Optimization is a complex task because ultimately it requires
understanding of the entire system to be optimized. Although it may
be possible to perform some local optimizations with little
knowledge of your system or application, the more optimal you want
your system to become, the more you must know about it.
This chapter tries to explain and give some examples of different
ways to optimize MySQL. Remember, however, that there are always
additional ways to make the system even faster, although they may
require increasing effort to achieve.
The most important factor in making a system fast is its basic
design. You must also know what kinds of processing your system is
doing, and what its bottlenecks are. In most cases, system
bottlenecks arise from these sources:
Disk seeks. It takes time for the disk to find a piece of
data. With modern disks, the mean time for this is usually
lower than 10ms, so we can in theory do about 100 seeks a
second. This time improves slowly with new disks and is very
hard to optimize for a single table. The way to optimize seek
time is to distribute the data onto more than one disk.
Disk reading and writing. When the disk is at the correct
position, we need to read the data. With modern disks, one
disk delivers at least 10–20MB/s throughput. This is
easier to optimize than seeks because you can read in parallel
from multiple disks.
CPU cycles. When we have the data in main memory, we need to
process it to get our result. Having small tables compared to
the amount of memory is the most common limiting factor. But
with small tables, speed is usually not the problem.
Memory bandwidth. When the CPU needs more data than can fit in
the CPU cache, main memory bandwidth becomes a bottleneck.
This is an uncommon bottleneck for most systems, but one to be
aware of.
When using the MyISAM storage engine, MySQL
uses extremely fast table locking that allows multiple readers
or a single writer. The biggest problem with this storage engine
occurs when you have a steady stream of mixed updates and slow
selects on a single table. If this is a problem for certain
tables, you can use another storage engine for them. See
Chapter 14, Storage Engines.
MySQL can work with both transactional and non-transactional
tables. To make it easier to work smoothly with
non-transactional tables (which cannot roll back if something
goes wrong), MySQL has the following rules. Note that these
rules apply only when not running in strict
SQL mode or if you use the IGNORE specifier
for INSERT or UPDATE.
All columns have default values.
If you insert an inappropriate or out-of-range value into a
column, MySQL sets the column to the “best possible
value” instead of reporting an error. For numerical
values, this is 0, the smallest possible value or the
largest possible value. For strings, this is either the
empty string or as much of the string as can be stored in
the column.
All calculated expressions return a value that can be used
instead of signaling an error condition. For example, 1/0
returns NULL.
Because all SQL servers implement different parts of standard
SQL, it takes work to write portable database applications. It
is very easy to achieve portability for very simple selects and
inserts, but becomes more difficult the more capabilities you
require. If you want an application that is fast with many
database systems, it becomes even more difficult.
All database systems have some weak points. That is, they have
different design compromises that lead to different behavior.
To make a complex application portable, you need to determine
which SQL servers it must work with, and then determine what
features those servers support. You can use the MySQL
crash-me program to find functions, types,
and limits that you can use with a selection of database
servers. crash-me does not check for every
possible feature, but it is still reasonably comprehensive,
performing about 450 tests. An example of the type of
information crash-me can provide is that you
should not use column names that are longer than 18 characters
if you want to be able to use Informix or DB2.
The crash-me program and the MySQL benchmarks
are all very database independent. By taking a look at how they
are written, you can get a feeling for what you must do to make
your own applications database independent. The programs can be
found in the sql-bench directory of MySQL
source distributions. They are written in Perl and use the DBI
database interface. Use of DBI in itself solves part of the
portability problem because it provides database-independent
access methods. See Section 7.1.4, “The MySQL Benchmark Suite”.
If you strive for database independence, you need to get a good
feeling for each SQL server's bottlenecks. For example, MySQL is
very fast in retrieving and updating rows for
MyISAM tables, but has a problem in mixing
slow readers and writers on the same table. Oracle, on the other
hand, has a big problem when you try to access rows that you
have recently updated (until they are flushed to disk).
Transactional database systems in general are not very good at
generating summary tables from log tables, because in this case
row locking is almost useless.
MySQL Enterprise
For expert advice on choosing the database engine suitable to
your circumstances subscribe to the MySQL Network Monitoring
and Advisory Service For more information see
http://www.mysql.com/products/enterprise/advisors.html.
To make your application really database
independent, you should define an easily extendable interface
through which you manipulate your data. For example, C++ is
available on most systems, so it makes sense to use a C++
class-based interface to the databases.
If you use some feature that is specific to a given database
system (such as the REPLACE statement, which
is specific to MySQL), you should implement the same feature for
other SQL servers by coding an alternative method. Although the
alternative might be slower, it enables the other servers to
perform the same tasks.
With MySQL, you can use the /*! */ syntax to
add MySQL-specific keywords to a statement. The code inside
/* */ is treated as a comment (and ignored)
by most other SQL servers. For information about writing
comments, see Section 9.5, “Comment Syntax”.
If high performance is more important than exactness, as for
some Web applications, it is possible to create an application
layer that caches all results to give you even higher
performance. By letting old results expire after a while, you
can keep the cache reasonably fresh. This provides a method to
handle high load spikes, in which case you can dynamically
increase the cache size and set the expiration timeout higher
until things get back to normal.
In this case, the table creation information should contain
information about the initial cache size and how often the table
should normally be refreshed.
An attractive alternative to implementing an application cache
is to use the MySQL query cache. By enabling the query cache,
the server handles the details of determining whether a query
result can be reused. This simplifies your application. See
Section 5.13, “The MySQL Query Cache”.
7.1.3. What We Have Used MySQL For
This section describes an early application for MySQL.
During MySQL initial development, the features of MySQL were
made to fit our largest customer, which handled data warehousing
for a couple of the largest retailers in Sweden.
From all stores, we got weekly summaries of all bonus card
transactions, and were expected to provide useful information
for the store owners to help them find how their advertising
campaigns were affecting their own customers.
The volume of data was quite huge (about seven million summary
transactions per month), and we had data for 4–10 years
that we needed to present to the users. We got weekly requests
from our customers, who wanted instant access to new reports
from this data.
We solved this problem by storing all information per month in
compressed “transaction tables.” We had a set of
simple macros that generated summary tables grouped by different
criteria (product group, customer id, store, and so on) from the
tables in which the transactions were stored. The reports were
Web pages that were dynamically generated by a small Perl
script. This script parsed a Web page, executed the SQL
statements in it, and inserted the results. We would have used
PHP or mod_perl instead, but they were not
available at the time.
For graphical data, we wrote a simple tool in C that could
process SQL query results and produce GIF images based on those
results. This tool also was dynamically executed from the Perl
script that parses the Web pages.
In most cases, a new report could be created simply by copying
an existing script and modifying the SQL query that it used. In
some cases, we needed to add more columns to an existing summary
table or generate a new one. This also was quite simple because
we kept all transaction-storage tables on disk. (This amounted
to about 50GB of transaction tables and 200GB of other customer
data.)
We also let our customers access the summary tables directly
with ODBC so that the advanced users could experiment with the
data themselves.
This system worked well and we had no problems handling the data
with quite modest Sun Ultra SPARCstation hardware
(2×200MHz). Eventually the system was migrated to Linux.
7.1.4. The MySQL Benchmark Suite
This benchmark suite is meant to tell any user what operations a
given SQL implementation performs well or poorly. You can get a
good idea for how the benchmarks work by looking at the code and
results in the sql-bench directory in any
MySQL source distribution.
Note that this benchmark is single-threaded, so it measures the
minimum time for the operations performed. We plan to add
multi-threaded tests to the benchmark suite in the future.
To use the benchmark suite, the following requirements must be
satisfied:
The benchmark scripts are written in Perl and use the Perl
DBI module to access database servers, so DBI must be
installed. You also need the server-specific DBD drivers for
each of the servers you want to test. For example, to test
MySQL, PostgreSQL, and DB2, you must have the
DBD::mysql, DBD::Pg,
and DBD::DB2 modules installed. See
Section 2.4.20, “Perl Installation Notes”.
After you obtain a MySQL source distribution, you can find the
benchmark suite located in its sql-bench
directory. To run the benchmark tests, build MySQL, and then
change location into the sql-bench
directory and execute the run-all-tests
script:
shell> cd sql-bench
shell> perl run-all-tests --server=server_name
server_name should be the name of one
of the supported servers. To get a list of all options and
supported servers, invoke this command:
shell> perl run-all-tests --help
The crash-me script also is located in the
sql-bench directory.
crash-me tries to determine what features a
database system supports and what its capabilities and
limitations are by actually running queries. For example, it
determines:
You should definitely benchmark your application and database to
find out where the bottlenecks are. After fixing one bottleneck
(or by replacing it with a “dummy” module), you can
proceed to identify the next bottleneck. Even if the overall
performance for your application currently is acceptable, you
should at least make a plan for each bottleneck and decide how
to solve it if someday you really need the extra performance.
For examples of portable benchmark programs, look at those in
the MySQL benchmark suite. See
Section 7.1.4, “The MySQL Benchmark Suite”. You can take any program
from this suite and modify it for your own needs. By doing this,
you can try different solutions to your problem and test which
really is fastest for you.
It is very common for a problem to occur only when the system is
very heavily loaded. We have had many customers who contact us
when they have a (tested) system in production and have
encountered load problems. In most cases, performance problems
turn out to be due to issues of basic database design (for
example, table scans are not good under high load) or problems
with the operating system or libraries. Most of the time, these
problems would be much easier to fix if the systems were not
already in production.
To avoid problems like this, you should put some effort into
benchmarking your whole application under the worst possible
load. You can use Super Smack, available at
http://jeremy.zawodny.com/mysql/super-smack/. As
suggested by its name, it can bring a system to its knees, so
make sure to use it only on your development systems.
First, one factor affects all statements: The more complex your
permissions setup, the more overhead you have. Using simpler
permissions when you issue GRANT statements
enables MySQL to reduce permission-checking overhead when clients
execute statements. For example, if you do not grant any
table-level or column-level privileges, the server need not ever
check the contents of the tables_priv and
columns_priv tables. Similarly, if you place no
resource limits on any accounts, the server does not have to
perform resource counting. If you have a very high
statement-processing load, it may be worth the time to use a
simplified grant structure to reduce permission-checking overhead.
If your problem is with a specific MySQL expression or function,
you can perform a timing test by invoking the
BENCHMARK() function using the
mysql client program. Its syntax is
BENCHMARK(loop_count,expression).
The return value is always zero, but mysql
prints a line displaying approximately how long the statement took
to execute. For example:
mysql> SELECT BENCHMARK(1000000,1+1);
+------------------------+
| BENCHMARK(1000000,1+1) |
+------------------------+
| 0 |
+------------------------+
1 row in set (0.32 sec)
This result was obtained on a Pentium II 400MHz system. It shows
that MySQL can execute 1,000,000 simple addition expressions in
0.32 seconds on that system.
All MySQL functions should be highly optimized, but there may be
some exceptions. BENCHMARK() is an excellent
tool for finding out if some function is a problem for your
queries.
7.2.1. Optimizing Queries with EXPLAIN
EXPLAIN tbl_name
Or:
EXPLAIN [EXTENDED] SELECT select_options
The EXPLAIN statement can be used either as a
synonym for DESCRIBE or as a way to obtain
information about how MySQL executes a SELECT
statement:
EXPLAIN
tbl_name is synonymous
with DESCRIBE
tbl_name or
SHOW COLUMNS FROM
tbl_name.
When you precede a SELECT statement with
the keyword EXPLAIN, MySQL displays
information from the optimizer about the query execution
plan. That is, MySQL explains how it would process the
SELECT, including information about how
tables are joined and in which order.
With the help of EXPLAIN, you can see where
you should add indexes to tables to get a faster
SELECT that uses indexes to find rows. You
can also use EXPLAIN to check whether the
optimizer joins the tables in an optimal order. To force the
optimizer to use a join order corresponding to the order in
which the tables are named in the SELECT
statement, begin the statement with SELECT
STRAIGHT_JOIN rather than just
SELECT.
If you have a problem with indexes not being used when you
believe that they should be, you should run ANALYZE
TABLE to update table statistics such as cardinality
of keys, that can affect the choices the optimizer makes. See
Section 13.5.2.1, “ANALYZE TABLE Syntax”.
EXPLAIN returns a row of information for each
table used in the SELECT statement. The
tables are listed in the output in the order that MySQL would
read them while processing the query. MySQL resolves all joins
using a single-sweep multi-join method.
This means that MySQL reads a row from the first table, and then
finds a matching row in the second table, the third table, and
so on. When all tables are processed, MySQL outputs the selected
columns and backtracks through the table list until a table is
found for which there are more matching rows. The next row is
read from this table and the process continues with the next
table.
When the EXTENDED keyword is used,
EXPLAIN produces extra information that can
be viewed by issuing a SHOW WARNINGS
statement following the EXPLAIN statement.
This information displays how the optimizer qualifies table and
column names in the SELECT statement, what
the SELECT looks like after the application
of rewriting and optimization rules, and possibly other notes
about the optimization process.
Each output row from EXPLAIN provides
information about one table, and each row contains the following
columns:
id
The SELECT identifier. This is the
sequential number of the SELECT within
the query.
select_type
The type of SELECT, which can be any of
those shown in the following table:
SIMPLE
Simple SELECT (not using UNION or
subqueries)
PRIMARY
Outermost SELECT
UNION
Second or later SELECT statement in a
UNION
DEPENDENT UNION
Second or later SELECT statement in a
UNION, dependent on outer query
UNION RESULT
Result of a UNION.
SUBQUERY
First SELECT in subquery
DEPENDENT SUBQUERY
First SELECT in subquery, dependent on outer query
DERIVED
Derived table SELECT (subquery in
FROM clause)
UNCACHEABLE SUBQUERY
A subquery for which the result cannot be cached and must be
re-evaluated for each row of the outer query
“DEPENDENT SUBQUERY” evaluation differs from
UNCACHEABLE SUBQUERY evaluation. For
“DEPENDENT SUBQUERY”, the subquery is
re-evaluated only once for each set of different values of
the variables from its outer context. For
UNCACHEABLE SUBQUERY, the subquery is
re-evaluated for each row of the outer context. Cacheability
of subqueries is subject to the restrictions detailed in
Section 5.13.1, “How the Query Cache Operates”. For example, referring to
user variables makes a subquery uncacheable.
table
The table to which the row of output refers.
type
The join type. The different join types are listed here,
ordered from the best type to the worst:
system
The table has only one row (= system table). This is a
special case of the const join type.
const
The table has at most one matching row, which is read at
the start of the query. Because there is only one row,
values from the column in this row can be regarded as
constants by the rest of the optimizer.
const tables are very fast because
they are read only once.
const is used when you compare all
parts of a PRIMARY KEY or
UNIQUE index to constant values. In
the following queries,
tbl_name can be used as a
const table:
SELECT * FROM tbl_name WHERE primary_key=1;
SELECT * FROM tbl_name
WHERE primary_key_part1=1 AND primary_key_part2=2;
eq_ref
One row is read from this table for each combination of
rows from the previous tables. Other than the
system and const
types, this is the best possible join type. It is used
when all parts of an index are used by the join and the
index is a PRIMARY KEY or
UNIQUE index.
eq_ref can be used for indexed
columns that are compared using the =
operator. The comparison value can be a constant or an
expression that uses columns from tables that are read
before this table. In the following examples, MySQL can
use an eq_ref join to process
ref_table:
SELECT * FROM ref_table,other_table
WHERE ref_table.key_column=other_table.column;
SELECT * FROM ref_table,other_table
WHERE ref_table.key_column_part1=other_table.column
AND ref_table.key_column_part2=1;
ref
All rows with matching index values are read from this
table for each combination of rows from the previous
tables. ref is used if the join uses
only a leftmost prefix of the key or if the key is not a
PRIMARY KEY or
UNIQUE index (in other words, if the
join cannot select a single row based on the key value).
If the key that is used matches only a few rows, this is
a good join type.
ref can be used for indexed columns
that are compared using the = or
<=> operator. In the following
examples, MySQL can use a ref join to
process ref_table:
SELECT * FROM ref_table WHERE key_column=expr;
SELECT * FROM ref_table,other_table
WHERE ref_table.key_column=other_table.column;
SELECT * FROM ref_table,other_table
WHERE ref_table.key_column_part1=other_table.column
AND ref_table.key_column_part2=1;
ref_or_null
This join type is like ref, but with
the addition that MySQL does an extra search for rows
that contain NULL values. This join
type optimization is used most often in resolving
subqueries. In the following examples, MySQL can use a
ref_or_null join to process
ref_table:
SELECT * FROM ref_table
WHERE key_column=expr OR key_column IS NULL;
This join type indicates that the Index Merge
optimization is used. In this case, the
key column in the output row contains
a list of indexes used, and key_len
contains a list of the longest key parts for the indexes
used. For more information, see
Section 7.2.6, “Index Merge Optimization”.
unique_subquery
This type replaces ref for some
IN subqueries of the following form:
value IN (SELECT primary_key FROM single_table WHERE some_expr)
unique_subquery is just an index
lookup function that replaces the subquery completely
for better efficiency.
index_subquery
This join type is similar to
unique_subquery. It replaces
IN subqueries, but it works for
non-unique indexes in subqueries of the following form:
value IN (SELECT key_column FROM single_table WHERE some_expr)
range
Only rows that are in a given range are retrieved, using
an index to select the rows. The key
column in the output row indicates which index is used.
The key_len contains the longest key
part that was used. The ref column is
NULL for this type.
range can be used when a key column
is compared to a constant using any of the
=, <>,
>, >=,
<, <=,
IS NULL,
<=>,
BETWEEN, or IN
operators:
SELECT * FROM tbl_name
WHERE key_column = 10;
SELECT * FROM tbl_name
WHERE key_column BETWEEN 10 and 20;
SELECT * FROM tbl_name
WHERE key_column IN (10,20,30);
SELECT * FROM tbl_name
WHERE key_part1= 10 AND key_part2 IN (10,20,30);
index
This join type is the same as ALL,
except that only the index tree is scanned. This usually
is faster than ALL because the index
file usually is smaller than the data file.
MySQL can use this join type when the query uses only
columns that are part of a single index.
ALL
A full table scan is done for each combination of rows
from the previous tables. This is normally not good if
the table is the first table not marked
const, and usually
very bad in all other cases.
Normally, you can avoid ALL by adding
indexes that allow row retrieval from the table based on
constant values or column values from earlier tables.
possible_keys
The possible_keys column indicates which
indexes MySQL can choose from use to find the rows in this
table. Note that this column is totally independent of the
order of the tables as displayed in the output from
EXPLAIN. That means that some of the keys
in possible_keys might not be usable in
practice with the generated table order.
If this column is NULL, there are no
relevant indexes. In this case, you may be able to improve
the performance of your query by examining the
WHERE clause to check whether it refers
to some column or columns that would be suitable for
indexing. If so, create an appropriate index and check the
query with EXPLAIN again. See
Section 13.1.2, “ALTER TABLE Syntax”.
To see what indexes a table has, use SHOW INDEX
FROM tbl_name.
key
The key column indicates the key (index)
that MySQL actually decided to use. If MySQL decides to use
one of the possible_keys indexes to look
up rows, that index is listed as the key value.
It is possible that key will name an
index that is not present in the
possible_keys value. This can happen if
none of the possible_keys indexes are
suitable for looking up rows, but all the columns selected
by the query are columns of some other index. That is, the
named index covers the selected columns, so although it is
not used to determine which rows to retrieve, an index scan
is more efficient than a data row scan.
For InnoDB, a secondary index might cover
the selected columns even if the query also selects the
primary key because InnoDB stores the
primary key value with each secondary index. If
key is NULL, MySQL
found no index to use for executing the query more
efficiently.
To force MySQL to use or ignore an index listed in the
possible_keys column, use FORCE
INDEX, USE INDEX, or
IGNORE INDEX in your query. See
Section 13.2.7.2, “Index Hint Syntax”.
The key_len column indicates the length
of the key that MySQL decided to use. The length is
NULL if the key column
says NULL. Note that the value of
key_len enables you to determine how many
parts of a multiple-part key MySQL actually uses.
ref
The ref column shows which columns or
constants are compared to the index named in the
key column to select rows from the table.
rows
The rows column indicates the number of
rows MySQL believes it must examine to execute the query.
Extra
This column contains additional information about how MySQL
resolves the query. The following list explains the values
that can appear in this column. If you want to make your
queries as fast as possible, you should look out for
Extra values of Using
filesort and Using temporary.
Distinct
MySQL is looking for distinct values, so it stops
searching for more rows for the current row combination
after it has found the first matching row.
Full scan on NULL key
This occurs for subquery optimization as a fallback
strategy when the optimizer cannot use an index-lookup
access method.
Impossible WHERE noticed after reading const
tables
MySQL has read all const (and
system) tables and notice that the
WHERE clause is always false.
No tables
The query has no FROM clause, or has
a FROM DUAL clause.
Not exists
MySQL was able to do a LEFT JOIN
optimization on the query and does not examine more rows
in this table for the previous row combination after it
finds one row that matches the LEFT
JOIN criteria. Here is an example of the type
of query that can be optimized this way:
SELECT * FROM t1 LEFT JOIN t2 ON t1.id=t2.id
WHERE t2.id IS NULL;
Assume that t2.id is defined as
NOT NULL. In this case, MySQL scans
t1 and looks up the rows in
t2 using the values of
t1.id. If MySQL finds a matching row
in t2, it knows that
t2.id can never be
NULL, and does not scan through the
rest of the rows in t2 that have the
same id value. In other words, for
each row in t1, MySQL needs to do
only a single lookup in t2,
regardless of how many rows actually match in
t2.
range checked for each record (index map:
N)
MySQL found no good index to use, but found that some of
indexes might be used after column values from preceding
tables are known. For each row combination in the
preceding tables, MySQL checks whether it is possible to
use a range or
index_merge access method to retrieve
rows. This is not very fast, but is faster than
performing a join with no index at all. The
applicability criteria are as described in
Section 7.2.5, “Range Optimization”, and
Section 7.2.6, “Index Merge Optimization”, with the
exception that all column values for the preceding table
are known and considered to be constants.
Select tables optimized away
The query contained only aggregate functions
(MIN(), MAX())
that were all resolved using an index, or
COUNT(*) for
MyISAM, and no GROUP
BY clause. The optimizer determined that only
one row should be returned.
Using filesort
MySQL must do an extra pass to find out how to retrieve
the rows in sorted order. The sort is done by going
through all rows according to the join type and storing
the sort key and pointer to the row for all rows that
match the WHERE clause. The keys then
are sorted and the rows are retrieved in sorted order.
See Section 7.2.11, “ORDER BY Optimization”.
Using index
The column information is retrieved from the table using
only information in the index tree without having to do
an additional seek to read the actual row. This strategy
can be used when the query uses only columns that are
part of a single index.
Using temporary
To resolve the query, MySQL needs to create a temporary
table to hold the result. This typically happens if the
query contains GROUP BY and
ORDER BY clauses that list columns
differently.
