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8.8.2 EXPLAIN Output Format
The EXPLAIN
statement provides information about how MySQL executes statements. EXPLAIN
works with SELECT
, DELETE
, INSERT
, REPLACE
, and UPDATE
statements.
EXPLAIN
returns a row of information for each table used in the SELECT
statement. It lists the tables in the output in the order that MySQL would read them while processing the statement. MySQL resolves all joins using a nested-loop 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.
EXPLAIN
output includes partition information. Also, for SELECT
statements, EXPLAIN
generates extended information that can be displayed with SHOW WARNINGS
following the EXPLAIN
(see Section 8.8.3, “Extended EXPLAIN Output Format”).
In older MySQL releases, partition and extended information was produced using EXPLAIN PARTITIONS
and EXPLAIN EXTENDED
. Those syntaxes are still recognized for backward compatibility but partition and extended output is now enabled by default, so the PARTITIONS
and EXTENDED
keywords are superfluous and deprecated. Their use results in a warning, and they will be removed from EXPLAIN
syntax in a future MySQL release.
You cannot use the deprecated PARTITIONS
and EXTENDED
keywords together in the same EXPLAIN
statement. In addition, neither of these keywords can be used together with the FORMAT
option.
MySQL Workbench has a Visual Explain capability that provides a visual representation of EXPLAIN
output. See Tutorial: Using Explain to Improve Query Performance .
This section describes the output columns produced by EXPLAIN
. Later sections provide additional information about the type
and Extra
columns.
Each output row from EXPLAIN
provides information about one table. Each row contains the values summarized in Table 8.1, “EXPLAIN Output Columns”, and described in more detail following the table. Column names are shown in the table's first column; the second column provides the equivalent property name shown in the output when FORMAT=JSON
is used.
Table 8.1 EXPLAIN Output Columns
Column | JSON Name | Meaning |
---|---|---|
id |
select_id |
The SELECT identifier |
select_type |
None | The SELECT type |
table |
table_name |
The table for the output row |
partitions |
partitions |
The matching partitions |
type |
access_type |
The join type |
possible_keys |
possible_keys |
The possible indexes to choose |
key |
key |
The index actually chosen |
key_len |
key_length |
The length of the chosen key |
ref |
ref |
The columns compared to the index |
rows |
rows |
Estimate of rows to be examined |
filtered |
filtered |
Percentage of rows filtered by table condition |
Extra |
None | Additional information |
JSON properties which are NULL
are not displayed in JSON-formatted EXPLAIN
output.
The
SELECT
identifier. This is the sequential number of theSELECT
within the query. The value can beNULL
if the row refers to the union result of other rows. In this case, thetable
column shows a value like<union
to indicate that the row refers to the union of the rows withM
,N
>id
values ofM
andN
.The type of
SELECT
, which can be any of those shown in the following table. A JSON-formattedEXPLAIN
exposes theSELECT
type as a property of aquery_block
, unless it isSIMPLE
orPRIMARY
. The JSON names (where applicable) are also shown in the table.select_type
ValueJSON Name Meaning SIMPLE
None Simple SELECT
(not usingUNION
or subqueries)PRIMARY
None Outermost SELECT
UNION
None Second or later SELECT
statement in aUNION
DEPENDENT UNION
dependent
(true
)Second or later SELECT
statement in aUNION
, dependent on outer queryUNION RESULT
union_result
Result of a UNION
.SUBQUERY
None First SELECT
in subqueryDEPENDENT SUBQUERY
dependent
(true
)First SELECT
in subquery, dependent on outer queryDERIVED
None Derived table MATERIALIZED
materialized_from_subquery
Materialized subquery UNCACHEABLE SUBQUERY
cacheable
(false
)A subquery for which the result cannot be cached and must be re-evaluated for each row of the outer query UNCACHEABLE UNION
cacheable
(false
)The second or later select in a UNION
that belongs to an uncacheable subquery (seeUNCACHEABLE SUBQUERY
)DEPENDENT
typically signifies the use of a correlated subquery. See Section 13.2.10.7, “Correlated Subqueries”.DEPENDENT SUBQUERY
evaluation differs fromUNCACHEABLE SUBQUERY
evaluation. ForDEPENDENT SUBQUERY
, the subquery is re-evaluated only once for each set of different values of the variables from its outer context. ForUNCACHEABLE SUBQUERY
, the subquery is re-evaluated for each row of the outer context.Cacheability of subqueries differs from caching of query results in the query cache (which is described in Section 8.10.3.1, “How the Query Cache Operates”). Subquery caching occurs during query execution, whereas the query cache is used to store results only after query execution finishes.
