If you have existing tables, and applications that use them, that
you want to convert to
InnoDB for better
reliability and scalability, use the following guidelines and tips.
This section assumes most such tables were originally
MyISAM, which was formerly the default.
As you transition away from
MyISAM tables, lower
the value of the
configuration option to free memory no longer needed for caching
results. Increase the value of the
configuration option, which performs a similar role of allocating
cache memory for
InnoDB tables. The
pool caches both table data and index data, so it does double
duty in speeding up lookups for queries and keeping query results in
memory for reuse.
Allocate as much memory to this option as you can afford, often up to 80% of physical memory on the server.
If the operating system runs short of memory for other processes
and begins to swap, reduce the
Swapping is such an expensive operation that it drastically
reduces the benefit of the cache memory.
value is several gigabytes or higher, consider increasing the
Doing so helps on busy servers where many connections are
reading data into the cache at the same time.
On a busy server, run benchmarks with the Query Cache turned
InnoDB buffer pool provides similar
benefits, so the Query Cache might be tying up memory
MyISAM tables do not support
transactions, you might not
have paid much attention to the
autocommit configuration option and
statements. These keywords are important to allow multiple sessions
to read and write
InnoDB tables concurrently,
providing substantial scalability benefits in write-heavy workloads.
While a transaction is open, the system keeps a snapshot of the data as seen at the beginning of the transaction, which can cause substantial overhead if the system inserts, updates, and deletes millions of rows while a stray transaction keeps running. Thus, take care to avoid transactions that run for too long:
If you are using a mysql session for
interactive experiments, always
COMMIT (to finalize the changes)
(to undo the changes) when finished. Close down interactive
sessions rather than leaving them open for long periods, to
avoid keeping transactions open for long periods by accident.
ROLLBACK is a
relatively expensive operation, because
DELETE operations are written to
InnoDB tables prior to the
COMMIT, with the expectation that
most changes will be committed successfully and rollbacks will
be rare. When experimenting with large volumes of data, avoid
making changes to large numbers of rows and then rolling back
When loading large volumes of data with a sequence of
INSERT statements, periodically
COMMIT the results to avoid
having transactions that last for hours. In typical load
operations for data warehousing, if something goes wrong, you
TRUNCATE TABLE and start over
from the beginning rather than doing a
The preceding tips save memory and disk space that can be wasted
during too-long transactions. When transactions are shorter than
they should be, the problem is excessive I/O. With each
COMMIT, MySQL makes sure each change
is safely recorded to disk, which involves some I/O.
For most operations on
InnoDB tables, you
should use the setting
autocommit=0. From an
efficiency perspective, this avoids unnecessary I/O when you
issue large numbers of consecutive
DELETE statements. From a safety
perspective, this allows you to issue a
statement to recover lost or garbled data if you make a mistake
on the mysql command line, or in an exception
handler in your application.
The time when
InnoDB tables is when running a
sequence of queries for generating reports or analyzing
statistics. In this situation, there is no I/O penalty related
the read-only workload.
If you make a series of related changes, finalize all those
changes at once with a single
COMMIT at the end. For example,
if you insert related pieces of information into several tables,
do a single
COMMIT after making
all the changes. Or if you run many consecutive
INSERT statements, do a single
COMMIT after all the data is
loaded; if you are doing millions of
INSERT statements, perhaps split
up the huge transaction by issuing a
COMMIT every ten thousand or
hundred thousand records, so the transaction does not grow too
You might see warning messages referring to “deadlocks”
in the MySQL error log, or the output of
SHOW ENGINE INNODB
STATUS. Despite the scary-sounding name, a
deadlock is not a serious issue
InnoDB tables, and often does not require any
corrective action. When two transactions start modifying multiple
tables, accessing the tables in a different order, they can reach a
state where each transaction is waiting for the other and neither
can proceed. MySQL immediately detects this condition and cancels
(rolls back) the
“smaller” transaction, allowing the other to proceed.
Your applications do need error-handling logic to restart a transaction that is forcibly cancelled like this. When you re-issue the same SQL statements as before, the original timing issue no longer applies: either the other transaction has already finished and yours can proceed, or the other transaction is still in progress and your transaction waits until it finishes.
If deadlock warnings occur constantly, you might review the application code to reorder the SQL operations in a consistent way, or to shorten the transactions.
To get the best performance from
you can adjust a number of parameters related to storage layout.
When you convert
MyISAM tables that are large,
frequently accessed, and hold vital data, investigate and consider
options, and the
KEY_BLOCK_SIZE clauses of the
CREATE TABLE statement.
During your initial experiments, the most important setting is
this option before creating new
ensures that the
files do not allocate disk space permanently for all the
InnoDB data. With
DROP TABLE and
TRUNCATE TABLE free disk space as you
To convert a non-
InnoDB table to use
Do not convert MySQL system tables in the
database (such as
host) to the
This is an unsupported operation. The system tables must always be
You might make an InnoDB table that is a clone of a MyISAM table,
rather than doing the
conversion, to test the old and new table side-by-side before
Create an empty
InnoDB table with identical
column and index definitions. Use
show create table
to see the full
CREATE TABLE statement to use. Change
ENGINE clause to
To transfer a large volume of data into an empty
InnoDB table created as shown in the previous
section, insert the rows with
innodb_table SELECT * FROM
myisam_table ORDER BY
You can also create the indexes for the
table after inserting the data. Historically, creating new secondary
indexes was a slow operation for InnoDB, but now you can create the
indexes after the data is loaded with relatively little overhead
from the index creation step.