Using where
A WHERE clause is used to restrict
which rows to match against the next table or send to
the client. Unless you specifically intend to fetch or
examine all rows from the table, you may have something
wrong in your query if the Extra
value is not Using where and the
table join type is ALL or
index.
Using sort_union(...), Using
union(...), Using
intersect(...)
Similar to the Using index way of
accessing a table, Using index for
group-by indicates that MySQL found an index
that can be used to retrieve all columns of a
GROUP BY or
DISTINCT query without any extra disk
access to the actual table. Additionally, the index is
used in the most efficient way so that for each group,
only a few index entries are read. For details, see
Section 7.2.12, “GROUP BY Optimization”.
Using where with pushed condition
This item applies to NDB Cluster
tables only. It means that MySQL
Cluster is using condition
pushdown to improve the efficiency of a
direct comparison (=) between a
non-indexed column and a constant. In such cases, the
condition is “pushed down” to the cluster's
data nodes where it is evaluated in all partitions
simultaneously. This eliminates the need to send
non-matching rows over the network, and can speed up
such queries by a factor of 5 to 10 times over cases
where condition pushdown could be but is not used.
Suppose that you have a Cluster table defined as
follows:
CREATE TABLE t1 (
a INT,
b INT,
KEY(a)
) ENGINE=NDBCLUSTER;
In this case, condition pushdown can be used with a
query such as this one:
SELECT a,b FROM t1 WHERE b = 10;
This can be seen in the output of EXPLAIN
SELECT, as shown here:
mysql> EXPLAIN SELECT a,b FROM t1 WHERE b = 10\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: t1
type: ALL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: 10
Extra: Using where with pushed condition
Condition pushdown cannot be used
with either of these two queries:
SELECT a,b FROM t1 WHERE a = 10;
SELECT a,b FROM t1 WHERE b + 1 = 10;
With regard to the first of these two queries, condition
pushdown is not applicable because an index exists on
column a. In the case of the second
query, a condition pushdown cannot be employed because
the comparison involving the non-indexed column
b is an indirect one. (However, it
would apply if you were to reduce b + 1 =
10 to b = 9 in the
WHERE clause.)
However, a condition pushdown may also be employed when
an indexed column is compared with a constant using a
> or <
operator:
mysql> EXPLAIN SELECT a,b FROM t1 WHERE a<2\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: t1
type: range
possible_keys: a
key: a
key_len: 5
ref: NULL
rows: 2
Extra: Using where with pushed condition
With regard to condition pushdown, keep in mind that:
Condition pushdown is relevant to MySQL Cluster
only, and does not occur when
executing queries against tables using any other
storage engine.
Condition pushdown capability is not used by
default. To enable it, you can start
mysqld with the
--engine-condition-pushdown option,
or execute the following statement:
SET engine_condition_pushdown=On;
Note: Condition
pushdown is not supported for columns of any of the
BLOB or TEXT
types.
Condition pushdown, Using where with pushed
condition, and
engine_condition_pushdown were all
introduced in MySQL 5.0 Cluster.
You can get a good indication of how good a join is by taking
the product of the values in the rows column
of the EXPLAIN output. This should tell you
roughly how many rows MySQL must examine to execute the query.
If you restrict queries with the
max_join_size system variable, this row
product also is used to determine which multiple-table
SELECT statements to execute and which to
abort. See Section 7.5.2, “Tuning Server Parameters”.
The following example shows how a multiple-table join can be
optimized progressively based on the information provided by
EXPLAIN.
Suppose that you have the SELECT statement
shown here and that you plan to examine it using
EXPLAIN:
EXPLAIN SELECT tt.TicketNumber, tt.TimeIn,
tt.ProjectReference, tt.EstimatedShipDate,
tt.ActualShipDate, tt.ClientID,
tt.ServiceCodes, tt.RepetitiveID,
tt.CurrentProcess, tt.CurrentDPPerson,
tt.RecordVolume, tt.DPPrinted, et.COUNTRY,
et_1.COUNTRY, do.CUSTNAME
FROM tt, et, et AS et_1, do
WHERE tt.SubmitTime IS NULL
AND tt.ActualPC = et.EMPLOYID
AND tt.AssignedPC = et_1.EMPLOYID
AND tt.ClientID = do.CUSTNMBR;
For this example, make the following assumptions:
The columns being compared have been declared as follows:
Table
Column
Data Type
tt
ActualPC
CHAR(10)
tt
AssignedPC
CHAR(10)
tt
ClientID
CHAR(10)
et
EMPLOYID
CHAR(15)
do
CUSTNMBR
CHAR(15)
The tables have the following indexes:
Table
Index
tt
ActualPC
tt
AssignedPC
tt
ClientID
et
EMPLOYID (primary key)
do
CUSTNMBR (primary key)
The tt.ActualPC values are not evenly
distributed.
Initially, before any optimizations have been performed, the
EXPLAIN statement produces the following
information:
table type possible_keys key key_len ref rows Extra
et ALL PRIMARY NULL NULL NULL 74
do ALL PRIMARY NULL NULL NULL 2135
et_1 ALL PRIMARY NULL NULL NULL 74
tt ALL AssignedPC, NULL NULL NULL 3872
ClientID,
ActualPC
range checked for each record (key map: 35)
Because type is ALL for
each table, this output indicates that MySQL is generating a
Cartesian product of all the tables; that is, every combination
of rows. This takes quite a long time, because the product of
the number of rows in each table must be examined. For the case
at hand, this product is 74 × 2135 × 74 × 3872
= 45,268,558,720 rows. If the tables were bigger, you can only
imagine how long it would take.
One problem here is that MySQL can use indexes on columns more
efficiently if they are declared as the same type and size. In
this context, VARCHAR and
CHAR are considered the same if they are
declared as the same size. tt.ActualPC is
declared as CHAR(10) and
et.EMPLOYID is CHAR(15),
so there is a length mismatch.
To fix this disparity between column lengths, use ALTER
TABLE to lengthen ActualPC from 10
characters to 15 characters:
mysql> ALTER TABLE tt MODIFY ActualPC VARCHAR(15);
Now tt.ActualPC and
et.EMPLOYID are both
VARCHAR(15). Executing the
EXPLAIN statement again produces this result:
table type possible_keys key key_len ref rows Extra
tt ALL AssignedPC, NULL NULL NULL 3872 Using
ClientID, where
ActualPC
do ALL PRIMARY NULL NULL NULL 2135
range checked for each record (key map: 1)
et_1 ALL PRIMARY NULL NULL NULL 74
range checked for each record (key map: 1)
et eq_ref PRIMARY PRIMARY 15 tt.ActualPC 1
This is not perfect, but is much better: The product of the
rows values is less by a factor of 74. This
version executes in a couple of seconds.
A second alteration can be made to eliminate the column length
mismatches for the tt.AssignedPC =
et_1.EMPLOYID and tt.ClientID =
do.CUSTNMBR comparisons:
After that modification, EXPLAIN produces the
output shown here:
table type possible_keys key key_len ref rows Extra
et ALL PRIMARY NULL NULL NULL 74
tt ref AssignedPC, ActualPC 15 et.EMPLOYID 52 Using
ClientID, where
ActualPC
et_1 eq_ref PRIMARY PRIMARY 15 tt.AssignedPC 1
do eq_ref PRIMARY PRIMARY 15 tt.ClientID 1
At this point, the query is optimized almost as well as
possible. The remaining problem is that, by default, MySQL
assumes that values in the tt.ActualPC column
are evenly distributed, and that is not the case for the
tt table. Fortunately, it is easy to tell
MySQL to analyze the key distribution:
mysql> ANALYZE TABLE tt;
With the additional index information, the join is perfect and
EXPLAIN produces this result:
table type possible_keys key key_len ref rows Extra
tt ALL AssignedPC NULL NULL NULL 3872 Using
ClientID, where
ActualPC
et eq_ref PRIMARY PRIMARY 15 tt.ActualPC 1
et_1 eq_ref PRIMARY PRIMARY 15 tt.AssignedPC 1
do eq_ref PRIMARY PRIMARY 15 tt.ClientID 1
Note that the rows column in the output from
EXPLAIN is an educated guess from the MySQL
join optimizer. You should check whether the numbers are even
close to the truth by comparing the rows
product with the actual number of rows that the query returns.
If the numbers are quite different, you might get better
performance by using STRAIGHT_JOIN in your
SELECT statement and trying to list the
tables in a different order in the FROM
clause.
In most cases, you can estimate query performance by counting
disk seeks. For small tables, you can usually find a row in one
disk seek (because the index is probably cached). For bigger
tables, you can estimate that, using B-tree indexes, you need
this many seeks to find a row:
log(row_count) /
log(index_block_length / 3 × 2
/ (index_length +
data_pointer_length)) + 1.
In MySQL, an index block is usually 1,024 bytes and the data
pointer is usually four bytes. For a 500,000-row table with an
index length of three bytes (the size of
MEDIUMINT), the formula indicates
log(500,000)/log(1024/3×2/(3+4)) + 1 =
4 seeks.
This index would require storage of about 500,000 × 7
× 3/2 = 5.2MB (assuming a typical index buffer fill ratio
of 2/3), so you probably have much of the index in memory and so
need only one or two calls to read data to find the row.
For writes, however, you need four seek requests to find where
to place a new index value and normally two seeks to update the
index and write the row.
Note that the preceding discussion does not mean that your
application performance slowly degenerates by log
N. As long as everything is cached by
the OS or the MySQL server, things become only marginally slower
as the table gets bigger. After the data gets too big to be
cached, things start to go much slower until your applications
are bound only by disk seeks (which increase by log
N). To avoid this, increase the key
cache size as the data grows. For MyISAM
tables, the key cache size is controlled by the
key_buffer_size system variable. See
Section 7.5.2, “Tuning Server Parameters”.
MySQL Enterprise
The MySQL Network Monitoring and Advisory Service provides a
number of advisors specifically designed to improve query
performance. For more information see
http://www.mysql.com/products/enterprise/advisors.html.
7.2.3. Speed of SELECT Queries
In general, when you want to make a slow SELECT ...
WHERE query faster, the first thing to check is
whether you can add an index. All references between different
tables should usually be done with indexes. You can use the
EXPLAIN statement to determine which indexes
are used for a SELECT. See
Section 7.2.1, “Optimizing Queries with EXPLAIN”, and Section 7.4.5, “How MySQL Uses Indexes”.
Some general tips for speeding up queries on
MyISAM tables:
To help MySQL better optimize queries, use ANALYZE
TABLE or run myisamchk
--analyze on a table after it has been loaded with
data. This updates a value for each index part that
indicates the average number of rows that have the same
value. (For unique indexes, this is always 1.) MySQL uses
this to decide which index to choose when you join two
tables based on a non-constant expression. You can check the
result from the table analysis by using SHOW INDEX
FROM tbl_name and
examining the Cardinality value.
myisamchk --description --verbose shows
index distribution information.
To sort an index and data according to an index, use
myisamchk --sort-index --sort-records=1
(assuming that you want to sort on index 1). This is a good
way to make queries faster if you have a unique index from
which you want to read all rows in order according to the
index. The first time you sort a large table this way, it
may take a long time.
7.2.4. WHERE Clause Optimization
This section discusses optimizations that can be made for
processing WHERE clauses. The examples use
SELECT statements, but the same optimizations
apply for WHERE clauses in
DELETE and UPDATE
statements.
Work on the MySQL optimizer is ongoing, so this section is
incomplete. MySQL performs a great many optimizations, not all
of which are documented here.
Some of the optimizations performed by MySQL follow:
Removal of unnecessary parentheses:
((a AND b) AND c OR (((a AND b) AND (c AND d))))
-> (a AND b AND c) OR (a AND b AND c AND d)
Constant folding:
(a<b AND b=c) AND a=5
-> b>5 AND b=c AND a=5
Constant condition removal (needed because of constant
folding):
(B>=5 AND B=5) OR (B=6 AND 5=5) OR (B=7 AND 5=6)
-> B=5 OR B=6
Constant expressions used by indexes are evaluated only
once.
COUNT(*) on a single table without a
WHERE is retrieved directly from the
table information for MyISAM and
MEMORY tables. This is also done for any
NOT NULL expression when used with only
one table.
Early detection of invalid constant expressions. MySQL
quickly detects that some SELECT
statements are impossible and returns no rows.
HAVING is merged with
WHERE if you do not use GROUP
BY or aggregate functions
(COUNT(), MIN(), and
so on).
For each table in a join, a simpler WHERE
is constructed to get a fast WHERE
evaluation for the table and also to skip rows as soon as
possible.
All constant tables are read first before any other tables
in the query. A constant table is any of the following:
An empty table or a table with one row.
A table that is used with a WHERE
clause on a PRIMARY KEY or a
UNIQUE index, where all index parts
are compared to constant expressions and are defined as
NOT NULL.
All of the following tables are used as constant tables:
SELECT * FROM t WHERE primary_key=1;
SELECT * FROM t1,t2
WHERE t1.primary_key=1 AND t2.primary_key=t1.id;
The best join combination for joining the tables is found by
trying all possibilities. If all columns in ORDER
BY and GROUP BY clauses come
from the same table, that table is preferred first when
joining.
If there is an ORDER BY clause and a
different GROUP BY clause, or if the
ORDER BY or GROUP BY
contains columns from tables other than the first table in
the join queue, a temporary table is created.
If you use the SQL_SMALL_RESULT option,
MySQL uses an in-memory temporary table.
Each table index is queried, and the best index is used
unless the optimizer believes that it is more efficient to
use a table scan. At one time, a scan was used based on
whether the best index spanned more than 30% of the table,
but a fixed percentage no longer determines the choice
between using an index or a scan. The optimizer now is more
complex and bases its estimate on additional factors such as
table size, number of rows, and I/O block size.
In some cases, MySQL can read rows from the index without
even consulting the data file. If all columns used from the
index are numeric, only the index tree is used to resolve
the query.
Before each row is output, those that do not match the
HAVING clause are skipped.
Some examples of queries that are very fast:
SELECT COUNT(*) FROM tbl_name;
SELECT MIN(key_part1),MAX(key_part1) FROM tbl_name;
SELECT MAX(key_part2) FROM tbl_name
WHERE key_part1=constant;
SELECT ... FROM tbl_name
ORDER BY key_part1,key_part2,... LIMIT 10;
SELECT ... FROM tbl_name
ORDER BY key_part1 DESC, key_part2 DESC, ... LIMIT 10;
MySQL resolves the following queries using only the index tree,
assuming that the indexed columns are numeric:
SELECT key_part1,key_part2 FROM tbl_name WHERE key_part1=val;
SELECT COUNT(*) FROM tbl_name
WHERE key_part1=val1 AND key_part2=val2;
SELECT key_part2 FROM tbl_name GROUP BY key_part1;
The following queries use indexing to retrieve the rows in
sorted order without a separate sorting pass:
SELECT ... FROM tbl_name
ORDER BY key_part1,key_part2,... ;
SELECT ... FROM tbl_name
ORDER BY key_part1 DESC, key_part2 DESC, ... ;
The range access method uses a single index
to retrieve a subset of table rows that are contained within one
or several index value intervals. It can be used for a
single-part or multiple-part index. The following sections give
a detailed description of how intervals are extracted from the
WHERE clause.
7.2.5.1. The Range Access Method for Single-Part Indexes
For a single-part index, index value intervals can be
conveniently represented by corresponding conditions in the
WHERE clause, so we speak of
range conditions rather than
“intervals.”
The definition of a range condition for a single-part index is
as follows:
For both BTREE and
HASH indexes, comparison of a key part
with a constant value is a range condition when using the
=, <=>,
IN, IS NULL, or
IS NOT NULL operators.
For BTREE indexes, comparison of a key
part with a constant value is a range condition when using
the >, <,
>=, <=,
BETWEEN, !=, or
<> operators, or LIKE
'pattern' (where
'pattern'
does not start with a wildcard).
For all types of indexes, multiple range conditions
combined with OR or
AND form a range condition.
“Constant value” in the preceding descriptions
means one of the following:
A constant from the query string
A column of a const or
system table from the same join
The result of an uncorrelated subquery
Any expression composed entirely from subexpressions of
the preceding types
Here are some examples of queries with range conditions in the
WHERE clause:
SELECT * FROM t1
WHERE key_col > 1
AND key_col < 10;
SELECT * FROM t1
WHERE key_col = 1
OR key_col IN (15,18,20);
SELECT * FROM t1
WHERE key_col LIKE 'ab%'
OR key_col BETWEEN 'bar' AND 'foo';
Note that some non-constant values may be converted to
constants during the constant propagation phase.
MySQL tries to extract range conditions from the
WHERE clause for each of the possible
indexes. During the extraction process, conditions that cannot
be used for constructing the range condition are dropped,
conditions that produce overlapping ranges are combined, and
conditions that produce empty ranges are removed.
Consider the following statement, where
key1 is an indexed column and
nonkey is not indexed:
SELECT * FROM t1 WHERE
(key1 < 'abc' AND (key1 LIKE 'abcde%' OR key1 LIKE '%b')) OR
(key1 < 'bar' AND nonkey = 4) OR
(key1 < 'uux' AND key1 > 'z');
The extraction process for key key1 is as
follows:
Start with original WHERE clause:
(key1 < 'abc' AND (key1 LIKE 'abcde%' OR key1 LIKE '%b')) OR
(key1 < 'bar' AND nonkey = 4) OR
(key1 < 'uux' AND key1 > 'z')
Remove nonkey = 4 and key1
LIKE '%b' because they cannot be used for a
range scan. The correct way to remove them is to replace
them with TRUE, so that we do not miss
any matching rows when doing the range scan. Having
replaced them with TRUE, we get:
(key1 < 'abc' AND (key1 LIKE 'abcde%' OR TRUE)) OR
(key1 < 'bar' AND TRUE) OR
(key1 < 'uux' AND key1 > 'z')
Collapse conditions that are always true or false:
(key1 LIKE 'abcde%' OR TRUE) is
always true
(key1 < 'uux' AND key1 > 'z')
is always false
Replacing these conditions with constants, we get:
(key1 < 'abc' AND TRUE) OR (key1 < 'bar' AND TRUE) OR (FALSE)
Removing unnecessary TRUE and
FALSE constants, we obtain:
(key1 < 'abc') OR (key1 < 'bar')
Combining overlapping intervals into one yields the final
condition to be used for the range scan:
(key1 < 'bar')
In general (and as demonstrated by the preceding example), the
condition used for a range scan is less restrictive than the
WHERE clause. MySQL performs an additional
check to filter out rows that satisfy the range condition but
not the full WHERE clause.
The range condition extraction algorithm can handle nested
AND/OR constructs of
arbitrary depth, and its output does not depend on the order
in which conditions appear in WHERE clause.
Currently, MySQL does not support merging multiple ranges for
the range access method for spatial
indexes. To work around this limitation, you can use a
UNION with identical
SELECT statements, except that you put each
spatial predicate in a different SELECT.
7.2.5.2. The Range Access Method for Multiple-Part Indexes
Range conditions on a multiple-part index are an extension of
range conditions for a single-part index. A range condition on
a multiple-part index restricts index rows to lie within one
or several key tuple intervals. Key tuple intervals are
defined over a set of key tuples, using ordering from the
index.
For example, consider a multiple-part index defined as
key1(key_part1,
key_part2,
key_part3), and the
following set of key tuples listed in key order:
The interval covers the 4th, 5th, and 6th tuples in the
preceding data set and can be used by the range access method.
By contrast, the condition
key_part3 =
'abc' does not define a single interval and cannot
be used by the range access method.
The following descriptions indicate how range conditions work
for multiple-part indexes in greater detail.
For HASH indexes, each interval
containing identical values can be used. This means that
the interval can be produced only for conditions in the
following form:
key_part1cmpconst1
AND key_part2cmpconst2
AND ...
AND key_partNcmpconstN;
Here, const1,
const2, … are constants,
cmp is one of the
=, <=>, or
IS NULL comparison operators, and the
conditions cover all index parts. (That is, there are
N conditions, one for each part
of an N-part index.) For
example, the following is a range condition for a
three-part HASH index:
key_part1 = 1 AND key_part2 IS NULL AND key_part3 = 'foo'
For a BTREE index, an interval might be
usable for conditions combined with
AND, where each condition compares a
key part with a constant value using =,
<=>, IS NULL,
>, <,
>=, <=,
!=, <>,
BETWEEN, or LIKE
'pattern' (where
'pattern'
does not start with a wildcard). An interval can be used
as long as it is possible to determine a single key tuple
containing all rows that match the condition (or two
intervals if <> or
!= is used). For example, for this
condition:
key_part1 = 'foo' AND key_part2 >= 10 AND key_part3 > 10
It is possible that the created interval contains more
rows than the initial condition. For example, the
preceding interval includes the value ('foo', 11,
0), which does not satisfy the original
condition.
If conditions that cover sets of rows contained within
intervals are combined with OR, they
form a condition that covers a set of rows contained
within the union of their intervals. If the conditions are
combined with AND, they form a
condition that covers a set of rows contained within the
intersection of their intervals. For example, for this
condition on a two-part index:
(key_part1 = 1 AND key_part2 < 2) OR (key_part1 > 5)
In this example, the interval on the first line uses one
key part for the left bound and two key parts for the
right bound. The interval on the second line uses only one
key part. The key_len column in the
EXPLAIN output indicates the maximum
length of the key prefix used.
In some cases, key_len may indicate
that a key part was used, but that might be not what you
would expect. Suppose that
key_part1 and
key_part2 can be
NULL. Then the
key_len column displays two key part
lengths for the following condition:
key_part1 >= 1 AND key_part2 < 2
But, in fact, the condition is converted to this:
key_part1 >= 1 AND key_part2 IS NOT NULL
Section 7.2.5.1, “The Range Access Method for Single-Part Indexes”, describes how
optimizations are performed to combine or eliminate intervals
for range conditions on a single-part index. Analogous steps
are performed for range conditions on multiple-part indexes.
The Index Merge method is used to
retrieve rows with several range scans and to
merge their results into one. The merge can produce unions,
intersections, or unions-of-intersections of its underlying
scans.
Note: If you have upgraded from
a previous version of MySQL, you should be aware that this type
of join optimization is first introduced in MySQL 5.0, and
represents a significant change in behavior with regard to
indexes. (Formerly, MySQL was able to use at most only one index
for each referenced table.)
In EXPLAIN output, the Index Merge method
appears as index_merge in the
type column. In this case, the
key column contains a list of indexes used,
and key_len contains a list of the longest
key parts for those indexes.
Examples:
SELECT * FROM tbl_name WHERE key1 = 10 OR key2 = 20;
SELECT * FROM tbl_name
WHERE (key1 = 10 OR key2 = 20) AND non_key=30;
SELECT * FROM t1, t2
WHERE (t1.key1 IN (1,2) OR t1.key2 LIKE 'value%')
AND t2.key1=t1.some_col;
SELECT * FROM t1, t2
WHERE t1.key1=1
AND (t2.key1=t1.some_col OR t2.key2=t1.some_col2);
The Index Merge method has several access algorithms (seen in
the Extra field of EXPLAIN
output):
Using intersect(...)
