESS THAN (1970),
    PARTITION p1 VALUES LESS THAN (1980),
    PARTITION p2 VALUES LESS THAN (1990)
);

Suppose further that the minimum age for members is 16. As the calendar approaches the end of 2005, you realize that you will soon be admitting members who were born in 1990 (and later in years to come). You can modify the members table to accommodate new members born in the years 1990 to 1999 as shown here:

ALTER TABLE members ADD PARTITION (PARTITION p3 VALUES LESS THAN (2000));

With tables that are partitioned by range, you can use ADD PARTITION to add new partitions to the high end of the partitions list only. Trying to add a new partition in this manner between or before existing partitions results in an error as shown here:

mysql> ALTER TABLE members
     >     ADD PARTITION (
     >     PARTITION n VALUES LESS THAN (1960));
ERROR 1463 (HY000): VALUES LESS THAN value must be strictly »
   increasing for each partition

You can work around this problem by reorganizing the first partition into two new ones that split the range between them, like this:

ALTER TABLE members
    REORGANIZE PARTITION p0 INTO (
        PARTITION n0 VALUES LESS THAN (1960),
        PARTITION n1 VALUES LESS THAN (1970)
);

Using SHOW CREATE TABLE you can see that the ALTER TABLE statement has had the desired effect:

mysql> SHOW CREATE TABLE members\G
*************************** 1. row ***************************
       Table: members
Create Table: CREATE TABLE `members` (
  `id` int(11) DEFAULT NULL,
  `fname` varchar(25) DEFAULT NULL,
  `lname` varchar(25) DEFAULT NULL,
  `dob` date DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=latin1
/*!50100 PARTITION BY RANGE ( YEAR(dob))
(PARTITION n0 VALUES LESS THAN (1960) ENGINE = InnoDB,
 PARTITION n1 VALUES LESS THAN (1970) ENGINE = InnoDB,
 PARTITION p1 VALUES LESS THAN (1980) ENGINE = InnoDB,
 PARTITION p2 VALUES LESS THAN (1990) ENGINE = InnoDB,
 PARTITION p3 VALUES LESS THAN (2000) ENGINE = InnoDB) */
1 row in set (0.00 sec)

See also Section 14.1.8.1, “ALTER TABLE Partition Operations”.

You can also use ALTER TABLE ... ADD PARTITION to add new partitions to a table that is partitioned by LIST. Suppose a table tt is defined using the following CREATE TABLE statement:

CREATE TABLE tt (
    id INT,
    data INT
)
PARTITION BY LIST(data) (
    PARTITION p0 VALUES IN (5, 10, 15),
    PARTITION p1 VALUES IN (6, 12, 18)
);

You can add a new partition in which to store rows having the data column values 7, 14, and 21 as shown:

ALTER TABLE tt ADD PARTITION (PARTITION p2 VALUES IN (7, 14, 21));

You cannot add a new LIST partition encompassing any values that are already included in the value list of an existing partition. If you attempt to do so, an error will result:

mysql> ALTER TABLE tt ADD PARTITION 
     >     (PARTITION np VALUES IN (4, 8, 12));
ERROR 1465 (HY000): Multiple definition of same constant »
                    in list partitioning

Because any rows with the data column value 12 have already been assigned to partition p1, you cannot create a new partition on table tt that includes 12 in its value list. To accomplish this, you could drop p1, and add np and then a new p1 with a modified definition. However, as discussed earlier, this would result in the loss of all data stored in p1—and it is often the case that this is not what you really want to do. Another solution might appear to be to make a copy of the table with the new partitioning and to copy the data into it using CREATE TABLE ... SELECT ..., then drop the old table and rename the new one, but this could be very time-consuming when dealing with a large amounts of data. This also might not be feasible in situations where high availability is a requirement.

You can add multiple partitions in a single ALTER TABLE ... ADD PARTITION statement as shown here:

CREATE TABLE employees (
  id INT NOT NULL,
  fname VARCHAR(50) NOT NULL,
  lname VARCHAR(50) NOT NULL,
  hired DATE NOT NULL
)
PARTITION BY RANGE( YEAR(hired) ) (
  PARTITION p1 VALUES LESS THAN (1991),
  PARTITION p2 VALUES LESS THAN (1996),
  PARTITION p3 VALUES LESS THAN (2001),
  PARTITION p4 VALUES LESS THAN (2005)
);

ALTER TABLE employees ADD PARTITION (
    PARTITION p5 VALUES LESS THAN (2010),
    PARTITION p6 VALUES LESS THAN MAXVALUE
);

Fortunately, MySQL's partitioning implementation provides ways to redefine partitions without losing data. Let us look first at a couple of simple examples involving RANGE partitioning. Recall the members table which is now defined as shown here:

mysql> SHOW CREATE TABLE members\G
*************************** 1. row ***************************
       Table: members
Create Table: CREATE TABLE `members` (
  `id` int(11) default NULL,
  `fname` varchar(25) default NULL,
  `lname` varchar(25) default NULL,
  `dob` date default NULL
) ENGINE=MyISAM DEFAULT CHARSET=latin1
PARTITION BY RANGE ( YEAR(dob) ) (
  PARTITION p0 VALUES LESS THAN (1970) ENGINE = MyISAM,
  PARTITION p1 VALUES LESS THAN (1980) ENGINE = MyISAM,
  PARTITION p2 VALUES LESS THAN (1990) ENGINE = MyISAM.
  PARTITION p3 VALUES LESS THAN (2000) ENGINE = MyISAM
)

Suppose that you would like to move all rows representing members born before 1960 into a separate partition. As we have already seen, this cannot be done using ALTER TABLE ... ADD PARTITION. However, you can use another partition-related extension to ALTER TABLE to accomplish this:

ALTER TABLE members REORGANIZE PARTITION p0 INTO (
    PARTITION s0 VALUES LESS THAN (1960),
    PARTITION s1 VALUES LESS THAN (1970)
);

In effect, this command splits partition p0 into two new partitions s0 and s1. It also moves the data that was stored in p0 into the new partitions according to the rules embodied in the two PARTITION ... VALUES ... clauses, so that s0 contains only those records for which YEAR(dob) is less than 1960 and s1 contains those rows in which YEAR(dob) is greater than or equal to 1960 but less than 1970.

A REORGANIZE PARTITION clause may also be used for merging adjacent partitions. You can return the members table to its previous partitioning as shown here:

ALTER TABLE members REORGANIZE PARTITION s0,s1 INTO (
    PARTITION p0 VALUES LESS THAN (1970)
);

No data is lost in splitting or merging partitions using REORGANIZE PARTITION. In executing the above statement, MySQL moves all of the records that were stored in partitions s0 and s1 into partition p0.

The general syntax for REORGANIZE PARTITION is shown here:

ALTER TABLE tbl_name
    REORGANIZE PARTITION partition_list
    INTO (partition_definitions);

Here, tbl_name is the name of the partitioned table, and partition_list is a comma-separated list of names of one or more existing partitions to be changed. partition_definitions is a comma-separated list of new partition definitions, which follow the same rules as for the partition_definitions list used in CREATE TABLE (see Section 14.1.18, “CREATE TABLE Syntax”). It should be noted that you are not limited to merging several partitions into one, or to splitting one partition into many, when using REORGANIZE PARTITION. For example, you can reorganize all four partitions of the members table into two, as follows:

ALTER TABLE members REORGANIZE PARTITION p0,p1,p2,p3 INTO (
    PARTITION m0 VALUES LESS THAN (1980),
    PARTITION m1 VALUES LESS THAN (2000)
);

You can also use REORGANIZE PARTITION with tables that are partitioned by LIST. Let us return to the problem of adding a new partition to the list-partitioned tt table and failing because the new partition had a value that was already present in the value-list of one of the existing partitions. We can handle this by adding a partition that contains only nonconflicting values, and then reorganizing the new partition and the existing one so that the value which was stored in the existing one is now moved to the new one:

ALTER TABLE tt ADD PARTITION (PARTITION np VALUES IN (4, 8));
ALTER TABLE tt REORGANIZE PARTITION p1,np INTO (
    PARTITION p1 VALUES IN (6, 18),
    PARTITION np VALUES in (4, 8, 12)
);

Here are some key points to keep in mind when using ALTER TABLE ... REORGANIZE PARTITION to repartition tables that are partitioned by RANGE or LIST:

  • The PARTITION clauses used to determine the new partitioning scheme are subject to the same rules as those used with a CREATE TABLE statement.

    Most importantly, you should remember that the new partitioning scheme cannot have any overlapping ranges (applies to tables partitioned by RANGE) or sets of values (when reorganizing tables partitioned by LIST).

  • The combination of partitions in the partition_definitions list should account for the same range or set of values overall as the combined partitions named in the partition_list.

    For instance, in the members table used as an example in this section, partitions p1 and p2 together cover the years 1980 through 1999. Therefore, any reorganization of these two partitions should cover the same range of years overall.

  • For tables partitioned by RANGE, you can reorganize only adjacent partitions; you cannot skip over range partitions.

    For instance, you could not reorganize the members table used as an example in this section using a statement beginning with ALTER TABLE members REORGANIZE PARTITION p0,p2 INTO ... because p0 covers the years prior to 1970 and p2 the years from 1990 through 1999 inclusive, and thus the two are not adjacent partitions.

  • You cannot use REORGANIZE PARTITION to change the table's partitioning type; that is, you cannot (for example) change RANGE partitions to HASH partitions or vice versa. You also cannot use this command to change the partitioning expression or column. To accomplish either of these tasks without dropping and re-creating the table, you can use ALTER TABLE ... PARTITION BY .... For example:

    ALTER TABLE members
        PARTITION BY HASH( YEAR(dob) )
        PARTITIONS 8;
    

21.3.2 Management of HASH and KEY Partitions

Tables which are partitioned by hash or by key are very similar to one another with regard to making changes in a partitioning setup, and both differ in a number of ways from tables which have been partitioned by range or list. For that reason, this section addresses the modification of tables partitioned by hash or by key only. For a discussion of adding and dropping of partitions of tables that are partitioned by range or list, see Section 21.3.1, “Management of RANGE and LIST Partitions”.

You cannot drop partitions from tables that are partitioned by HASH or KEY in the same way that you can from tables that are partitioned by RANGE or LIST. However, you can merge HASH or KEY partitions using the ALTER TABLE ... COALESCE PARTITION statement. Suppose that you have a table containing data about clients, which is divided into twelve partitions. The clients table is defined as shown here:

CREATE TABLE clients (
    id INT,
    fname VARCHAR(30),
    lname VARCHAR(30),
    signed DATE
)
PARTITION BY HASH( MONTH(signed) )
PARTITIONS 12;

To reduce the number of partitions from twelve to eight, execute the following ALTER TABLE command:

mysql> ALTER TABLE clients COALESCE PARTITION 4;
Query OK, 0 rows affected (0.02 sec)

COALESCE works equally well with tables that are partitioned by HASH, KEY, LINEAR HASH, or LINEAR KEY. Here is an example similar to the previous one, differing only in that the table is partitioned by LINEAR KEY:

mysql> CREATE TABLE clients_lk (
    ->     id INT,
    ->     fname VARCHAR(30),
    ->     lname VARCHAR(30),
    ->     signed DATE
    -> )
    -> PARTITION BY LINEAR KEY(signed)
    -> PARTITIONS 12;
Query OK, 0 rows affected (0.03 sec)

mysql> ALTER TABLE clients_lk COALESCE PARTITION 4;
Query OK, 0 rows affected (0.06 sec)
Records: 0  Duplicates: 0  Warnings: 0

The number following COALESCE PARTITION is the number of partitions to merge into the remainder—in other words, it is the number of partitions to remove from the table.