If you have
UNIQUE constraints on secondary keys,
you can speed up a table import by turning off the uniqueness checks
temporarily during the import operation:
... import operation ...SET unique_checks=1;
For big tables, this saves disk I/O because
InnoDB can use its
insert buffer to write
secondary index records as a batch. Be certain that the data
contains no duplicate keys.
unique_checks permits but does not
require storage engines to ignore duplicate keys.
To get better control over the insertion process, you might insert big tables in pieces:
INSERT INTO newtable SELECT * FROM oldtable WHERE yourkey >
somethingAND yourkey <=
After all records have been inserted, you can rename the tables.
During the conversion of big tables, increase the size of the
InnoDB buffer pool to reduce disk I/O, to a
maximum of 80% of physical memory. You can also increase the sizes
InnoDB log files.
By this point, as already mentioned, you should already have the
enabled, so that if you temporarily make several copies of your data
InnoDB tables, you can recover all that disk
space by dropping unneeded tables afterward.
Whether you convert the
MyISAM table directly or
create a cloned
InnoDB table, make sure that you
have sufficient disk space to hold both the old and new tables
during the process.
InnoDB tables require more
disk space than
MyISAM tables. If an
ALTER TABLE operation runs out of
space, it starts a rollback, and that can take hours if it is
disk-bound. For inserts,
InnoDB uses the insert
buffer to merge secondary index records to indexes in batches. That
saves a lot of disk I/O. For rollback, no such mechanism is used,
and the rollback can take 30 times longer than the insertion.
In the case of a runaway rollback, if you do not have valuable data in your database, it may be advisable to kill the database process rather than wait for millions of disk I/O operations to complete. For the complete procedure, see Section 14.21.2, “Forcing InnoDB Recovery”.
PRIMARY KEY clause is a critical factor
affecting the performance of MySQL queries and the space usage for
tables and indexes. Perhaps you have phoned a financial institution
where you are asked for an account number. If you do not have the
number, you are asked for a dozen different pieces of information to
“uniquely identify” yourself. The primary key is like
that unique account number that lets you get straight down to
business when querying or modifying the information in a table.
Every row in the table must have a primary key value, and no two
rows can have the same primary key value.
Here are some guidelines for the primary key, followed by more detailed explanations.
PRIMARY KEY for each table.
Typically, it is the most important column that you refer to in
WHERE clauses when looking up a single row.
Choose the column and its data type carefully. Prefer numeric columns over character or string ones.
Consider using an auto-increment column if there is not another stable, unique, non-null, numeric column to use.
An auto-increment column is also a good choice if there is any doubt whether the value of the primary key column could ever change. Changing the value of a primary key column is an expensive operation, possibly involving rearranging data within the table and within each secondary index.
Consider adding a primary key to any table that does not already have one. Use the smallest practical numeric type based on the maximum projected size of the table. This can make each row slightly more compact, which can yield substantial space savings for large tables. The space savings are multiplied if the table has any secondary indexes, because the primary key value is repeated in each secondary index entry. In addition to reducing data size on disk, a small primary key also lets more data fit into the buffer pool, speeding up all kinds of operations and improving concurrency.
If the table already has a primary key on some longer column, such
VARCHAR, consider adding a new unsigned
AUTO_INCREMENT column and switching the primary
key to that, even if that column is not referenced in queries. This
design change can produce substantial space savings in the secondary
indexes. You can designate the former primary key columns as
UNIQUE NOT NULL to enforce the same constraints
PRIMARY KEY clause, that is, to prevent
duplicate or null values across all those columns.
If you spread related information across multiple tables, typically each table uses the same column for its primary key. For example, a personnel database might have several tables, each with a primary key of employee number. A sales database might have some tables with a primary key of customer number, and other tables with a primary key of order number. Because lookups using the primary key are very fast, you can construct efficient join queries for such tables.
If you leave the
PRIMARY KEY clause out entirely,
MySQL creates an invisible one for you. It is a 6-byte value that
might be longer than you need, thus wasting space. Because it is
hidden, you cannot refer to it in queries.
The extra reliability and scalability features of
InnoDB do require more disk storage than
MyISAM tables. You might change the
column and index definitions slightly, for better space utilization,
reduced I/O and memory consumption when processing result sets, and
better query optimization plans making efficient use of index
If you do set up a numeric ID column for the primary key, use that
value to cross-reference with related values in any other tables,
particularly for join queries. For
example, rather than accepting a country name as input and doing
queries searching for the same name, do one lookup to determine the
country ID, then do other queries (or a single join query) to look
up relevant information across several tables. Rather than storing a
customer or catalog item number as a string of digits, potentially
using up several bytes, convert it to a numeric ID for storing and
querying. A 4-byte unsigned
column can index over 4 billion items (with the US meaning of
billion: 1000 million). For the ranges of the different integer
types, see Section 11.2.1, “Integer Types (Exact Value) - INTEGER, INT, SMALLINT, TINYINT,
InnoDB files require more care and planning than
MyISAM files do.
Methods of copying or moving
InnoDB tables to
a different server are described in
Section 14.9.2, “Moving or Copying InnoDB Tables to Another Machine”.