Using union(...)
Using sort_union(...)
The following sections describe these methods in greater detail.
Note: The Index Merge
optimization algorithm has the following known deficiencies:
If a range scan is possible on some key, an Index Merge is
not considered. For example, consider this query:
SELECT * FROM t1 WHERE (goodkey1 < 10 OR goodkey2 < 20) AND badkey < 30;
For this query, two plans are possible:
An Index Merge scan using the (goodkey1 < 10
OR goodkey2 < 20) condition.
A range scan using the badkey < 30
condition.
However, the optimizer considers only the second plan.
If your query has a complex WHERE clause
with deep AND/OR
nesting and MySQL doesn't choose the optimal plan, try
distributing terms using the following identity laws:
(x AND y) OR z = (x OR z) AND (y OR z)
(x OR y) AND z = (x AND z) OR (y AND z)
Index Merge is not applicable to fulltext indexes. We plan
to extend it to cover these in a future MySQL release.
The choice between different possible variants of the Index
Merge access method and other access methods is based on cost
estimates of various available options.
7.2.6.1. The Index Merge Intersection Access Algorithm
This access algorithm can be employed when a
WHERE clause was converted to several range
conditions on different keys combined with
AND, and each condition is one of the
following:
In this form, where the index has exactly
N parts (that is, all index
parts are covered):
key_part1=const1 AND key_part2=const2 ... AND key_partN=constN
Any range condition over a primary key of an
InnoDB or BDB table.
Examples:
SELECT * FROM innodb_table WHERE primary_key < 10 AND key_col1=20;
SELECT * FROM tbl_name
WHERE (key1_part1=1 AND key1_part2=2) AND key2=2;
The Index Merge intersection algorithm performs simultaneous
scans on all used indexes and produces the intersection of row
sequences that it receives from the merged index scans.
If all columns used in the query are covered by the used
indexes, full table rows are not retrieved
(EXPLAIN output contains Using
index in Extra field in this
case). Here is an example of such a query:
SELECT COUNT(*) FROM t1 WHERE key1=1 AND key2=1;
If the used indexes don't cover all columns used in the query,
full rows are retrieved only when the range conditions for all
used keys are satisfied.
If one of the merged conditions is a condition over a primary
key of an InnoDB or BDB
table, it is not used for row retrieval, but is used to filter
out rows retrieved using other conditions.
7.2.6.2. The Index Merge Union Access Algorithm
The applicability criteria for this algorithm are similar to
those for the Index Merge method intersection algorithm. The
algorithm can be employed when the table's
WHERE clause was converted to several range
conditions on different keys combined with
OR, and each condition is one of the
following:
In this form, where the index has exactly
N parts (that is, all index
parts are covered):
key_part1=const1 AND key_part2=const2 ... AND key_partN=constN
Any range condition over a primary key of an
InnoDB or BDB table.
A condition for which the Index Merge method intersection
algorithm is applicable.
Examples:
SELECT * FROM t1 WHERE key1=1 OR key2=2 OR key3=3;
SELECT * FROM innodb_table WHERE (key1=1 AND key2=2) OR
(key3='foo' AND key4='bar') AND key5=5;
7.2.6.3. The Index Merge Sort-Union Access Algorithm
This access algorithm is employed when the
WHERE clause was converted to several range
conditions combined by OR, but for which
the Index Merge method union algorithm is not applicable.
Examples:
SELECT * FROM tbl_name WHERE key_col1 < 10 OR key_col2 < 20;
SELECT * FROM tbl_name
WHERE (key_col1 > 10 OR key_col2 = 20) AND nonkey_col=30;
The difference between the sort-union algorithm and the union
algorithm is that the sort-union algorithm must first fetch
row IDs for all rows and sort them before returning any rows.
7.2.7. IS NULL Optimization
MySQL can perform the same optimization on
col_nameIS NULL
that it can use for col_name=constant_value.
For example, MySQL can use indexes and ranges to search for
NULL with IS NULL.
Examples:
SELECT * FROM tbl_name WHERE key_col IS NULL;
SELECT * FROM tbl_name WHERE key_col <=> NULL;
SELECT * FROM tbl_name
WHERE key_col=const1 OR key_col=const2 OR key_col IS NULL;
If a WHERE clause includes a
col_nameIS NULL
condition for a column that is declared as NOT
NULL, that expression is optimized away. This
optimization does not occur in cases when the column might
produce NULL anyway; for example, if it comes
from a table on the right side of a LEFT
JOIN.
MySQL can also optimize the combination
col_name =
expr AND
col_name IS NULL, a form
that is common in resolved subqueries.
EXPLAIN shows ref_or_null
when this optimization is used.
This optimization can handle one IS NULL for
any key part.
Some examples of queries that are optimized, assuming that there
is an index on columns a and
b of table t2:
SELECT * FROM t1 WHERE t1.a=expr OR t1.a IS NULL;
SELECT * FROM t1, t2 WHERE t1.a=t2.a OR t2.a IS NULL;
SELECT * FROM t1, t2
WHERE (t1.a=t2.a OR t2.a IS NULL) AND t2.b=t1.b;
SELECT * FROM t1, t2
WHERE t1.a=t2.a AND (t2.b=t1.b OR t2.b IS NULL);
SELECT * FROM t1, t2
WHERE (t1.a=t2.a AND t2.a IS NULL AND ...)
OR (t1.a=t2.a AND t2.a IS NULL AND ...);
ref_or_null works by first doing a read on
the reference key, and then a separate search for rows with a
NULL key value.
Note that the optimization can handle only one IS
NULL level. In the following query, MySQL uses key
lookups only on the expression (t1.a=t2.a AND t2.a IS
NULL) and is not able to use the key part on
b:
SELECT * FROM t1, t2
WHERE (t1.a=t2.a AND t2.a IS NULL)
OR (t1.b=t2.b AND t2.b IS NULL);
7.2.8. LEFT JOIN and RIGHT JOIN
Optimization
MySQL implements an A LEFT
JOIN B join_condition as
follows:
Table B is set to depend on table
A and all tables on which
A depends.
Table A is set to depend on all
tables (except B) that are used
in the LEFT JOIN condition.
The LEFT JOIN condition is used to decide
how to retrieve rows from table
B. (In other words, any condition
in the WHERE clause is not used.)
All standard join optimizations are performed, with the
exception that a table is always read after all tables on
which it depends. If there is a circular dependence, MySQL
issues an error.
All standard WHERE optimizations are
performed.
If there is a row in A that
matches the WHERE clause, but there is no
row in B that matches the
ON condition, an extra
B row is generated with all
columns set to NULL.
If you use LEFT JOIN to find rows that do
not exist in some table and you have the following test:
col_name IS
NULL in the WHERE part, where
col_name is a column that is
declared as NOT NULL, MySQL stops
searching for more rows (for a particular key combination)
after it has found one row that matches the LEFT
JOIN condition.
The implementation of RIGHT JOIN is analogous
to that of LEFT JOIN with the roles of the
tables reversed.
The join optimizer calculates the order in which tables should
be joined. The table read order forced by LEFT
JOIN or STRAIGHT_JOIN helps the
join optimizer do its work much more quickly, because there are
fewer table permutations to check. Note that this means that if
you do a query of the following type, MySQL does a full scan on
b because the LEFT JOIN
forces it to be read before d:
SELECT *
FROM a JOIN b LEFT JOIN c ON (c.key=a.key) LEFT JOIN d ON (d.key=a.key)
WHERE b.key=d.key;
The fix in this case is reverse the order in which
a and b are listed in the
FROM clause:
SELECT *
FROM b JOIN a LEFT JOIN c ON (c.key=a.key) LEFT JOIN d ON (d.key=a.key)
WHERE b.key=d.key;
For a LEFT JOIN, if the
WHERE condition is always false for the
generated NULL row, the LEFT
JOIN is changed to a normal join. For example, the
WHERE clause would be false in the following
query if t2.column1 were
NULL:
SELECT * FROM t1 LEFT JOIN t2 ON (column1) WHERE t2.column2=5;
Therefore, it is safe to convert the query to a normal join:
SELECT * FROM t1, t2 WHERE t2.column2=5 AND t1.column1=t2.column1;
This can be made faster because MySQL can use table
t2 before table t1 if
doing so would result in a better query plan. To force a
specific table order, use STRAIGHT_JOIN.
7.2.9. Nested Join Optimization
As of MySQL 5.0.1, the syntax for expressing joins allows nested
joins. The following discussion refers to the join syntax
described in Section 13.2.7.1, “JOIN Syntax”.
The syntax of table_factor is
extended in comparison with the SQL Standard. The latter accepts
only table_reference, not a list of
them inside a pair of parentheses. This is a conservative
extension if we consider each comma in a list of
table_reference items as equivalent
to an inner join. For example:
SELECT * FROM t1 LEFT JOIN (t2, t3, t4)
ON (t2.a=t1.a AND t3.b=t1.b AND t4.c=t1.c)
is equivalent to:
SELECT * FROM t1 LEFT JOIN (t2 CROSS JOIN t3 CROSS JOIN t4)
ON (t2.a=t1.a AND t3.b=t1.b AND t4.c=t1.c)
In MySQL, CROSS JOIN is a syntactic
equivalent to INNER JOIN (they can replace
each other). In standard SQL, they are not equivalent.
INNER JOIN is used with an
ON clause; CROSS JOIN is
used otherwise.
In versions of MySQL prior to 5.0.1, parentheses in
table_references were just omitted
and all join operations were grouped to the left. In general,
parentheses can be ignored in join expressions containing only
inner join operations.
After removing parentheses and grouping operations to the left,
the join expression:
t1 LEFT JOIN (t2 LEFT JOIN t3 ON t2.b=t3.b OR t2.b IS NULL)
ON t1.a=t2.a
transforms into the expression:
(t1 LEFT JOIN t2 ON t1.a=t2.a) LEFT JOIN t3
ON t2.b=t3.b OR t2.b IS NULL
Yet, the two expressions are not equivalent. To see this,
suppose that the tables t1,
t2, and t3 have the
following state:
Table t1 contains rows
(1), (2)
Table t2 contains row
(1,101)
Table t3 contains row
(101)
In this case, the first expression returns a result set
including the rows (1,1,101,101),
(2,NULL,NULL,NULL), whereas the second
expression returns the rows (1,1,101,101),
(2,NULL,NULL,101):
mysql> SELECT *
-> FROM t1
-> LEFT JOIN
-> (t2 LEFT JOIN t3 ON t2.b=t3.b OR t2.b IS NULL)
-> ON t1.a=t2.a;
+------+------+------+------+
| a | a | b | b |
+------+------+------+------+
| 1 | 1 | 101 | 101 |
| 2 | NULL | NULL | NULL |
+------+------+------+------+
mysql> SELECT *
-> FROM (t1 LEFT JOIN t2 ON t1.a=t2.a)
-> LEFT JOIN t3
-> ON t2.b=t3.b OR t2.b IS NULL;
+------+------+------+------+
| a | a | b | b |
+------+------+------+------+
| 1 | 1 | 101 | 101 |
| 2 | NULL | NULL | 101 |
+------+------+------+------+
In the following example, an outer join operation is used
together with an inner join operation:
t1 LEFT JOIN (t2, t3) ON t1.a=t2.a
That expression cannot be transformed into the following
expression:
t1 LEFT JOIN t2 ON t1.a=t2.a, t3.
For the given table states, the two expressions return different
sets of rows:
mysql> SELECT *
-> FROM t1 LEFT JOIN (t2, t3) ON t1.a=t2.a;
+------+------+------+------+
| a | a | b | b |
+------+------+------+------+
| 1 | 1 | 101 | 101 |
| 2 | NULL | NULL | NULL |
+------+------+------+------+
mysql> SELECT *
-> FROM t1 LEFT JOIN t2 ON t1.a=t2.a, t3;
+------+------+------+------+
| a | a | b | b |
+------+------+------+------+
| 1 | 1 | 101 | 101 |
| 2 | NULL | NULL | 101 |
+------+------+------+------+
Therefore, if we omit parentheses in a join expression with
outer join operators, we might change the result set for the
original expression.
More exactly, we cannot ignore parentheses in the right operand
of the left outer join operation and in the left operand of a
right join operation. In other words, we cannot ignore
parentheses for the inner table expressions of outer join
operations. Parentheses for the other operand (operand for the
outer table) can be ignored.
The following expression:
(t1,t2) LEFT JOIN t3 ON P(t2.b,t3.b)
is equivalent to this expression:
t1, t2 LEFT JOIN t3 ON P(t2.b,t3.b)
for any tables t1,t2,t3 and any condition
P over attributes t2.b and
t3.b.
Whenever the order of execution of the join operations in a join
expression (join_table) is not from
left to right, we talk about nested joins. Consider the
following queries:
SELECT * FROM t1 LEFT JOIN (t2 LEFT JOIN t3 ON t2.b=t3.b) ON t1.a=t2.a
WHERE t1.a > 1
SELECT * FROM t1 LEFT JOIN (t2, t3) ON t1.a=t2.a
WHERE (t2.b=t3.b OR t2.b IS NULL) AND t1.a > 1
Those queries are considered to contain these nested joins:
t2 LEFT JOIN t3 ON t2.b=t3.b
t2, t3
The nested join is formed in the first query with a left join
operation, whereas in the second query it is formed with an
inner join operation.
In the first query, the parentheses can be omitted: The
grammatical structure of the join expression will dictate the
same order of execution for join operations. For the second
query, the parentheses cannot be omitted, although the join
expression here can be interpreted unambiguously without them.
(In our extended syntax the parentheses in (t2,
t3) of the second query are required, although
theoretically the query could be parsed without them: We still
would have unambiguous syntactical structure for the query
because LEFT JOIN and ON
would play the role of the left and right delimiters for the
expression (t2,t3).)
The preceding examples demonstrate these points:
For join expressions involving only inner joins (and not
outer joins), parentheses can be removed. You can remove
parentheses and evaluate left to right (or, in fact, you can
evaluate the tables in any order).
The same is not true, in general, for outer joins or for
outer joins mixed with inner joins. Removal of parentheses
may change the result.
Queries with nested outer joins are executed in the same
pipeline manner as queries with inner joins. More exactly, a
variation of the nested-loop join algorithm is exploited. Recall
by what algorithmic schema the nested-loop join executes a
query. Suppose that we have a join query over 3 tables
T1,T2,T3 of the form:
SELECT * FROM T1 INNER JOIN T2 ON P1(T1,T2)
INNER JOIN T3 ON P2(T2,T3)
WHERE P(T1,T2,T3).
Here, P1(T1,T2) and
P2(T3,T3) are some join conditions (on
expressions), whereas P(t1,t2,t3) is a
condition over columns of tables T1,T2,T3.
The nested-loop join algorithm would execute this query in the
following manner:
FOR each row t1 in T1 {
FOR each row t2 in T2 such that P1(t1,t2) {
FOR each row t3 in T3 such that P2(t2,t3) {
IF P(t1,t2,t3) {
t:=t1||t2||t3; OUTPUT t;
}
}
}
}
The notation t1||t2||t3 means “a row
constructed by concatenating the columns of rows
t1, t2, and
t3.” In some of the following
examples, NULL where a row name appears means
that NULL is used for each column of that
row. For example, t1||t2||NULL means “a
row constructed by concatenating the columns of rows
t1 and t2, and
NULL for each column of
t3.”
Now let's consider a query with nested outer joins:
SELECT * FROM T1 LEFT JOIN
(T2 LEFT JOIN T3 ON P2(T2,T3))
ON P1(T1,T2)
WHERE P(T1,T2,T3).
For this query, we modify the nested-loop pattern to get:
FOR each row t1 in T1 {
BOOL f1:=FALSE;
FOR each row t2 in T2 such that P1(t1,t2) {
BOOL f2:=FALSE;
FOR each row t3 in T3 such that P2(t2,t3) {
IF P(t1,t2,t3) {
t:=t1||t2||t3; OUTPUT t;
}
f2=TRUE;
f1=TRUE;
}
IF (!f2) {
IF P(t1,t2,NULL) {
t:=t1||t2||NULL; OUTPUT t;
}
f1=TRUE;
}
}
IF (!f1) {
IF P(t1,NULL,NULL) {
t:=t1||NULL||NULL; OUTPUT t;
}
}
}
In general, for any nested loop for the first inner table in an
outer join operation, a flag is introduced that is turned off
before the loop and is checked after the loop. The flag is
turned on when for the current row from the outer table a match
from the table representing the inner operand is found. If at
the end of the loop cycle the flag is still off, no match has
been found for the current row of the outer table. In this case,
the row is complemented by NULL values for
the columns of the inner tables. The result row is passed to the
final check for the output or into the next nested loop, but
only if the row satisfies the join condition of all embedded
outer joins.
In our example, the outer join table expressed by the following
expression is embedded:
(T2 LEFT JOIN T3 ON P2(T2,T3))
Note that for the query with inner joins, the optimizer could
choose a different order of nested loops, such as this one:
FOR each row t3 in T3 {
FOR each row t2 in T2 such that P2(t2,t3) {
FOR each row t1 in T1 such that P1(t1,t2) {
IF P(t1,t2,t3) {
t:=t1||t2||t3; OUTPUT t;
}
}
}
}
For the queries with outer joins, the optimizer can choose only
such an order where loops for outer tables precede loops for
inner tables. Thus, for our query with outer joins, only one
nesting order is possible. For the following query, the
optimizer will evaluate two different nestings:
SELECT * T1 LEFT JOIN (T2,T3) ON P1(T1,T2) AND P2(T1,T3)
WHERE P(T1,T2,T3)
The nestings are these:
FOR each row t1 in T1 {
BOOL f1:=FALSE;
FOR each row t2 in T2 such that P1(t1,t2) {
FOR each row t3 in T3 such that P2(t1,t3) {
IF P(t1,t2,t3) {
t:=t1||t2||t3; OUTPUT t;
}
f1:=TRUE
}
}
IF (!f1) {
IF P(t1,NULL,NULL) {
t:=t1||NULL||NULL; OUTPUT t;
}
}
}
and:
FOR each row t1 in T1 {
BOOL f1:=FALSE;
FOR each row t3 in T3 such that P2(t1,t3) {
FOR each row t2 in T2 such that P1(t1,t2) {
IF P(t1,t2,t3) {
t:=t1||t2||t3; OUTPUT t;
}
f1:=TRUE
}
}
IF (!f1) {
IF P(t1,NULL,NULL) {
t:=t1||NULL||NULL; OUTPUT t;
}
}
}
In both nestings, T1 must be processed in the
outer loop because it is used in an outer join.
T2 and T3 are used in an
inner join, so that join must be processed in the inner loop.
However, because the join is an inner join,
T2 and T3 can be processed
in either order.
When discussing the nested-loop algorithm for inner joins, we
omitted some details whose impact on the performance of query
execution may be huge. We did not mention so-called
“pushed-down” conditions. Suppose that our
WHERE condition
P(T1,T2,T3) can be represented by a
conjunctive formula:
P(T1,T2,T2) = C1(T1) AND C2(T2) AND C3(T3).
In this case, MySQL actually uses the following nested-loop
schema for the execution of the query with inner joins:
FOR each row t1 in T1 such that C1(t1) {
FOR each row t2 in T2 such that P1(t1,t2) AND C2(t2) {
FOR each row t3 in T3 such that P2(t2,t3) AND C3(t3) {
IF P(t1,t2,t3) {
t:=t1||t2||t3; OUTPUT t;
}
}
}
}
You see that each of the conjuncts C1(T1),
C2(T2), C3(T3) are pushed
out of the most inner loop to the most outer loop where it can
be evaluated. If C1(T1) is a very restrictive
condition, this condition pushdown may greatly reduce the number
of rows from table T1 passed to the inner
loops. As a result, the execution time for the query may improve
immensely.
For a query with outer joins, the WHERE
condition is to be checked only after it has been found that the
current row from the outer table has a match in the inner
tables. Thus, the optimization of pushing conditions out of the
inner nested loops cannot be applied directly to queries with
outer joins. Here we have to introduce conditional pushed-down
predicates guarded by the flags that are turned on when a match
has been encountered.
For our example with outer joins with:
P(T1,T2,T3)=C1(T1) AND C(T2) AND C3(T3)
the nested-loop schema using guarded pushed-down conditions
looks like this:
FOR each row t1 in T1 such that C1(t1) {
BOOL f1:=FALSE;
FOR each row t2 in T2
such that P1(t1,t2) AND (f1?C2(t2):TRUE) {
BOOL f2:=FALSE;
FOR each row t3 in T3
such that P2(t2,t3) AND (f1&&f2?C3(t3):TRUE) {
IF (f1&&f2?TRUE:(C2(t2) AND C3(t3))) {
t:=t1||t2||t3; OUTPUT t;
}
f2=TRUE;
f1=TRUE;
}
IF (!f2) {
IF (f1?TRUE:C2(t2) && P(t1,t2,NULL)) {
t:=t1||t2||NULL; OUTPUT t;
}
f1=TRUE;
}
}
IF (!f1 && P(t1,NULL,NULL)) {
t:=t1||NULL||NULL; OUTPUT t;
}
}
In general, pushed-down predicates can be extracted from join
conditions such as P1(T1,T2) and
P(T2,T3). In this case, a pushed-down
predicate is guarded also by a flag that prevents checking the
predicate for the NULL-complemented row
generated by the corresponding outer join operation.
Note that access by key from one inner table to another in the
same nested join is prohibited if it is induced by a predicate
from the WHERE condition. (We could use
conditional key access in this case, but this technique is not
employed yet in MySQL 5.0.)
7.2.10. Outer Join Simplification
Table expressions in the FROM clause of a
query are simplified in many cases.
At the parser stage, queries with right outer joins operations
are converted to equivalent queries containing only left join
operations. In the general case, the conversion is performed
according to the following rule:
(T1, ...) RIGHT JOIN (T2,...) ON P(T1,...,T2,...) =
(T2, ...) LEFT JOIN (T1,...) ON P(T1,...,T2,...)
All inner join expressions of the form T1 INNER JOIN T2
ON P(T1,T2) are replaced by the list
T1,T2, P(T1,T2) being
joined as a conjunct to the WHERE condition
(or to the join condition of the embedding join, if there is
any).
When the optimizer evaluates plans for join queries with outer
join operation, it takes into consideration only the plans
where, for each such operation, the outer tables are accessed
before the inner tables. The optimizer options are limited
because only such plans enables us to execute queries with outer
joins operations by the nested loop schema.
Suppose that we have a query of the form:
SELECT * T1 LEFT JOIN T2 ON P1(T1,T2)
WHERE P(T1,T2) AND R(T2)
with R(T2) narrowing greatly the number of
matching rows from table T2. If we executed
the query as it is, the optimizer would have no other choice
besides to access table T1 before table
T2 that may lead to a very inefficient
execution plan.