If you attempt to remove more partitions than the table has, the result is an error like the one shown:

mysql> ALTER TABLE clients COALESCE PARTITION 18;
ERROR 1478 (HY000): Cannot remove all partitions, use DROP TABLE instead

To increase the number of partitions for the clients table from 12 to 18. use ALTER TABLE ... ADD PARTITION as shown here:

ALTER TABLE clients ADD PARTITION PARTITIONS 6;

21.3.3 Exchanging Partitions and Subpartitions with Tables

In MySQL 5.7, it is possible to exchange a table partition or subpartition with a table using ALTER TABLE pt EXCHANGE PARTITION p WITH TABLE nt, where pt is the partitioned table and p is the partition or subpartition of pt to be exchanged with unpartitioned table nt, provided that the following statements are true:

  1. Table nt is not itself partitioned.

  2. Table nt is not a temporary table.

  3. The structures of tables pt and nt are otherwise identical.

  4. Table nt contains no foreign key references, and no other table has any foreign keys that refer to nt.

  5. There are no rows in nt that lie outside the boundaries of the partition definition for p. This condition does not apply if the WITHOUT VALIDATION option is used. The [{WITH|WITHOUT} VALIDATION] option was added in MySQL 5.7.5.

In addition to the ALTER, INSERT, and CREATE privileges usually required for ALTER TABLE statements, you must have the DROP privilege to perform ALTER TABLE ... EXCHANGE PARTITION.

You should also be aware of the following effects of ALTER TABLE ... EXCHANGE PARTITION:

  • Executing ALTER TABLE ... EXCHANGE PARTITION does not invoke any triggers on either the partitioned table or the table to be exchanged.

  • Any AUTO_INCREMENT columns in the exchanged table are reset.

  • The IGNORE keyword has no effect when used with ALTER TABLE ... EXCHANGE PARTITION.

The syntax of the ALTER TABLE ... EXCHANGE PARTITION statement is shown here, where pt is the partitioned table, p is the partition or subpartition to be exchanged, and nt is the nonpartitioned table to be exchanged with p:

ALTER TABLE pt
    EXCHANGE PARTITION p
    WITH TABLE nt;

Optionally, you can append a WITH VALIDATION or WITHOUT VALIDATION clause. When WITHOUT VALIDATION is specified, the ALTER TABLE ... EXCHANGE PARTITION operation does not perform row-by-row validation when exchanging a partition a nonpartitioned table, allowing database administrators to assume responsibility for ensuring that rows are within the boundaries of the partition definition. WITH VALIDATION is the default behavior and need not be specified explicitly. The [{WITH|WITHOUT} VALIDATION] option was added in MySQL 5.7.5.

One and only one partition or subpartition may be exchanged with one and only one nonpartitioned table in a single ALTER TABLE EXCHANGE PARTITION statement. To exchange multiple partitions or subpartitions, use multiple ALTER TABLE EXCHANGE PARTITION statements. EXCHANGE PARTITION may not be combined with other ALTER TABLE options. The partitioning and (if applicable) subpartitioning used by the partitioned table may be of any type or types supported in MySQL 5.7.

Exchanging a Partition with a Nonpartitioned Table

Suppose that a partitioned table e has been created and populated using the following SQL statements:

CREATE TABLE e (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30)
)
    PARTITION BY RANGE (id) (
        PARTITION p0 VALUES LESS THAN (50),
        PARTITION p1 VALUES LESS THAN (100),
        PARTITION p2 VALUES LESS THAN (150),
        PARTITION p3 VALUES LESS THAN (MAXVALUE)
);

INSERT INTO e VALUES
    (1669, "Jim", "Smith"),
    (337, "Mary", "Jones"),
    (16, "Frank", "White"),
    (2005, "Linda", "Black");

Now we create a nonpartitioned copy of e named e2. This can be done using the mysql client as shown here:

mysql> CREATE TABLE e2 LIKE e;
Query OK, 0 rows affected (1.34 sec)

mysql> ALTER TABLE e2 REMOVE PARTITIONING;
Query OK, 0 rows affected (0.90 sec)
Records: 0  Duplicates: 0  Warnings: 0

You can see which partitions in table e contain rows by querying the INFORMATION_SCHEMA.PARTITIONS table, like this:

mysql> SELECT PARTITION_NAME, TABLE_ROWS
    ->     FROM INFORMATION_SCHEMA.PARTITIONS
    ->     WHERE TABLE_NAME = 'e';
+----------------+------------+
| PARTITION_NAME | TABLE_ROWS |
+----------------+------------+
| p0             |          1 |
| p1             |          0 |
| p2             |          0 |
| p3             |          3 |
+----------------+------------+
4 rows in set (0.00 sec)
Note

For partitioned InnoDB tables, the row count given in the TABLE_ROWS column of the INFORMATION_SCHEMA.PARTITIONS table is only an estimated value used in SQL optimization, and is not always exact.

To exchange partition p0 in table e with table e2, you can use the ALTER TABLE statement shown here:

mysql> ALTER TABLE e EXCHANGE PARTITION p0 WITH TABLE e2;
Query OK, 0 rows affected (0.28 sec)

More precisely, the statement just issued causes any rows found in the partition to be swapped with those found in the table. You can observe how this has happened by querying the INFORMATION_SCHEMA.PARTITIONS table, as before. The table row that was previously found in partition p0 is no longer present:

mysql> SELECT PARTITION_NAME, TABLE_ROWS
    ->     FROM INFORMATION_SCHEMA.PARTITIONS
    ->     WHERE TABLE_NAME = 'e';
+----------------+------------+
| PARTITION_NAME | TABLE_ROWS |
+----------------+------------+
| p0             |          0 |
| p1             |          0 |
| p2             |          0 |
| p3             |          3 |
+----------------+------------+
4 rows in set (0.00 sec)

If you query table e2, you can see that the missing row can now be found there:

mysql> SELECT * FROM e2;
+----+-------+-------+
| id | fname | lname |
+----+-------+-------+
| 16 | Frank | White |
+----+-------+-------+
1 row in set (0.00 sec)

The table to be exchanged with the partition does not necessarily have to be empty. To demonstrate this, we first insert a new row into table e, making sure that this row is stored in partition p0 by choosing an id column value that is less than 50, and verifying this afterward by querying the PARTITIONS table:

                                                          
mysql> INSERT INTO e VALUES (41, "Michael", "Green");            
Query OK, 1 row affected (0.05 sec)                              

mysql> SELECT PARTITION_NAME, TABLE_ROWS
    ->     FROM INFORMATION_SCHEMA.PARTITIONS
    ->     WHERE TABLE_NAME = 'e';            
+----------------+------------+             
| PARTITION_NAME | TABLE_ROWS |             
+----------------+------------+             
| p0             |          1 |             
| p1             |          0 |             
| p2             |          0 |             
| p3             |          3 |             
+----------------+------------+             
4 rows in set (0.00 sec)        

Now we once again exchange partition p0 with table e2 using the same ALTER TABLE statement as previously:

mysql> ALTER TABLE e EXCHANGE PARTITION p0 WITH TABLE e2;
Query OK, 0 rows affected (0.28 sec)

The output of the following queries shows that the table row that was stored in partition p0 and the table row that was stored in table e2, prior to issuing the ALTER TABLE statement, have now switched places:

mysql> SELECT * FROM e;
+------+-------+-------+
| id   | fname | lname |
+------+-------+-------+
|   16 | Frank | White |
| 1669 | Jim   | Smith |
|  337 | Mary  | Jones |
| 2005 | Linda | Black |
+------+-------+-------+
4 rows in set (0.00 sec)

mysql> SELECT PARTITION_NAME, TABLE_ROWS
    ->     FROM INFORMATION_SCHEMA.PARTITIONS
    ->     WHERE TABLE_NAME = 'e';
+----------------+------------+
| PARTITION_NAME | TABLE_ROWS |
+----------------+------------+
| p0             |          1 |
| p1             |          0 |
| p2             |          0 |
| p3             |          3 |
+----------------+------------+
4 rows in set (0.00 sec)

mysql> SELECT * FROM e2;
+----+---------+-------+
| id | fname   | lname |
+----+---------+-------+
| 41 | Michael | Green |
+----+---------+-------+
1 row in set (0.00 sec)

Nonmatching Rows

You should keep in mind that any rows found in the nonpartitioned table prior to issuing the ALTER TABLE ... EXCHANGE PARTITION statement must meet the conditions required for them to be stored in the target partition; otherwise, the statement fails. To see how this occurs, first insert a row into e2 that is outside the boundaries of the partition definition for partition p0 of table e. For example, insert a row with an id column value that is too large; then, try to exchange the table with the partition again:

mysql> INSERT INTO e2 VALUES (51, "Ellen", "McDonald");
Query OK, 1 row affected (0.08 sec)

mysql> ALTER TABLE e EXCHANGE PARTITION p0 WITH TABLE e2;
ERROR 1707 (HY000): Found row that does not match the partition

Only the WITHOUT VALIDATION option would permit this operation to succeed:

mysql> ALTER TABLE e EXCHANGE PARTITION p0 WITH TABLE e2 WITHOUT VALIDATION;
Query OK, 0 rows affected (0.02 sec)

When a partition is exchanged with a table that contains rows that do not match the partition definition, it is the responsibility of the database administrator to fix the non-matching rows, which can be performed using REPAIR TABLE or ALTER TABLE ... REPAIR PARTITION.

Exchanging Partitions Without Row-By-Row Validation

To avoid time consuming validation when exchanging a partition with a table that has many rows, it is possible to skip the row-by-row validation step by appending WITHOUT VALIDATION to the ALTER TABLE ... EXCHANGE PARTITION statement.

The following example compares the difference between execution times when exchanging a partition with a nonpartitioned table, with and without validation. The partitioned table (table e) contains two partitions of 1 million rows each. The rows in p0 of table e are removed and p0 is exchanged with a nonpartitioned table of 1 million rows. The WITH VALIDATION operation takes 0.74 seconds. By comparison, the WITHOUT VALIDATION operation takes 0.01 seconds.

# Create a partitioned table with 1 million rows in each partition

CREATE TABLE e (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30)
)
    PARTITION BY RANGE (id) (
        PARTITION p0 VALUES LESS THAN (1000001),
        PARTITION p1 VALUES LESS THAN (2000001),
);

mysql> SELECT COUNT(*) FROM e;                                             
| COUNT(*) |
+----------+
|  2000000 |
+----------+
1 row in set (0.27 sec)

# View the rows in each partition

SELECT PARTITION_NAME, TABLE_ROWS FROM INFORMATION_SCHEMA.PARTITIONS WHERE TABLE_NAME = 'e';
+----------------+-------------+
| PARTITION_NAME | TABLE_ROWS  |
+----------------+-------------+
| p0             |     1000000 |
| p1             |     1000000 |
+----------------+-------------+
2 rows in set (0.00 sec)

# Create a nonpartitioned table of the same structure and populate it with 1 million rows

CREATE TABLE e2 (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30)
);

mysql> SELECT COUNT(*) FROM e2;
+----------+
| COUNT(*) |
+----------+
|  1000000 |
+----------+
1 row in set (0.24 sec)

# Create another nonpartitioned table of the same structure and populate it with 1 million rows

CREATE TABLE e3 (
    id INT NOT NULL,
    fname VARCHAR(30),
    lname VARCHAR(30)
);
    
mysql> SELECT COUNT(*) FROM e3;
+----------+
| COUNT(*) |
+----------+
|  1000000 |
+----------+
1 row in set (0.25 sec)

# Drop the rows from p0 of table e

mysql> DELETE FROM e WHERE id < 1000001;
Query OK, 1000000 rows affected (5.55 sec)

# Confirm that there are no rows in partition p0

mysql> SELECT PARTITION_NAME, TABLE_ROWS FROM INFORMATION_SCHEMA.PARTITIONS WHERE TABLE_NAME = 'e';
+----------------+------------+
| PARTITION_NAME | TABLE_ROWS |
+----------------+------------+
| p0             |          0 |
| p1             |    1000000 |
+----------------+------------+
2 rows in set (0.00 sec)
    
# Exchange partition p0 of table e with the table e2 'WITH VALIDATION'

mysql> ALTER TABLE e EXCHANGE PARTITION p0 WITH TABLE e2 WITH VALIDATION;
Query OK, 0 rows affected (0.74 sec)

# Confirm that the partition was exchanged with table e2

mysql> SELECT PARTITION_NAME, TABLE_ROWS FROM INFORMATION_SCHEMA.PARTITIONS WHERE TABLE_NAME = 'e';
+----------------+------------+
| PARTITION_NAME | TABLE_ROWS |
+----------------+------------+
| p0             |    1000000 |
| p1             |    1000000 |
+----------------+------------+
2 rows in set (0.00 sec)

# Once again, drop the rows from p0 of table e

mysql> DELETE FROM e WHERE id < 1000001;
Query OK, 1000000 rows affected (5.55 sec)

# Confirm that there are no rows in partition p0

mysql> SELECT PARTITION_NAME, TABLE_ROWS FROM INFORMATION_SCHEMA.PARTITIONS WHERE TABLE_NAME = 'e';
+----------------+------------+
| PARTITION_NAME | TABLE_ROWS |
+----------------+------------+
| p0             |          0 |
| p1             |    1000000 |
+----------------+------------+
2 rows in set (0.00 sec)

# Exchange partition p0 of table e with the table e3 'WITHOUT VALIDATION'

mysql> ALTER TABLE e EXCHANGE PARTITION p0 WITH TABLE e3 WITHOUT VALIDATION;
Query OK, 0 rows affected (0.01 sec)

# Confirm that the partition was exchanged with table e3

mysql> SELECT PARTITION_NAME, TABLE_ROWS FROM INFORMATION_SCHEMA.PARTITIONS WHERE TABLE_NAME = 'e';
+----------------+------------+
| PARTITION_NAME | TABLE_ROWS |
+----------------+------------+
| p0             |    1000000 |
| p1             |    1000000 |
+----------------+------------+
2 rows in set (0.00 sec)    
      

If a partition is exchanged with a table that contains rows that do not match the partition definition, it is the responsibility of the database administrator to fix the non-matching rows, which can be performed using REPAIR TABLE or ALTER TABLE ... REPAIR PARTITION.

