7.24 EXPLAIN syntax (Get information about a SELECT)

    EXPLAIN tbl_name
or  EXPLAIN SELECT select_options

EXPLAIN tbl_name is a synonym for 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.

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 the records. You can also see if the optimizer joins the tables in an optimal order. To force the optimizer to use a specific join order for a SELECT statement, add a STRAIGHT_JOIN clause.

For non-simple joins, EXPLAIN returns a row of information for each table used in the SELECT statement. The tables are listed in the order they would be read. 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.

Output from EXPLAIN includes the following columns:

table
The table to which the row of output refers.
type
The join type. Information about the various types is given below.
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. That means that some of the keys in possible_keys may not be useable in practice with the generated table order. If this column is empty, 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 if 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 7.8 ALTER TABLE syntax. To see what indexes a table has, use SHOW INDEX FROM tbl_name.
key
The key column indicates the key that MySQL actually decided to use. The key is NULL if no index was chosen. If MySQL chooses the wrong index, you can probably force MySQL to use another index by using myisamchk --analyze, See section 15.1.1 myisamchk invocation syntax, or by using USE INDEX/IGNORE INDEX. See section 7.15 JOIN syntax.
key_len
The key_len column indicates the length of the key that MySQL decided to use. The length is NULL if the key is NULL. Note that this tell us how many parts of a multi-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 of how MySQL will resolve the query. Here follows an explanation of the different text strings that can be found in this column:
Not exists
MySQL was able to do a LEFT JOIN optimisation on the query and will not examine more rows in this table for a row combination after it founds one rows that matches the LEFT JOIN criteria.
range checked for each record (index map: #)
MySQL didn't find a real good index to use. It will instead for each row combination in the preceding tables do a check which index to use (if any) use this index to retrieve the rows from the table. This isn't very fast but is of course faster than having to do a join without an index.
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 + pointer to the row for all rows that match the WHERE. Then the keys are sorted. Finally 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 actually row. This can be done when all the used columns for the table are part of the same index.
Using temporary
To be able to resolve the query MySQL will need to create a temporary table to hold the result. This typically happens if you do an ORDER BY on a different column set than you did an GROUP BY on.
where used
A WHERE clause will be used to restrict which rows will be matched against the next table or sent to the client. If you don't have this information and the the table is of type ALL or index you may have something wrong in your query (if you don't intend to fetch/examine all rows from the table).
If you want to get your queries as fast as possible, you should look out for Using filesort and Using temporary.

The different join types are listed below, ordered from best to worst type:

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 as they are read only once!
eq_ref
One row will be read from this table for each combination of rows from the previous tables. This the best possible join type, other than the const types. It is used when all parts of an index are used by the join and the index is UNIQUE or a PRIMARY KEY.
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 UNIQUE or a PRIMARY KEY (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 join type is good.
range
Only rows that are in a given range will be retrieved, using an index to select the rows. The ref column indicates which index is used.
index
This is the same as ALL, except that only the index tree is scanned. This is usually faster than ALL, as the index file is usually smaller than the data file.
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. You normally can avoid ALL by adding more indexes, so that the row can be retrieved based on constant values or column values from earlier tables.

You can get a good indication of how good a join is by multiplying all values in the rows column of the EXPLAIN output. This should tell you roughly how many rows MySQL must examine to execute the query. This number is also used when you restrict queries with the max_join_size variable. See section 12.2.3 Tuning server parameters.

The following example shows how a JOIN can be optimized progressively using the information provided by EXPLAIN.

Suppose you have the SELECT statement shown below, that you examine 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, assume that:

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,ClientID,ActualPC NULL NULL    NULL 3872
      range checked for each record (key map: 35)

Because type is ALL for each table, this output indicates that MySQL is doing a full join for all tables! This will take quite a long time, as the product of the number of rows in each table must be examined! For the case at hand, this 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't (yet) use indexes on columns efficiently if they are declared differently. 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,ClientID,ActualPC NULL NULL NULL 3872    where used
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 below: 

table type   possible_keys   key     key_len ref            rows     Extra
et    ALL    PRIMARY         NULL    NULL    NULL           74
tt    ref    AssignedPC,ClientID,ActualPC ActualPC 15 et.EMPLOYID 52 where used
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 isn't the case for the tt table. Fortunately, it is easy to tell MySQL about this: 

shell> myisamchk --analyze PATH_TO_MYSQL_DATABASE/tt
shell> mysqladmin refresh

Now the join is ``perfect'', and EXPLAIN produces this result: 

table type   possible_keys   key     key_len ref            rows    Extra
tt    ALL    AssignedPC,ClientID,ActualPC NULL NULL NULL    3872    where used
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; To optimize a query, you should check if 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.