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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.
This section describes the second use of `EXPLAIN' for obtaining query
execution plan information. For a description of the `DESCRIBE' and
`SHOW COLUMNS' statements, see describe, and
show-columns.
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 analyze-table.
`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 First `SELECT' in subquery, dependent on outer
SUBQUERY' query
`DERIVED' Derived table `SELECT' (subquery in `FROM' clause)
`DEPENDENT' typically signifies the use of a correlated subquery.
See correlated-subqueries.
* `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;
See is-null-optimization.
* `index_merge'
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 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 alter-table.
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. The key is `NULL' if no index was chosen. 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 select.
For `MyISAM' and `BDB' tables, running `ANALYZE TABLE' helps the
optimizer choose better indexes. For `MyISAM' tables, `myisamchk
--analyze' does the same. See analyze-table, and
table-maintenance.
* `key_len'
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. Here is an explanation of the values that can
appear in this column:
* `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.
* `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
range-optimization, and index-merge-optimization,
with the exception that all column values for the preceding
table are known and considered to be constants.
* `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 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'.
If you want to make your queries as fast as possible, you
should look out for `Extra' values of `Using filesort' and
`Using temporary'.
* `Using sort_union(...)', `Using union(...)', `Using
intersect(...)'
These indicate how index scans are merged for the
`index_merge' join type. See
index-merge-optimization, for more information.
* `Using index for group-by'
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 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 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;
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
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:
mysql> ALTER TABLE tt MODIFY AssignedPC VARCHAR(15),
-> MODIFY ClientID VARCHAR(15);
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.
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