Fortunately, MySQL converts such a query into a query without an
outer join operation if the WHERE condition
is null-rejected. A condition is called null-rejected for an
outer join operation if it evaluates to FALSE
or to UNKNOWN for any
NULL-complemented row built for the
operation.
Thus, for this outer join:
T1 LEFT JOIN T2 ON T1.A=T2.A
Conditions such as these are null-rejected:
T2.B IS NOT NULL,
T2.B > 3,
T2.C <= T1.C,
T2.B < 2 OR T2.C > 1
Conditions such as these are not null-rejected:
T2.B IS NULL,
T1.B < 3 OR T2.B IS NOT NULL,
T1.B < 3 OR T2.B > 3
The general rules for checking whether a condition is
null-rejected for an outer join operation are simple. A
condition is null-rejected in the following cases:
If it is of the form A IS NOT NULL, where
A is an attribute of any of the inner
tables
If it is a predicate containing a reference to an inner
table that evaluates to UNKNOWN when one
of its arguments is NULL
If it is a conjunction containing a null-rejected condition
as a conjunct
If it is a disjunction of null-rejected conditions
A condition can be null-rejected for one outer join operation in
a query and not null-rejected for another. In the query:
SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A
LEFT JOIN T3 ON T3.B=T1.B
WHERE T3.C > 0
the WHERE condition is null-rejected for the
second outer join operation but is not null-rejected for the
first one.
If the WHERE condition is null-rejected for
an outer join operation in a query, the outer join operation is
replaced by an inner join operation.
For example, the preceding query is replaced with the query:
SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A
INNER JOIN T3 ON T3.B=T1.B
WHERE T3.C > 0
For the original query, the optimizer would evaluate plans
compatible with only one access order
T1,T2,T3. For the replacing query, it
additionally considers the access sequence
T3,T1,T2.
A conversion of one outer join operation may trigger a
conversion of another. Thus, the query:
SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A
LEFT JOIN T3 ON T3.B=T2.B
WHERE T3.C > 0
will be first converted to the query:
SELECT * FROM T1 LEFT JOIN T2 ON T2.A=T1.A
INNER JOIN T3 ON T3.B=T2.B
WHERE T3.C > 0
which is equivalent to the query:
SELECT * FROM (T1 LEFT JOIN T2 ON T2.A=T1.A), T3
WHERE T3.C > 0 AND T3.B=T2.B
Now the remaining outer join operation can be replaced by an
inner join, too, because the condition
T3.B=T2.B is null-rejected and we get a query
without outer joins at all:
SELECT * FROM (T1 INNER JOIN T2 ON T2.A=T1.A), T3
WHERE T3.C > 0 AND T3.B=T2.B
Sometimes we succeed in replacing an embedded outer join
operation, but cannot convert the embedding outer join. The
following query:
SELECT * FROM T1 LEFT JOIN
(T2 LEFT JOIN T3 ON T3.B=T2.B)
ON T2.A=T1.A
WHERE T3.C > 0
is converted to:
SELECT * FROM T1 LEFT JOIN
(T2 INNER JOIN T3 ON T3.B=T2.B)
ON T2.A=T1.A
WHERE T3.C > 0,
That can be rewritten only to the form still containing the
embedding outer join operation:
SELECT * FROM T1 LEFT JOIN
(T2,T3)
ON (T2.A=T1.A AND T3.B=T2.B)
WHERE T3.C > 0.
When trying to convert an embedded outer join operation in a
query, we must take into account the join condition for the
embedding outer join together with the WHERE
condition. In the query:
SELECT * FROM T1 LEFT JOIN
(T2 LEFT JOIN T3 ON T3.B=T2.B)
ON T2.A=T1.A AND T3.C=T1.C
WHERE T3.D > 0 OR T1.D > 0
the WHERE condition is not null-rejected for
the embedded outer join, but the join condition of the embedding
outer join T2.A=T1.A AND T3.C=T1.C is
null-rejected. So the query can be converted to:
SELECT * FROM T1 LEFT JOIN
(T2, T3)
ON T2.A=T1.A AND T3.C=T1.C AND T3.B=T2.B
WHERE T3.D > 0 OR T1.D > 0
The algorithm that converts outer join operations into inner
joins was implemented in full measure, as it has been described
here, in MySQL 5.0.1. MySQL 4.1 performs only some simple
conversions.
7.2.11. ORDER BY Optimization
In some cases, MySQL can use an index to satisfy an
ORDER BY clause without doing any extra
sorting.
The index can also be used even if the ORDER
BY does not match the index exactly, as long as all of
the unused portions of the index and all the extra
ORDER BY columns are constants in the
WHERE clause. The following queries use the
index to resolve the ORDER BY part:
SELECT * FROM t1
ORDER BY key_part1,key_part2,... ;
SELECT * FROM t1
WHERE key_part1=constant
ORDER BY key_part2;
SELECT * FROM t1
ORDER BY key_part1 DESC, key_part2 DESC;
SELECT * FROM t1
WHERE key_part1=1
ORDER BY key_part1 DESC, key_part2 DESC;
In some cases, MySQL cannot use indexes to
resolve the ORDER BY, although it still uses
indexes to find the rows that match the WHERE
clause. These cases include the following:
You use ORDER BY on different keys:
SELECT * FROM t1 ORDER BY key1, key2;
You use ORDER BY on non-consecutive parts
of a key:
SELECT * FROM t1 WHERE key2=constant ORDER BY key_part2;
You mix ASC and DESC:
SELECT * FROM t1 ORDER BY key_part1 DESC, key_part2 ASC;
The key used to fetch the rows is not the same as the one
used in the ORDER BY:
SELECT * FROM t1 WHERE key2=constant ORDER BY key1;
You are joining many tables, and the columns in the
ORDER BY are not all from the first
non-constant table that is used to retrieve rows. (This is
the first table in the EXPLAIN output
that does not have a const join type.)
You have different ORDER BY and
GROUP BY expressions.
The type of table index used does not store rows in order.
For example, this is true for a HASH
index in a MEMORY table.
With EXPLAIN SELECT ... ORDER BY, you can
check whether MySQL can use indexes to resolve the query. It
cannot if you see Using filesort in the
Extra column. See Section 7.2.1, “Optimizing Queries with EXPLAIN”.
MySQL has two filesort algorithms for sorting
and retrieving results. The original method uses only the
ORDER BY columns. The modified method uses
not just the ORDER BY columns, but all the
columns used in the query.
The optimizer selects which filesort
algorithm to use. It normally uses the modified algorithm except
when BLOB or TEXT columns
are involved, in which case it uses the original algorithm.
The original filesort algorithm works as
follows:
Read all rows according to key or by table scanning. Rows
that do not match the WHERE clause are
skipped.
For each row, store a pair of values in a buffer (the sort
key and the row pointer). The size of the buffer is the
value of the sort_buffer_size system
variable.
When the buffer gets full, run a qsort (quicksort) on it and
store the result in a temporary file. Save a pointer to the
sorted block. (If all pairs fit into the sort buffer, no
temporary file is created.)
Repeat the preceding steps until all rows have been read.
Do a multi-merge of up to MERGEBUFF (7)
regions to one block in another temporary file. Repeat until
all blocks from the first file are in the second file.
Repeat the following until there are fewer than
MERGEBUFF2 (15) blocks left.
On the last multi-merge, only the pointer to the row (the
last part of the sort key) is written to a result file.
Read the rows in sorted order by using the row pointers in
the result file. To optimize this, we read in a big block of
row pointers, sort them, and use them to read the rows in
sorted order into a row buffer. The size of the buffer is
the value of the read_rnd_buffer_size
system variable. The code for this step is in the
sql/records.cc source file.
One problem with this approach is that it reads rows twice: One
time when evaluating the WHERE clause, and
again after sorting the pair values. And even if the rows were
accessed successively the first time (for example, if a table
scan is done), the second time they are accessed randomly. (The
sort keys are ordered, but the row positions are not.)
The modified filesort algorithm incorporates
an optimization such that it records not only the sort key value
and row position, but also the columns required for the query.
This avoids reading the rows twice. The modified
filesort algorithm works like this:
Read the rows that match the WHERE
clause.
For each row, record a tuple of values consisting of the
sort key value and row position, and also the columns
required for the query.
Sort the tuples by sort key value
Retrieve the rows in sorted order, but read the required
columns directly from the sorted tuples rather than by
accessing the table a second time.
Using the modified filesort algorithm, the
tuples are longer than the pairs used in the original method,
and fewer of them fit in the sort buffer (the size of which is
given by sort_buffer_size). As a result, it
is possible for the extra I/O to make the modified approach
slower, not faster. To avoid a slowdown, the optimization is
used only if the total size of the extra columns in the sort
tuple does not exceed the value of the
max_length_for_sort_data system variable. (A
symptom of setting the value of this variable too high is that
you should see high disk activity and low CPU activity.)
If you want to increase ORDER BY speed, check
whether you can get MySQL to use indexes rather than an extra
sorting phase. If this is not possible, you can try the
following strategies:
Increase the size of the sort_buffer_size
variable.
Increase the size of the
read_rnd_buffer_size variable.
Change tmpdir to point to a dedicated
filesystem with large amounts of empty space. This option
accepts several paths that are used in round-robin fashion.
Paths should be separated by colon characters
(‘:’) on Unix and semicolon
characters (‘;’) on Windows,
NetWare, and OS/2. You can use this feature to spread the
load across several directories. Note:
The paths should be for directories in filesystems that are
located on different physical disks,
not different partitions on the same disk.
By default, MySQL sorts all GROUP BY
col1,
col2, ... queries as if you
specified ORDER BY col1,
col2, ... in the query as
well. If you include an ORDER BY clause
explicitly that contains the same column list, MySQL optimizes
it away without any speed penalty, although the sorting still
occurs. If a query includes GROUP BY but you
want to avoid the overhead of sorting the result, you can
suppress sorting by specifying ORDER BY NULL.
For example:
INSERT INTO foo
SELECT a, COUNT(*) FROM bar GROUP BY a ORDER BY NULL;
The most general way to satisfy a GROUP BY
clause is to scan the whole table and create a new temporary
table where all rows from each group are consecutive, and then
use this temporary table to discover groups and apply aggregate
functions (if any). In some cases, MySQL is able to do much
better than that and to avoid creation of temporary tables by
using index access.
The most important preconditions for using indexes for
GROUP BY are that all GROUP
BY columns reference attributes from the same index,
and that the index stores its keys in order (for example, this
is a BTREE index and not a
HASH index). Whether use of temporary tables
can be replaced by index access also depends on which parts of
an index are used in a query, the conditions specified for these
parts, and the selected aggregate functions.
There are two ways to execute a GROUP BY
query via index access, as detailed in the following sections.
In the first method, the grouping operation is applied together
with all range predicates (if any). The second method first
performs a range scan, and then groups the resulting tuples.
7.2.12.1. Loose index scan
The most efficient way to process GROUP BY
is when the index is used to directly retrieve the group
fields. With this access method, MySQL uses the property of
some index types that the keys are ordered (for example,
BTREE). This property enables use of lookup
groups in an index without having to consider all keys in the
index that satisfy all WHERE conditions.
This access method considers only a fraction of the keys in an
index, so it is called a loose index
scan. When there is no WHERE
clause, a loose index scan reads as many keys as the number of
groups, which may be a much smaller number than that of all
keys. If the WHERE clause contains range
predicates (see the discussion of the range
join type in Section 7.2.1, “Optimizing Queries with EXPLAIN”), a loose index scan
looks up the first key of each group that satisfies the range
conditions, and again reads the least possible number of keys.
This is possible under the following conditions:
The query is over a single table.
The GROUP BY includes the first
consecutive parts of the index. (If, instead of
GROUP BY, the query has a
DISTINCT clause, all distinct
attributes refer to the beginning of the index.)
The only aggregate functions used (if any) are
MIN() and MAX(), and
all of them refer to the same column.
Any other parts of the index than those from the
GROUP BY referenced in the query must
be constants (that is, they must be referenced in
equalities with constants), except for the argument of
MIN() or MAX()
functions.
The EXPLAIN output for such queries shows
Using index for group-by in the
Extra column.
The following queries fall into this category, assuming that
there is an index idx(c1,c2,c3) on table
t1(c1,c2,c3,c4):
SELECT c1, c2 FROM t1 GROUP BY c1, c2;
SELECT DISTINCT c1, c2 FROM t1;
SELECT c1, MIN(c2) FROM t1 GROUP BY c1;
SELECT c1, c2 FROM t1 WHERE c1 < const GROUP BY c1, c2;
SELECT MAX(c3), MIN(c3), c1, c2 FROM t1 WHERE c2 > const GROUP BY c1, c2;
SELECT c2 FROM t1 WHERE c1 < const GROUP BY c1, c2;
SELECT c1, c2 FROM t1 WHERE c3 = const GROUP BY c1, c2;
The following queries cannot be executed with this quick
select method, for the reasons given:
There are aggregate functions other than
MIN() or MAX(), for
example:
SELECT c1, SUM(c2) FROM t1 GROUP BY c1;
The fields in the GROUP BY clause do
not refer to the beginning of the index, as shown here:
SELECT c1,c2 FROM t1 GROUP BY c2, c3;
The query refers to a part of a key that comes after the
GROUP BY part, and for which there is
no equality with a constant, an example being:
SELECT c1,c3 FROM t1 GROUP BY c1, c2;
7.2.12.2. Tight index scan
A tight index scan may be either a full index scan or a range
index scan, depending on the query conditions.
When the conditions for a loose index scan are not met, it is
still possible to avoid creation of temporary tables for
GROUP BY queries. If there are range
conditions in the WHERE clause, this method
reads only the keys that satisfy these conditions. Otherwise,
it performs an index scan. Because this method reads all keys
in each range defined by the WHERE clause,
or scans the whole index if there are no range conditions, we
term it a tight index scan. Notice that
with a tight index scan, the grouping operation is performed
only after all keys that satisfy the range conditions have
been found.
For this method to work, it is sufficient that there is a
constant equality condition for all columns in a query
referring to parts of the key coming before or in between
parts of the GROUP BY key. The constants
from the equality conditions fill in any “gaps”
in the search keys so that it is possible to form complete
prefixes of the index. These index prefixes then can be used
for index lookups. If we require sorting of the GROUP
BY result, and it is possible to form search keys
that are prefixes of the index, MySQL also avoids extra
sorting operations because searching with prefixes in an
ordered index already retrieves all the keys in order.
The following queries do not work with the loose index scan
access method described earlier, but still work with the tight
index scan access method (assuming that there is an index
idx(c1,c2,c3) on table
t1(c1,c2,c3,c4)).
There is a gap in the GROUP BY, but it
is covered by the condition c2 = 'a':
SELECT c1, c2, c3 FROM t1 WHERE c2 = 'a' GROUP BY c1, c3;
The GROUP BY does not begin with the
first part of the key, but there is a condition that
provides a constant for that part:
SELECT c1, c2, c3 FROM t1 WHERE c1 = 'a' GROUP BY c2, c3;
7.2.13. DISTINCT Optimization
DISTINCT combined with ORDER
BY needs a temporary table in many cases.
In most cases, a DISTINCT clause can be
considered as a special case of GROUP BY. For
example, the following two queries are equivalent:
SELECT DISTINCT c1, c2, c3 FROM t1
WHERE c1 > const;
SELECT c1, c2, c3 FROM t1
WHERE c1 > const GROUP BY c1, c2, c3;
Due to this equivalence, the optimizations applicable to
GROUP BY queries can be also applied to
queries with a DISTINCT clause. Thus, for
more details on the optimization possibilities for
DISTINCT queries, see
Section 7.2.12, “GROUP BY Optimization”.
When combining LIMIT
row_count with
DISTINCT, MySQL stops as soon as it finds
row_count unique rows.
If you do not use columns from all tables named in a query,
MySQL stops scanning any unused tables as soon as it finds the
first match. In the following case, assuming that
t1 is used before t2
(which you can check with EXPLAIN), MySQL
stops reading from t2 (for any particular row
in t1) when it finds the first row in
t2:
SELECT DISTINCT t1.a FROM t1, t2 where t1.a=t2.a;
7.2.14. Optimizing IN/=ANY Subqueries
Certain optimizations are applicable to comparisons that use the
IN operator to test subquery results (or that
use =ANY, which is equivalent). This section
discusses these optimizations, particularly with regard to the
challenges that NULL values present.
Suggestions on what you can do to help the optimizer are given
at the end of the discussion.
Consider the following subquery comparison:
outer_expr IN (SELECT inner_expr FROM ... WHERE subquery_where)
MySQL evaluates queries “from outside to inside.”
That is, it first obtains the value of the outer expression
outer_expr, and then runs the
subquery and captures the rows that it produces.
A very useful optimization is to “inform” the
subquery that the only rows of interest are those where the
inner expression inner_expr is equal
to outer_expr. This is done by
pushing down an appropriate equality into the subquery's
WHERE clause. That is, the comparison is
converted to this:
EXISTS (SELECT 1 FROM ... WHERE subquery_where AND outer_expr=inner_expr)
After the conversion, MySQL can use the pushed-down equality to
limit the number of rows that it must examine when evaluating
the subquery.
More generally, a comparison of N
values to a subquery that returns
N-value rows is subject to the same
conversion. If oe_i and
ie_i represent corresponding outer
and inner expression values, this subquery comparison:
(oe_1, ..., oe_N) IN
(SELECT ie_1, ..., ie_N FROM ... WHERE subquery_where)
Becomes:
EXISTS (SELECT 1 FROM ... WHERE subquery_where
AND oe_1 = ie_1
AND ...
AND oe_N = ie_N)
The following discussion assumes a single pair of outer and
inner expression values for simplicity.
The conversion just described has its limitations. It is valid
only if we ignore possible NULL values. That
is, the “pushdown” strategy works as long as both
of these two conditions are true:
outer_expr and
inner_expr cannot be
NULL.
You do not need to distinguish NULL from
FALSE subquery results. (If the subquery
is a part of an OR or
AND expression in the
WHERE clause, MySQL assumes that you
don't care.)
When either or both of those conditions do not hold,
optimization is more complex.
Suppose that outer_expr is known to
be a non-NULL value but the subquery does not
produce a row such that outer_expr =
inner_expr. Then
outer_expr IN (SELECT
...) evaluates as follows:
NULL, if the SELECT
produces any row where inner_expr
is NULL
FALSE, if the SELECT
produces only non-NULL values or produces
nothing
In this situation, the approach of looking for rows with
outer_expr =
inner_expr is no longer
valid. It is necessary to look for such rows, but if none are
found, also look for rows where
inner_expr is
NULL. Roughly speaking, the subquery can be
converted to:
EXISTS (SELECT 1 FROM ... WHERE subquery_where AND
(outer_expr=inner_expr OR inner_expr IS NULL))
The need to evaluate the extra IS NULL
condition is why MySQL has the ref_or_null
access method:
mysql> EXPLAIN
-> SELECT outer_expr IN (SELECT t2.maybe_null_key
-> FROM t2, t3 WHERE ...)
-> FROM t1;
*************************** 1. row ***************************
id: 1
select_type: PRIMARY
table: t1
...
*************************** 2. row ***************************
id: 2
select_type: DEPENDENT SUBQUERY
table: t2
type: ref_or_null
possible_keys: maybe_null_key
key: maybe_null_key
key_len: 5
ref: func
rows: 2
Extra: Using where; Using index
...
The unique_subquery and
index_subquery subqery-specific access
methods also have or-null variants. However, they are not
visible in EXPLAIN output, so you must use
EXPLAIN EXTENDED followed by SHOW
WARNINGS (note the checking NULL in
the warning message):
mysql> EXPLAIN EXTENDED
-> SELECT outer_expr IN (SELECT maybe_null_key FROM t2) FROM t1\G
*************************** 1. row ***************************
id: 1
select_type: PRIMARY
table: t1
...
*************************** 2. row ***************************
id: 2
select_type: DEPENDENT SUBQUERY
table: t2
type: index_subquery
possible_keys: maybe_null_key
key: maybe_null_key
key_len: 5
ref: func
rows: 2
Extra: Using index
mysql> SHOW WARNINGS\G
*************************** 1. row ***************************
Level: Note
Code: 1003
Message: select (`test`.`t1`.`outer_expr`,
(((`test`.`t1`.`outer_expr`) in t2 on
maybe_null_key checking NULL))) AS `outer_expr IN (SELECT
maybe_null_key FROM t2)` from `test`.`t1`
The additional OR ... IS NULL condition makes
query execution slightly more complicated (and some
optimizations within the subquery become inapplicable), but
generally this is tolerable.
The situation is much worse when
outer_expr can be
NULL. According to the SQL interpretation of
NULL as “unknown value,”
NULL IN (SELECT inner_expr
...) should evaluate to:
NULL, if the SELECT
produces any rows
FALSE, if the SELECT
produces no rows
For proper evaluation, it is necessary to be able to check
whether the SELECT has produced any rows at
all, so outer_expr =
inner_expr cannot be pushed
down into the subquery. This is a problem, because many real
world subqueries become very slow unless the equality can be
pushed down.
Essentially, there must be different ways to execute the
subquery depending on the value of
outer_expr. In MySQL 5.0
before 5.0.36, the optimizer chose speed over distinguishing a
NULL from FALSE result, so
for some queries, you might get a FALSE
result rather than NULL.
As of MySQL 5.0.36, the optimizer chooses SQL compliance over
speed, so it accounts for the possibility that
outer_expr might be
NULL.
If outer_expr is
NULL, to evaluate the following expression,
it is necessary to run the SELECT to
determine whether it produces any rows:
NULL IN (SELECT inner_expr FROM ... WHERE subquery_where)
It is necessary to run the original SELECT
here, without any pushed-down equalities of the kind mentioned
earlier.
On the other hand, when outer_expr is
not NULL, it is absolutely essential that
this comparison:
outer_expr IN (SELECT inner_expr FROM ... WHERE subquery_where)
be converted to this expression that uses a pushed-down
condition:
EXISTS (SELECT 1 FROM ... WHERE subquery_where AND outer_expr=inner_expr)
Without this conversion, subqueries will be slow. To solve the
dilemma of whether to push down or not push down conditions into
the subquery, the conditions are wrapped in
“trigger” functions. Thus, an expression of the
following form:
outer_expr IN (SELECT inner_expr FROM ... WHERE subquery_where)
is converted into:
EXISTS (SELECT 1 FROM ... WHERE subquery_where
AND trigcond(outer_expr=inner_expr))
More generally, if the subquery comparison is based on several
pairs of outer and inner expressions, the conversion takes this
comparison:
(oe_1, ..., oe_N) IN (SELECT ie_1, ..., ie_N FROM ... WHERE subquery_where)
and converts it to this expression:
EXISTS (SELECT 1 FROM ... WHERE subquery_where
AND trigcond(oe_1=ie_1)
AND ...
