MEMORY storage engine (formerly known as
HEAP) creates special-purpose tables with
contents that are stored in memory. Because the data is vulnerable
to crashes, hardware issues, or power outages, only use these tables
as temporary work areas or read-only caches for data pulled from
Table 15.4 MEMORY Storage Engine Features
|Backup/point-in-time recovery (Implemented in the server, rather than in the storage engine.)||Yes|
|Cluster database support||No|
|Encrypted data||Yes (Implemented in the server via encryption functions.)|
|Foreign key support||No|
|Full-text search indexes||No|
|Geospatial data type support||No|
|Geospatial indexing support||No|
|Replication support (Implemented in the server, rather than in the storage engine.)||Limited (See the discussion later in this section.)|
|Update statistics for data dictionary||Yes|
Developers looking to deploy applications that use the
MEMORY storage engine for important, highly
available, or frequently updated data should consider whether NDB
Cluster is a better choice. A typical use case for the
MEMORY engine involves these characteristics:
Operations involving transient, non-critical data such as session management or caching. When the MySQL server halts or restarts, the data in
MEMORYtables is lost.
In-memory storage for fast access and low latency. Data volume can fit entirely in memory without causing the operating system to swap out virtual memory pages.
A read-only or read-mostly data access pattern (limited updates).
NDB Cluster offers the same features as the
MEMORY engine with higher performance levels,
and provides additional features not available with
Row-level locking and multiple-thread operation for low contention between clients.
Scalability even with statement mixes that include writes.
Optional disk-backed operation for data durability.
Shared-nothing architecture and multiple-host operation with no single point of failure, enabling 99.999% availability.
Automatic data distribution across nodes; application developers need not craft custom sharding or partitioning solutions.
MEMORY performance is constrained by contention
resulting from single-thread execution and table lock overhead
when processing updates. This limits scalability when load
increases, particularly for statement mixes that include writes.
Despite the in-memory processing for
tables, they are not necessarily faster than
InnoDB tables on a busy server, for
general-purpose queries, or under a read/write workload. In
particular, the table locking involved with performing updates can
slow down concurrent usage of
from multiple sessions.
Depending on the kinds of queries performed on a
MEMORY table, you might create indexes as
either the default hash data structure (for looking up single
values based on a unique key), or a general-purpose B-tree data
structure (for all kinds of queries involving equality,
inequality, or range operators such as less than or greater than).
The following sections illustrate the syntax for creating both
kinds of indexes. A common performance issue is using the default
hash indexes in workloads where B-tree indexes are more efficient.
MEMORY storage engine associates each table
with one disk file, which stores the table definition (not the
data). The file name begins with the table name and has an
MEMORY tables have the following
MEMORYtables is allocated in small blocks. Tables use 100% dynamic hashing for inserts. No overflow area or extra key space is needed. No extra space is needed for free lists. Deleted rows are put in a linked list and are reused when you insert new data into the table.
MEMORYtables also have none of the problems commonly associated with deletes plus inserts in hashed tables.
MEMORYtables use a fixed-length row-storage format. Variable-length types such as
VARCHARare stored using a fixed length.
MEMORYincludes support for
MEMORYtables are shared among all clients, just like any other non-
To create a
MEMORY table, specify the clause
ENGINE=MEMORY on the
CREATE TABLE statement.
CREATE TABLE t (i INT) ENGINE = MEMORY;
As indicated by the engine name,
are stored in memory. They use hash indexes by default, which
makes them very fast for single-value lookups, and very useful for
creating temporary tables. However, when the server shuts down,
all rows stored in
MEMORY tables are lost. The
tables themselves continue to exist because their definitions are
.frm files on disk, but they are
empty when the server restarts.
This example shows how you might create, use, and remove a
mysql> CREATE TABLE test ENGINE=MEMORY SELECT ip,SUM(downloads) AS down FROM log_table GROUP BY ip; mysql> SELECT COUNT(ip),AVG(down) FROM test; mysql> DROP TABLE test;
The maximum size of
MEMORY tables is limited by
variable, which has a default value of 16MB. To enforce different
size limits for
MEMORY tables, change the value
of this variable. The value in effect for
CREATE TABLE, or a subsequent
ALTER TABLE or
TRUNCATE TABLE, is the value used
for the life of the table. A server restart also sets the maximum
size of existing
MEMORY tables to the global
max_heap_table_size value. You
can set the size for individual tables as described later in this
MEMORY storage engine supports both
BTREE indexes. You
can specify one or the other for a given index by adding a
USING clause as shown here:
CREATE TABLE lookup (id INT, INDEX USING HASH (id)) ENGINE = MEMORY; CREATE TABLE lookup (id INT, INDEX USING BTREE (id)) ENGINE = MEMORY;
For general characteristics of B-tree and hash indexes, see Section 8.3.1, “How MySQL Uses Indexes”.