When you specify
FORMAT=JSON
withEXPLAIN
, the output has no single property directly equivalent toselect_type
; thequery_block
property corresponds to a givenSELECT
. Properties equivalent to most of theSELECT
subquery types just shown are available (an example beingmaterialized_from_subquery
forMATERIALIZED
), and are displayed when appropriate. There are no JSON equivalents forSIMPLE
orPRIMARY
.The
select_type
value for non-SELECT
statements displays the statement type for affected tables. For example,select_type
isDELETE
forDELETE
statements.The name of the table to which the row of output refers. This can also be one of the following values:
<union
: The row refers to the union of the rows withM
,N
>id
values ofM
andN
.<derived
: The row refers to the derived table result for the row with anN
>id
value ofN
. A derived table may result, for example, from a subquery in theFROM
clause.<subquery
: The row refers to the result of a materialized subquery for the row with anN
>id
value ofN
. See Section 8.2.2.2, “Optimizing Subqueries with Materialization”.
partitions
(JSON name:partitions
)The partitions from which records would be matched by the query. The value is
NULL
for nonpartitioned tables. See Section 22.3.5, “Obtaining Information About Partitions”.The join type. For descriptions of the different types, see
EXPLAIN
Join Types.possible_keys
(JSON name:possible_keys
)The
possible_keys
column indicates the indexes from which MySQL can choose 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 fromEXPLAIN
. That means that some of the keys inpossible_keys
might not be usable in practice with the generated table order.If this column is
NULL
(or undefined in JSON-formatted output), there are no relevant indexes. In this case, you may be able to improve the performance of your query by examining theWHERE
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 withEXPLAIN
again. See Section 13.1.8, “ALTER TABLE Statement”.To see what indexes a table has, use
SHOW INDEX FROM
.tbl_name
The
key
column indicates the key (index) that MySQL actually decided to use. If MySQL decides to use one of thepossible_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 thepossible_keys
value. This can happen if none of thepossible_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 becauseInnoDB
stores the primary key value with each secondary index. Ifkey
isNULL
, 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, useFORCE INDEX
,USE INDEX
, orIGNORE INDEX
in your query. See Section 8.9.4, “Index Hints”.For
MyISAM
tables, runningANALYZE TABLE
helps the optimizer choose better indexes. ForMyISAM
tables, myisamchk --analyze does the same. See Section 13.7.2.1, “ANALYZE TABLE Statement”, and Section 7.6, “MyISAM Table Maintenance and Crash Recovery”.key_len
(JSON name:key_length
)The
key_len
column indicates the length of the key that MySQL decided to use. The value ofkey_len
enables you to determine how many parts of a multiple-part key MySQL actually uses. If thekey
column saysNULL
, thekey_len
column also saysNULL
.Due to the key storage format, the key length is one greater for a column that can be
NULL
than for aNOT NULL
column.The
ref
column shows which columns or constants are compared to the index named in thekey
column to select rows from the table.If the value is
func
, the value used is the result of some function. To see which function, useSHOW WARNINGS
followingEXPLAIN
to see the extendedEXPLAIN
output. The function might actually be an operator such as an arithmetic operator.The
rows
column indicates the number of rows MySQL believes it must examine to execute the query.For
InnoDB
tables, this number is an estimate, and may not always be exact.filtered
(JSON name:filtered
)The
filtered
column indicates an estimated percentage of table rows that will be filtered by the table condition. The maximum value is 100, which means no filtering of rows occurred. Values decreasing from 100 indicate increasing amounts of filtering.rows
shows the estimated number of rows examined androws
×filtered
shows the number of rows that will be joined with the following table. For example, ifrows
is 1000 andfiltered
is 50.00 (50%), the number of rows to be joined with the following table is 1000 × 50% = 500.This column contains additional information about how MySQL resolves the query. For descriptions of the different values, see
EXPLAIN
Extra Information.There is no single JSON property corresponding to the
Extra
column; however, values that can occur in this column are exposed as JSON properties, or as the text of themessage
property.
