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7.2.1. Optimizing Queries with EXPLAIN

EXPLAIN tbl_name

Or:

EXPLAIN [EXTENDED | PARTITIONS] 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.

  • EXPLAIN PARTITIONS is available beginning with MySQL 5.1.5. It is useful only when examining queries involving partitioned tables. For details, see Section 17.3.4, “Obtaining Information About Partitions”.

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 Section 13.3.1, “DESCRIBE Syntax”, and Section 13.5.4.3, “SHOW COLUMNS Syntax”.

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.

Note: You cannot use the EXTENDED and PARTITIONS keywords together in the same EXPLAIN statement.

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)

    DEPENDENT typically signifies the use of a correlated subquery. See Section 13.2.8.7, “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 Section 7.2.7, “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 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. 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 Section 13.2.7, “SELECT Syntax”.

    For MyISAM and BDB tables, running ANALYZE TABLE helps the optimizer choose better indexes. For MyISAM tables, myisamchk --analyze does the same. See Section 13.5.2.1, “ANALYZE TABLE Syntax”, and Section 5.9.4, “Table Maintenance and Crash Recovery”.

  • 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 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.

    • 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 7.2.12, “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 Section 7.2.6, “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 Section 7.2.13, “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;
        

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:

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.


 
 
  Published under the terms of the GNU General Public License Design by Interspire