AND trigcond(oe_N=ie_N)
)
Each trigcond(X)
is a special function that evaluates to the following values:
X when the “linked”
outer expression oe_i is not
NULL
TRUE when the “linked” outer
expression oe_i is
NULL
Note that trigger functions are not
triggers of the kind that you create with CREATE
TRIGGER.
Equalities that are wrapped into trigcond()
functions are not first class predicates for the query
optimizer. Most optimizations cannot deal with predicates that
may be turned on and off at query execution time, so they assume
any trigcond(X) to
be an unknown function and ignore it. At the moment, triggered
equalities can be used by those optimizations:
Reference optimizations:
trigcond(X=Y
[OR Y IS NULL]) can be
used to construct ref,
eq_ref, or ref_or_null
table accesses.
Index lookup-based subquery execution engines:
trigcond(X=Y)
can be used to construct unique_subquery
or index_subquery accesses.
Table-condition generator: If the subquery is a join of
several tables, the triggered condition will be checked as
soon as possible.
When the optimizer uses a triggered condition to create some
kind of index lookup-based access (as for the first two items of
the preceding list), it must have a fallback strategy for the
case when the condition is turned off. This fallback strategy is
always the same: Do a full table scan. In
EXPLAIN output, the fallback shows up as
Full scan on NULL key in the
Extra column:
mysql> EXPLAIN SELECT t1.col1,
-> t1.col1 IN (SELECT t2.key1 FROM t2 WHERE t2.col2=t1.col2) FROM t1\G
*************************** 1. row ***************************
id: 1
select_type: PRIMARY
table: t1
...
*************************** 2. row ***************************
id: 2
select_type: DEPENDENT SUBQUERY
table: t2
type: index_subquery
possible_keys: key1
key: key1
key_len: 5
ref: func
rows: 2
Extra: Using where; Full scan on NULL key
If you run EXPLAIN EXTENDED followed by
SHOW WARNINGS, you can see the triggered
condition:
*************************** 1. row ***************************
Level: Note
Code: 1003
Message: select `test`.`t1`.`col1` AS `col1`,
<in_optimizer>(`test`.`t1`.`col1`,
<exists>(<index_lookup>(<cache>(`test`.`t1`.`col1`) in t2
on key1 checking NULL
where (`test`.`t2`.`col2` = `test`.`t1`.`col2`) having
trigcond(<is_not_null_test>(`test`.`t2`.`key1`))))) AS
`t1.col1 IN (select t2.key1 from t2 where t2.col2=t1.col2)`
from `test`.`t1`
The use of triggered conditions has some performance
implications. A NULL IN (SELECT ...)
expression now may cause a full table scan (which is slow) when
it previously did not. This is the price paid for correct
results (the goal of the trigger-condition strategy was to
improve compliance and not speed).
For multiple-table subqueries, execution of NULL IN
(SELECT ...) will be particularly slow because the
join optimizer doesn't optimize for the case where the outer
expression is NULL. It assumes that subquery
evaluations with NULL on the left side are
very rare, even if there are statistics that indicate otherwise.
On the other hand, if the outer expression might be
NULL but never actually is, there is no
performance penalty.
To help the query optimizer better execute your queries, use
these tips:
A column must be declared as NOT NULL if
it really is. (This also helps other aspects of the
optimizer.)
If you don't need to distinguish a NULL
from FALSE subquery result, you can
easily avoid the slow execution path. Replace a comparison
that looks like this:
outer_expr IN (SELECT inner_expr FROM ...)
with this expression:
(outer_expr IS NOT NULL) AND (outer_expr IN (SELECT inner_expr FROM ...))
Then NULL IN (SELECT ...) will never be
evaluated because MySQL stops evaluating
AND parts as soon as the expression
result is clear.
7.2.15. LIMIT Optimization
In some cases, MySQL handles a query differently when you are
using LIMIT
row_count and not using
HAVING:
If you are selecting only a few rows with
LIMIT, MySQL uses indexes in some cases
when normally it would prefer to do a full table scan.
If you use LIMIT
row_count with
ORDER BY, MySQL ends the sorting as soon
as it has found the first
row_count rows of the sorted
result, rather than sorting the entire result. If ordering
is done by using an index, this is very fast. If a filesort
must be done, all rows that match the query without the
LIMIT clause must be selected, and most
or all of them must be sorted, before it can be ascertained
that the first row_count rows
have been found. In either case, after the initial rows have
been found, there is no need to sort any remainder of the
result set, and MySQL does not do so.
When combining LIMIT
row_count with
DISTINCT, MySQL stops as soon as it finds
row_count unique rows.
In some cases, a GROUP BY can be resolved
by reading the key in order (or doing a sort on the key) and
then calculating summaries until the key value changes. In
this case, LIMIT
row_count does not
calculate any unnecessary GROUP BY
values.
As soon as MySQL has sent the required number of rows to the
client, it aborts the query unless you are using
SQL_CALC_FOUND_ROWS.
LIMIT 0 quickly returns an empty set.
This can be useful for checking the validity of a query.
When using one of the MySQL APIs, it can also be employed
for obtaining the types of the result columns. (This trick
does not work in the MySQL Monitor (the
mysql program), which merely displays
Empty set in such cases; you should
instead use SHOW COLUMNS or
DESCRIBE for this purpose.)
When the server uses temporary tables to resolve the query,
it uses the LIMIT
row_count clause to
calculate how much space is required.
7.2.16. How to Avoid Table Scans
The output from EXPLAIN shows
ALL in the type column
when MySQL uses a table scan to resolve a query. This usually
happens under the following conditions:
The table is so small that it is faster to perform a table
scan than to bother with a key lookup. This is common for
tables with fewer than 10 rows and a short row length.
There are no usable restrictions in the
ON or WHERE clause for
indexed columns.
You are comparing indexed columns with constant values and
MySQL has calculated (based on the index tree) that the
constants cover too large a part of the table and that a
table scan would be faster. See
Section 7.2.4, “WHERE Clause Optimization”.
You are using a key with low cardinality (many rows match
the key value) through another column. In this case, MySQL
assumes that by using the key it probably will do many key
lookups and that a table scan would be faster.
For small tables, a table scan often is appropriate and the
performance impact is negligible. For large tables, try the
following techniques to avoid having the optimizer incorrectly
choose a table scan:
Start mysqld with the
--max-seeks-for-key=1000 option or use
SET max_seeks_for_key=1000 to tell the
optimizer to assume that no key scan causes more than 1,000
key seeks. See Section 5.2.3, “System Variables”.
7.2.17. Speed of INSERT Statements
The time required for inserting a row is determined by the
following factors, where the numbers indicate approximate
proportions:
Connecting: (3)
Sending query to server: (2)
Parsing query: (2)
Inserting row: (1 × size of row)
Inserting indexes: (1 × number of indexes)
Closing: (1)
This does not take into consideration the initial overhead to
open tables, which is done once for each concurrently running
query.
The size of the table slows down the insertion of indexes by log
N, assuming B-tree indexes.
You can use the following methods to speed up inserts:
If you are inserting many rows from the same client at the
same time, use INSERT statements with
multiple VALUES lists to insert several
rows at a time. This is considerably faster (many times
faster in some cases) than using separate single-row
INSERT statements. If you are adding data
to a non-empty table, you can tune the
bulk_insert_buffer_size variable to make
data insertion even faster. See
Section 5.2.3, “System Variables”.
For a MyISAM table, you can use
concurrent inserts to add rows at the same time that
SELECT statements are running, if there
are no deleted rows in middle of the data file. See
Section 7.3.3, “Concurrent Inserts”.
When loading a table from a text file, use LOAD
DATA INFILE. This is usually 20 times faster than
using INSERT statements. See
Section 13.2.5, “LOAD DATA INFILE Syntax”.
With some extra work, it is possible to make LOAD
DATA INFILE run even faster for a
MyISAM table when the table has many
indexes. Use the following procedure:
Optionally create the table with CREATE
TABLE.
Execute a FLUSH TABLES statement or a
mysqladmin flush-tables command.
Use myisamchk --keys-used=0 -rq
/path/to/db/tbl_name.
This removes all use of indexes for the table.
Insert data into the table with LOAD DATA
INFILE. This does not update any indexes and
therefore is very fast.
Re-create the indexes with myisamchk -rq
/path/to/db/tbl_name.
This creates the index tree in memory before writing it
to disk, which is much faster that updating the index
during LOAD DATA INFILE because it
avoids lots of disk seeks. The resulting index tree is
also perfectly balanced.
Execute a FLUSH TABLES statement or a
mysqladmin flush-tables command.
LOAD DATA INFILE performs the preceding
optimization automatically if the MyISAM
table into which you insert data is empty. The main
difference between automatic optimization and using the
procedure explicitly is that you can let
myisamchk allocate much more temporary
memory for the index creation than you might want the server
to allocate for index re-creation when it executes the
LOAD DATA INFILE statement.
You can also disable or enable the indexes for a
MyISAM table by using the following
statements rather than myisamchk. If you
use these statements, you can skip the FLUSH
TABLE operations:
ALTER TABLE tbl_name DISABLE KEYS;
ALTER TABLE tbl_name ENABLE KEYS;
To speed up INSERT operations that are
performed with multiple statements for non-transactional
tables, lock your tables:
LOCK TABLES a WRITE;
INSERT INTO a VALUES (1,23),(2,34),(4,33);
INSERT INTO a VALUES (8,26),(6,29);
...
UNLOCK TABLES;
This benefits performance because the index buffer is
flushed to disk only once, after all
INSERT statements have completed.
Normally, there would be as many index buffer flushes as
there are INSERT statements. Explicit
locking statements are not needed if you can insert all rows
with a single INSERT.
To obtain faster insertions for transactional tables, you
should use START TRANSACTION and
COMMIT instead of LOCK
TABLES.
Locking also lowers the total time for multiple-connection
tests, although the maximum wait time for individual
connections might go up because they wait for locks. Suppose
that five clients attempt to perform inserts simultaneously
as follows:
Connection 1 does 1000 inserts
Connections 2, 3, and 4 do 1 insert
Connection 5 does 1000 inserts
If you do not use locking, connections 2, 3, and 4 finish
before 1 and 5. If you use locking, connections 2, 3, and 4
probably do not finish before 1 or 5, but the total time
should be about 40% faster.
INSERT, UPDATE, and
DELETE operations are very fast in MySQL,
but you can obtain better overall performance by adding
locks around everything that does more than about five
successive inserts or updates. If you do very many
successive inserts, you could do a LOCK
TABLES followed by an UNLOCK
TABLES once in a while (each 1,000 rows or so) to
allow other threads access to the table. This would still
result in a nice performance gain.
INSERT is still much slower for loading
data than LOAD DATA INFILE, even when
using the strategies just outlined.
To increase performance for MyISAM
tables, for both LOAD DATA INFILE and
INSERT, enlarge the key cache by
increasing the key_buffer_size system
variable. See Section 7.5.2, “Tuning Server Parameters”.
MySQL Enterprise
For more advice on optimizing the performance of your server,
subscribe to the MySQL Network Monitoring and Advisory
Service. Numerous advisors are dedicated to monitoring
performance. For more information see
http://www.mysql.com/products/enterprise/advisors.html.
7.2.18. Speed of UPDATE Statements
An update statement is optimized like a
SELECT query with the additional overhead of
a write. The speed of the write depends on the amount of data
being updated and the number of indexes that are updated.
Indexes that are not changed do not get updated.
Another way to get fast updates is to delay updates and then do
many updates in a row later. Performing multiple updates
together is much quicker than doing one at a time if you lock
the table.
For a MyISAM table that uses dynamic row
format, updating a row to a longer total length may split the
row. If you do this often, it is very important to use
OPTIMIZE TABLE occasionally. See
Section 13.5.2.5, “OPTIMIZE TABLE Syntax”.
7.2.19. Speed of DELETE Statements
The time required to delete individual rows is exactly
proportional to the number of indexes. To delete rows more
quickly, you can increase the size of the key cache by
increasing the key_buffer_size system
variable. See Section 7.5.2, “Tuning Server Parameters”.
To delete all rows from a table, TRUNCATE TABLE
tbl_name is faster than
than DELETE FROM Truncate operations are not
transaction-safe; an error occurs when attempting one in the
course of an active transaction or active table lock.
tbl_name. See
Section 13.2.9, “TRUNCATE Syntax”.
7.2.20. Other Optimization Tips
This section lists a number of miscellaneous tips for improving
query processing speed:
Use persistent connections to the database to avoid
connection overhead. If you cannot use persistent
connections and you are initiating many new connections to
the database, you may want to change the value of the
thread_cache_size variable. See
Section 7.5.2, “Tuning Server Parameters”.
Always check whether all your queries really use the indexes
that you have created in the tables. In MySQL, you can do
this with the EXPLAIN statement. See
Section 7.2.1, “Optimizing Queries with EXPLAIN”.
Try to avoid complex SELECT queries on
MyISAM tables that are updated
frequently, to avoid problems with table locking that occur
due to contention between readers and writers.
MyISAM supports concurrent inserts: If a
table has no free blocks in the middle of the data file, you
can INSERT new rows into it at the same
time that other threads are reading from the table. If it is
important to be able to do this, you should consider using
the table in ways that avoid deleting rows. Another
possibility is to run OPTIMIZE TABLE to
defragment the table after you have deleted a lot of rows
from it. This behavior is altered by setting the
concurrent_insert variable. You can force
new rows to be appended (and therefore allow concurrent
inserts), even in tables that have deleted rows. See
Section 7.3.3, “Concurrent Inserts”.
MySQL Enterprise
For optimization tips geared to your specific
circumstances subscribe to the MySQL Network Monitoring
and Advisory Service. For more information see
http://www.mysql.com/products/enterprise/advisors.html.
Use ALTER TABLE ... ORDER BY
expr1,
expr2, ... if you
usually retrieve rows in
expr1,
expr2, ... order. By
using this option after extensive changes to the table, you
may be able to get higher performance.
In some cases, it may make sense to introduce a column that
is “hashed” based on information from other
columns. If this column is short, reasonably unique, and
indexed, it may be much faster than a “wide”
index on many columns. In MySQL, it is very easy to use this
extra column:
SELECT * FROM tbl_name
WHERE hash_col=MD5(CONCAT(col1,col2))
AND col1='constant' AND col2='constant';
For MyISAM tables that change frequently,
you should try to avoid all variable-length columns
(VARCHAR, BLOB, and
TEXT). The table uses dynamic row format
if it includes even a single variable-length column. See
Chapter 14, Storage Engines.
It is normally not useful to split a table into different
tables just because the rows become large. In accessing a
row, the biggest performance hit is the disk seek needed to
find the first byte of the row. After finding the data, most
modern disks can read the entire row fast enough for most
applications. The only cases where splitting up a table
makes an appreciable difference is if it is a
MyISAM table using dynamic row format
that you can change to a fixed row size, or if you very
often need to scan the table but do not need most of the
columns. See Chapter 14, Storage Engines.
If you often need to calculate results such as counts based
on information from a lot of rows, it may be preferable to
introduce a new table and update the counter in real time.
An update of the following form is very fast:
UPDATE tbl_name SET count_col=count_col+1 WHERE key_col=constant;
This is very important when you use MySQL storage engines
such as MyISAM that has only table-level
locking (multiple readers with single writers). This also
gives better performance with most database systems, because
the row locking manager in this case has less to do.
If you need to collect statistics from large log tables, use
summary tables instead of scanning the entire log table.
Maintaining the summaries should be much faster than trying
to calculate statistics “live.” Regenerating
new summary tables from the logs when things change
(depending on business decisions) is faster than changing
the running application.
If possible, you should classify reports as
“live” or as “statistical,” where
data needed for statistical reports is created only from
summary tables that are generated periodically from the live
data.
Take advantage of the fact that columns have default values.
Insert values explicitly only when the value to be inserted
differs from the default. This reduces the parsing that
MySQL must do and improves the insert speed.
In some cases, it is convenient to pack and store data into
a BLOB column. In this case, you must
provide code in your application to pack and unpack
information, but this may save a lot of accesses at some
stage. This is practical when you have data that does not
conform well to a rows-and-columns table structure.
Normally, you should try to keep all data non-redundant
(observing what is referred to in database theory as
third normal form). However, there
may be situations in which it can be advantageous to
duplicate information or create summary tables to gain more
speed.
You can increase performance by caching queries or answers
in your application and then executing many inserts or
updates together. If your database system supports table
locks (as do MySQL and Oracle), this should help to ensure
that the index cache is only flushed once after all updates.
You can also take advantage of MySQL's query cache to
achieve similar results; see Section 5.13, “The MySQL Query Cache”.
Use INSERT DELAYED when you do not need
to know when your data is written. This reduces the overall
insertion impact because many rows can be written with a
single disk write.
Use INSERT LOW_PRIORITY when you want to
give SELECT statements higher priority
than your inserts.
Use SELECT HIGH_PRIORITY to get
retrievals that jump the queue. That is, the
SELECT is executed even if there is
another client waiting to do a write.
LOW_PRIORITY and
HIGH_PRIORITY have an effect only for
storage engines that use only table-level locking
(MyISAM, MEMORY,
MERGE).
Use multiple-row INSERT statements to
store many rows with one SQL statement. Many SQL servers
support this, including MySQL.
Use LOAD DATA INFILE to load large
amounts of data. This is faster than using
INSERT statements.
Use AUTO_INCREMENT columns so that each
row in a table can be identified by a single unique value.
unique values.
Use MEMORY (HEAP)
tables when possible to get more speed. See
Section 14.4, “The MEMORY (HEAP) Storage Engine”.
MEMORY tables are useful for non-critical
data that is accessed often, such as information about the
last displayed banner for users who don't have cookies
enabled in their Web browser. User sessions are another
alternative available in many Web application environments
for handling volatile state data.
With Web servers, images and other binary assets should
normally be stored as files. That is, store only a reference
to the file rather than the file itself in the database.
Most Web servers are better at caching files than database
contents, so using files is generally faster.
Columns with identical information in different tables
should be declared to have identical data types so that
joins based on the corresponding columns will be faster.
Try to keep column names simple. For example, in a table
named customer, use a column name of
name instead of
customer_name. To make your names
portable to other SQL servers, you should keep them shorter
than 18 characters.
If you need really high speed, you should take a look at the
low-level interfaces for data storage that the different SQL
servers support. For example, by accessing the MySQL
MyISAM storage engine directly, you could
get a speed increase of two to five times compared to using
the SQL interface. To be able to do this, the data must be
on the same server as the application, and usually it should
only be accessed by one process (because external file
locking is really slow). One could eliminate these problems
by introducing low-level MyISAM commands
in the MySQL server (this could be one easy way to get more
performance if needed). By carefully designing the database
interface, it should be quite easy to support this type of
optimization.
If you are using numerical data, it is faster in many cases
to access information from a database (using a live
connection) than to access a text file. Information in the
database is likely to be stored in a more compact format
than in the text file, so accessing it involves fewer disk
accesses. You also save code in your application because you
need not parse your text files to find line and column
boundaries.
Replication can provide a performance benefit for some
operations. You can distribute client retrievals among
replication servers to split up the load. To avoid slowing
down the master while making backups, you can make backups
using a slave server. See Chapter 6, Replication.
Declaring a MyISAM table with the
DELAY_KEY_WRITE=1 table option makes
index updates faster because they are not flushed to disk
until the table is closed. The downside is that if something
kills the server while such a table is open, you should
ensure that the table is okay by running the server with the
--myisam-recover option, or by running
myisamchk before restarting the server.
(However, even in this case, you should not lose anything by
using DELAY_KEY_WRITE, because the key
information can always be generated from the data rows.)
MySQL manages contention for table contents using locking:
Internal locking; is performed within the MySQL server itself
to manage contention for table contents by multiple threads.
This type of locking is internal because it is performed
entirely by the server and involves no other programs. See
Section 7.3.1, “Internal Locking Methods”.
This section discusses internal locking; that is, locking
performed within the MySQL server itself to manage contention
for table contents by multiple threads. This type of locking is
internal because it is performed entirely by the server and
involves no other programs. External locking occurs when the
server and other programs lock table files to coordinate among
themselves which program can access the tables at which time.
See Section 7.3.4, “External Locking”.
MySQL uses table-level locking for MyISAM and
MEMORY tables, page-level locking for
BDB tables, and row-level locking for
InnoDB tables.
In many cases, you can make an educated guess about which
locking type is best for an application, but generally it is
difficult to say that a given lock type is better than another.
Everything depends on the application and different parts of an
application may require different lock types.
To decide whether you want to use a storage engine with
row-level locking, you should look at what your application does
and what mix of select and update statements it uses. For
example, most Web applications perform many selects, relatively
few deletes, updates based mainly on key values, and inserts
into a few specific tables. The base MySQL
MyISAM setup is very well tuned for this.
MySQL Enterprise
The MySQL Network Monitoring and Advisory Service provides
expert advice on when to use table-level locking and when to
use row-level locking. To subscribe see
http://www.mysql.com/products/enterprise/advisors.html.
Table locking in MySQL is deadlock-free for storage engines that
use table-level locking. Deadlock avoidance is managed by always
requesting all needed locks at once at the beginning of a query
and always locking the tables in the same order.
MySQL grants table WRITE locks as follows:
If there are no locks on the table, put a write lock on it.
Otherwise, put the lock request in the write lock queue.
MySQL grants table READ locks as follows:
If there are no write locks on the table, put a read lock on
it.
Otherwise, put the lock request in the read lock queue.
When a lock is released, the lock is made available to the
threads in the write lock queue and then to the threads in the
read lock queue. This means that if you have many updates for a
table, SELECT statements wait until there are
no more updates.
You can analyze the table lock contention on your system by
checking the Table_locks_waited and
Table_locks_immediate status variables:
mysql> SHOW STATUS LIKE 'Table%';
+-----------------------+---------+
| Variable_name | Value |
+-----------------------+---------+
| Table_locks_immediate | 1151552 |
| Table_locks_waited | 15324 |
+-----------------------+---------+
The MyISAM storage engine supports concurrent
inserts to reduce contention between readers and writers for a
given table: If a MyISAM table has no free
blocks in the middle of the data file, rows are always inserted
at the end of the data file. In this case, you can freely mix
concurrent INSERT and
SELECT statements for a
MyISAM table without locks. That is, you can
insert rows into a MyISAM table at the same
time other clients are reading from it. Holes can result from
rows having been deleted from or updated in the middle of the
table. If there are holes, concurrent inserts are disabled but
are re-enabled automatically when all holes have been filled
with new data. This behavior is altered by the
concurrent_insert system variable. See
Section 7.3.3, “Concurrent Inserts”.
If you want to perform many INSERT and
SELECT operations on a table
real_table when concurrent inserts are not
possible, you can insert rows into a temporary table
temp_table and update the real table with the
rows from the temporary table periodically. This can be done
with the following code:
mysql> LOCK TABLES real_table WRITE, temp_table WRITE;
mysql> INSERT INTO real_table SELECT * FROM temp_table;
mysql> DELETE FROM temp_table;
mysql> UNLOCK TABLES;
InnoDB uses row locks and
BDB uses page locks. Deadlocks are possible
for these storage engines because they automatically acquire
locks during the processing of SQL statements, not at the start
of the transaction.
