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
        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.
      
        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.
        EXPLAIN
        EXTENDED also displays the filtered
        column.
      
          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 - SELECTidentifier. This is the sequential number of the- SELECTwithin the query.
- select_type- The type of - SELECT, which can be any of those shown in the following table.- select_typeValue- Meaning - SIMPLE- Simple - SELECT(not using- UNIONor subqueries)- PRIMARY- Outermost - SELECT- UNION- Second or later - SELECTstatement in a- UNION- DEPENDENT UNION- Second or later - SELECTstatement in a- UNION, dependent on outer query- UNION RESULT- Result of a - UNION.- SUBQUERY- First - SELECTin subquery- DEPENDENT SUBQUERY- First - SELECTin subquery, dependent on outer query- DERIVED- Derived table - SELECT(subquery in- FROMclause)- UNCACHEABLE SUBQUERY- A subquery for which the result cannot be cached and must be re-evaluated for each row of the outer query - UNCACHEABLE UNION- The second or later select in a - UNIONthat belongs to an uncacheable subquery (see- UNCACHEABLE SUBQUERY)- DEPENDENTtypically signifies the use of a correlated subquery. See Section 12.2.10.7, “Correlated Subqueries”.- DEPENDENT SUBQUERYevaluation differs from- UNCACHEABLE SUBQUERYevaluation. For- DEPENDENT SUBQUERY, the subquery is re-evaluated only once for each set of different values of the variables from its outer context. For- UNCACHEABLE SUBQUERY, the subquery is re-evaluated for each row of the outer context. Cacheability of subqueries is subject to the restrictions detailed in Section 7.9.3.1, “How the Query Cache Operates”. For example, referring to user variables makes a subquery uncacheable.
- 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: - The table has only one row (= system table). This is a special case of the - constjoin 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. - consttables are very fast because they are read only once.- constis used when you compare all parts of a- PRIMARY KEYor- UNIQUEindex to constant values. In the following queries,- tbl_namecan be used as a- consttable:- SELECT * FROM - tbl_nameWHERE- primary_key=1; SELECT * FROM- tbl_nameWHERE- 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 - systemand- consttypes, 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 KEYor- UNIQUE NOT NULLindex.- eq_refcan 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_refjoin to process- ref_table:- SELECT * FROM - ref_table,- other_tableWHERE- ref_table.- key_column=- other_table.- column; SELECT * FROM- ref_table,- other_tableWHERE- ref_table.- key_column_part1=- other_table.- columnAND- 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. - refis used if the join uses only a leftmost prefix of the key or if the key is not a- PRIMARY KEYor- UNIQUEindex (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.- refcan be used for indexed columns that are compared using the- =or- <=>operator. In the following examples, MySQL can use a- refjoin to process- ref_table:- SELECT * FROM - ref_tableWHERE- key_column=- expr; SELECT * FROM- ref_table,- other_tableWHERE- ref_table.- key_column=- other_table.- column; SELECT * FROM- ref_table,- other_tableWHERE- ref_table.- key_column_part1=- other_table.- columnAND- ref_table.- key_column_part2=1;
- The join is performed using a - FULLTEXTindex.
- This join type is like - ref, but with the addition that MySQL does an extra search for rows that contain- NULLvalues. This join type optimization is used most often in resolving subqueries. In the following examples, MySQL can use a- ref_or_nulljoin to process- ref_table:- SELECT * FROM - ref_tableWHERE- key_column=- exprOR- key_columnIS NULL;
- This join type indicates that the Index Merge optimization is used. In this case, the - keycolumn in the output row contains a list of indexes used, and- key_lencontains a list of the longest key parts for the indexes used. For more information, see Section 7.13.2, “Index Merge Optimization”.
- This type replaces - reffor some- INsubqueries of the following form:- valueIN (SELECT- primary_keyFROM- single_tableWHERE- some_expr)- unique_subqueryis just an index lookup function that replaces the subquery completely for better efficiency.
- This join type is similar to - unique_subquery. It replaces- INsubqueries, but it works for nonunique indexes in subqueries of the following form:- valueIN (SELECT- key_columnFROM- single_tableWHERE- some_expr)
- Only rows that are in a given range are retrieved, using an index to select the rows. The - keycolumn in the output row indicates which index is used. The- key_lencontains the longest key part that was used. The- refcolumn is- NULLfor this type.- rangecan be used when a key column is compared to a constant using any of the- =,- <>,- >,- >=,- <,- <=,- IS NULL,- <=>,- BETWEEN, or- IN()operators:- SELECT * FROM - tbl_nameWHERE- key_column= 10; SELECT * FROM- tbl_nameWHERE- key_columnBETWEEN 10 and 20; SELECT * FROM- tbl_nameWHERE- key_columnIN (10,20,30); SELECT * FROM- tbl_nameWHERE- key_part1= 10 AND- key_part2IN (10,20,30);
- This join type is the same as - ALL, except that only the index tree is scanned. This usually is faster than- ALLbecause 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. 
- 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- ALLby adding indexes that enable row retrieval from the table based on constant values or column values from earlier tables.
 