The type
column of EXPLAIN
output describes how tables are joined. In JSON-formatted output, these are found as values of the access_type
property. The following list describes the join types, ordered from the best type to the worst:
The table has only one row (= system table). This is a special case of the
const
join type.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 aPRIMARY KEY
orUNIQUE
index to constant values. In the following queries,tbl_name
can be used as aconst
table:SELECT * FROM tbl_name WHERE primary_key=1; SELECT * FROM tbl_name WHERE primary_key_part1=1 AND primary_key_part2=2;
One row is read from this table for each combination of rows from the previous tables. Other than the
system
andconst
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 aPRIMARY KEY
orUNIQUE NOT NULL
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 aneq_ref
join to processref_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;
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 aPRIMARY KEY
orUNIQUE
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 aref
join to processref_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;
The join is performed using a
FULLTEXT
index.This join type is like
ref
, but with the addition that MySQL does an extra search for rows that containNULL
values. This join type optimization is used most often in resolving subqueries. In the following examples, MySQL can use aref_or_null
join to processref_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, andkey_len
contains a list of the longest key parts for the indexes used. For more information, see Section 8.2.1.3, “Index Merge Optimization”.This type replaces
eq_ref
for someIN
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.This join type is similar to
unique_subquery
. It replacesIN
subqueries, but it works for nonunique indexes in subqueries of the following form:value IN (SELECT key_column FROM single_table WHERE some_expr)
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. Thekey_len
contains the longest key part that was used. Theref
column isNULL
for this type.range
can be used when a key column is compared to a constant using any of the=
,<>
,>
,>=
,<
,<=
,IS NULL
,<=>
,BETWEEN
,LIKE
, orIN()
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);
The
index
join type is the same asALL
, except that the index tree is scanned. This occurs two ways:If the index is a covering index for the queries and can be used to satisfy all data required from the table, only the index tree is scanned. In this case, the
Extra
column saysUsing index
. An index-only scan usually is faster thanALL
because the size of the index usually is smaller than the table data.A full table scan is performed using reads from the index to look up data rows in index order.
Uses index
does not appear in theExtra
column.
MySQL can use this join type when the query uses only columns that are part of a single index.
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 avoidALL
by adding indexes that enable row retrieval from the table based on constant values or column values from earlier tables.
The Extra
column of EXPLAIN
output contains additional information about how MySQL resolves the query. The following list explains the values that can appear in this column. Each item also indicates for JSON-formatted output which property displays the Extra
value. For some of these, there is a specific property. The others display as the text of the message
property.
If you want to make your queries as fast as possible, look out for Extra
column values of Using filesort
and Using temporary
, or, in JSON-formatted EXPLAIN
output, for using_filesort
and using_temporary_table
properties equal to true
.
Child of '
(JSON:table
' pushed join@1message
text)This table is referenced as the child of
table
in a join that can be pushed down to the NDB kernel. Applies only in NDB Cluster, when pushed-down joins are enabled. See the description of thendb_join_pushdown
server system variable for more information and examples.const row not found
(JSON property:const_row_not_found
)For a query such as
SELECT ... FROM
, the table was empty.tbl_name
Deleting all rows
(JSON property:message
)For
DELETE
, some storage engines (such asMyISAM
) support a handler method that removes all table rows in a simple and fast way. ThisExtra
value is displayed if the engine uses this optimization.Distinct
(JSON property: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.
FirstMatch(
(JSON property:tbl_name
)first_match
)The semijoin FirstMatch join shortcutting strategy is used for
tbl_name
.Full scan on NULL key
(JSON property:message
)This occurs for subquery optimization as a fallback strategy when the optimizer cannot use an index-lookup access method.
Impossible HAVING
(JSON property:message
)The
HAVING
clause is always false and cannot select any rows.Impossible WHERE
(JSON property:message
)The
WHERE
clause is always false and cannot select any rows.Impossible WHERE noticed after reading const tables
(JSON property:message
)MySQL has read all
const
(andsystem
) tables and notice that theWHERE
clause is always false.LooseScan(
(JSON property:m
..n
)message
)The semijoin LooseScan strategy is used.
m
andn
are key part numbers.No matching min/max row
(JSON property:message
)No row satisfies the condition for a query such as
SELECT MIN(...) FROM ... WHERE
.condition
no matching row in const table
(JSON property:message
)For a query with a join, there was an empty table or a table with no rows satisfying a unique index condition.