Exchanging a Subpartition with a Nonpartitioned Table

You can also exchange a subpartition of a subpartitioned table (see Section 21.2.6, “Subpartitioning”) with a nonpartitioned table using an ALTER TABLE ... EXCHANGE PARTITION statement. In the following example, we first create a table es that is partitioned by RANGE and subpartitioned by KEY, populate this table as we did table e, and then create an empty, nonpartitioned copy es2 of the table, as shown here:

mysql> CREATE TABLE es (
    ->     id INT NOT NULL,
    ->     fname VARCHAR(30),
    ->     lname VARCHAR(30)
    -> )
    ->     PARTITION BY RANGE (id)
    ->     SUBPARTITION BY KEY (lname)
    ->     SUBPARTITIONS 2 (
    ->         PARTITION p0 VALUES LESS THAN (50),
    ->         PARTITION p1 VALUES LESS THAN (100),
    ->         PARTITION p2 VALUES LESS THAN (150),
    ->         PARTITION p3 VALUES LESS THAN (MAXVALUE)
    ->     );
Query OK, 0 rows affected (2.76 sec)

mysql> INSERT INTO es VALUES
    ->     (1669, "Jim", "Smith"),
    ->     (337, "Mary", "Jones"),
    ->     (16, "Frank", "White"),
    ->     (2005, "Linda", "Black");
Query OK, 4 rows affected (0.04 sec)
Records: 4  Duplicates: 0  Warnings: 0

mysql> CREATE TABLE es2 LIKE es;
Query OK, 0 rows affected (1.27 sec)

mysql> ALTER TABLE es2 REMOVE PARTITIONING;
Query OK, 0 rows affected (0.70 sec)
Records: 0  Duplicates: 0  Warnings: 0

Although we did not explicitly name any of the subpartitions when creating table es, we can obtain generated names for these by including the SUBPARTITION_NAME of the PARTITIONS table from INFORMATION_SCHEMA when selecting from that table, as shown here:

mysql> SELECT PARTITION_NAME, SUBPARTITION_NAME, TABLE_ROWS
    ->     FROM INFORMATION_SCHEMA.PARTITIONS
    ->     WHERE TABLE_NAME = 'es';
+----------------+-------------------+------------+
| PARTITION_NAME | SUBPARTITION_NAME | TABLE_ROWS |
+----------------+-------------------+------------+
| p0             | p0sp0             |          1 |
| p0             | p0sp1             |          0 |
| p1             | p1sp0             |          0 |
| p1             | p1sp1             |          0 |
| p2             | p2sp0             |          0 |
| p2             | p2sp1             |          0 |
| p3             | p3sp0             |          3 |
| p3             | p3sp1             |          0 |
+----------------+-------------------+------------+
8 rows in set (0.00 sec)

The following ALTER TABLE statement exchanges subpartition p3sp0 table es with the nonpartitioned table es2:

mysql> ALTER TABLE es EXCHANGE PARTITION p3sp0 WITH TABLE es2;
Query OK, 0 rows affected (0.29 sec)

You can verify that the rows were exchanged by issuing the following queries:

mysql> SELECT PARTITION_NAME, SUBPARTITION_NAME, TABLE_ROWS
    ->     FROM INFORMATION_SCHEMA.PARTITIONS
    ->     WHERE TABLE_NAME = 'es';
+----------------+-------------------+------------+
| PARTITION_NAME | SUBPARTITION_NAME | TABLE_ROWS |
+----------------+-------------------+------------+
| p0             | p0sp0             |          1 |
| p0             | p0sp1             |          0 |
| p1             | p1sp0             |          0 |
| p1             | p1sp1             |          0 |
| p2             | p2sp0             |          0 |
| p2             | p2sp1             |          0 |
| p3             | p3sp0             |          0 |
| p3             | p3sp1             |          0 |
+----------------+-------------------+------------+
8 rows in set (0.00 sec)

mysql> SELECT * FROM es2;
+------+-------+-------+
| id   | fname | lname |
+------+-------+-------+
| 1669 | Jim   | Smith |
|  337 | Mary  | Jones |
| 2005 | Linda | Black |
+------+-------+-------+
3 rows in set (0.00 sec)

If a table is subpartitioned, you can exchange only a subpartition of the table—not an entire partition—with an unpartitioned table, as shown here:

mysql> ALTER TABLE es EXCHANGE PARTITION p3 WITH TABLE es2;
ERROR 1704 (HY000): Subpartitioned table, use subpartition instead of partition

The comparison of table structures used by MySQL is very strict. The number, order, names, and types of columns and indexes of the partitioned table and the nonpartitioned table must match exactly. In addition, both tables must use the same storage engine:

mysql> CREATE TABLE es3 LIKE e;
Query OK, 0 rows affected (1.31 sec)

mysql> ALTER TABLE es3 REMOVE PARTITIONING;
Query OK, 0 rows affected (0.53 sec)
Records: 0  Duplicates: 0  Warnings: 0

mysql> SHOW CREATE TABLE es3\G
*************************** 1. row ***************************
       Table: es3
Create Table: CREATE TABLE `es3` (
  `id` int(11) NOT NULL,
  `fname` varchar(30) DEFAULT NULL,
  `lname` varchar(30) DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=latin1
1 row in set (0.00 sec)

mysql> ALTER TABLE es3 ENGINE = MyISAM;
Query OK, 0 rows affected (0.15 sec)
Records: 0  Duplicates: 0  Warnings: 0

mysql> ALTER TABLE es EXCHANGE PARTITION p3sp0 WITH TABLE es3;
ERROR 1497 (HY000): The mix of handlers in the partitions is not allowed in this version of MySQL

21.3.4 Maintenance of Partitions

A number of table and partition maintenance tasks can be carried out using SQL statements intended for such purposes on partitioned tables in MySQL 5.7.

Table maintenance of partitioned tables can be accomplished using the statements CHECK TABLE, OPTIMIZE TABLE, ANALYZE TABLE, and REPAIR TABLE, which are supported for partitioned tables.

You can use a number of extensions to ALTER TABLE for performing operations of this type on one or more partitions directly, as described in the following list:

  • Rebuilding partitions.  Rebuilds the partition; this has the same effect as dropping all records stored in the partition, then reinserting them. This can be useful for purposes of defragmentation.

    Example:

    ALTER TABLE t1 REBUILD PARTITION p0, p1;
    
  • Optimizing partitions.  If you have deleted a large number of rows from a partition or if you have made many changes to a partitioned table with variable-length rows (that is, having VARCHAR, BLOB, or TEXT columns), you can use ALTER TABLE ... OPTIMIZE PARTITION to reclaim any unused space and to defragment the partition data file.

    Example:

    ALTER TABLE t1 OPTIMIZE PARTITION p0, p1;
    

    Using OPTIMIZE PARTITION on a given partition is equivalent to running CHECK PARTITION, ANALYZE PARTITION, and REPAIR PARTITION on that partition.

    Some MySQL storage engines, including InnoDB, do not support per-partition optimization; in these cases, ALTER TABLE ... OPTIMIZE PARTITION analyzes and rebuilds the entire table, and causes an appropriate warning to be issued. (Bug #11751825, Bug #42822) Use ALTER TABLE ... REBUILD PARTITION and ALTER TABLE ... ANALYZE PARTITION instead, to avoid this issue.

  • Analyzing partitions.  This reads and stores the key distributions for partitions.

    Example:

    ALTER TABLE t1 ANALYZE PARTITION p3;
    
  • Repairing partitions.  This repairs corrupted partitions.

    Example:

    ALTER TABLE t1 REPAIR PARTITION p0,p1;
    

    Normally, REPAIR PARTITION fails when the partition contains duplicate key errors. In MySQL 5.7.2 and later, you can use ALTER IGNORE TABLE with this option, in which case all rows that cannot be moved due to the presence of duplicate keys are removed from the partition (Bug #16900947).

  • Checking partitions.  You can check partitions for errors in much the same way that you can use CHECK TABLE with nonpartitioned tables.

    Example:

    ALTER TABLE trb3 CHECK PARTITION p1;
    

    This command will tell you if the data or indexes in partition p1 of table t1 are corrupted. If this is the case, use ALTER TABLE ... REPAIR PARTITION to repair the partition.

    Normally, CHECK PARTITION fails when the partition contains duplicate key errors. In MySQL 5.7.2 and later, you can use ALTER IGNORE TABLE with this option, in which case the statement returns the contents of each row in the partition where a duplicate key violation is found. Note that only the values for the columns in the partitioning expression for the table are reported. (Bug #16900947)

Each of the statements in the list just shown also supports the keyword ALL in place of the list of partition names. Using ALL causes the statement to act on all partitions in the table.

The use of mysqlcheck and myisamchk is not supported with partitioned tables.

In MySQL 5.7, you can also truncate partitions using ALTER TABLE ... TRUNCATE PARTITION. This statement can be used to delete all rows from one or more partitions in much the same way that TRUNCATE TABLE deletes all rows from a table.

ALTER TABLE ... TRUNCATE PARTITION ALL truncates all partitions in the table.

Prior to MySQL 5.7.2, ANALYZE, CHECK, OPTIMIZE, REBUILD, REPAIR, and TRUNCATE operations were not permitted on subpartitions (Bug #14028340, Bug #65184).

21.3.5 Obtaining Information About Partitions

This section discusses obtaining information about existing partitions, which can be done in a number of ways. Methods of obtaining such information include the following:

As discussed elsewhere in this chapter, SHOW CREATE TABLE includes in its output the PARTITION BY clause used to create a partitioned table. For example:

mysql> SHOW CREATE TABLE trb3\G
*************************** 1. row ***************************
       Table: trb3
Create Table: CREATE TABLE `trb3` (
  `id` int(11) default NULL,
  `name` varchar(50) default NULL,
  `purchased` date default NULL
) ENGINE=MyISAM DEFAULT CHARSET=latin1
PARTITION BY RANGE (YEAR(purchased)) (
  PARTITION p0 VALUES LESS THAN (1990) ENGINE = MyISAM,
  PARTITION p1 VALUES LESS THAN (1995) ENGINE = MyISAM,
  PARTITION p2 VALUES LESS THAN (2000) ENGINE = MyISAM,
  PARTITION p3 VALUES LESS THAN (2005) ENGINE = MyISAM
)
1 row in set (0.00 sec)

The output from SHOW TABLE STATUS for partitioned tables is the same as that for nonpartitioned tables, except that the Create_options column contains the string partitioned. The Engine column contains the name of the storage engine used by all partitions of the table. (See Section 14.7.5.36, “SHOW TABLE STATUS Syntax”, for more information about this statement.)