MEMORY tables can have up to 64 indexes per
table, 16 columns per index and a maximum key length of 3072
MEMORY table hash index has a high degree
of key duplication (many index entries containing the same value),
updates to the table that affect key values and all deletes are
significantly slower. The degree of this slowdown is proportional
to the degree of duplication (or, inversely proportional to the
index cardinality). You can use a
to avoid this problem.
MEMORY tables can have nonunique keys. (This is
an uncommon feature for implementations of hash indexes.)
Columns that are indexed can contain
MEMORY table contents are stored in memory,
which is a property that
MEMORY tables share
with internal temporary tables that the server creates on the fly
while processing queries. However, the two types of tables differ
MEMORY tables are not subject to
storage conversion, whereas internal temporary tables are:
If an internal temporary table becomes too large, the server automatically converts it to on-disk storage, as described in Section 8.4.4, “Internal Temporary Table Use in MySQL”.
MEMORYtables are never converted to disk tables.
To populate a
MEMORY table when the MySQL
server starts, you can use the
--init-file option. For example,
you can put statements such as
INSERT INTO ...
LOAD DATA into
this file to load the table from a persistent data source. See
Section 5.1.6, “Server Command Options”, and Section 13.2.6, “LOAD DATA Syntax”.
MEMORY tables become empty when it
is shut down and restarted. If the server is a replication master,
its slaves are not aware that these tables have become empty, so
you see out-of-date content if you select data from the tables on
the slaves. To synchronize master and slave
MEMORY tables, when a
table is used on a master for the first time since it was started,
DELETE statement is written to
the master's binary log, to empty the table on the slaves also.
The slave still has outdated data in the table during the interval
between the master's restart and its first use of the table. To
avoid this interval when a direct query to the slave could return
stale data, use the
option to populate the
MEMORY table on the
master at startup.
The server needs sufficient memory to maintain all
MEMORY tables that are in use at the same time.
Memory is not reclaimed if you delete individual rows from a
MEMORY table. Memory is reclaimed only when the
entire table is deleted. Memory that was previously used for
deleted rows is re-used for new rows within the same table. To
free all the memory used by a
MEMORY table when
you no longer require its contents, execute
TRUNCATE TABLE to remove all rows,
or remove the table altogether using
TABLE. To free up the memory used by deleted rows, use
ALTER TABLE ENGINE=MEMORY to force a table
The memory needed for one row in a
is calculated using the following expression:
SUM_OVER_ALL_BTREE_KEYS(max_length_of_key + sizeof(char*) * 4) + SUM_OVER_ALL_HASH_KEYS(sizeof(char*) * 2) + ALIGN(length_of_row+1, sizeof(char*))
ALIGN() represents a round-up factor to cause
the row length to be an exact multiple of the
char pointer size.
sizeof(char*) is 4 on 32-bit machines and 8 on
As mentioned earlier, the
variable sets the limit on the maximum size of
MEMORY tables. To control the maximum size for
individual tables, set the session value of this variable before
creating each table. (Do not change the global
max_heap_table_size value unless
you intend the value to be used for
tables created by all clients.) The following example creates two
MEMORY tables, with a maximum size of 1MB and
mysql> SET max_heap_table_size = 1024*1024; Query OK, 0 rows affected (0.00 sec) mysql> CREATE TABLE t1 (id INT, UNIQUE(id)) ENGINE = MEMORY; Query OK, 0 rows affected (0.01 sec) mysql> SET max_heap_table_size = 1024*1024*2; Query OK, 0 rows affected (0.00 sec) mysql> CREATE TABLE t2 (id INT, UNIQUE(id)) ENGINE = MEMORY; Query OK, 0 rows affected (0.00 sec)
Both tables revert to the server's global
max_heap_table_size value if the
You can also specify a
MAX_ROWS table option in
CREATE TABLE statements for
MEMORY tables to provide a hint about the
number of rows you plan to store in them. This does not enable the
table to grow beyond the
max_heap_table_size value, which
still acts as a constraint on maximum table size. For maximum
flexibility in being able to use
max_heap_table_size at least as
high as the value to which you want each
table to be able to grow.
A forum dedicated to the
MEMORY storage engine
is available at https://forums.mysql.com/list.php?92.