No matching rows after partition pruning
(JSON property:message
)For
DELETE
orUPDATE
, the optimizer found nothing to delete or update after partition pruning. It is similar in meaning toImpossible WHERE
forSELECT
statements.No tables used
(JSON property:message
)The query has no
FROM
clause, or has aFROM DUAL
clause.For
INSERT
orREPLACE
statements,EXPLAIN
displays this value when there is noSELECT
part. For example, it appears forEXPLAIN INSERT INTO t VALUES(10)
because that is equivalent toEXPLAIN INSERT INTO t SELECT 10 FROM DUAL
.Not exists
(JSON property:message
)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 theLEFT 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 asNOT NULL
. In this case, MySQL scanst1
and looks up the rows int2
using the values oft1.id
. If MySQL finds a matching row int2
, it knows thatt2.id
can never beNULL
, and does not scan through the rest of the rows int2
that have the sameid
value. In other words, for each row int1
, MySQL needs to do only a single lookup int2
, regardless of how many rows actually match int2
.Plan isn't ready yet
(JSON property: none)This value occurs with
EXPLAIN FOR CONNECTION
when the optimizer has not finished creating the execution plan for the statement executing in the named connection. If execution plan output comprises multiple lines, any or all of them could have thisExtra
value, depending on the progress of the optimizer in determining the full execution plan.Range checked for each record (index map:
(JSON property:N
)message
)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
orindex_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 8.2.1.2, “Range Optimization”, and Section 8.2.1.3, “Index Merge Optimization”, with the exception that all column values for the preceding table are known and considered to be constants.Indexes are numbered beginning with 1, in the same order as shown by
SHOW INDEX
for the table. The index map valueN
is a bitmask value that indicates which indexes are candidates. For example, a value of0x19
(binary 11001) means that indexes 1, 4, and 5 will be considered.Scanned
(JSON property:N
databasesmessage
)This indicates how many directory scans the server performs when processing a query for
INFORMATION_SCHEMA
tables, as described in Section 8.2.3, “Optimizing INFORMATION_SCHEMA Queries”. The value ofN
can be 0, 1, orall
.Select tables optimized away
(JSON property:message
)The optimizer determined 1) that at most one row should be returned, and 2) that to produce this row, a deterministic set of rows must be read. When the rows to be read can be read during the optimization phase (for example, by reading index rows), there is no need to read any tables during query execution.
The first condition is fulfilled when the query is implicitly grouped (contains an aggregate function but no
GROUP BY
clause). The second condition is fulfilled when one row lookup is performed per index used. The number of indexes read determines the number of rows to read.Consider the following implicitly grouped query:
SELECT MIN(c1), MIN(c2) FROM t1;
Suppose that
MIN(c1)
can be retrieved by reading one index row andMIN(c2)
can be retrieved by reading one row from a different index. That is, for each columnc1
andc2
, there exists an index where the column is the first column of the index. In this case, one row is returned, produced by reading two deterministic rows.This
Extra
value does not occur if the rows to read are not deterministic. Consider this query:SELECT MIN(c2) FROM t1 WHERE c1 <= 10;
Suppose that
(c1, c2)
is a covering index. Using this index, all rows withc1 <= 10
must be scanned to find the minimumc2
value. By contrast, consider this query:SELECT MIN(c2) FROM t1 WHERE c1 = 10;
In this case, the first index row with
c1 = 10
contains the minimumc2
value. Only one row must be read to produce the returned row.For storage engines that maintain an exact row count per table (such as
MyISAM
, but notInnoDB
), thisExtra
value can occur forCOUNT(*)
queries for which theWHERE
clause is missing or always true and there is noGROUP BY
clause. (This is an instance of an implicitly grouped query where the storage engine influences whether a deterministic number of rows can be read.)Skip_open_table
,Open_frm_only
,Open_full_table
(JSON property:message
)These values indicate file-opening optimizations that apply to queries for
INFORMATION_SCHEMA
tables, as described in Section 8.2.3, “Optimizing INFORMATION_SCHEMA Queries”.Skip_open_table
: Table files do not need to be opened. The information has already become available within the query by scanning the database directory.Open_frm_only
: Only the table's.frm
file need be opened.Open_full_table
: The unoptimized information lookup. The.frm
,.MYD
, and.MYI
files must be opened.