You can also obtain information about partitions from INFORMATION_SCHEMA, which contains a PARTITIONS table. See Section 23.15, “The INFORMATION_SCHEMA PARTITIONS Table”.

It is possible to determine which partitions of a partitioned table are involved in a given SELECT query using EXPLAIN. The partitions column in the EXPLAIN output lists the partitions from which records would be matched by the query.

Suppose that you have a table trb1 created and populated as follows:

CREATE TABLE trb1 (id INT, name VARCHAR(50), purchased DATE)
    PARTITION BY RANGE(id)
    (
        PARTITION p0 VALUES LESS THAN (3),
        PARTITION p1 VALUES LESS THAN (7),
        PARTITION p2 VALUES LESS THAN (9),
        PARTITION p3 VALUES LESS THAN (11)
    );

INSERT INTO trb1 VALUES
    (1, 'desk organiser', '2003-10-15'),
    (2, 'CD player', '1993-11-05'),
    (3, 'TV set', '1996-03-10'),
    (4, 'bookcase', '1982-01-10'),
    (5, 'exercise bike', '2004-05-09'),
    (6, 'sofa', '1987-06-05'),
    (7, 'popcorn maker', '2001-11-22'),
    (8, 'aquarium', '1992-08-04'),
    (9, 'study desk', '1984-09-16'),
    (10, 'lava lamp', '1998-12-25');

You can see which partitions are used in a query such as SELECT * FROM trb1;, as shown here:

mysql> EXPLAIN SELECT * FROM trb1\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: trb1
   partitions: p0,p1,p2,p3
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 10
        Extra: Using filesort

In this case, all four partitions are searched. However, when a limiting condition making use of the partitioning key is added to the query, you can see that only those partitions containing matching values are scanned, as shown here:

mysql> EXPLAIN SELECT * FROM trb1 WHERE id < 5\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: trb1
   partitions: p0,p1
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 10
        Extra: Using where

EXPLAIN also provides information about keys used and possible keys:

mysql> ALTER TABLE trb1 ADD PRIMARY KEY (id);
Query OK, 10 rows affected (0.03 sec)
Records: 10  Duplicates: 0  Warnings: 0

mysql> EXPLAIN SELECT * FROM trb1 WHERE id < 5\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: trb1
   partitions: p0,p1
         type: range
possible_keys: PRIMARY
          key: PRIMARY
      key_len: 4
          ref: NULL
         rows: 7
        Extra: Using where

If EXPLAIN PARTITIONS is used to examine a query against a nonpartitioned table, no error is produced, but the value of the partitions column is always NULL.

The rows column of EXPLAIN output displays the total number of rows in the table.

See also Section 14.8.2, “EXPLAIN Syntax”.

21.4 Partition Pruning

This section discusses an optimization known as partition pruning. The core concept behind partition pruning is relatively simple, and can be described as Do not scan partitions where there can be no matching values. Suppose that you have a partitioned table t1 defined by this statement:

CREATE TABLE t1 (
    fname VARCHAR(50) NOT NULL,
    lname VARCHAR(50) NOT NULL,
    region_code TINYINT UNSIGNED NOT NULL,
    dob DATE NOT NULL
)
PARTITION BY RANGE( region_code ) (
    PARTITION p0 VALUES LESS THAN (64),
    PARTITION p1 VALUES LESS THAN (128),
    PARTITION p2 VALUES LESS THAN (192),
    PARTITION p3 VALUES LESS THAN MAXVALUE
);

Consider the case where you wish to obtain results from a SELECT statement such as this one:

SELECT fname, lname, region_code, dob
    FROM t1
    WHERE region_code > 125 AND region_code < 130;

It is easy to see that none of the rows which ought to be returned will be in either of the partitions p0 or p3; that is, we need to search only in partitions p1 and p2 to find matching rows. By doing so, it is possible to expend much less time and effort in finding matching rows than would be required to scan all partitions in the table. This cutting away of unneeded partitions is known as pruning. When the optimizer can make use of partition pruning in performing this query, execution of the query can be an order of magnitude faster than the same query against a nonpartitioned table containing the same column definitions and data.

Note

When pruning is performed on a partitioned MyISAM table, all partitions are opened, whether or not they are examined, due to the design of the MyISAM storage engine. This means that you must have a sufficient number of file descriptors available to cover all partitions of the table. See MyISAM and partition file descriptor usage.

This limitation does not apply to partitioned tables using other MySQL storage engines such as InnoDB.

The optimizer can perform pruning whenever a WHERE condition can be reduced to either one of the following two cases:

  • partition_column = constant

  • partition_column IN (constant1, constant2, ..., constantN)

In the first case, the optimizer simply evaluates the partitioning expression for the value given, determines which partition contains that value, and scans only this partition. In many cases, the equal sign can be replaced with another arithmetic comparison, including <, >, <=, >=, and <>. Some queries using BETWEEN in the WHERE clause can also take advantage of partition pruning. See the examples later in this section.

In the second case, the optimizer evaluates the partitioning expression for each value in the list, creates a list of matching partitions, and then scans only the partitions in this partition list.

MySQL can apply partition pruning to SELECT, DELETE, and UPDATE statements. INSERT statements currently cannot be pruned.

Pruning can also be applied to short ranges, which the optimizer can convert into equivalent lists of values. For instance, in the previous example, the WHERE clause can be converted to WHERE region_code IN (126, 127, 128, 129). Then the optimizer can determine that the first two values in the list are found in partition p1, the remaining two values in partition p2, and that the other partitions contain no relevant values and so do not need to be searched for matching rows.

The optimizer can also perform pruning for WHERE conditions that involve comparisons of the preceding types on multiple columns for tables that use RANGE COLUMNS or LIST COLUMNS partitioning.

This type of optimization can be applied whenever the partitioning expression consists of an equality or a range which can be reduced to a set of equalities, or when the partitioning expression represents an increasing or decreasing relationship. Pruning can also be applied for tables partitioned on a DATE or DATETIME column when the partitioning expression uses the YEAR() or TO_DAYS() function. In addition, in MySQL 5.7, pruning can be applied for such tables when the partitioning expression uses the TO_SECONDS() function.

Suppose that table t2, defined as shown here, is partitioned on a DATE column:

CREATE TABLE t2 (
    fname VARCHAR(50) NOT NULL,
    lname VARCHAR(50) NOT NULL,
    region_code TINYINT UNSIGNED NOT NULL,
    dob DATE NOT NULL
)
PARTITION BY RANGE( YEAR(dob) ) (
    PARTITION d0 VALUES LESS THAN (1970),
    PARTITION d1 VALUES LESS THAN (1975),
    PARTITION d2 VALUES LESS THAN (1980),
    PARTITION d3 VALUES LESS THAN (1985),
    PARTITION d4 VALUES LESS THAN (1990),
    PARTITION d5 VALUES LESS THAN (2000),
    PARTITION d6 VALUES LESS THAN (2005),
    PARTITION d7 VALUES LESS THAN MAXVALUE
);

The following statements using t2 can make of use partition pruning:

SELECT * FROM t2 WHERE dob = '1982-06-23';

UPDATE t2 SET region_code = 8 WHERE dob BETWEEN '1991-02-15' AND '1997-04-25';

DELETE FROM t2 WHERE dob >= '1984-06-21' AND dob <= '1999-06-21'

In the case of the last statement, the optimizer can also act as follows:

  1. Find the partition containing the low end of the range.

    YEAR('1984-06-21') yields the value 1984, which is found in partition d3.

  2. Find the partition containing the high end of the range.

    YEAR('1999-06-21') evaluates to 1999, which is found in partition d5.

  3. Scan only these two partitions and any partitions that may lie between them.

    In this case, this means that only partitions d3, d4, and d5 are scanned. The remaining partitions may be safely ignored (and are ignored).

Important

Invalid DATE and DATETIME values referenced in the WHERE condition of a statement against a partitioned table are treated as NULL. This means that a query such as SELECT * FROM partitioned_table WHERE date_column < '2008-12-00' does not return any values (see Bug #40972).

So far, we have looked only at examples using RANGE partitioning, but pruning can be applied with other partitioning types as well.

Consider a table that is partitioned by LIST, where the partitioning expression is increasing or decreasing, such as the table t3 shown here. (In this example, we assume for the sake of brevity that the region_code column is limited to values between 1 and 10 inclusive.)

CREATE TABLE t3 (
    fname VARCHAR(50) NOT NULL,
    lname VARCHAR(50) NOT NULL,
    region_code TINYINT UNSIGNED NOT NULL,
    dob DATE NOT NULL
)
PARTITION BY LIST(region_code) (
    PARTITION r0 VALUES IN (1, 3),
    PARTITION r1 VALUES IN (2, 5, 8),
    PARTITION r2 VALUES IN (4, 9),
    PARTITION r3 VALUES IN (6, 7, 10)
);

For a statement such as SELECT * FROM t3 WHERE region_code BETWEEN 1 AND 3, the optimizer determines in which partitions the values 1, 2, and 3 are found (r0 and r1) and skips the remaining ones (r2 and r3).

For tables that are partitioned by HASH or [LINEAR] KEY, partition pruning is also possible in cases in which the WHERE clause uses a simple = relation against a column used in the partitioning expression. Consider a table created like this:

CREATE TABLE t4 (
    fname VARCHAR(50) NOT NULL,
    lname VARCHAR(50) NOT NULL,
    region_code TINYINT UNSIGNED NOT NULL,
    dob DATE NOT NULL
)
PARTITION BY KEY(region_code)
PARTITIONS 8;

A statement that compares a column value with a constant can be pruned:

UPDATE t4 WHERE region_code = 7;

Pruning can also be employed for short ranges, because the optimizer can turn such conditions into IN relations. For example, using the same table t4 as defined previously, queries such as these can be pruned:

SELECT * FROM t4 WHERE region_code > 2 AND region_code < 6;

SELECT * FROM t4 WHERE region_code BETWEEN 3 AND 5;

In both these cases, the WHERE clause is transformed by the optimizer into WHERE region_code IN (3, 4, 5).

Important

This optimization is used only if the range size is smaller than the number of partitions. Consider this statement:

DELETE FROM t4 WHERE region_code BETWEEN 4 AND 12;

The range in the WHERE clause covers 9 values (4, 5, 6, 7, 8, 9, 10, 11, 12), but t4 has only 8 partitions. This means that the DELETE cannot be pruned.

When a table is partitioned by HASH or [LINEAR] KEY, pruning can be used only on integer columns. For example, this statement cannot use pruning because dob is a DATE column:

SELECT * FROM t4 WHERE dob >= '2001-04-14' AND dob <= '2005-10-15';

However, if the table stores year values in an INT column, then a query having WHERE year_col >= 2001 AND year_col <= 2005 can be pruned.

Prior to MySQL 5.7.1, partition pruning was disabled for all tables using a storage engine that provides automatic partitioning, such as the NDB storage engine used by MySQL Cluster. (Bug #14672885) Beginning with MySQL 5.7.1, such tables can be pruned if they are explicitly partitioned. (Bug #14827952)

21.5 Partition Selection

MySQL 5.7 supports explicit selection of partitions and subpartitions that, when executing a statement, should be checked for rows matching a given WHERE condition. Partition selection is similar to partition pruning, in that only specific partitions are checked for matches, but differs in two key respects:

  1. The partitions to be checked are specified by the issuer of the statement, unlike partition pruning, which is automatic.

  2. Whereas partition pruning applies only to queries, explicit selection of partitions is supported for both queries and a number of DML statements.

SQL statements supporting explicit partition selection are listed here:

The remainder of this section discusses explicit partition selection as it applies generally to the statements just listed, and provides some examples.

Explicit partition selection is implemented using a PARTITION option. For all supported statements, this option uses the syntax shown here:

      PARTITION (partition_names)

      partition_names:
          partition_name, ...

This option always follows the name of the table to which the partition or partitions belong. partition_names is a comma-separated list of partitions or subpartitions to be used. Each name in this list must be the name of an existing partition or subpartition of the specified table; if any of the partitions or subpartitions are not found, the statement fails with an error (partition 'partition_name' doesn't exist). Partitions and subpartitions named in partition_names may be listed in any order, and may overlap.