- possible_keys- The - possible_keyscolumn 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_keysmight 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- WHEREclause 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- EXPLAINagain. See Section 12.1.6, “- ALTER TABLESyntax”.- To see what indexes a table has, use - SHOW INDEX FROM.- tbl_name
- key- The - keycolumn indicates the key (index) that MySQL actually decided to use. If MySQL decides to use one of the- possible_keysindexes to look up rows, that index is listed as the key value.- It is possible that - keywill name an index that is not present in the- possible_keysvalue. This can happen if none of the- possible_keysindexes 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 because- InnoDBstores the primary key value with each secondary index. If- keyis- NULL, 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_keyscolumn, use- FORCE INDEX,- USE INDEX, or- IGNORE INDEXin your query. See Section 12.2.9.2, “Index Hint Syntax”.- For - MyISAMtables, running- ANALYZE TABLEhelps the optimizer choose better indexes. For- MyISAMtables, myisamchk --analyze does the same. See Section 12.4.2.1, “- ANALYZE TABLESyntax”, and Section 6.6, “- MyISAMTable Maintenance and Crash Recovery”.
- key_len- The - key_lencolumn indicates the length of the key that MySQL decided to use. The length is- NULLif the- keycolumn says- NULL. Note that the value of- key_lenenables you to determine how many parts of a multiple-part key MySQL actually uses.
- ref- The - refcolumn shows which columns or constants are compared to the index named in the- keycolumn to select rows from the table.
- rows- The - rowscolumn indicates the number of rows MySQL believes it must examine to execute the query.- For - InnoDBtables, this number is an estimate, and may not always be exact.
- filtered- The - filteredcolumn indicates an estimated percentage of table rows that will be filtered by the table condition. That is,- rowsshows the estimated number of rows examined and- rows×- filtered/- 100shows the number of rows that will be joined with previous tables. This column is displayed if you use- EXPLAIN EXTENDED.
- Extra- This column contains additional information about how MySQL resolves the query. The following list explains the values that can appear in this column. If you want to make your queries as fast as possible, look out for - Extravalues of- Using filesortand- Using temporary.- const row not found- For a query such as - SELECT ... FROM, the table was empty.- tbl_name
- 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. 
- Full scan on NULL key- This occurs for subquery optimization as a fallback strategy when the optimizer cannot use an index-lookup access method. 
- Impossible HAVING- The - HAVINGclause is always false and cannot select any rows.
- Impossible WHERE- The - WHEREclause is always false and cannot select any rows.
- Impossible WHERE noticed after reading const tables- MySQL has read all - const(and- system) tables and notice that the- WHEREclause is always false.
- No matching min/max row- No row satisfies the condition for a query such as - SELECT MIN(...) FROM ... WHERE.- condition
- no matching row in const table- For a query with a join, there was an empty table or a table with no rows satisfying a unique index condition. 
- No tables used- The query has no - FROMclause, or has a- FROM DUALclause.
- Not exists- MySQL was able to do a - LEFT JOINoptimization 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 JOINcriteria. 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.idis defined as- NOT NULL. In this case, MySQL scans- t1and looks up the rows in- t2using the values of- t1.id. If MySQL finds a matching row in- t2, it knows that- t2.idcan never be- NULL, and does not scan through the rest of the rows in- t2that have the same- idvalue. 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 - rangeor- index_mergeaccess 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 7.13.1, “Range Optimization”, and Section 7.13.2, “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 INDEXfor the table. The index map value- Nis a bitmask value that indicates which indexes are candidates. For example, a value of- 0x19(binary 11001) means that indexes 1, 4, and 5 will be considered.
- Scanned- Ndatabases- This indicates how many directory scans the server performs when processing a query for - INFORMATION_SCHEMAtables, as described in Section 7.2.4, “Optimizing- INFORMATION_SCHEMAQueries”. The value of- Ncan be 0, 1, or- all.
- Select tables optimized away- The query contained only aggregate functions ( - MIN(),- MAX()) that were all resolved using an index, or- COUNT(*)for- MyISAM, and no- GROUP BYclause. The optimizer determined that only one row should be returned.
- Skip_open_table,- Open_frm_only,- Open_trigger_only,- Open_full_table- These values indicate file-opening optimizations that apply to queries for - INFORMATION_SCHEMAtables, as described in Section 7.2.4, “Optimizing- INFORMATION_SCHEMAQueries”.- 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- .frmfile need be opened.
- Open_trigger_only: Only the table's- .TRGfile need be opened.
- Open_full_table: The unoptimized information lookup. The- .frm,- .MYD, and- .MYIfiles must be opened.
 
- unique row not found- For a query such as - SELECT ... FROM, no rows satisfy the condition for a- tbl_name- UNIQUEindex or- PRIMARY KEYon the table.
- 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 - WHEREclause. The keys then are sorted and the rows are retrieved in sorted order. See Section 7.13.9, “- ORDER BYOptimization”.
- 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 - InnoDBtables that have a user-defined clustered index, that index can be used even when- Using indexis absent from the- Extracolumn. This is the case if- typeis- indexand- keyis- PRIMARY.
- Using index for group-by- Similar to the - Using indextable access method,- Using index for group-byindicates that MySQL found an index that can be used to retrieve all columns of a- GROUP BYor- DISTINCTquery 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 7.13.10, “- GROUP BYOptimization”.
- 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. 
- Using sort_union(...),- Using union(...),- Using intersect(...)- These indicate how index scans are merged for the - index_mergejoin type. See Section 7.13.2, “Index Merge Optimization”.
- 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 BYand- ORDER BYclauses that list columns differently.
- Using where- A - WHEREclause 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- Extravalue is not- Using whereand the table join type is- ALLor- index. Even if you are using an index for all parts of a- WHEREclause, you may see- Using whereif the column can be- NULL.
- Using where with pushed condition- This item applies to - NDBCLUSTERtables only. It means that MySQL 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 7.13.3, “Engine Condition Pushdown Optimization”.
 
        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 7.11.2, “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.ActualPCvalues 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
        
        Note that 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.
      
        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 12.2.10.8, “Subqueries in the FROM Clause”.