15.12.2 Online DDL Performance and Concurrency

Online DDL improves several aspects of MySQL operation:

Applications that access the table are more responsive because queries and DML operations on the table can proceed while the DDL operation is in progress. Reduced locking and waiting for MySQL server resources leads to greater scalability, even for operations that are not involved in the DDL operation.
Instant operations only modify metadata in the data dictionary. No metadata locks are taken on the table, and table data is unaffected, making operations instantaneous. Concurrent DML is unaffected.
Online operations avoid the disk I/O and CPU cycles associated with the table-copy method, which minimizes overall load on the database. Minimizing load helps maintain good performance and high throughput during the DDL operation.
Online operations read less data into the buffer pool than table-copy operations, which reduces purging of frequently accessed data from memory. Purging of frequently accessed data can cause a temporary performance dip after a DDL operation.

The LOCK clause

By default, MySQL uses as little locking as possible during a DDL operation. The LOCK clause can be specified for in-place operations and some copy operations to enforce more restrictive locking, if required. If the LOCK clause specifies a less restrictive level of locking than is permitted for a particular DDL operation, the statement fails with an error. LOCK clauses are described below, in order of least to most restrictive:

LOCK=NONE:
Permits concurrent queries and DML.
For example, use this clause for tables involving customer signups or purchases, to avoid making the tables unavailable during lengthy DDL operations.
LOCK=SHARED:
Permits concurrent queries but blocks DML.
For example, use this clause on data warehouse tables, where you can delay data load operations until the DDL operation is finished, but queries cannot be delayed for long periods.
LOCK=DEFAULT:
Permits as much concurrency as possible (concurrent queries, DML, or both). Omitting the LOCK clause is the same as specifying LOCK=DEFAULT.
Use this clause when you know that the default locking level of the DDL statement will not cause availability problems for the table.
LOCK=EXCLUSIVE:
Blocks concurrent queries and DML.
Use this clause if the primary concern is finishing the DDL operation in the shortest amount of time possible, and concurrent query and DML access is not necessary. You might also use this clause if the server is supposed to be idle, to avoid unexpected table accesses.

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Online DDL and Metadata Locks

Online DDL operations can be viewed as having three phases:

Phase 1: Initialization
In the initialization phase, the server determines how much concurrency is permitted during the operation, taking into account storage engine capabilities, operations specified in the statement, and user-specified ALGORITHM and LOCK options. During this phase, a shared upgradeable metadata lock is taken to protect the current table definition.
Phase 2: Execution
In this phase, the statement is prepared and executed. Whether the metadata lock is upgraded to exclusive depends on the factors assessed in the initialization phase. If an exclusive metadata lock is required, it is only taken briefly during statement preparation.
Phase 3: Commit Table Definition
In the commit table definition phase, the metadata lock is upgraded to exclusive to evict the old table definition and commit the new one. Once granted, the duration of the exclusive metadata lock is brief.

Due to the exclusive metadata lock requirements outlined above, an online DDL operation may have to wait for concurrent transactions that hold metadata locks on the table to commit or rollback. Transactions started before or during the DDL operation can hold metadata locks on the table being altered. In the case of a long running or inactive transaction, an online DDL operation can time out waiting for an exclusive metadata lock. Additionally, a pending exclusive metadata lock requested by an online DDL operation blocks subsequent transactions on the table.

The following example demonstrates an online DDL operation waiting for an exclusive metadata lock, and how a pending metadata lock blocks subsequent transactions on the table.

Session 1:

mysql> CREATE TABLE t1 (c1 INT) ENGINE=InnoDB;
mysql> START TRANSACTION;
mysql> SELECT * FROM t1;

The session 1 SELECT statement takes a shared metadata lock on table t1.

Session 2:

mysql> ALTER TABLE t1 ADD COLUMN x INT, ALGORITHM=INPLACE, LOCK=NONE;

The online DDL operation in session 2, which requires an exclusive metadata lock on table t1 to commit table definition changes, must wait for the session 1 transaction to commit or roll back.

Session 3:

mysql> SELECT * FROM t1;

The SELECT statement issued in session 3 is blocked waiting for the exclusive metadata lock requested by the ALTER TABLE operation in session 2 to be granted.

You can use SHOW FULL PROCESSLIST to determine if transactions are waiting for a metadata lock.

mysql> SHOW FULL PROCESSLIST\G
...
*************************** 2. row ***************************
Id: 5
User: root
Host: localhost
db: test
Command: Query
Time: 44
State: Waiting for table metadata lock
Info: ALTER TABLE t1 ADD COLUMN x INT, ALGORITHM=INPLACE, LOCK=NONE
...
*************************** 4. row ***************************
Id: 7
User: root
Host: localhost
db: test
Command: Query
Time: 5
State: Waiting for table metadata lock
Info: SELECT * FROM t1
4 rows in set (0.00 sec)

Metadata lock information is also exposed through the Performance Schema metadata_locks table, which provides information about metadata lock dependencies between sessions, the metadata lock a session is waiting for, and the session that currently holds the metadata lock. For more information, see Section 26.12.12.3, “The metadata_locks Table”.

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Online DDL Performance

The performance of a DDL operation is largely determined by whether the operation is performed instantly, in place, and whether it rebuilds the table.

To assess the relative performance of a DDL operation, you can compare results using ALGORITHM=INSTANT, ALGORITHM=INPLACE, and ALGORITHM=COPY. A statement can also be run with old_alter_table enabled to force the use of ALGORITHM=COPY.

For DDL operations that modify table data, you can determine whether a DDL operation performs changes in place or performs a table copy by looking at the “rows affected” value displayed after the command finishes. For example:

Changing the default value of a column (fast, does not affect the table data):
1. Query OK, 0 rows affected (0.07 sec)
Adding an index (takes time, but 0 rows affected shows that the table is not copied):
1. Query OK, 0 rows affected (21.42 sec)
Changing the data type of a column (takes substantial time and requires rebuilding all the rows of the table):
1. Query OK, 1671168 rows affected (1 min 35.54 sec)

Before running a DDL operation on a large table, check whether the operation is fast or slow as follows:

Clone the table structure.
Populate the cloned table with a small amount of data.
Run the DDL operation on the cloned table.
Check whether the “rows affected” value is zero or not. A nonzero value means the operation copies table data, which might require special planning. For example, you might do the DDL operation during a period of scheduled downtime, or on each replication slave server one at a time.

Note

For a greater understanding of the MySQL processing associated with a DDL operation, examine Performance Schema and INFORMATION_SCHEMA tables related to InnoDB before and after DDL operations to see the number of physical reads, writes, memory allocations, and so on.

Performance Schema stage events can be used to monitor ALTER TABLE progress. See Section 15.15.1, “Monitoring ALTER TABLE Progress for InnoDB Tables Using Performance Schema”.

Because there is some processing work involved with recording the changes made by concurrent DML operations, then applying those changes at the end, an online DDL operation could take longer overall than the table-copy mechanism that blocks table access from other sessions. The reduction in raw performance is balanced against better responsiveness for applications that use the table. When evaluating the techniques for changing table structure, consider end-user perception of performance, based on factors such as load times for web pages.

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