When the PARTITION option is used, only the partitions and subpartitions listed are checked for matching rows. This option can be used in a SELECT statement to determine which rows belong to a given partition. Consider a partitioned table named employees, created and populated using the statements shown here:

SET @@SQL_MODE = '';

CREATE TABLE employees  (
    id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,
    fname VARCHAR(25) NOT NULL,
    lname VARCHAR(25) NOT NULL,
    store_id INT NOT NULL,
    department_id INT NOT NULL
) 
    PARTITION BY RANGE(id)  (
        PARTITION p0 VALUES LESS THAN (5),
        PARTITION p1 VALUES LESS THAN (10),
        PARTITION p2 VALUES LESS THAN (15),
        PARTITION p3 VALUES LESS THAN MAXVALUE
);

INSERT INTO employees VALUES
    ('', 'Bob', 'Taylor', 3, 2), ('', 'Frank', 'Williams', 1, 2),
    ('', 'Ellen', 'Johnson', 3, 4), ('', 'Jim', 'Smith', 2, 4),
    ('', 'Mary', 'Jones', 1, 1), ('', 'Linda', 'Black', 2, 3),
    ('', 'Ed', 'Jones', 2, 1), ('', 'June', 'Wilson', 3, 1),
    ('', 'Andy', 'Smith', 1, 3), ('', 'Lou', 'Waters', 2, 4),
    ('', 'Jill', 'Stone', 1, 4), ('', 'Roger', 'White', 3, 2),
    ('', 'Howard', 'Andrews', 1, 2), ('', 'Fred', 'Goldberg', 3, 3),
    ('', 'Barbara', 'Brown', 2, 3), ('', 'Alice', 'Rogers', 2, 2),
    ('', 'Mark', 'Morgan', 3, 3), ('', 'Karen', 'Cole', 3, 2);

You can see which rows are stored in partition p1 like this:

mysql> SELECT * FROM employees PARTITION (p1);
+----+-------+--------+----------+---------------+
| id | fname | lname  | store_id | department_id |
+----+-------+--------+----------+---------------+
|  5 | Mary  | Jones  |        1 |             1 |
|  6 | Linda | Black  |        2 |             3 |
|  7 | Ed    | Jones  |        2 |             1 |
|  8 | June  | Wilson |        3 |             1 |
|  9 | Andy  | Smith  |        1 |             3 |
+----+-------+--------+----------+---------------+
5 rows in set (0.00 sec)

The result is the same as obtained by the query SELECT * FROM employees WHERE id BETWEEN 5 AND 9.

To obtain rows from multiple partitions, supply their names as a comma-delimited list. For example, SELECT * FROM employees PARTITION (p1, p2) returns all rows from partitions p1 and p2 while excluding rows from the remaining partitions.

Any valid query against a partitioned table can be rewritten with a PARTITION option to restrict the result to one or more desired partitions. You can use WHERE conditions, ORDER BY and LIMIT options, and so on. You can also use aggregate functions with HAVING and GROUP BY options. Each of the following queries produces a valid result when run on the employees table as previously defined:

mysql> SELECT * FROM employees PARTITION (p0, p2)
    ->     WHERE lname LIKE 'S%';
+----+-------+-------+----------+---------------+
| id | fname | lname | store_id | department_id |
+----+-------+-------+----------+---------------+
|  4 | Jim   | Smith |        2 |             4 |
| 11 | Jill  | Stone |        1 |             4 |
+----+-------+-------+----------+---------------+
2 rows in set (0.00 sec)

mysql> SELECT id, CONCAT(fname, ' ', lname) AS name
    ->     FROM employees PARTITION (p0) ORDER BY lname;
+----+----------------+
| id | name           |
+----+----------------+
|  3 | Ellen Johnson  |
|  4 | Jim Smith      |
|  1 | Bob Taylor     |
|  2 | Frank Williams |
+----+----------------+
4 rows in set (0.06 sec)

mysql> SELECT store_id, COUNT(department_id) AS c
    ->     FROM employees PARTITION (p1,p2,p3)
    ->     GROUP BY store_id HAVING c > 4;
+---+----------+
| c | store_id |
+---+----------+
| 5 |        2 |
| 5 |        3 |
+---+----------+
2 rows in set (0.00 sec)

Statements using partition selection can be employed with tables using any of the partitioning types supported in MySQL 5.7. When a table is created using [LINEAR] HASH or [LINEAR] KEY partitioning and the names of the partitions are not specified, MySQL automatically names the partitions p0, p1, p2, ..., pN-1, where N is the number of partitions. For subpartitions not explicitly named, MySQL assigns automatically to the subpartitions in each partition pX the names pXsp0, pXsp1, pXsp2, ..., pXspM-1, where M is the number of subpartitions. When executing against this table a SELECT (or other SQL statement for which explicit partition selection is allowed), you can use these generated names in a PARTITION option, as shown here:

mysql> CREATE TABLE employees_sub  (
    ->     id INT NOT NULL AUTO_INCREMENT,
    ->     fname VARCHAR(25) NOT NULL,
    ->     lname VARCHAR(25) NOT NULL,
    ->     store_id INT NOT NULL,
    ->     department_id INT NOT NULL,
    ->     PRIMARY KEY pk (id, lname)
    -> ) 
    ->     PARTITION BY RANGE(id)
    ->     SUBPARTITION BY KEY (lname)
    ->     SUBPARTITIONS 2 (
    ->         PARTITION p0 VALUES LESS THAN (5),
    ->         PARTITION p1 VALUES LESS THAN (10),
    ->         PARTITION p2 VALUES LESS THAN (15),
    ->         PARTITION p3 VALUES LESS THAN MAXVALUE
    -> );
Query OK, 0 rows affected (1.14 sec)

mysql> INSERT INTO employees_sub   # re-use data in employees table
    ->     SELECT * FROM employees;
Query OK, 18 rows affected (0.09 sec)
Records: 18  Duplicates: 0  Warnings: 0

mysql> SELECT id, CONCAT(fname, ' ', lname) AS name
    ->     FROM employees_sub PARTITION (p2sp1);
+----+---------------+
| id | name          |
+----+---------------+
| 10 | Lou Waters    |
| 14 | Fred Goldberg |
+----+---------------+
2 rows in set (0.00 sec)

You may also use a PARTITION option in the SELECT portion of an INSERT ... SELECT statement, as shown here:

mysql> CREATE TABLE employees_copy LIKE employees;
Query OK, 0 rows affected (0.28 sec)

mysql> INSERT INTO employees_copy
    ->     SELECT * FROM employees PARTITION (p2);
Query OK, 5 rows affected (0.04 sec)
Records: 5  Duplicates: 0  Warnings: 0

mysql> SELECT * FROM employees_copy;
+----+--------+----------+----------+---------------+
| id | fname  | lname    | store_id | department_id |
+----+--------+----------+----------+---------------+
| 10 | Lou    | Waters   |        2 |             4 |
| 11 | Jill   | Stone    |        1 |             4 |
| 12 | Roger  | White    |        3 |             2 |
| 13 | Howard | Andrews  |        1 |             2 |
| 14 | Fred   | Goldberg |        3 |             3 |
+----+--------+----------+----------+---------------+
5 rows in set (0.00 sec)

Partition selection can also be used with joins. Suppose we create and populate two tables using the statements shown here:

CREATE TABLE stores (
    id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,
    city VARCHAR(30) NOT NULL
)
    PARTITION BY HASH(id)
    PARTITIONS 2;
  
INSERT INTO stores VALUES
    ('', 'Nambucca'), ('', 'Uranga'),
    ('', 'Bellingen'), ('', 'Grafton');
  
CREATE TABLE departments  (
    id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,
    name VARCHAR(30) NOT NULL
)
    PARTITION BY KEY(id)
    PARTITIONS 2;
  
INSERT INTO departments VALUES
    ('', 'Sales'), ('', 'Customer Service'),
    ('', 'Delivery'), ('', 'Accounting');

You can explicitly select partitions (or subpartitions, or both) from any or all of the tables in a join. (Note that the PARTITION option used to select partitions from a given table immediately follows the name of the table, before all other options, including any table alias.) For example, the following query gets the name, employee ID, department, and city of all employees who work in the Sales or Delivery department (partition p1 of the departments table) at the stores in either of the cities of Nambucca and Bellingen (partition p0 of the stores table):

mysql> SELECT
    ->     e.id AS 'Employee ID', CONCAT(e.fname, ' ', e.lname) AS Name,
    ->     s.city AS City, d.name AS department
    -> FROM employees AS e
    ->     JOIN stores PARTITION (p1) AS s ON e.store_id=s.id
    ->     JOIN departments PARTITION (p0) AS d ON e.department_id=d.id
    -> ORDER BY e.lname;
+-------------+---------------+-----------+------------+
| Employee ID | Name          | City      | department |
+-------------+---------------+-----------+------------+
|          14 | Fred Goldberg | Bellingen | Delivery   |
|           5 | Mary Jones    | Nambucca  | Sales      |
|          17 | Mark Morgan   | Bellingen | Delivery   |
|           9 | Andy Smith    | Nambucca  | Delivery   |
|           8 | June Wilson   | Bellingen | Sales      |
+-------------+---------------+-----------+------------+
5 rows in set (0.00 sec)

For general information about joins in MySQL, see Section 14.2.9.2, “JOIN Syntax”.

When the PARTITION option is used with DELETE statements, only those partitions (and subpartitions, if any) listed with the option are checked for rows to be deleted. Any other partitions are ignored, as shown here:

mysql> SELECT * FROM employees WHERE fname LIKE 'j%';
+----+-------+--------+----------+---------------+
| id | fname | lname  | store_id | department_id |
+----+-------+--------+----------+---------------+
|  4 | Jim   | Smith  |        2 |             4 |
|  8 | June  | Wilson |        3 |             1 |
| 11 | Jill  | Stone  |        1 |             4 |
+----+-------+--------+----------+---------------+
3 rows in set (0.00 sec)

mysql> DELETE FROM employees PARTITION (p0, p1)
    ->     WHERE fname LIKE 'j%';
Query OK, 2 rows affected (0.09 sec)

mysql> SELECT * FROM employees WHERE fname LIKE 'j%';
+----+-------+-------+----------+---------------+
| id | fname | lname | store_id | department_id |
+----+-------+-------+----------+---------------+
| 11 | Jill  | Stone |        1 |             4 |
+----+-------+-------+----------+---------------+
1 row in set (0.00 sec)

Only the two rows in partitions p0 and p1 matching the WHERE condition were deleted. As you can see from the result when the SELECT is run a second time, there remains a row in the table matching the WHERE condition, but residing in a different partition (p2).

UPDATE statements using explicit partition selection behave in the same way; only rows in the partitions referenced by the PARTITION option are considered when determining the rows to be updated, as can be seen by executing the following statements:

mysql> UPDATE employees PARTITION (p0) 
    ->     SET store_id = 2 WHERE fname = 'Jill';
Query OK, 0 rows affected (0.00 sec)
Rows matched: 0  Changed: 0  Warnings: 0

mysql> SELECT * FROM employees WHERE fname = 'Jill';
+----+-------+-------+----------+---------------+
| id | fname | lname | store_id | department_id |
+----+-------+-------+----------+---------------+
| 11 | Jill  | Stone |        1 |             4 |
+----+-------+-------+----------+---------------+
1 row in set (0.00 sec)

mysql> UPDATE employees PARTITION (p2)
    ->     SET store_id = 2 WHERE fname = 'Jill';
Query OK, 1 row affected (0.09 sec)
Rows matched: 1  Changed: 1  Warnings: 0

mysql> SELECT * FROM employees WHERE fname = 'Jill';
+----+-------+-------+----------+---------------+
| id | fname | lname | store_id | department_id |
+----+-------+-------+----------+---------------+
| 11 | Jill  | Stone |        2 |             4 |
+----+-------+-------+----------+---------------+
1 row in set (0.00 sec)

In the same way, when PARTITION is used with DELETE, only rows in the partition or partitions named in the partition list are checked for deletion.

