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6.2.1 EXPLAIN Syntax (Get Information About a SELECT) - MySQL

While optimization is possible with limited knowledge of your system or application, the more you know about your system, the better your optimization will be. This article, the first of two parts, covers some of the different points you will need to know for optimizing MySQL. It is excerpted from chapter six of the book MySQL Administrator's Guide, by MySQL AB (Sams, 2004; ISBN: 0672326345)

TABLE OF CONTENTS:
  1. MySQL Optimization, part 1
  2. 6.1.4 The MySQL Benchmark Suite
  3. 6.2.1 EXPLAIN Syntax (Get Information About a SELECT)
  4. 6.2.2 Estimating Query Performance
  5. 6.2.6 How MySQL Optimizes IS NULL
  6. 6.2.9 How MySQL Optimizes ORDER BY
  7. 6.2.12 Speed of INSERT Queries
  8. 6.2.15 Other Optimization Tips
By: Sams Publishing
Rating: starstarstarstarstar / 58
April 13, 2005

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EXPLAIN Syntax (Get Information About a SELECT)

EXPLAIN tbl_name

Or:

EXPLAIN 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 will execute a SELECT statement:

  • The EXPLAIN tbl_name syntax is synonymous with DESCRIBE tbl_name or SHOW COLUMNS FROM tbl_name.

  • When you precede a SELECT statement with the keyword EXPLAIN, MySQL explains how it would process the SELECT, providing information about how tables are joined and in which order.

This section provides information about the second use of EXPLAIN.

With the help of EXPLAIN, you can see when you must add indexes to tables to get a faster SELECT that uses indexes to find records.

You should frequently run ANALYZE TABLE to update table statistics such as cardinality of keys, which can affect the choices the optimizer makes.

You can also see 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.

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, then finds a matching row in the second table, then in the third table, and so on. When all tables are processed, it 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.

In MySQL version 4.1, the EXPLAIN output format was changed to work better with constructs such as UNION statements, subqueries, and derived tables. Most notable is the addition of two new columns: id and select_type. You will not see these columns when using servers older than MySQL 4.1.

Each output row from EXPLAIN provides information about one table, and each row consists of 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 the following:

    • 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 subquery

    • SUBQUERY

      First SELECT in subquery

    • DEPENDENT SUBQUERY

      First SELECT in subquery, dependent on outer subquery

    • DERIVED

      Derived table SELECT (subquery in FROM clause)

  • 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 will be 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 with 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 will be read from this table for each combination of rows from the previous tables. Other than the 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 will be 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 = 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 will do an extra search for rows that contain NULL values. This join type optimization is new for MySQL 4.1.1 and is mostly used when 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 6.2.6, "How MySQL Optimizes IS NULL."

    • index_merge

      This join type indicates that the Index Merge optimization is used. In this case, the key column 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 6.2.5, "How MySQL Optimizes OR Clauses."

    • 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 will be retrieved, using an index to select the rows. The key column indicates which index is used. The key_len contains the longest key part that was used. The ref column will be NULL for this type.

      range can be used for 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 will be 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 could 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 see 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.

    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.

    For MyISAM and BDB tables, running ANALYZE TABLE will help the optimizer choose better indexes. For MyISAM tables, myisamchk --analyze will do the same. See Section 4.6.2, "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 allows you to determine how many parts of a multiple-part key MySQL will actually use.

  • ref

    The ref column shows which columns or constants are used with the key 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 will resolve the query. Here is an explanation of the different text strings that can appear in this column:

    • Distinct

      MySQL will stop 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 will 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 will scan t1 and look 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 will 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: #)

      MySQL found no good index to use. Instead, for each row combination in the preceding tables, it will do a check to determine which index to use (if any), and use it to retrieve the rows from the table. This is not very fast, but is faster than performing a join with no index at all.

    • Using filesort

      MySQL will need to 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.

    • 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 will need 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 will be 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.

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 product also is used to determine which multiple-table SELECT statements to execute. See Section 6.5.2, "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 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

    Column 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 will take 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 the same. (For ISAM tables, indexes may not be used at all unless the columns are declared the same.) In this context, VARCHAR and CHAR are the same unless they are declared as different lengths. Because tt.ActualPC is declared as CHAR(10) and et.EMPLOYID is declared as CHAR(15), 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 now less by a factor of 74. This version is executed 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);

Now 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

This is almost as good as it can get.

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;

Now 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. If not, you may 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.

This article is excerpted from MySQL Administrator's Guide, by MySQL AB (editor) (Sams, 2004; ISBN 0672326345). Check it out at your favorite bookstore today. Buy this book now.



 
 
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