Advantages of row-level locking:
Fewer lock conflicts when accessing different rows in many
threads
Fewer changes for rollbacks
Possible to lock a single row for a long time
Disadvantages of row-level locking:
Requires more memory than page-level or table-level locks
Slower than page-level or table-level locks when used on a
large part of the table because you must acquire many more
locks
Definitely much slower than other locks if you often do
GROUP BY operations on a large part of
the data or if you must scan the entire table frequently
Table locks are superior to page-level or row-level locks in the
following cases:
Most statements for the table are reads
Statements for the table are a mix of reads and writes,
where writes are updates or deletes for a single row that
can be fetched with one key read:
UPDATE tbl_name SET column=value WHERE unique_key_col=key_value;
DELETE FROM tbl_name WHERE unique_key_col=key_value;
SELECT combined with concurrent
INSERT statements, and very few
UPDATE or DELETE
statements
Many scans or GROUP BY operations on the
entire table without any writers
With higher-level locks, you can more easily tune applications
by supporting locks of different types, because the lock
overhead is less than for row-level locks.
Options other than row-level or page-level locking:
Versioning (such as that used in MySQL for concurrent
inserts) where it is possible to have one writer at the same
time as many readers. This means that the database or table
supports different views for the data depending on when
access begins. Other common terms for this are “time
travel,” “copy on write,” or “copy
on demand.”
Copy on demand is in many cases superior to page-level or
row-level locking. However, in the worst case, it can use
much more memory than using normal locks.
Instead of using row-level locks, you can employ
application-level locks, such as those provided by
GET_LOCK() and
RELEASE_LOCK() in MySQL. These are
advisory locks, so they work only in well-behaved
applications. See Section 12.10.4, “Miscellaneous Functions”.
7.3.2. Table Locking Issues
To achieve a very high lock speed, MySQL uses table locking
(instead of page, row, or column locking) for all storage
engines except InnoDB,
BDB, and NDBCLUSTER.
For InnoDB and BDB tables,
MySQL only uses table locking if you explicitly lock the table
with LOCK TABLES. For these storage engines,
we recommend that you not use LOCK TABLES at
all, because InnoDB uses automatic row-level
locking and BDB uses page-level locking to
ensure transaction isolation.
For large tables, table locking is much better than row locking
for most applications, but there are some pitfalls:
Table locking enables many threads to read from a table at
the same time, but if a thread wants to write to a table, it
must first get exclusive access. During the update, all
other threads that want to access this particular table must
wait until the update is done.
Table updates normally are considered to be more important
than table retrievals, so they are given higher priority.
This should ensure that updates to a table are not
“starved” even if there is heavy
SELECT activity for the table.
Table locking causes problems in cases such as when a thread
is waiting because the disk is full and free space needs to
become available before the thread can proceed. In this
case, all threads that want to access the problem table are
also put in a waiting state until more disk space is made
available.
Table locking is also disadvantageous under the following
scenario:
A client issues a SELECT that takes a
long time to run.
Another client then issues an UPDATE on
the same table. This client waits until the
SELECT is finished.
Another client issues another SELECT
statement on the same table. Because
UPDATE has higher priority than
SELECT, this SELECT
waits for the UPDATE to finish,
and for the first
SELECT to finish.
The following items describe some ways to avoid or reduce
contention caused by table locking:
Try to get the SELECT statements to run
faster so that they lock tables for a shorter time. You
might have to create some summary tables to do this.
Start mysqld with
--low-priority-updates. For storage engines
that use only table-level locking
(MyISAM, MEMORY,
MERGE), this gives all statements that
update (modify) a table lower priority than
SELECT statements. In this case, the
second SELECT statement in the preceding
scenario would execute before the UPDATE
statement, and would not need to wait for the first
SELECT to finish.
You can specify that all updates issued in a specific
connection should be done with low priority by using the
SET LOW_PRIORITY_UPDATES=1 statement. See
Section 13.5.3, “SET Syntax”.
You can give a specific INSERT,
UPDATE, or DELETE
statement lower priority with the
LOW_PRIORITY attribute.
You can give a specific SELECT statement
higher priority with the HIGH_PRIORITY
attribute. See Section 13.2.7, “SELECT Syntax”.
You can start mysqld with a low value for
the max_write_lock_count system variable
to force MySQL to temporarily elevate the priority of all
SELECT statements that are waiting for a
table after a specific number of inserts to the table occur.
This allows READ locks after a certain
number of WRITE locks.
If you have problems with INSERT combined
with SELECT, you might want to consider
switching to MyISAM tables, which support
concurrent SELECT and
INSERT statements. (See
Section 7.3.3, “Concurrent Inserts”.)
If you have problems with mixed SELECT
and DELETE statements, the
LIMIT option to DELETE
may help. See Section 13.2.1, “DELETE Syntax”.
Using SQL_BUFFER_RESULT with
SELECT statements can help to make the
duration of table locks shorter. See
Section 13.2.7, “SELECT Syntax”.
You could change the locking code in
mysys/thr_lock.c to use a single queue.
In this case, write locks and read locks would have the same
priority, which might help some applications.
Here are some tips concerning table locks in MySQL:
Concurrent users are not a problem if you do not mix updates
with selects that need to examine many rows in the same
table.
You can use LOCK TABLES to increase
speed, because many updates within a single lock is much
faster than updating without locks. Splitting table contents
into separate tables may also help.
MySQL Enterprise
Lock contention can seriously degrade performance. The
MySQL Network Monitoring and Advisory Service provides
expert advice on avoiding this problem. To subscribe see
http://www.mysql.com/products/enterprise/advisors.html.
7.3.3. Concurrent Inserts
The MyISAM storage engine supports concurrent
inserts to reduce contention between readers and writers for a
given table: If a MyISAM table has no holes
in the data file (deleted rows in the middle), inserts can be
performed to add rows to the end of the table at the same time
that SELECT statements are reading rows from
the table.
The concurrent_insert system variable can be
set to modify the concurrent-insert processing. By default, the
variable is set to 1 and concurrent inserts are handled as just
described. If concurrent_inserts is set to 0,
concurrent inserts are disabled. If the variable is set to 2,
concurrent inserts at the end of the table are allowed even for
tables that have deleted rows. See also the description of the
concurrent_insert
system variable.
Under circumstances where concurrent inserts can be used, there
is seldom any need to use the DELAYED
modifier for INSERT statements. See
Section 13.2.4.2, “INSERT DELAYED Syntax”.
If you are using the binary log, concurrent inserts are
converted to normal inserts for CREATE ...
SELECT or INSERT ... SELECT
statements. This is done to ensure that you can re-create an
exact copy of your tables by applying the log during a backup
operation. See Section 5.11.3, “The Binary Log”.
With LOAD DATA INFILE, if you specify
CONCURRENT with a MyISAM
table that satisfies the condition for concurrent inserts (that
is, it contains no free blocks in the middle), other threads can
retrieve data from the table while LOAD DATA
is executing. Use of the CONCURRENT option
affects the performance of LOAD DATA a bit,
even if no other thread is using the table at the same time.
If you specify HIGH_PRIORITY, it overrides
the effect of the --low-priority-updates option
if the server was started with that option. It also causes
concurrent inserts not to be used.
For LOCK TABLE, the difference between
READ LOCAL and READ is
that READ LOCAL allows non-conflicting
INSERT statements (concurrent inserts) to
execute while the lock is held. However, this cannot be used if
you are going to manipulate the database using processes
external to the server while you hold the lock.
7.3.4. External Locking
External locking is the use of filesystem locking to manage
contention for database tables by multiple processes. External
locking is used in situations where a single process such as the
MySQL server cannot be assumed to be the only process that
requires access to tables. Here are some examples:
If you run multiple servers that use the same database
directory (not recommended), each server must have external
locking enabled.
If you use myisamchk to perform table
maintenance operations on MyISAM tables,
you must either ensure that the server is not running, or
that the server has external locking enabled so that it
locks table files as necessary to coordinate with
myisamchk for access to the tables. The
same is true for use of myisampack to
pack MyISAM tables.
With external locking in effect, each process that requires
access to a table acquires a filesystem lock for the table files
before proceeding to access the table. If all necessary locks
cannot be acquired, the process is blocked from accessing the
table until the locks can be obtained (after the process that
currently holds the locks releases them).
External locking affects server performance because the server
must sometimes wait for other processes before it can access
tables.
External locking is unnecessary if you run a single server to
access a given data directory (which is the usual case) and if
no other programs such as myisamchk need to
modify tables while the server is running. If you only
read tables with other programs, external
locking is not required, although myisamchk
might report warnings if the server changes tables while
myisamchk is reading them.
With external locking disabled, to use
myisamchk, you must either stop the server
while myisamchk executes or else lock and
flush the tables before running myisamchk.
(See Section 7.5.1, “System Factors and Startup Parameter Tuning”.) To avoid this
requirement, use the CHECK TABLE and
REPAIR TABLE statements to check and repair
MyISAM tables.
For mysqld, external locking is controlled by
the value of the skip_external_locking system
variable. (Before MySQL 4.0.3, this variable is named
skip_locking.) When this variable is enabled,
external locking is disabled, and vice versa. From MySQL 4.0 on,
external locking is disabled by default. Before MySQL 4.0,
external locking is enabled by default on Linux or when MySQL is
configured to use MIT-pthreads.
Use of external locking can be controlled at server startup by
using the --external-locking or
--skip-external-locking option. (Before MySQL
4.0.3, these options are named --enable-locking
and --skip-locking.)
If you do use external locking option to enable updates to
MyISAM tables from many MySQL processes, you
must ensure that the following conditions are satisfied:
You should not use the query cache for queries that use
tables that are updated by another process.
You should not start the server with the
--delay-key-write=ALL option or use the
DELAY_KEY_WRITE=1 table option for any
shared tables. Otherise, index corruption can occur.
The easiest way to satisfy these conditions is to always use
--external-locking together with
--delay-key-write=OFF and
--query-cache-size=0. (This is not done by
default because in many setups it is useful to have a mixture of
the preceding options.)
MySQL keeps row data and index data in separate files. Many
(almost all) other database systems mix row and index data in
the same file. We believe that the MySQL choice is better for a
very wide range of modern systems.
Another way to store the row data is to keep the information for
each column in a separate area (examples are SDBM and Focus).
This causes a performance hit for every query that accesses more
than one column. Because this degenerates so quickly when more
than one column is accessed, we believe that this model is not
good for general-purpose databases.
The more common case is that the index and data are stored
together (as in Oracle/Sybase, et al). In this case, you find
the row information at the leaf page of the index. The good
thing with this layout is that it, in many cases, depending on
how well the index is cached, saves a disk read. The bad things
with this layout are:
Table scanning is much slower because you have to read
through the indexes to get at the data.
You cannot use only the index table to retrieve data for a
query.
You use more space because you must duplicate indexes from
the nodes (you cannot store the row in the nodes).
Deletes degenerate the table over time (because indexes in
nodes are usually not updated on delete).
It is more difficult to cache only the index data.
7.4.2. Make Your Data as Small as Possible
One of the most basic optimizations is to design your tables to
take as little space on the disk as possible. This can result in
huge improvements because disk reads are faster, and smaller
tables normally require less main memory while their contents
are being actively processed during query execution. Indexing
also is a lesser resource burden if done on smaller columns.
MySQL supports many different storage engines (table types) and
row formats. For each table, you can decide which storage and
indexing method to use. Choosing the proper table format for
your application may give you a big performance gain. See
Chapter 14, Storage Engines.
You can get better performance for a table and minimize storage
space by using the techniques listed here:
Use the most efficient (smallest) data types possible. MySQL
has many specialized types that save disk space and memory.
For example, use the smaller integer types if possible to
get smaller tables. MEDIUMINT is often a
better choice than INT because a
MEDIUMINT column uses 25% less space.
Declare columns to be NOT NULL if
possible. It makes everything faster and you save one bit
per column. If you really need NULL in
your application, you should definitely use it. Just avoid
having it on all columns by default.
For MyISAM tables, if you do not have any
variable-length columns (VARCHAR,
TEXT, or BLOB
columns), a fixed-size row format is used. This is faster
but unfortunately may waste some space. See
Section 14.1.3, “MyISAM Table Storage Formats”. You can hint that
you want to have fixed length rows even if you have
VARCHAR columns with the CREATE
TABLE option ROW_FORMAT=FIXED.
Starting with MySQL 5.0.3, InnoDB tables
use a more compact storage format. In earlier versions of
MySQL, InnoDB rows contain some redundant
information, such as the number of columns and the length of
each column, even for fixed-size columns. By default, tables
are created in the compact format
(ROW_FORMAT=COMPACT). If you wish to
downgrade to older versions of MySQL, you can request the
old format with ROW_FORMAT=REDUNDANT.
The compact InnoDB format also changes
how CHAR columns containing UTF-8 data
are stored. With ROW_FORMAT=REDUNDANT, a
UTF-8 CHAR(N)
occupies 3 × N bytes, given
that the maximum length of a UTF-8 encoded character is
three bytes. Many languages can be written primarily using
single-byte UTF-8 characters, so a fixed storage length
often wastes space. With
ROW_FORMAT=COMPACT format,
InnoDB allocates a variable amount of
storage in the range from N to 3
× N bytes for these columns
by stripping trailing spaces if necessary. The minimum
storage length is kept as N bytes
to facilitate in-place updates in typical cases.
The primary index of a table should be as short as possible.
This makes identification of each row easy and efficient.
Create only the indexes that you really need. Indexes are
good for retrieval but bad when you need to store data
quickly. If you access a table mostly by searching on a
combination of columns, create an index on them. The first
part of the index should be the column most used. If you
always use many columns when selecting
from the table, you should use the column with more
duplicates first to obtain better compression of the index.
If it is very likely that a string column has a unique
prefix on the first number of characters, it's better to
index only this prefix, using MySQL's support for creating
an index on the leftmost part of the column (see
Section 13.1.4, “CREATE INDEX Syntax”). Shorter indexes are faster,
not only because they require less disk space, but because
they also give you more hits in the index cache, and thus
fewer disk seeks. See Section 7.5.2, “Tuning Server Parameters”.
In some circumstances, it can be beneficial to split into
two a table that is scanned very often. This is especially
true if it is a dynamic-format table and it is possible to
use a smaller static format table that can be used to find
the relevant rows when scanning the table.
7.4.3. Column Indexes
All MySQL data types can be indexed. Use of indexes on the
relevant columns is the best way to improve the performance of
SELECT operations.
The maximum number of indexes per table and the maximum index
length is defined per storage engine. See
Chapter 14, Storage Engines. All storage engines support
at least 16 indexes per table and a total index length of at
least 256 bytes. Most storage engines have higher limits.
With
col_name(N)
syntax in an index specification, you can create an index that
uses only the first N characters of a
string column. Indexing only a prefix of column values in this
way can make the index file much smaller. When you index a
BLOB or TEXT column, you
must specify a prefix length for the index.
For example:
CREATE TABLE test (blob_col BLOB, INDEX(blob_col(10)));
Prefixes can be up to 1000 bytes long (767 bytes for
InnoDB tables). Note that prefix limits are
measured in bytes, whereas the prefix length in CREATE
TABLE statements is interpreted as number of
characters. Be sure to take this into account when
specifying a prefix length for a column that uses a multi-byte
character set.
You can also create FULLTEXT indexes. These
are used for full-text searches. Only the
MyISAM storage engine supports
FULLTEXT indexes and only for
CHAR, VARCHAR, and
TEXT columns. Indexing always takes place
over the entire column and column prefix indexing is not
supported. For details, see Section 12.8, “Full-Text Search Functions”.
You can also create indexes on spatial data types. Currently,
only MyISAM supports R-tree indexes on
spatial types. As of MySQL 5.0.16, other storage engines use
B-trees for indexing spatial types (except for
ARCHIVE and NDBCLUSTER,
which do not support spatial type indexing).
The MEMORY storage engine uses
HASH indexes by default, but also supports
BTREE indexes.
7.4.4. Multiple-Column Indexes
MySQL can create composite indexes (that is, indexes on multiple
columns). An index may consist of up to 15 columns. For certain
data types, you can index a prefix of the column (see
Section 7.4.3, “Column Indexes”).
A multiple-column index can be considered a sorted array
containing values that are created by concatenating the values
of the indexed columns.
MySQL uses multiple-column indexes in such a way that queries
are fast when you specify a known quantity for the first column
of the index in a WHERE clause, even if you
do not specify values for the other columns.
Suppose that a table has the following specification:
CREATE TABLE test (
id INT NOT NULL,
last_name CHAR(30) NOT NULL,
first_name CHAR(30) NOT NULL,
PRIMARY KEY (id),
INDEX name (last_name,first_name)
);
The name index is an index over the
last_name and first_name
columns. The index can be used for queries that specify values
in a known range for last_name, or for both
last_name and first_name.
Therefore, the name index is used in the
following queries:
SELECT * FROM test WHERE last_name='Widenius';
SELECT * FROM test
WHERE last_name='Widenius' AND first_name='Michael';
SELECT * FROM test
WHERE last_name='Widenius'
AND (first_name='Michael' OR first_name='Monty');
SELECT * FROM test
WHERE last_name='Widenius'
AND first_name >='M' AND first_name < 'N';
However, the name index is
not used in the following queries:
SELECT * FROM test WHERE first_name='Michael';
SELECT * FROM test
WHERE last_name='Widenius' OR first_name='Michael';
Indexes are used to find rows with specific column values
quickly. Without an index, MySQL must begin with the first row
and then read through the entire table to find the relevant
rows. The larger the table, the more this costs. If the table
has an index for the columns in question, MySQL can quickly
determine the position to seek to in the middle of the data file
without having to look at all the data. If a table has 1,000
rows, this is at least 100 times faster than reading
sequentially. If you need to access most of the rows, it is
faster to read sequentially, because this minimizes disk seeks.
Most MySQL indexes (PRIMARY KEY,
UNIQUE, INDEX, and
FULLTEXT) are stored in B-trees. Exceptions
are that indexes on spatial data types use R-trees, and that
MEMORY tables also support hash indexes.
In general, indexes are used as described in the following
discussion. Characteristics specific to hash indexes (as used in
MEMORY tables) are described at the end of
this section.
MySQL uses indexes for these operations:
To find the rows matching a WHERE clause
quickly.
To eliminate rows from consideration. If there is a choice
between multiple indexes, MySQL normally uses the index that
finds the smallest number of rows.
To retrieve rows from other tables when performing joins.
MySQL can use indexes on columns more efficiently if they
are declared as the same type and size. In this context,
VARCHAR and CHAR are
considered the same if they are declared as the same size.
For example, VARCHAR(10) and
CHAR(10) are the same size, but
VARCHAR(10) and
CHAR(15) are not.
Comparison of dissimilar columns may prevent use of indexes
if values cannot be compared directly without conversion.
Suppose that a numeric column is compared to a string
column. For a given value such as 1 in
the numeric column, it might compare equal to any number of
values in the string column such as '1',
' 1', '00001', or
'01.e1'. This rules out use of any
indexes for the string column.
To find the MIN() or
MAX() value for a specific indexed column
key_col. This is optimized by a
preprocessor that checks whether you are using
WHERE key_part_N =
constant on all key
parts that occur before key_col
in the index. In this case, MySQL does a single key lookup
for each MIN() or
MAX() expression and replaces it with a
constant. If all expressions are replaced with constants,
the query returns at once. For example:
SELECT MIN(key_part2),MAX(key_part2)
FROM tbl_name WHERE key_part1=10;
To sort or group a table if the sorting or grouping is done
on a leftmost prefix of a usable key (for example,
ORDER BY key_part1,
key_part2). If all key
parts are followed by DESC, the key is
read in reverse order. See
Section 7.2.11, “ORDER BY Optimization”.
In some cases, a query can be optimized to retrieve values
without consulting the data rows. If a query uses only
columns from a table that are numeric and that form a
leftmost prefix for some key, the selected values may be
retrieved from the index tree for greater speed:
SELECT key_part3 FROM tbl_name
WHERE key_part1=1
Suppose that you issue the following SELECT
statement:
mysql> SELECT * FROM tbl_name WHERE col1=val1 AND col2=val2;
If a multiple-column index exists on col1 and
col2, the appropriate rows can be fetched
directly. If separate single-column indexes exist on
col1 and col2, the
optimizer tries to find the most restrictive index by deciding
which index finds fewer rows and using that index to fetch the
rows.
If the table has a multiple-column index, any leftmost prefix of
the index can be used by the optimizer to find rows. For
example, if you have a three-column index on (col1,
col2, col3), you have indexed search capabilities on
(col1), (col1, col2), and
(col1, col2, col3).
MySQL cannot use an index if the columns do not form a leftmost
prefix of the index. Suppose that you have the
SELECT statements shown here:
SELECT * FROM tbl_name WHERE col1=val1;
SELECT * FROM tbl_name WHERE col1=val1 AND col2=val2;
SELECT * FROM tbl_name WHERE col2=val2;
SELECT * FROM tbl_name WHERE col2=val2 AND col3=val3;
If an index exists on (col1, col2, col3),
only the first two queries use the index. The third and fourth
queries do involve indexed columns, but
(col2) and (col2, col3)
are not leftmost prefixes of (col1, col2,
col3).
A B-tree index can be used for column comparisons in expressions
that use the =, >,
>=, <,
<=, or BETWEEN
operators. The index also can be used for
LIKE comparisons if the argument to
LIKE is a constant string that does not start
with a wildcard character. For example, the following
SELECT statements use indexes:
SELECT * FROM tbl_name WHERE key_col LIKE 'Patrick%';
SELECT * FROM tbl_name WHERE key_col LIKE 'Pat%_ck%';
In the first statement, only rows with 'Patrick' <=
key_col < 'Patricl' are
considered. In the second statement, only rows with
'Pat' <= key_col <
'Pau' are considered.
The following SELECT statements do not use
indexes:
SELECT * FROM tbl_name WHERE key_col LIKE '%Patrick%';
SELECT * FROM tbl_name WHERE key_col LIKE other_col;
In the first statement, the LIKE value begins
with a wildcard character. In the second statement, the
LIKE value is not a constant.
If you use ... LIKE
'%string%' and
string is longer than three
characters, MySQL uses the Turbo Boyer-Moore
algorithm to initialize the pattern for the string
and then uses this pattern to perform the search more quickly.
A search using col_name IS
NULL employs indexes if
col_name is indexed.
Any index that does not span all AND levels
in the WHERE clause is not used to optimize
the query. In other words, to be able to use an index, a prefix
of the index must be used in every AND group.