For statements that insert rows, the behavior differs in that failure to find a suitable partition causes the statement to fail. This is true for both INSERT and REPLACE statements, as shown here:

mysql> INSERT INTO employees PARTITION (p2) VALUES (20, 'Jan', 'Jones', 1, 3);
ERROR 1729 (HY000): Found a row not matching the given partition set
mysql> INSERT INTO employees PARTITION (p3) VALUES (20, 'Jan', 'Jones', 1, 3);
Query OK, 1 row affected (0.07 sec)

mysql> REPLACE INTO employees PARTITION (p0) VALUES (20, 'Jan', 'Jones', 3, 2);
ERROR 1729 (HY000): Found a row not matching the given partition set

mysql> REPLACE INTO employees PARTITION (p3) VALUES (20, 'Jan', 'Jones', 3, 2);
Query OK, 2 rows affected (0.09 sec)

For statements that write multiple rows to a partitioned table that uses the InnoDB storage engine: If any row in the list following VALUES cannot be written to one of the partitions specified in the partition_names list, the entire statement fails and no rows are written. This is shown for INSERT statements in the following example, reusing the employees table created previously:

mysql> ALTER TABLE employees
    ->     REORGANIZE PARTITION p3 INTO (
    ->         PARTITION p3 VALUES LESS THAN (20),
    ->         PARTITION p4 VALUES LESS THAN (25),
    ->         PARTITION p5 VALUES LESS THAN MAXVALUE
    ->     );
Query OK, 6 rows affected (2.09 sec)
Records: 6  Duplicates: 0  Warnings: 0

mysql> SHOW CREATE TABLE employees\G
*************************** 1. row ***************************
       Table: employees
Create Table: CREATE TABLE `employees` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `fname` varchar(25) NOT NULL,
  `lname` varchar(25) NOT NULL,
  `store_id` int(11) NOT NULL,
  `department_id` int(11) NOT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=27 DEFAULT CHARSET=latin1
/*!50100 PARTITION BY RANGE (id)
(PARTITION p0 VALUES LESS THAN (5) ENGINE = InnoDB,
 PARTITION p1 VALUES LESS THAN (10) ENGINE = InnoDB,
 PARTITION p2 VALUES LESS THAN (15) ENGINE = InnoDB,
 PARTITION p3 VALUES LESS THAN (20) ENGINE = InnoDB,
 PARTITION p4 VALUES LESS THAN (25) ENGINE = InnoDB,
 PARTITION p5 VALUES LESS THAN MAXVALUE ENGINE = InnoDB) */
1 row in set (0.00 sec)

mysql> INSERT INTO employees PARTITION (p3, p4) VALUES
    ->     (24, 'Tim', 'Greene', 3, 1),  (26, 'Linda', 'Mills', 2, 1);
ERROR 1729 (HY000): Found a row not matching the given partition set

mysql> INSERT INTO employees PARTITION (p3, p4. p5) VALUES
    ->     (24, 'Tim', 'Greene', 3, 1),  (26, 'Linda', 'Mills', 2, 1);
Query OK, 2 rows affected (0.06 sec)
Records: 2  Duplicates: 0  Warnings: 0

The preceding is true for both INSERT statements and REPLACE statements that write multiple rows.

In MySQL 5.7.1 and later, partition selection is disabled for tables employing a storage engine that supplies automatic partitioning, such as NDB. (Bug #14827952)

21.6 Restrictions and Limitations on Partitioning

This section discusses current restrictions and limitations on MySQL partitioning support.

Prohibited constructs.  The following constructs are not permitted in partitioning expressions:

  • Stored procedures, stored functions, UDFs, or plugins.

  • Declared variables or user variables.

For a list of SQL functions which are permitted in partitioning expressions, see Section 21.6.3, “Partitioning Limitations Relating to Functions”.

Arithmetic and logical operators.  Use of the arithmetic operators +, -, and * is permitted in partitioning expressions. However, the result must be an integer value or NULL (except in the case of [LINEAR] KEY partitioning, as discussed elsewhere in this chapter; see Section 21.2, “Partitioning Types”, for more information).

The DIV operator is also supported, and the / operator is not permitted. (Bug #30188, Bug #33182)

The bit operators |, &, ^, <<, >>, and ~ are not permitted in partitioning expressions.

HANDLER statements.  Previously, the HANDLER statement was not supported with partitioned tables. This limitation is removed beginning with MySQL 5.7.1.

Server SQL mode.  Tables employing user-defined partitioning do not preserve the SQL mode in effect at the time that they were created. As discussed in Section 6.1.8, “Server SQL Modes”, the results of many MySQL functions and operators may change according to the server SQL mode. Therefore, a change in the SQL mode at any time after the creation of partitioned tables may lead to major changes in the behavior of such tables, and could easily lead to corruption or loss of data. For these reasons, it is strongly recommended that you never change the server SQL mode after creating partitioned tables.

Examples.  The following examples illustrate some changes in behavior of partitioned tables due to a change in the server SQL mode:

  1. Error handling.  Suppose that you create a partitioned table whose partitioning expression is one such as column DIV 0 or column MOD 0, as shown here:

    mysql> CREATE TABLE tn (c1 INT)
        ->     PARTITION BY LIST(1 DIV c1) (
        ->       PARTITION p0 VALUES IN (NULL),
        ->       PARTITION p1 VALUES IN (1)
        -> );
    Query OK, 0 rows affected (0.05 sec)
    

    The default behavior for MySQL is to return NULL for the result of a division by zero, without producing any errors:

    mysql> SELECT @@sql_mode;
    +------------+
    | @@sql_mode |
    +------------+
    |            |
    +------------+
    1 row in set (0.00 sec)
    
    
    mysql> INSERT INTO tn VALUES (NULL), (0), (1);
    Query OK, 3 rows affected (0.00 sec)
    Records: 3  Duplicates: 0  Warnings: 0
    

    However, changing the server SQL mode to treat division by zero as an error and to enforce strict error handling causes the same INSERT statement to fail, as shown here:

    mysql> SET sql_mode='STRICT_ALL_TABLES,ERROR_FOR_DIVISION_BY_ZERO';
    Query OK, 0 rows affected (0.00 sec)
    
    mysql> INSERT INTO tn VALUES (NULL), (0), (1);
    ERROR 1365 (22012): Division by 0
    
  2. Table accessibility.  Sometimes a change in the server SQL mode can make partitioned tables unusable. The following CREATE TABLE statement can be executed successfully only if the NO_UNSIGNED_SUBTRACTION mode is in effect:

    mysql> SELECT @@sql_mode;
    +------------+
    | @@sql_mode |
    +------------+
    |            |
    +------------+
    1 row in set (0.00 sec)
    
    mysql> CREATE TABLE tu (c1 BIGINT UNSIGNED)
        ->   PARTITION BY RANGE(c1 - 10) (
        ->     PARTITION p0 VALUES LESS THAN (-5),
        ->     PARTITION p1 VALUES LESS THAN (0),
        ->     PARTITION p2 VALUES LESS THAN (5),
        ->     PARTITION p3 VALUES LESS THAN (10),
        ->     PARTITION p4 VALUES LESS THAN (MAXVALUE)
        -> );
    ERROR 1563 (HY000): Partition constant is out of partition function domain
    
    mysql> SET sql_mode='NO_UNSIGNED_SUBTRACTION';
    Query OK, 0 rows affected (0.00 sec)
    
    mysql> SELECT @@sql_mode;
    +-------------------------+
    | @@sql_mode              |
    +-------------------------+
    | NO_UNSIGNED_SUBTRACTION |
    +-------------------------+
    1 row in set (0.00 sec)
    
    mysql> CREATE TABLE tu (c1 BIGINT UNSIGNED)
        ->   PARTITION BY RANGE(c1 - 10) (
        ->     PARTITION p0 VALUES LESS THAN (-5),
        ->     PARTITION p1 VALUES LESS THAN (0),
        ->     PARTITION p2 VALUES LESS THAN (5),
        ->     PARTITION p3 VALUES LESS THAN (10),
        ->     PARTITION p4 VALUES LESS THAN (MAXVALUE)
        -> );
    Query OK, 0 rows affected (0.05 sec)
    

    If you remove the NO_UNSIGNED_SUBTRACTION server SQL mode after creating tu, you may no longer be able to access this table:

    mysql> SET sql_mode='';
    Query OK, 0 rows affected (0.00 sec)
    
    mysql> SELECT * FROM tu;
    ERROR 1563 (HY000): Partition constant is out of partition function domain
    mysql> INSERT INTO tu VALUES (20);
    ERROR 1563 (HY000): Partition constant is out of partition function domain
    

Server SQL modes also impact replication of partitioned tables. Differing SQL modes on master and slave can lead to partitioning expressions being evaluated differently; this can cause the distribution of data among partitions to be different in the master's and slave's copies of a given table, and may even cause inserts into partitioned tables that succeed on the master to fail on the slave. For best results, you should always use the same server SQL mode on the master and on the slave.

Performance considerations.  Some effects of partitioning operations on performance are given in the following list:

  • File system operations.  Partitioning and repartitioning operations (such as ALTER TABLE with PARTITION BY ..., REORGANIZE PARTITION, or REMOVE PARTITIONING) depend on file system operations for their implementation. This means that the speed of these operations is affected by such factors as file system type and characteristics, disk speed, swap space, file handling efficiency of the operating system, and MySQL server options and variables that relate to file handling. In particular, you should make sure that large_files_support is enabled and that open_files_limit is set properly. For partitioned tables using the MyISAM storage engine, increasing myisam_max_sort_file_size may improve performance; partitioning and repartitioning operations involving InnoDB tables may be made more efficient by enabling innodb_file_per_table.

    See also Maximum number of partitions.

  • MyISAM and partition file descriptor usage.  For a partitioned MyISAM table, MySQL uses 2 file descriptors for each partition, for each such table that is open. This means that you need many more file descriptors to perform operations on a partitioned MyISAM table than on a table which is identical to it except that the latter table is not partitioned, particularly when performing ALTER TABLE operations.

    Assume a MyISAM table t with 100 partitions, such as the table created by this SQL statement:

    CREATE TABLE t (c1 VARCHAR(50))
    PARTITION BY KEY (c1) PARTITIONS 100
    ENGINE=MYISAM;
    
    Note

    For brevity, we use KEY partitioning for the table shown in this example, but file descriptor usage as described here applies to all partitioned MyISAM tables, regardless of the type of partitioning that is employed. Partitioned tables using other storage engines such as InnoDB are not affected by this issue.

    Now assume that you wish to repartition t so that it has 101 partitions, using the statement shown here:

    ALTER TABLE t PARTITION BY KEY (c1) PARTITIONS 101;
    

    To process this ALTER TABLE statement, MySQL uses 402 file descriptors—that is, two for each of the 100 original partitions, plus two for each of the 101 new partitions. This is because all partitions (old and new) must be opened concurrently during the reorganization of the table data. It is recommended that, if you expect to perform such operations, you should make sure that --open-files-limit is not set too low to accommodate them.

  • Table locks.  The process executing a partitioning operation on a table takes a write lock on the table. Reads from such tables are relatively unaffected; pending INSERT and UPDATE operations are performed as soon as the partitioning operation has completed.

  • Storage engine.  Partitioning operations, queries, and update operations generally tend to be faster with MyISAM tables than with InnoDB or NDB tables.

  • Indexes; partition pruning.  As with nonpartitioned tables, proper use of indexes can speed up queries on partitioned tables significantly. In addition, designing partitioned tables and queries on these tables to take advantage of partition pruning can improve performance dramatically. See Section 21.4, “Partition Pruning”, for more information.

    Previously, index condition pushdown was not supported for partitioned tables. This limitation was removed in MySQL 5.7.3. See Section 9.2.1.5, “Index Condition Pushdown Optimization”.

  • Performance with LOAD DATA.  In MySQL 5.7, LOAD DATA uses buffering to improve performance. You should be aware that the buffer uses 130 KB memory per partition to achieve this.

Maximum number of partitions.  The maximum possible number of partitions for a given table not using the NDB storage engine is 8192. This number includes subpartitions.