Start temporary
,End temporary
(JSON property:message
)This indicates temporary table use for the semijoin Duplicate Weedout strategy.
unique row not found
(JSON property:message
)For a query such as
SELECT ... FROM
, no rows satisfy the condition for atbl_name
UNIQUE
index orPRIMARY KEY
on the table.Using filesort
(JSON property: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 8.2.1.14, “ORDER BY Optimization”.Using index
(JSON property: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.
For
InnoDB
tables that have a user-defined clustered index, that index can be used even whenUsing index
is absent from theExtra
column. This is the case iftype
isindex
andkey
isPRIMARY
.Using index condition
(JSON property:using_index_condition
)Tables are read by accessing index tuples and testing them first to determine whether to read full table rows. In this way, index information is used to defer (“push down”) reading full table rows unless it is necessary. See Section 8.2.1.5, “Index Condition Pushdown Optimization”.
Using index for group-by
(JSON property:using_index_for_group_by
)Similar to the
Using index
table access method,Using index for group-by
indicates that MySQL found an index that can be used to retrieve all columns of aGROUP BY
orDISTINCT
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 8.2.1.15, “GROUP BY Optimization”.Using join buffer (Block Nested Loop)
,Using join buffer (Batched Key Access)
(JSON property:using_join_buffer
)Tables from earlier joins are read in portions into the join buffer, and then their rows are used from the buffer to perform the join with the current table.
(Block Nested Loop)
indicates use of the Block Nested-Loop algorithm and(Batched Key Access)
indicates use of the Batched Key Access algorithm. That is, the keys from the table on the preceding line of theEXPLAIN
output are buffered, and the matching rows are fetched in batches from the table represented by the line in whichUsing join buffer
appears.In JSON-formatted output, the value of
using_join_buffer
is always either one ofBlock Nested Loop
orBatched Key Access
.For more information about these algorithms, see Block Nested-Loop Join Algorithm, and Batched Key Access Joins.
Using MRR
(JSON property:message
)Tables are read using the Multi-Range Read optimization strategy. See Section 8.2.1.10, “Multi-Range Read Optimization”.
Using sort_union(...)
,Using union(...)
,Using intersect(...)
(JSON property:message
)These indicate the particular algorithm showing how index scans are merged for the
index_merge
join type. See Section 8.2.1.3, “Index Merge Optimization”.Using temporary
(JSON property:using_temporary_table
)To resolve the query, MySQL needs to create a temporary table to hold the result. This typically happens if the query contains
GROUP BY
andORDER BY
clauses that list columns differently.Using where
(JSON property:attached_condition
)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 theExtra
value is notUsing where
and the table join type isALL
orindex
.Using where
has no direct counterpart in JSON-formatted output; theattached_condition
property contains anyWHERE
condition used.Using where with pushed condition
(JSON property:message
)This item applies to
NDB
tables only. It means that NDB Cluster is using the Condition Pushdown optimization to improve the efficiency of a direct comparison between a nonindexed column and a constant. In such cases, the condition is “pushed down” to the cluster's data nodes and is evaluated on all data nodes simultaneously. This eliminates the need to send nonmatching 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. For more information, see Section 8.2.1.4, “Engine Condition Pushdown Optimization”.Zero limit
(JSON property:message
)The query had a
LIMIT 0
clause and cannot select any rows.
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 5.1.1, “Configuring the Server”.
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 (index map: 0x23)
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 (index map: 0x1)
et_1 ALL PRIMARY NULL NULL NULL 74
Range checked for each record (index map: 0x1)
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
The rows
column in the output from EXPLAIN
is an educated guess from the MySQL join optimizer. 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. (However, STRAIGHT_JOIN
may prevent indexes from being used because it disables semijoin transformations. See Section 8.2.2.1, “Optimizing Subqueries, Derived Tables, and View References with Semijoin Transformations”.)
It is possible in some cases to execute statements that modify data when EXPLAIN SELECT
is used with a subquery; for more information, see Section 13.2.10.8, “Derived Tables”.