The following WHERE clauses use indexes:
... WHERE index_part1=1 AND index_part2=2 AND other_column=3
/* index = 1 OR index = 2 */
... WHERE index=1 OR A=10 AND index=2
/* optimized like "index_part1='hello'" */
... WHERE index_part1='hello' AND index_part3=5
/* Can use index on index1 but not on index2 or index3 */
... WHERE index1=1 AND index2=2 OR index1=3 AND index3=3;
These WHERE clauses do
not use indexes:
/* index_part1 is not used */
... WHERE index_part2=1 AND index_part3=2
/* Index is not used in both parts of the WHERE clause */
... WHERE index=1 OR A=10
/* No index spans all rows */
... WHERE index_part1=1 OR index_part2=10
Sometimes MySQL does not use an index, even if one is available.
One circumstance under which this occurs is when the optimizer
estimates that using the index would require MySQL to access a
very large percentage of the rows in the table. (In this case, a
table scan is likely to be much faster because it requires fewer
seeks.) However, if such a query uses LIMIT
to retrieve only some of the rows, MySQL uses an index anyway,
because it can much more quickly find the few rows to return in
the result.
Hash indexes have somewhat different characteristics from those
just discussed:
They are used only for equality comparisons that use the
= or <=>
operators (but are very fast). They are
not used for comparison operators such as
< that find a range of values.
The optimizer cannot use a hash index to speed up
ORDER BY operations. (This type of index
cannot be used to search for the next entry in order.)
MySQL cannot determine approximately how many rows there are
between two values (this is used by the range optimizer to
decide which index to use). This may affect some queries if
you change a MyISAM table to a
hash-indexed MEMORY table.
Only whole keys can be used to search for a row. (With a
B-tree index, any leftmost prefix of the key can be used to
find rows.)
MySQL Enterprise
Often, it is not possible to predict exactly what indexes will
be required or will be most efficient — actual table
usage is the best indicator. The MySQL Network Monitoring and
Advisory Service provides expert advice on this topic. For
more information see
http://www.mysql.com/products/enterprise/advisors.html.
To minimize disk I/O, the MyISAM storage
engine exploits a strategy that is used by many database
management systems. It employs a cache mechanism to keep the
most frequently accessed table blocks in memory:
For index blocks, a special structure called the
key cache (or key
buffer) is maintained. The structure contains a
number of block buffers where the most-used index blocks are
placed.
For data blocks, MySQL uses no special cache. Instead it
relies on the native operating system filesystem cache.
This section first describes the basic operation of the
MyISAM key cache. Then it discusses features
that improve key cache performance and that enable you to better
control cache operation:
Access to the key cache no longer is serialized among
threads. Multiple threads can access the cache concurrently.
You can set up multiple key caches and assign table indexes
to specific caches.
To control the size of the key cache, use the
key_buffer_size system variable. If this
variable is set equal to zero, no key cache is used. The key
cache also is not used if the key_buffer_size
value is too small to allocate the minimal number of block
buffers (8).
When the key cache is not operational, index files are accessed
using only the native filesystem buffering provided by the
operating system. (In other words, table index blocks are
accessed using the same strategy as that employed for table data
blocks.)
An index block is a contiguous unit of access to the
MyISAM index files. Usually the size of an
index block is equal to the size of nodes of the index B-tree.
(Indexes are represented on disk using a B-tree data structure.
Nodes at the bottom of the tree are leaf nodes. Nodes above the
leaf nodes are non-leaf nodes.)
All block buffers in a key cache structure are the same size.
This size can be equal to, greater than, or less than the size
of a table index block. Usually one these two values is a
multiple of the other.
When data from any table index block must be accessed, the
server first checks whether it is available in some block buffer
of the key cache. If it is, the server accesses data in the key
cache rather than on disk. That is, it reads from the cache or
writes into it rather than reading from or writing to disk.
Otherwise, the server chooses a cache block buffer containing a
different table index block (or blocks) and replaces the data
there by a copy of required table index block. As soon as the
new index block is in the cache, the index data can be accessed.
If it happens that a block selected for replacement has been
modified, the block is considered “dirty.” In this
case, prior to being replaced, its contents are flushed to the
table index from which it came.
Usually the server follows an LRU (Least Recently
Used) strategy: When choosing a block for
replacement, it selects the least recently used index block. To
make this choice easier, the key cache module maintains a
special queue (LRU chain) of all used
blocks. When a block is accessed, it is placed at the end of the
queue. When blocks need to be replaced, blocks at the beginning
of the queue are the least recently used and become the first
candidates for eviction.
7.4.6.1. Shared Key Cache Access
Threads can access key cache buffers simultaneously, subject
to the following conditions:
A buffer that is not being updated can be accessed by
multiple threads.
A buffer that is being updated causes threads that need to
use it to wait until the update is complete.
Multiple threads can initiate requests that result in
cache block replacements, as long as they do not interfere
with each other (that is, as long as they need different
index blocks, and thus cause different cache blocks to be
replaced).
Shared access to the key cache enables the server to improve
throughput significantly.
7.4.6.2. Multiple Key Caches
Shared access to the key cache improves performance but does
not eliminate contention among threads entirely. They still
compete for control structures that manage access to the key
cache buffers. To reduce key cache access contention further,
MySQL also provides multiple key caches. This feature enables
you to assign different table indexes to different key caches.
Where there are multiple key caches, the server must know
which cache to use when processing queries for a given
MyISAM table. By default, all
MyISAM table indexes are cached in the
default key cache. To assign table indexes to a specific key
cache, use the CACHE INDEX statement (see
Section 13.5.5.1, “CACHE INDEX Syntax”). For example, the following
statement assigns indexes from the tables
t1, t2, and
t3 to the key cache named
hot_cache:
mysql> CACHE INDEX t1, t2, t3 IN hot_cache;
+---------+--------------------+----------+----------+
| Table | Op | Msg_type | Msg_text |
+---------+--------------------+----------+----------+
| test.t1 | assign_to_keycache | status | OK |
| test.t2 | assign_to_keycache | status | OK |
| test.t3 | assign_to_keycache | status | OK |
+---------+--------------------+----------+----------+
The key cache referred to in a CACHE INDEX
statement can be created by setting its size with a
SET GLOBAL parameter setting statement or
by using server startup options. For example:
mysql> SET GLOBAL keycache1.key_buffer_size=128*1024;
To destroy a key cache, set its size to zero:
mysql> SET GLOBAL keycache1.key_buffer_size=0;
Note that you cannot destroy the default key cache. Any
attempt to do this will be ignored:
mysql> SET GLOBAL key_buffer_size = 0;
mysql> SHOW VARIABLES LIKE 'key_buffer_size';
+-----------------+---------+
| Variable_name | Value |
+-----------------+---------+
| key_buffer_size | 8384512 |
+-----------------+---------+
Key cache variables are structured system variables that have
a name and components. For
keycache1.key_buffer_size,
keycache1 is the cache variable name and
key_buffer_size is the cache component. See
Section 5.2.4.1, “Structured System Variables”, for a
description of the syntax used for referring to structured key
cache system variables.
By default, table indexes are assigned to the main (default)
key cache created at the server startup. When a key cache is
destroyed, all indexes assigned to it are reassigned to the
default key cache.
For a busy server, we recommend a strategy that uses three key
caches:
A “hot” key cache that takes up 20% of the
space allocated for all key caches. Use this for tables
that are heavily used for searches but that are not
updated.
A “cold” key cache that takes up 20% of the
space allocated for all key caches. Use this cache for
medium-sized, intensively modified tables, such as
temporary tables.
A “warm” key cache that takes up 60% of the
key cache space. Employ this as the default key cache, to
be used by default for all other tables.
One reason the use of three key caches is beneficial is that
access to one key cache structure does not block access to the
others. Statements that access tables assigned to one cache do
not compete with statements that access tables assigned to
another cache. Performance gains occur for other reasons as
well:
The hot cache is used only for retrieval queries, so its
contents are never modified. Consequently, whenever an
index block needs to be pulled in from disk, the contents
of the cache block chosen for replacement need not be
flushed first.
For an index assigned to the hot cache, if there are no
queries requiring an index scan, there is a high
probability that the index blocks corresponding to
non-leaf nodes of the index B-tree remain in the cache.
An update operation most frequently executed for temporary
tables is performed much faster when the updated node is
in the cache and need not be read in from disk first. If
the size of the indexes of the temporary tables are
comparable with the size of cold key cache, the
probability is very high that the updated node is in the
cache.
CACHE INDEX sets up an association between
a table and a key cache, but the association is lost each time
the server restarts. If you want the association to take
effect each time the server starts, one way to accomplish this
is to use an option file: Include variable settings that
configure your key caches, and an init-file
option that names a file containing CACHE
INDEX statements to be executed. For example:
MySQL Enterprise
For advice on how best to configure your
my.cnf/my.ini option file subscribe to
MySQL Network Monitoring and Advisory Service.
Recommendations are based on actual table usage. For more
information see
http://www.mysql.com/products/enterprise/advisors.html.
The statements in mysqld_init.sql are
executed each time the server starts. The file should contain
one SQL statement per line. The following example assigns
several tables each to hot_cache and
cold_cache:
CACHE INDEX db1.t1, db1.t2, db2.t3 IN hot_cache
CACHE INDEX db1.t4, db2.t5, db2.t6 IN cold_cache
7.4.6.3. Midpoint Insertion Strategy
By default, the key cache management system uses the LRU
strategy for choosing key cache blocks to be evicted, but it
also supports a more sophisticated method called the
midpoint insertion strategy.
When using the midpoint insertion strategy, the LRU chain is
divided into two parts: a hot sub-chain and a warm sub-chain.
The division point between two parts is not fixed, but the key
cache management system takes care that the warm part is not
“too short,” always containing at least
key_cache_division_limit percent of the key
cache blocks. key_cache_division_limit is a
component of structured key cache variables, so its value is a
parameter that can be set per cache.
When an index block is read from a table into the key cache,
it is placed at the end of the warm sub-chain. After a certain
number of hits (accesses of the block), it is promoted to the
hot sub-chain. At present, the number of hits required to
promote a block (3) is the same for all index blocks.
A block promoted into the hot sub-chain is placed at the end
of the chain. The block then circulates within this sub-chain.
If the block stays at the beginning of the sub-chain for a
long enough time, it is demoted to the warm chain. This time
is determined by the value of the
key_cache_age_threshold component of the
key cache.
The threshold value prescribes that, for a key cache
containing N blocks, the block at
the beginning of the hot sub-chain not accessed within the
last N ×
key_cache_age_threshold / 100 hits is to be moved to
the beginning of the warm sub-chain. It then becomes the first
candidate for eviction, because blocks for replacement always
are taken from the beginning of the warm sub-chain.
The midpoint insertion strategy allows you to keep more-valued
blocks always in the cache. If you prefer to use the plain LRU
strategy, leave the
key_cache_division_limit value set to its
default of 100.
The midpoint insertion strategy helps to improve performance
when execution of a query that requires an index scan
effectively pushes out of the cache all the index blocks
corresponding to valuable high-level B-tree nodes. To avoid
this, you must use a midpoint insertion strategy with the
key_cache_division_limit set to much less
than 100. Then valuable frequently hit nodes are preserved in
the hot sub-chain during an index scan operation as well.
7.4.6.4. Index Preloading
If there are enough blocks in a key cache to hold blocks of an
entire index, or at least the blocks corresponding to its
non-leaf nodes, it makes sense to preload the key cache with
index blocks before starting to use it. Preloading allows you
to put the table index blocks into a key cache buffer in the
most efficient way: by reading the index blocks from disk
sequentially.
Without preloading, the blocks are still placed into the key
cache as needed by queries. Although the blocks will stay in
the cache, because there are enough buffers for all of them,
they are fetched from disk in random order, and not
sequentially.
To preload an index into a cache, use the LOAD INDEX
INTO CACHE statement. For example, the following
statement preloads nodes (index blocks) of indexes of the
tables t1 and t2:
mysql> LOAD INDEX INTO CACHE t1, t2 IGNORE LEAVES;
+---------+--------------+----------+----------+
| Table | Op | Msg_type | Msg_text |
+---------+--------------+----------+----------+
| test.t1 | preload_keys | status | OK |
| test.t2 | preload_keys | status | OK |
+---------+--------------+----------+----------+
The IGNORE LEAVES modifier causes only
blocks for the non-leaf nodes of the index to be preloaded.
Thus, the statement shown preloads all index blocks from
t1, but only blocks for the non-leaf nodes
from t2.
If an index has been assigned to a key cache using a
CACHE INDEX statement, preloading places
index blocks into that cache. Otherwise, the index is loaded
into the default key cache.
7.4.6.5. Key Cache Block Size
It is possible to specify the size of the block buffers for an
individual key cache using the
key_cache_block_size variable. This permits
tuning of the performance of I/O operations for index files.
The best performance for I/O operations is achieved when the
size of read buffers is equal to the size of the native
operating system I/O buffers. But setting the size of key
nodes equal to the size of the I/O buffer does not always
ensure the best overall performance. When reading the big leaf
nodes, the server pulls in a lot of unnecessary data,
effectively preventing reading other leaf nodes.
Currently, you cannot control the size of the index blocks in
a table. This size is set by the server when the
.MYI index file is created, depending on
the size of the keys in the indexes present in the table
definition. In most cases, it is set equal to the I/O buffer
size.
7.4.6.6. Restructuring a Key Cache
A key cache can be restructured at any time by updating its
parameter values. For example:
mysql> SET GLOBAL cold_cache.key_buffer_size=4*1024*1024;
If you assign to either the key_buffer_size
or key_cache_block_size key cache component
a value that differs from the component's current value, the
server destroys the cache's old structure and creates a new
one based on the new values. If the cache contains any dirty
blocks, the server saves them to disk before destroying and
re-creating the cache. Restructuring does not occur if you
change other key cache parameters.
When restructuring a key cache, the server first flushes the
contents of any dirty buffers to disk. After that, the cache
contents become unavailable. However, restructuring does not
block queries that need to use indexes assigned to the cache.
Instead, the server directly accesses the table indexes using
native filesystem caching. Filesystem caching is not as
efficient as using a key cache, so although queries execute, a
slowdown can be anticipated. After the cache has been
restructured, it becomes available again for caching indexes
assigned to it, and the use of filesystem caching for the
indexes ceases.
7.4.7. MyISAM Index Statistics Collection
Storage engines collect statistics about tables for use by the
optimizer. Table statistics are based on value groups, where a
value group is a set of rows with the same key prefix value. For
optimizer purposes, an important statistic is the average value
group size.
MySQL uses the average value group size in the following ways:
To estimate how may rows must be read for each
ref access
To estimate how many row a partial join will produce; that
is, the number of rows that an operation of this form will
produce:
(...) JOIN tbl_name ON tbl_name.key = expr
As the average value group size for an index increases, the
index is less useful for those two purposes because the average
number of rows per lookup increases: For the index to be good
for optimization purposes, it is best that each index value
target a small number of rows in the table. When a given index
value yields a large number of rows, the index is less useful
and MySQL is less likely to use it.
The average value group size is related to table cardinality,
which is the number of value groups. The SHOW
INDEX statement displays a cardinality value based on
N/S, where
N is the number of rows in the table
and S is the average value group
size. That ratio yields an approximate number of value groups in
the table.
For a join based on the <=> comparison
operator, NULL is not treated differently
from any other value: NULL <=> NULL,
just as N <=>
N for any other
N.
However, for a join based on the = operator,
NULL is different from
non-NULL values:
expr1 =
expr2 is not true when
expr1 or
expr2 (or both) are
NULL. This affects ref
accesses for comparisons of the form
tbl_name.key =
expr: MySQL will not access
the table if the current value of
expr is NULL,
because the comparison cannot be true.
For = comparisons, it does not matter how
many NULL values are in the table. For
optimization purposes, the relevant value is the average size of
the non-NULL value groups. However, MySQL
does not currently allow that average size to be collected or
used.
For MyISAM tables, you have some control over
collection of table statistics by means of the
myisam_stats_method system variable. This
variable has two possible values, which differ as follows:
When myisam_stats_method is
nulls_equal, all NULL
values are treated as identical (that is, they all form a
single value group).
If the NULL value group size is much
higher than the average non-NULL value
group size, this method skews the average value group size
upward. This makes index appear to the optimizer to be less
useful than it really is for joins that look for
non-NULL values. Consequently, the
nulls_equal method may cause the
optimizer not to use the index for ref
accesses when it should.
When myisam_stats_method is
nulls_unequal, NULL
values are not considered the same. Instead, each
NULL value forms a separate value group
of size 1.
If you have many NULL values, this method
skews the average value group size downward. If the average
non-NULL value group size is large,
counting NULL values each as a group of
size 1 causes the optimizer to overestimate the value of the
index for joins that look for non-NULL
values. Consequently, the nulls_unequal
method may cause the optimizer to use this index for
ref lookups when other methods may be
better.
If you tend to use many joins that use
<=> rather than =,
NULL values are not special in comparisons
and one NULL is equal to another. In this
case, nulls_equal is the appropriate
statistics method.
The myisam_stats_method system variable has
global and session values. Setting the global value affects
MyISAM statistics collection for all
MyISAM tables. Setting the session value
affects statistics collection only for the current client
connection. This means that you can force a table's statistics
to be regenerated with a given method without affecting other
clients by setting the session value of
myisam_stats_method.
To regenerate table statistics, you can use any of the following
methods:
Set myisam_stats_method, and then issue a
CHECK TABLE statement
Change the table to cause its statistics to go out of date
(for example, insert a row and then delete it), and then set
myisam_stats_method and issue an
ANALYZE TABLE statement
Some caveats regarding the use of
myisam_stats_method:
You can force table statistics to be collected explicitly,
as just described. However, MySQL may also collect
statistics automatically. For example, if during the course
of executing statements for a table, some of those
statements modify the table, MySQL may collect statistics.
(This may occur for bulk inserts or deletes, or some
ALTER TABLE statements, for example.) If
this happens, the statistics are collected using whatever
value myisam_stats_method has at the
time. Thus, if you collect statistics using one method, but
myisam_stats_method is set to the other
method when a table's statistics are collected automatically
later, the other method will be used.
There is no way to tell which method was used to generate
statistics for a given MyISAM table.
myisam_stats_method applies only to
MyISAM tables. Other storage engines have
only one method for collecting table statistics. Usually it
is closer to the nulls_equal method.
7.4.8. How MySQL Opens and Closes Tables
When you execute a mysqladmin status command,
you should see something like this:
The Open tables value of 12 can be somewhat
puzzling if you have only six tables.
MySQL is multi-threaded, so there may be many clients issuing
queries for a given table simultaneously. To minimize the
problem with multiple client threads having different states on
the same table, the table is opened independently by each
concurrent thread. This uses additional memory but normally
increases performance. With MyISAM tables,
one extra file descriptor is required for the data file for each
client that has the table open. (By contrast, the index file
descriptor is shared between all threads.)
The table_cache,
max_connections, and
max_tmp_tables system variables affect the
maximum number of files the server keeps open. If you increase
one or more of these values, you may run up against a limit
imposed by your operating system on the per-process number of
open file descriptors. Many operating systems allow you to
increase the open-files limit, although the method varies widely
from system to system. Consult your operating system
documentation to determine whether it is possible to increase
the limit and how to do so.
table_cache is related to
max_connections. For example, for 200
concurrent running connections, you should have a table cache
size of at least 200 ×
N, where
N is the maximum number of tables per
join in any of the queries which you execute. You must also
reserve some extra file descriptors for temporary tables and
files.
Make sure that your operating system can handle the number of
open file descriptors implied by the
table_cache setting. If
table_cache is set too high, MySQL may run
out of file descriptors and refuse connections, fail to perform
queries, and be very unreliable. You also have to take into
account that the MyISAM storage engine needs
two file descriptors for each unique open table. You can
increase the number of file descriptors available to MySQL using
the --open-files-limit startup option to
mysqld. See
Section B.1.2.17, “'File' Not Found and
Similar Errors”.
The cache of open tables is kept at a level of
table_cache entries. The default value is 64;
this can be changed with the --table_cache
option to mysqld. Note that MySQL may
temporarily open more tables than this to execute queries.
MySQL Enterprise
Performance may suffer if table_cache is
set too low. For expert advice on the optimum value for this
variable, subscribe to the MySQL Network Monitoring and
Advisory Service. For more information see
http://www.mysql.com/products/enterprise/advisors.html.
MySQL closes an unused table and removes it from the table cache
under the following circumstances:
When the cache is full and a thread tries to open a table
that is not in the cache.
When the cache contains more than
table_cache entries and a table in the
cache is no longer being used by any threads.
When a table flushing operation occurs. This happens when
someone issues a FLUSH TABLES statement
or executes a mysqladmin flush-tables or
mysqladmin refresh command.
When the table cache fills up, the server uses the following
procedure to locate a cache entry to use:
Tables that are not currently in use are released, beginning
with the table least recently used.
If a new table needs to be opened, but the cache is full and
no tables can be released, the cache is temporarily extended
as necessary.
When the cache is in a temporarily extended state and a table
goes from a used to unused state, the table is closed and
released from the cache.
A table is opened for each concurrent access. This means the
table needs to be opened twice if two threads access the same
table or if a thread accesses the table twice in the same query
(for example, by joining the table to itself). Each concurrent
open requires an entry in the table cache. The first open of any
MyISAM table takes two file descriptors: one
for the data file and one for the index file. Each additional
use of the table takes only one file descriptor for the data
file. The index file descriptor is shared among all threads.
If you are opening a table with the HANDLER
tbl_name OPEN statement, a
dedicated table object is allocated for the thread. This table
object is not shared by other threads and is not closed until
the thread calls HANDLER
tbl_name CLOSE or the
thread terminates. When this happens, the table is put back in
the table cache (if the cache is not full). See
Section 13.2.3, “HANDLER Syntax”.
You can determine whether your table cache is too small by
checking the mysqld status variable
Opened_tables:
mysql> SHOW GLOBAL STATUS LIKE 'Opened_tables';
+---------------+-------+
| Variable_name | Value |
+---------------+-------+
| Opened_tables | 2741 |
+---------------+-------+
7.4.9. Drawbacks to Creating Many Tables in the Same Database
If you have many MyISAM tables in the same
database directory, open, close, and create operations are slow.
If you execute SELECT statements on many
different tables, there is a little overhead when the table
cache is full, because for every table that has to be opened,
another must be closed. You can reduce this overhead by making
the table cache larger.
7.5.1. System Factors and Startup Parameter Tuning
We start with system-level factors, because some of these
decisions must be made very early to achieve large performance
gains. In other cases, a quick look at this section may suffice.
However, it is always nice to have a sense of how much can be
gained by changing factors that apply at this level.
The operating system to use is very important. To get the best
use of multiple-CPU machines, you should use Solaris (because
its threads implementation works well) or Linux (because the 2.4
and later kernels have good SMP support). Note that older Linux
kernels have a 2GB filesize limit by default. If you have such a
kernel and a need for files larger than 2GB, you should get the
Large File Support (LFS) patch for the ext2 filesystem. Other
filesystems such as ReiserFS and XFS do not have this 2GB
limitation.