The maximum possible number of user-defined partitions for a table using the NDB storage engine is determined according to the version of the MySQL Cluster software being used, the number of data nodes, and other factors. See NDB and user-defined partitioning, for more information.

If, when creating tables with a large number of partitions (but less than the maximum), you encounter an error message such as Got error ... from storage engine: Out of resources when opening file, you may be able to address the issue by increasing the value of the open_files_limit system variable. However, this is dependent on the operating system, and may not be possible or advisable on all platforms; see Section B.5.2.18, “File Not Found and Similar Errors”, for more information. In some cases, using large numbers (hundreds) of partitions may also not be advisable due to other concerns, so using more partitions does not automatically lead to better results.

See also File system operations.

Query cache not supported.  The query cache is not supported for partitioned tables, and is automatically disabled for queries involving partitioned tables. The query cache cannot be enabled for such queries.

Per-partition key caches.  In MySQL 5.7, key caches are supported for partitioned MyISAM tables, using the CACHE INDEX and LOAD INDEX INTO CACHE statements. Key caches may be defined for one, several, or all partitions, and indexes for one, several, or all partitions may be preloaded into key caches.

Foreign keys not supported for partitioned InnoDB tables.  Partitioned tables using the InnoDB storage engine do not support foreign keys. More specifically, this means that the following two statements are true:

  1. No definition of an InnoDB table employing user-defined partitioning may contain foreign key references; no InnoDB table whose definition contains foreign key references may be partitioned.

  2. No InnoDB table definition may contain a foreign key reference to a user-partitioned table; no InnoDB table with user-defined partitioning may contain columns referenced by foreign keys.

The scope of the restrictions just listed includes all tables that use the InnoDB storage engine. CREATE TABLE and ALTER TABLE statements that would result in tables violating these restrictions are not allowed.

ALTER TABLE ... ORDER BY.  An ALTER TABLE ... ORDER BY column statement run against a partitioned table causes ordering of rows only within each partition.

Effects on REPLACE statements by modification of primary keys.  It can be desirable in some cases (see Section 21.6.1, “Partitioning Keys, Primary Keys, and Unique Keys”) to modify a table's primary key. Be aware that, if your application uses REPLACE statements and you do this, the results of these statements can be drastically altered. See Section 14.2.8, “REPLACE Syntax”, for more information and an example.

FULLTEXT indexes.  Partitioned tables do not support FULLTEXT indexes or searches, even for partitioned tables employing the InnoDB or MyISAM storage engine.

Spatial columns.  Columns with spatial data types such as POINT or GEOMETRY cannot be used in partitioned tables.

Temporary tables.  Temporary tables cannot be partitioned. (Bug #17497)

Log tables.  It is not possible to partition the log tables; an ALTER TABLE ... PARTITION BY ... statement on such a table fails with an error.

Data type of partitioning key.  A partitioning key must be either an integer column or an expression that resolves to an integer. Expressions employing ENUM columns cannot be used. The column or expression value may also be NULL. (See Section 21.2.7, “How MySQL Partitioning Handles NULL”.)

There are two exceptions to this restriction:

  1. When partitioning by [LINEAR] KEY, it is possible to use columns of any valid MySQL data type other than TEXT or BLOB as partitioning keys, because MySQL's internal key-hashing functions produce the correct data type from these types. For example, the following two CREATE TABLE statements are valid:

    CREATE TABLE tkc (c1 CHAR)
    PARTITION BY KEY(c1)
    PARTITIONS 4;
    
    CREATE TABLE tke
        ( c1 ENUM('red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet') )
    PARTITION BY LINEAR KEY(c1)
    PARTITIONS 6;
    
  2. When partitioning by RANGE COLUMNS or LIST COLUMNS, it is possible to use string, DATE, and DATETIME columns. For example, each of the following CREATE TABLE statements is valid:

    CREATE TABLE rc (c1 INT, c2 DATE)
    PARTITION BY RANGE COLUMNS(c2) (
        PARTITION p0 VALUES LESS THAN('1990-01-01'),
        PARTITION p1 VALUES LESS THAN('1995-01-01'),
        PARTITION p2 VALUES LESS THAN('2000-01-01'),
        PARTITION p3 VALUES LESS THAN('2005-01-01'),
        PARTITION p4 VALUES LESS THAN(MAXVALUE)
    );
    
    CREATE TABLE lc (c1 INT, c2 CHAR(1))
    PARTITION BY LIST COLUMNS(c2) (
        PARTITION p0 VALUES IN('a', 'd', 'g', 'j', 'm', 'p', 's', 'v', 'y'),
        PARTITION p1 VALUES IN('b', 'e', 'h', 'k', 'n', 'q', 't', 'w', 'z'),
        PARTITION p2 VALUES IN('c', 'f', 'i', 'l', 'o', 'r', 'u', 'x', NULL)
    );
    

Neither of the preceding exceptions applies to BLOB or TEXT column types.

Subqueries.  A partitioning key may not be a subquery, even if that subquery resolves to an integer value or NULL.

Issues with subpartitions.  Subpartitions must use HASH or KEY partitioning. Only RANGE and LIST partitions may be subpartitioned; HASH and KEY partitions cannot be subpartitioned.

SUBPARTITION BY KEY requires that the subpartitioning column or columns be specified explicitly, unlike the case with PARTITION BY KEY, where it can be omitted (in which case the table's primary key column is used by default). Consider the table created by this statement:

CREATE TABLE ts (
    id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,
    name VARCHAR(30)
);

You can create a table having the same columns, partitioned by KEY, using a statement such as this one:

CREATE TABLE ts (
    id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,
    name VARCHAR(30)
)
PARTITION BY KEY()
PARTITIONS 4;
        

The previous statement is treated as though it had been written like this, with the table's primary key column used as the partitioning column:

CREATE TABLE ts (
    id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,
    name VARCHAR(30)
)
PARTITION BY KEY(id)
PARTITIONS 4;
        

However, the following statement that attempts to create a subpartitioned table using the default column as the subpartitioning column fails, and the column must be specified for the statement to succeed, as shown here:

mysql> CREATE TABLE ts (
    ->     id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,
    ->     name VARCHAR(30)
    -> )
    -> PARTITION BY RANGE(id)
    -> SUBPARTITION BY KEY()
    -> SUBPARTITIONS 4
    -> (
    ->     PARTITION p0 VALUES LESS THAN (100),
    ->     PARTITION p1 VALUES LESS THAN (MAXVALUE)
    -> );
ERROR 1064 (42000): You have an error in your SQL syntax; check the manual that
corresponds to your MySQL server version for the right syntax to use near ')

mysql> CREATE TABLE ts (
    ->     id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,
    ->     name VARCHAR(30)
    -> )
    -> PARTITION BY RANGE(id)
    -> SUBPARTITION BY KEY(id)
    -> SUBPARTITIONS 4
    -> (
    ->     PARTITION p0 VALUES LESS THAN (100),
    ->     PARTITION p1 VALUES LESS THAN (MAXVALUE)
    -> );
Query OK, 0 rows affected (0.07 sec)

This is a known issue (see Bug #51470).

DATA DIRECTORY and INDEX DIRECTORY options.  DATA DIRECTORY and INDEX DIRECTORY are subject to the following restrictions when used with partitioned tables:

  • Table-level DATA DIRECTORY and INDEX DIRECTORY options are ignored (see Bug #32091).

  • On Windows, the DATA DIRECTORY and INDEX DIRECTORY options are not supported for individual partitions or subpartitions of MyISAM tables. However, you can use DATA DIRECTORY for individual partitions or subpartitions of InnoDB tables.

Repairing and rebuilding partitioned tables.  The statements CHECK TABLE, OPTIMIZE TABLE, ANALYZE TABLE, and REPAIR TABLE are supported for partitioned tables.

In addition, you can use ALTER TABLE ... REBUILD PARTITION to rebuild one or more partitions of a partitioned table; ALTER TABLE ... REORGANIZE PARTITION also causes partitions to be rebuilt. See Section 14.1.8, “ALTER TABLE Syntax”, for more information about these two statements.

Starting in MySQL 5.7.2, ANALYZE, CHECK, OPTIMIZE, REPAIR, and TRUNCATE operations are supported with subpartitions. REBUILD was also accepted syntax prior to MySQL 5.7.5, although this had no effect. (Bug #19075411, Bug #73130) See also Section 14.1.8.1, “ALTER TABLE Partition Operations”.

mysqlcheck, myisamchk, and myisampack are not supported with partitioned tables.

FOR EXPORT option (FLUSH TABLES).  The FLUSH TABLES statement's FOR EXPORT option is not supported for partitioned InnoDB tables in MySQL 5.7.4 and earlier. (Bug #16943907)

21.6.1 Partitioning Keys, Primary Keys, and Unique Keys

This section discusses the relationship of partitioning keys with primary keys and unique keys. The rule governing this relationship can be expressed as follows: All columns used in the partitioning expression for a partitioned table must be part of every unique key that the table may have.

In other words, every unique key on the table must use every column in the table's partitioning expression. (This also includes the table's primary key, since it is by definition a unique key. This particular case is discussed later in this section.) For example, each of the following table creation statements is invalid:

CREATE TABLE t1 (
    col1 INT NOT NULL,
    col2 DATE NOT NULL,
    col3 INT NOT NULL,
    col4 INT NOT NULL,
    UNIQUE KEY (col1, col2)
)
PARTITION BY HASH(col3)
PARTITIONS 4;

CREATE TABLE t2 (
    col1 INT NOT NULL,
    col2 DATE NOT NULL,
    col3 INT NOT NULL,
    col4 INT NOT NULL,
    UNIQUE KEY (col1),
    UNIQUE KEY (col3)
)
PARTITION BY HASH(col1 + col3)
PARTITIONS 4;

In each case, the proposed table would have at least one unique key that does not include all columns used in the partitioning expression.

Each of the following statements is valid, and represents one way in which the corresponding invalid table creation statement could be made to work:

CREATE TABLE t1 (
    col1 INT NOT NULL,
    col2 DATE NOT NULL,
    col3 INT NOT NULL,
    col4 INT NOT NULL,
    UNIQUE KEY (col1, col2, col3)
)
PARTITION BY HASH(col3)
PARTITIONS 4;

CREATE TABLE t2 (
    col1 INT NOT NULL,
    col2 DATE NOT NULL,
    col3 INT NOT NULL,
    col4 INT NOT NULL,
    UNIQUE KEY (col1, col3)
)
PARTITION BY HASH(col1 + col3)
PARTITIONS 4;

This example shows the error produced in such cases:

mysql> CREATE TABLE t3 (
    ->     col1 INT NOT NULL,
    ->     col2 DATE NOT NULL,
    ->     col3 INT NOT NULL,
    ->     col4 INT NOT NULL,
    ->     UNIQUE KEY (col1, col2),
    ->     UNIQUE KEY (col3)
    -> )
    -> PARTITION BY HASH(col1 + col3)
    -> PARTITIONS 4;
ERROR 1491 (HY000): A PRIMARY KEY must include all columns in the table's partitioning function

The CREATE TABLE statement fails because both col1 and col3 are included in the proposed partitioning key, but neither of these columns is part of both of unique keys on the table. This shows one possible fix for the invalid table definition:

mysql> CREATE TABLE t3 (
    ->     col1 INT NOT NULL,
    ->     col2 DATE NOT NULL,
    ->     col3 INT NOT NULL,
    ->     col4 INT NOT NULL,
    ->     UNIQUE KEY (col1, col2, col3),
    ->     UNIQUE KEY (col3)
    -> )
    -> PARTITION BY HASH(col3)
    -> PARTITIONS 4;
Query OK, 0 rows affected (0.05 sec)

In this case, the proposed partitioning key col3 is part of both unique keys, and the table creation statement succeeds.