Before using MySQL in production, we advise you to test it on
your intended platform.
Other tips:
If you have enough RAM, you could remove all swap devices.
Some operating systems use a swap device in some contexts
even if you have free memory.
Avoid external locking. Since MySQL 4.0, the default has
been for external locking to be disabled on all systems. The
--external-locking and
--skip-external-locking options explicitly
enable and disable external locking.
Note that disabling external locking does not affect MySQL's
functionality as long as you run only one server. Just
remember to take down the server (or lock and flush the
relevant tables) before you run
myisamchk. On some systems it is
mandatory to disable external locking because it does not
work, anyway.
The only case in which you cannot disable external locking
is when you run multiple MySQL servers
(not clients) on the same data, or if you run
myisamchk to check (not repair) a table
without telling the server to flush and lock the tables
first. Note that using multiple MySQL servers to access the
same data concurrently is generally not
recommended, except when using MySQL Cluster.
The LOCK TABLES and UNLOCK
TABLES statements use internal locking, so you can
use them even if external locking is disabled.
7.5.2. Tuning Server Parameters
You can determine the default buffer sizes used by the
mysqld server using this command:
shell> mysqld --verbose --help
This command produces a list of all mysqld
options and configurable system variables. The output includes
the default variable values and looks something like this:
MySQL uses algorithms that are very scalable, so you can usually
run with very little memory. However, normally you get better
performance by giving MySQL more memory.
When tuning a MySQL server, the two most important variables to
configure are key_buffer_size and
table_cache. You should first feel confident
that you have these set appropriately before trying to change
any other variables.
The following examples indicate some typical variable values for
different runtime configurations.
If you have at least 256MB of memory and many tables and
want maximum performance with a moderate number of clients,
you should use something like this:
If there are very many simultaneous connections, swapping
problems may occur unless mysqld has been
configured to use very little memory for each connection.
mysqld performs better if you have enough
memory for all connections.
With little memory and lots of connections, use something
like this:
If you are performing GROUP BY or
ORDER BY operations on tables that are much
larger than your available memory, you should increase the value
of read_rnd_buffer_size to speed up the
reading of rows following sorting operations.
If you specify an option on the command line for
mysqld or mysqld_safe, it
remains in effect only for that invocation of the server. To use
the option every time the server runs, put it in an option file.
To see the effects of a parameter change, do something like
this:
The variable values are listed near the end of the output. Make
sure that the --verbose and
--help options are last. Otherwise, the effect
of any options listed after them on the command line are not
reflected in the output.
The task of the query optimizer is to find an optimal plan for
executing an SQL query. Because the difference in performance
between “good” and “bad” plans can be
orders of magnitude (that is, seconds versus hours or even
days), most query optimizers, including that of MySQL, perform a
more or less exhaustive search for an optimal plan among all
possible query evaluation plans. For join queries, the number of
possible plans investigated by the MySQL optimizer grows
exponentially with the number of tables referenced in a query.
For small numbers of tables (typically less than 7–10)
this is not a problem. However, when larger queries are
submitted, the time spent in query optimization may easily
become the major bottleneck in the server's performance.
MySQL 5.0.1 introduces a more flexible method for query
optimization that allows the user to control how exhaustive the
optimizer is in its search for an optimal query evaluation plan.
The general idea is that the fewer plans that are investigated
by the optimizer, the less time it spends in compiling a query.
On the other hand, because the optimizer skips some plans, it
may miss finding an optimal plan.
The behavior of the optimizer with respect to the number of
plans it evaluates can be controlled via two system variables:
The optimizer_prune_level variable tells
the optimizer to skip certain plans based on estimates of
the number of rows accessed for each table. Our experience
shows that this kind of “educated guess” rarely
misses optimal plans, and may dramatically reduce query
compilation times. That is why this option is on
(optimizer_prune_level=1) by default.
However, if you believe that the optimizer missed a better
query plan, this option can be switched off
(optimizer_prune_level=0) with the risk
that query compilation may take much longer. Note that, even
with the use of this heuristic, the optimizer still explores
a roughly exponential number of plans.
The optimizer_search_depth variable tells
how far into the “future” of each incomplete
plan the optimizer should look to evaluate whether it should
be expanded further. Smaller values of
optimizer_search_depth may result in
orders of magnitude smaller query compilation times. For
example, queries with 12, 13, or more tables may easily
require hours and even days to compile if
optimizer_search_depth is close to the
number of tables in the query. At the same time, if compiled
with optimizer_search_depth equal to 3 or
4, the optimizer may compile in less than a minute for the
same query. If you are unsure of what a reasonable value is
for optimizer_search_depth, this variable
can be set to 0 to tell the optimizer to determine the value
automatically.
7.5.4. How Compiling and Linking Affects the Speed of MySQL
Most of the following tests were performed on Linux with the
MySQL benchmarks, but they should give some indication for other
operating systems and workloads.
You obtain the fastest executables when you link with
-static.
On Linux, it is best to compile the server with
pgcc and -O3. You need about
200MB memory to compile sql_yacc.cc with
these options, because gcc or
pgcc needs a great deal of memory to make all
functions inline. You should also set CXX=gcc
when configuring MySQL to avoid inclusion of the
libstdc++ library, which is not needed. Note
that with some versions of pgcc, the
resulting binary runs only on true Pentium processors, even if
you use the compiler option indicating that you want the
resulting code to work on all x586-type processors (such as
AMD).
By using a better compiler and compilation options, you can
obtain a 10–30% speed increase in applications. This is
particularly important if you compile the MySQL server yourself.
When we tested both the Cygnus CodeFusion and Fujitsu compilers,
neither was sufficiently bug-free to allow MySQL to be compiled
with optimizations enabled.
The standard MySQL binary distributions are compiled with
support for all character sets. When you compile MySQL yourself,
you should include support only for the character sets that you
are going to use. This is controlled by the
--with-charset option to
configure.
Here is a list of some measurements that we have made:
If you use pgcc and compile everything
with -O6, the mysqld
server is 1% faster than with gcc 2.95.2.
If you link dynamically (without -static),
the result is 13% slower on Linux. Note that you still can
use a dynamically linked MySQL library for your client
applications. It is the server that is most critical for
performance.
For a connection from a client to a server running on the
same host, if you connect using TCP/IP rather than a Unix
socket file, performance is 7.5% slower. (On Unix, if you
connect to the hostname localhost, MySQL
uses a socket file by default.)
For TCP/IP connections from a client to a server, connecting
to a remote server on another host is 8–11% slower
than connecting to a server on the same host, even for
connections over 100Mb/s Ethernet.
When running our benchmark tests using secure connections
(all data encrypted with internal SSL support) performance
was 55% slower than with unencrypted connections.
If you compile with --with-debug=full, most
queries are 20% slower. Some queries may take substantially
longer; for example, the MySQL benchmarks run 35% slower. If
you use --with-debug (without
=full), the speed decrease is only 15%.
For a version of mysqld that has been
compiled with --with-debug=full, you can
disable memory checking at runtime by starting it with the
--skip-safemalloc option. The execution
speed should then be close to that obtained when configuring
with --with-debug.
On a Sun UltraSPARC-IIe, a server compiled with Forte 5.0 is
4% faster than one compiled with gcc 3.2.
On a Sun UltraSPARC-IIe, a server compiled with Forte 5.0 is
4% faster in 32-bit mode than in 64-bit mode.
Compiling with gcc 2.95.2 for UltraSPARC
with the -mcpu=v8 -Wa,-xarch=v8plusa
options gives 4% more performance.
On Solaris 2.5.1, MIT-pthreads is 8–12% slower than
Solaris native threads on a single processor. With greater
loads or more CPUs, the difference should be larger.
Compiling on Linux-x86 using gcc without
frame pointers (-fomit-frame-pointer or
-fomit-frame-pointer -ffixed-ebp) makes
mysqld 1–4% faster.
Binary MySQL distributions for Linux that are provided by MySQL
AB used to be compiled with pgcc. We had to
go back to regular gcc due to a bug in
pgcc that would generate binaries that do not
run on AMD. We will continue using gcc until
that bug is resolved. In the meantime, if you have a non-AMD
machine, you can build a faster binary by compiling with
pgcc. The standard MySQL Linux binary is
linked statically to make it faster and more portable.
7.5.5. How MySQL Uses Memory
The following list indicates some of the ways that the
mysqld server uses memory. Where applicable,
the name of the system variable relevant to the memory use is
given:
The key buffer is shared by all threads; its size is
determined by the key_buffer_size
variable. Other buffers used by the server are allocated as
needed. See Section 7.5.2, “Tuning Server Parameters”.
Each connection uses some thread-specific space. The
following list indicates these and which variables control
their size:
A stack (default 192KB, variable
thread_stack)
A connection buffer (variable
net_buffer_length)
A result buffer (variable
net_buffer_length)
The connection buffer and result buffer both begin with a
size given by net_buffer_length but are
dynamically enlarged up to
max_allowed_packet bytes as needed. The
result buffer shrinks to
net_buffer_length after each SQL
statement. While a statement is running, a copy of the
current statement string is also allocated.
All threads share the same base memory.
When a thread is no longer needed, the memory allocated to
it is released and returned to the system unless the thread
goes back into the thread cache. In that case, the memory
remains allocated.
Only compressed MyISAM tables are memory
mapped. This is because the 32-bit memory space of 4GB is
not large enough for most big tables. When systems with a
64-bit address space become more common, we may add general
support for memory mapping.
Each request that performs a sequential scan of a table
allocates a read buffer (variable
read_buffer_size).
When reading rows in an arbitrary sequence (for example,
following a sort), a random-read
buffer (variable
read_rnd_buffer_size) may be allocated in
order to avoid disk seeks.
All joins are executed in a single pass, and most joins can
be done without even using a temporary table. Most temporary
tables are memory-based hash tables. Temporary tables with a
large row length (calculated as the sum of all column
lengths) or that contain BLOB columns are
stored on disk.
If an internal heap table exceeds the size of
tmp_table_size, MySQL handles this
automatically by changing the in-memory heap table to a
disk-based MyISAM table as necessary. You
can also increase the temporary table size by setting the
tmp_table_size option to
mysqld, or by setting the SQL option
SQL_BIG_TABLES in the client program. See
Section 13.5.3, “SET Syntax”.
MySQL Enterprise
Subscribers to the MySQL Network Monitoring and Advisory
Service are alerted when temporary tables exceed
tmp_table_size. Advisors make
recommendations for the optimum value of
tmp_table_size based on actual table
usage. For more information about the MySQL Network
Monitoring and Advisory Service please see
http://www.mysql.com/products/enterprise/advisors.html.
Almost all parsing and calculating is done in a local memory
store. No memory overhead is needed for small items, so the
normal slow memory allocation and freeing is avoided. Memory
is allocated only for unexpectedly large strings. This is
done with malloc() and
free().
For each MyISAM table that is opened, the
index file is opened once; the data file is opened once for
each concurrently running thread. For each concurrent
thread, a table structure, column structures for each
column, and a buffer of size 3 ×
N are allocated (where
N is the maximum row length, not
counting BLOB columns). A
BLOB column requires five to eight bytes
plus the length of the BLOB data. The
MyISAM storage engine maintains one extra
row buffer for internal use.
For each table having BLOB columns, a
buffer is enlarged dynamically to read in larger
BLOB values. If you scan a table, a
buffer as large as the largest BLOB value
is allocated.
Handler structures for all in-use tables are saved in a
cache and managed as a FIFO. By default, the cache has 64
entries. If a table has been used by two running threads at
the same time, the cache contains two entries for the table.
See Section 7.4.8, “How MySQL Opens and Closes Tables”.
A FLUSH TABLES statement or
mysqladmin flush-tables command closes
all tables that are not in use at once and marks all in-use
tables to be closed when the currently executing thread
finishes. This effectively frees most in-use memory.
FLUSH TABLES does not return until all
tables have been closed.
ps and other system status programs may
report that mysqld uses a lot of memory. This
may be caused by thread stacks on different memory addresses.
For example, the Solaris version of ps counts
the unused memory between stacks as used memory. You can verify
this by checking available swap with swap -s.
We test mysqld with several memory-leakage
detectors (both commercial and Open Source), so there should be
no memory leaks.
7.5.6. How MySQL Uses Internal Temporary Tables
In some cases, the server creates internal temporary tables
while processing queries. A temporary table can be held in
memory and processed by the MEMORY storage
engine, or stored on disk and processed by the
MyISAM storage engine. Temporary tables can
be created under conditions such as these:
If there is an ORDER BY clause and a
different GROUP BY clause, or if the
ORDER BY or GROUP BY
contains columns from tables other than the first table in
the join queue, a temporary table is created.
If you use the SQL_SMALL_RESULT option,
MySQL uses an in-memory temporary table.
DISTINCT combined with ORDER
BY may require a temporary table.
You can tell whether a query requires a temporary table by using
EXPLAIN and checking the
Extra column to see whether it says
Using temporary. See
Section 7.2.1, “Optimizing Queries with EXPLAIN”.
Some conditions prevent the use of a MEMORY
temporary table, in which case the server uses a
MyISAM table instead:
Presence of a TEXT or
BLOB column in the table
Presence of any column in a GROUP BY or
DISTINCT clause larger than 512 bytes
A temporary table that is created initially as a
MEMORY table might be converted to a
MyISAM table and stored on disk if it becomes
too large. The max_heap_table_size system
variable determines how large MEMORY tables
are allowed to grow. It applies to all MEMORY
tables, including those created with CREATE
TABLE. However, for internal MEMORY
tables, the actual maximum size is determined by
max_heap_table_size in combination with
tmp_table_size: Whichever value is smaller is
the one that applies. If the size of an internal
MEMORY table exceeds the limit, MySQL
automatically converts it to an on-disk
MyISAM table.
7.5.7. How MySQL Uses DNS
When a new client connects to mysqld,
mysqld spawns a new thread to handle the
request. This thread first checks whether the hostname is in the
hostname cache. If not, the thread attempts to resolve the
hostname:
If the operating system supports the thread-safe
gethostbyaddr_r() and
gethostbyname_r() calls, the thread uses
them to perform hostname resolution.
If the operating system does not support the thread-safe
calls, the thread locks a mutex and calls
gethostbyaddr() and
gethostbyname() instead. In this case, no
other thread can resolve hostnames that are not in the
hostname cache until the first thread unlocks the mutex.
You can disable DNS hostname lookups by starting
mysqld with the
--skip-name-resolve option. However, in this
case, you can use only IP numbers in the MySQL grant tables.
If you have a very slow DNS and many hosts, you can get more
performance by either disabling DNS lookups with
--skip-name-resolve or by increasing the
HOST_CACHE_SIZE define (default value: 128)
and recompiling mysqld.
You can disable the hostname cache by starting the server with
the --skip-host-cache option. To clear the
hostname cache, issue a FLUSH HOSTS statement
or execute the mysqladmin flush-hosts
command.
To disallow TCP/IP connections entirely, start
mysqld with the
--skip-networking option.
Disk seeks are a huge performance bottleneck. This problem
becomes more apparent when the amount of data starts to grow
so large that effective caching becomes impossible. For large
databases where you access data more or less randomly, you can
be sure that you need at least one disk seek to read and a
couple of disk seeks to write things. To minimize this
problem, use disks with low seek times.
Increase the number of available disk spindles (and thereby
reduce the seek overhead) by either symlinking files to
different disks or striping the disks:
Using symbolic links
This means that, for MyISAM tables, you
symlink the index file and data files from their usual
location in the data directory to another disk (that may
also be striped). This makes both the seek and read times
better, assuming that the disk is not used for other
purposes as well. See Section 7.6.1, “Using Symbolic Links”.
Striping
Striping means that you have many disks and put the first
block on the first disk, the second block on the second
disk, and the N-th block on the
(N MOD
number_of_disks)
disk, and so on. This means if your normal data size is
less than the stripe size (or perfectly aligned), you get
much better performance. Striping is very dependent on the
operating system and the stripe size, so benchmark your
application with different stripe sizes. See
Section 7.1.5, “Using Your Own Benchmarks”.
The speed difference for striping is
very dependent on the parameters.
Depending on how you set the striping parameters and
number of disks, you may get differences measured in
orders of magnitude. You have to choose to optimize for
random or sequential access.
For reliability, you may want to use RAID 0+1 (striping plus
mirroring), but in this case, you need 2 ×
N drives to hold
N drives of data. This is probably
the best option if you have the money for it. However, you may
also have to invest in some volume-management software to
handle it efficiently.
A good option is to vary the RAID level according to how
critical a type of data is. For example, store semi-important
data that can be regenerated on a RAID 0 disk, but store
really important data such as host information and logs on a
RAID 0+1 or RAID N disk. RAID
N can be a problem if you have many
writes, due to the time required to update the parity bits.
On Linux, you can get much more performance by using
hdparm to configure your disk's interface.
(Up to 100% under load is not uncommon.) The following
hdparm options should be quite good for
MySQL, and probably for many other applications:
hdparm -m 16 -d 1
Note that performance and reliability when using this command
depend on your hardware, so we strongly suggest that you test
your system thoroughly after using hdparm.
Please consult the hdparm manual page for
more information. If hdparm is not used
wisely, filesystem corruption may result, so back up
everything before experimenting!
You can also set the parameters for the filesystem that the
database uses:
If you do not need to know when files were last accessed
(which is not really useful on a database server), you can
mount your filesystems with the -o noatime
option. That skips updates to the last access time in inodes
on the filesystem, which avoids some disk seeks.
On many operating systems, you can set a filesystem to be
updated asynchronously by mounting it with the -o
async option. If your computer is reasonably stable,
this should give you more performance without sacrificing too
much reliability. (This flag is on by default on Linux.)
You can move tables and databases from the database directory to
other locations and replace them with symbolic links to the new
locations. You might want to do this, for example, to move a
database to a file system with more free space or increase the
speed of your system by spreading your tables to different disk.
The recommended way to do this is simply to symlink databases to
a different disk. Symlink tables only as a last resort.
7.6.1.1. Using Symbolic Links for Databases on Unix
On Unix, the way to symlink a database is first to create a
directory on some disk where you have free space and then to
create a symlink to it from the MySQL data directory.
MySQL does not support linking one directory to multiple
databases. Replacing a database directory with a symbolic link
works as long as you do not make a symbolic link between
databases. Suppose that you have a database
db1 under the MySQL data directory, and
then make a symlink db2 that points to
db1:
shell> cd /path/to/datadir
shell> ln -s db1 db2
The result is that, or any table tbl_a in
db1, there also appears to be a table
tbl_a in db2. If one
client updates db1.tbl_a and another client
updates db2.tbl_a, problems are likely to
occur.
However, if you really need to do this, it is possible by
altering the source file
mysys/my_symlink.c, in which you should
look for the following statement:
if (!(MyFlags & MY_RESOLVE_LINK) ||
(!lstat(filename,&stat_buff) && S_ISLNK(stat_buff.st_mode)))
Change the statement to this:
if (1)
7.6.1.2. Using Symbolic Links for Tables on Unix
You should not symlink tables on systems that do not have a
fully operational realpath() call. (Linux
and Solaris support realpath()). You can
check whether your system supports symbolic links by issuing a
SHOW VARIABLES LIKE 'have_symlink'
statement.
Symlinks are fully supported only for
MyISAM tables. For files used by tables for
other storage engines, you may get strange problems if you try
to use symbolic links.
The handling of symbolic links for MyISAM
tables works as follows:
In the data directory, you always have the table format
(.frm) file, the data
(.MYD) file, and the index
(.MYI) file. The data file and index
file can be moved elsewhere and replaced in the data
directory by symlinks. The format file cannot.
You can symlink the data file and the index file
independently to different directories.
You can instruct a running MySQL server to perform the
symlinking by using the DATA DIRECTORY
and INDEX DIRECTORY options to
CREATE TABLE. See
Section 13.1.5, “CREATE TABLE Syntax”. Alternatively, symlinking
can be accomplished manually from the command line using
ln -s if mysqld is
not running.
myisamchk does not replace a symlink
with the data file or index file. It works directly on the
file to which the symlink points. Any temporary files are
created in the directory where the data file or index file
is located. The same is true for the ALTER
TABLE, OPTIMIZE TABLE, and
REPAIR TABLE statements.
Note: When you drop a
table that is using symlinks, both the symlink
and the file to which the symlink points are
dropped. This is an extremely good reason why
you should not run
mysqld as the system
root or allow system users to have
write access to MySQL database directories.
If you rename a table with ALTER TABLE ...
RENAME and you do not move the table to another
database, the symlinks in the database directory are
renamed to the new names and the data file and index file
are renamed accordingly.
If you use ALTER TABLE ... RENAME to
move a table to another database, the table is moved to
the other database directory. The old symlinks and the
files to which they pointed are deleted. In other words,
the new table is not symlinked.
If you are not using symlinks, you should use the
--skip-symbolic-links option to
mysqld to ensure that no one can use
mysqld to drop or rename a file outside
of the data directory.
Table symlink operations that are not yet supported:
ALTER TABLE ignores the DATA
DIRECTORY and INDEX DIRECTORY
table options.
BACKUP TABLE and RESTORE
TABLE do not respect symbolic links.
The .frm file must
never be a symbolic link (as
indicated previously, only the data and index files can be
symbolic links). Attempting to do this (for example, to
make synonyms) produces incorrect results. Suppose that
you have a database db1 under the MySQL
data directory, a table tbl1 in this
database, and in the db1 directory you
make a symlink tbl2 that points to
tbl1:
Problems result if one thread reads
db1.tbl1 and another thread updates
db1.tbl2:
The query cache is “fooled” (it has no
way of knowing that tbl1 has not
been updated, so it returns outdated results).
ALTER statements on
tbl2 fail.
7.6.1.3. Using Symbolic Links for Databases on Windows
Symbolic links are enabled by default for all Windows servers.
This enables you to put a database directory on a different
disk by setting up a symbolic link to it. This is similar to
the way that database symbolic links work on Unix, although
the procedure for setting up the link is different. If you do
not need symbolic links, you can disable them using the
--skip-symbolic-links option.
On Windows, create a symbolic link to a MySQL database by
creating a file in the data directory that contains the path
to the destination directory. The file should be named
db_name.sym,
where db_name is the database name.
Suppose that the MySQL data directory is
C:\mysql\data and you want to have
database foo located at
D:\data\foo. Set up a symlink using this
procedure
Make sure that the D:\data\foo
directory exists by creating it if necessary. If you
already have a database directory named
foo in the data directory, you should
move it to D:\data. Otherwise, the
symbolic link will be ineffective. To avoid problems, make
sure that the server is not running when you move the
database directory.
Create a text file
C:\mysql\data\foo.sym that contains
the pathname D:\data\foo\.
After this, all tables created in the database
foo are created in
D:\data\foo. Note that the
symbolic link is not used if a directory with the same name as
the database exists in the MySQL data directory.