The following table cannot be partitioned at all, because there is no way to include in a partitioning key any columns that belong to both unique keys:

CREATE TABLE t4 (
    col1 INT NOT NULL,
    col2 INT NOT NULL,
    col3 INT NOT NULL,
    col4 INT NOT NULL,
    UNIQUE KEY (col1, col3),
    UNIQUE KEY (col2, col4)
);

Since every primary key is by definition a unique key, this restriction also includes the table's primary key, if it has one. For example, the next two statements are invalid:

CREATE TABLE t5 (
    col1 INT NOT NULL,
    col2 DATE NOT NULL,
    col3 INT NOT NULL,
    col4 INT NOT NULL,
    PRIMARY KEY(col1, col2)
)
PARTITION BY HASH(col3)
PARTITIONS 4;

CREATE TABLE t6 (
    col1 INT NOT NULL,
    col2 DATE NOT NULL,
    col3 INT NOT NULL,
    col4 INT NOT NULL,
    PRIMARY KEY(col1, col3),
    UNIQUE KEY(col2)
)
PARTITION BY HASH( YEAR(col2) )
PARTITIONS 4;

In both cases, the primary key does not include all columns referenced in the partitioning expression. However, both of the next two statements are valid:

CREATE TABLE t7 (
    col1 INT NOT NULL,
    col2 DATE NOT NULL,
    col3 INT NOT NULL,
    col4 INT NOT NULL,
    PRIMARY KEY(col1, col2)
)
PARTITION BY HASH(col1 + YEAR(col2))
PARTITIONS 4;

CREATE TABLE t8 (
    col1 INT NOT NULL,
    col2 DATE NOT NULL,
    col3 INT NOT NULL,
    col4 INT NOT NULL,
    PRIMARY KEY(col1, col2, col4),
    UNIQUE KEY(col2, col1)
)
PARTITION BY HASH(col1 + YEAR(col2))
PARTITIONS 4;

If a table has no unique keys—this includes having no primary key—then this restriction does not apply, and you may use any column or columns in the partitioning expression as long as the column type is compatible with the partitioning type.

For the same reason, you cannot later add a unique key to a partitioned table unless the key includes all columns used by the table's partitioning expression. Consider the partitioned table created as shown here:

mysql> CREATE TABLE t_no_pk (c1 INT, c2 INT)
    ->     PARTITION BY RANGE(c1) (
    ->         PARTITION p0 VALUES LESS THAN (10),
    ->         PARTITION p1 VALUES LESS THAN (20),
    ->         PARTITION p2 VALUES LESS THAN (30),
    ->         PARTITION p3 VALUES LESS THAN (40)
    ->     );
Query OK, 0 rows affected (0.12 sec)

It is possible to add a primary key to t_no_pk using either of these ALTER TABLE statements:

#  possible PK
mysql> ALTER TABLE t_no_pk ADD PRIMARY KEY(c1);
Query OK, 0 rows affected (0.13 sec)
Records: 0  Duplicates: 0  Warnings: 0

# drop this PK
mysql> ALTER TABLE t_no_pk DROP PRIMARY KEY;
Query OK, 0 rows affected (0.10 sec)
Records: 0  Duplicates: 0  Warnings: 0

#  use another possible PK
mysql> ALTER TABLE t_no_pk ADD PRIMARY KEY(c1, c2);
Query OK, 0 rows affected (0.12 sec)
Records: 0  Duplicates: 0  Warnings: 0

# drop this PK
mysql> ALTER TABLE t_no_pk DROP PRIMARY KEY;
Query OK, 0 rows affected (0.09 sec)
Records: 0  Duplicates: 0  Warnings: 0

However, the next statement fails, because c1 is part of the partitioning key, but is not part of the proposed primary key:

#  fails with error 1503
mysql> ALTER TABLE t_no_pk ADD PRIMARY KEY(c2);
ERROR 1503 (HY000): A PRIMARY KEY must include all columns in the table's partitioning function

Since t_no_pk has only c1 in its partitioning expression, attempting to adding a unique key on c2 alone fails. However, you can add a unique key that uses both c1 and c2.

These rules also apply to existing nonpartitioned tables that you wish to partition using ALTER TABLE ... PARTITION BY. Consider a table np_pk created as shown here:

mysql> CREATE TABLE np_pk (
    ->     id INT NOT NULL AUTO_INCREMENT,
    ->     name VARCHAR(50),
    ->     added DATE,
    ->     PRIMARY KEY (id)
    -> );
Query OK, 0 rows affected (0.08 sec)

The following ALTER TABLE statement fails with an error, because the added column is not part of any unique key in the table:

mysql> ALTER TABLE np_pk
    ->     PARTITION BY HASH( TO_DAYS(added) )
    ->     PARTITIONS 4;
ERROR 1503 (HY000): A PRIMARY KEY must include all columns in the table's partitioning function

However, this statement using the id column for the partitioning column is valid, as shown here:

mysql> ALTER TABLE np_pk
    ->     PARTITION BY HASH(id)
    ->     PARTITIONS 4;
Query OK, 0 rows affected (0.11 sec)
Records: 0  Duplicates: 0  Warnings: 0

In the case of np_pk, the only column that may be used as part of a partitioning expression is id; if you wish to partition this table using any other column or columns in the partitioning expression, you must first modify the table, either by adding the desired column or columns to the primary key, or by dropping the primary key altogether.

21.6.2 Partitioning Limitations Relating to Storage Engines

The following limitations apply to the use of storage engines with user-defined partitioning of tables.

MERGE storage engine.  User-defined partitioning and the MERGE storage engine are not compatible. Tables using the MERGE storage engine cannot be partitioned. Partitioned tables cannot be merged.

FEDERATED storage engine.  Partitioning of FEDERATED tables is not supported; it is not possible to create partitioned FEDERATED tables.

CSV storage engine.  Partitioned tables using the CSV storage engine are not supported; it is not possible to create partitioned CSV tables.

InnoDB storage engine.  InnoDB foreign keys and MySQL partitioning are not compatible. Partitioned InnoDB tables cannot have foreign key references, nor can they have columns referenced by foreign keys. InnoDB tables which have or which are referenced by foreign keys cannot be partitioned.

InnoDB does not support the use of multiple disks for subpartitions. (This is currently supported only by MyISAM.)

In addition, ALTER TABLE ... OPTIMIZE PARTITION does not work correctly with partitioned tables that use the InnoDB storage engine. Use ALTER TABLE ... REBUILD PARTITION and ALTER TABLE ... ANALYZE PARTITION, instead, for such tables. For more information, see Section 14.1.8.1, “ALTER TABLE Partition Operations”.

User-defined partitioning and the NDB storage engine (MySQL Cluster).  Partitioning by KEY (including LINEAR KEY) is the only type of partitioning supported for the NDB storage engine. It is not possible under normal circumstances in MySQL Cluster to create a MySQL Cluster table using any partitioning type other than [LINEAR] KEY, and attempting to do so fails with an error.

Exception (not for production): It is possible to override this restriction by setting the new system variable on MySQL Cluster SQL nodes to ON. If you choose to do this, you should be aware that tables using partitioning types other than [LINEAR] KEY are not supported in production. In such cases, you can create and use tables with partitioning types other than KEY or LINEAR KEY, but you do this entirely at your own risk.

The maximum number of partitions that can be defined for an NDB table depends on the number of data nodes and node groups in the cluster, the version of the MySQL Cluster software in use, and other factors. See NDB and user-defined partitioning, for more information.

As of MySQL NDB Cluster 7.5.2, the maximum amount of fixed-size data that can be stored per partition in an NDB table is 128 TB. Previously, this was 16 GB.

CREATE TABLE and ALTER TABLE statements that would cause a user-partitioned NDB table not to meet either or both of the following two requirements are not permitted, and fail with an error:

  1. The table must have an explicit primary key.

  2. All columns listed in the table's partitioning expression must be part of the primary key.

Exception.  If a user-partitioned NDB table is created using an empty column-list (that is, using PARTITION BY KEY() or PARTITION BY LINEAR KEY()), then no explicit primary key is required.

Upgrading partitioned tables.  When performing an upgrade, tables which are partitioned by KEY and which use any storage engine other than NDB must be dumped and reloaded.

Same storage engine for all partitions.  All partitions of a partitioned table must use the same storage engine and it must be the same storage engine used by the table as a whole. In addition, if one does not specify an engine on the table level, then one must do either of the following when creating or altering a partitioned table:

  • Do not specify any engine for any partition or subpartition

  • Specify the engine for all partitions or subpartitions

21.6.3 Partitioning Limitations Relating to Functions

This section discusses limitations in MySQL Partitioning relating specifically to functions used in partitioning expressions.

Only the MySQL functions shown in the following table are allowed in partitioning expressions.

In MySQL 5.7, partition pruning is supported for the TO_DAYS(), TO_SECONDS(), YEAR(), and UNIX_TIMESTAMP() functions. See Section 21.4, “Partition Pruning”, for more information.

CEILING() and FLOOR().  Each of these functions returns an integer only if it is passed an argument of an exact numeric type, such as one of the INT types or DECIMAL. This means, for example, that the following CREATE TABLE statement fails with an error, as shown here:

mysql> CREATE TABLE t (c FLOAT) PARTITION BY LIST( FLOOR(c) )(
    ->     PARTITION p0 VALUES IN (1,3,5),
    ->     PARTITION p1 VALUES IN (2,4,6)
    -> );
ERROR 1490 (HY000): The PARTITION function returns the wrong type

EXTRACT() function with WEEK specifier.  The value returned by the EXTRACT() function, when used as EXTRACT(WEEK FROM col), depends on the value of the default_week_format system variable. For this reason, EXTRACT() is not permitted as a partitioning function when it specifies the unit as WEEK. (Bug #54483)

See Section 13.6.2, “Mathematical Functions”, for more information about the return types of these functions, as well as Section 12.2, “Numeric Types”.

21.6.4 Partitioning and Locking

For storage engines such as MyISAM that actually execute table-level locks when executing DML or DDL statements, such a statement in older versions of MySQL (5.6.5 and earlier)that affected a partitioned table imposed a lock on the table as a whole; that is, all partitions were locked until the statement was finished. In MySQL 5.7, partition lock pruning eliminates unneeded locks in many cases, and most statements reading from or updating a partitioned MyISAM table cause only the effected partitions to be locked. For example, a SELECT from a partitioned MyISAM table locks only those partitions actually containing rows that satisfy the SELECT statement's WHERE condition are locked.

For statements effecting partitioned tables using storage engines such as InnoDB, that employ row-level locking and do not actually perform (or need to perform) the locks prior to partition pruning, this is not an issue.

The next few paragraphs discuss the effects of partition lock pruning for various MySQL statements on tables using storage engines that employ table-level locks.

Effects on DML statements

SELECT statements (including those containing unions or joins) lock only those partitions that actually need to be read. This also applies to SELECT ... PARTITION.

An UPDATE prunes locks only for tables on which no partitioning columns are updated.

REPLACE and INSERT lock only those partitions having rows to be inserted or replaced. However, if an AUTO_INCREMENT value is generated for any partitioning column then all partitions are locked.

INSERT ... ON DUPLICATE KEY UPDATE is pruned as long as no partitioning column is updated.

INSERT ... SELECT locks only those partitions in the source table that need to be read, although all partitions in the target table are locked.

Locks imposed by LOAD DATA statements on partitioned tables cannot be pruned.

The presence of BEFORE INSERT or BEFORE UPDATE triggers using any partitioning column of a partitioned table means that locks on INSERT and UPDATE statements updating this table cannot be pruned, since the trigger can alter its values: A BEFORE INSERT trigger on any of the table's partitioning columns means that locks set by INSERT or REPLACE cannot be pruned, since the BEFORE INSERT trigger may change a row's partitioning columns before the row is inserted, forcing the row into a different partition than it would be otherwise. A BEFORE UPDATE trigger on a partitioning column means that locks imposed by UPDATE or INSERT ... ON DUPLICATE KEY UPDATE cannot be pruned.

Affected DDL statements

CREATE VIEW does not cause any locks.

ALTER TABLE ... EXCHANGE PARTITION prunes locks; only the exchanged table and the exchanged partition are locked.

ALTER TABLE ... TRUNCATE PARTITION prunes locks; only the partitions to be emptied are locked.

In addition, ALTER TABLE statements take metadata locks on the table level.

Other statements

LOCK TABLES cannot prune partition locks.

CALL stored_procedure(expr) supports lock pruning, but evaluating expr does not.

DO and SET statements do not support partitioning lock pruning.