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MySQL 调优（视野一 explain ）

 

https://docs.oracle.com/cd/E17952_01/mysql-5.6-en/explain-output.html#explain-join-types

简单举例：

 

 

 

 

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.

When the EXTENDED keyword is used, EXPLAIN produces extra information that can be viewed by issuing a SHOW WARNINGS statement following the EXPLAIN statement. EXPLAIN EXTENDED also displays the filtered column. See Section 8.8.3, “Extended EXPLAIN Output Format”.

Note

You cannot use the EXTENDED and PARTITIONS keywords together in the same EXPLAIN statement. Neither of these keywords can be used together with the FORMAT option. (FORMAT=JSON causes EXPLAIN to display extended and partition information automatically; using FORMAT=TRADITIONAL has no effect on EXPLAIN output.)

Note

MySQL Workbench has a Visual Explain capability that provides a visual representation of EXPLAIN output. See Tutorial: Using Explain to Improve Query Performance.

EXPLAIN Output Columns

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

ColumnJSON NameMeaning
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

Note

JSON properties which are NULL are not displayed in JSON-formatted EXPLAIN output.

  • id (JSON name: select_id)

    The SELECT identifier. This is the sequential number of the SELECT within the query. The value can be NULL if the row refers to the union result of other rows. In this case, the table column shows a value like <unionM,N> to indicate that the row refers to the union of the rows with id values of M and N.

  • select_type (JSON name: none)

    The type of SELECT, which can be any of those shown in the following table. A JSON-formatted EXPLAIN exposes the SELECT type as a property of a query_block, unless it is SIMPLE or PRIMARY. The JSON names (where applicable) are also shown in the table.

    select_type ValueJSON NameMeaning
    SIMPLE None Simple SELECT (not using UNION or subqueries)
    PRIMARY None Outermost SELECT
    UNION None Second or later SELECT statement in a UNION
    DEPENDENT UNION dependent (true) Second or later SELECT statement in a UNION, dependent on outer query
    UNION RESULT union_result Result of a UNION.
    SUBQUERY None First SELECT in subquery
    DEPENDENT SUBQUERY dependent (true) First SELECT in subquery, dependent on outer query
    DERIVED 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 (see UNCACHEABLE SUBQUERY)

    DEPENDENT typically signifies the use of a correlated subquery. See Section 13.2.10.7, “Correlated Subqueries”.

    DEPENDENT SUBQUERY evaluation differs from UNCACHEABLE SUBQUERY evaluation. 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 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 with EXPLAIN, the output has no single property directly equivalent to select_type; the query_block property corresponds to a given SELECT. Properties equivalent to most of the SELECT subquery types just shown are available (an example being materialized_from_subquery for MATERIALIZED), and are displayed when appropriate. There are no JSON equivalents for SIMPLE or PRIMARY.

  • table (JSON name: table_name)

    The name of the table to which the row of output refers. This can also be one of the following values:

    • <unionM,N>: The row refers to the union of the rows with id values of M and N.

    • <derivedN>: The row refers to the derived table result for the row with an id value of N. A derived table may result, for example, from a subquery in the FROM clause.

    • <subqueryN>: The row refers to the result of a materialized subquery for the row with an id value of N. 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. This column is displayed only if the PARTITIONS keyword is used. The value is NULL for nonpartitioned tables. See Section 19.3.5, “Obtaining Information About Partitions”.

  • type (JSON name: access_type)

    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 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 (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 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 13.1.7, “ALTER TABLE Statement”.

    To see what indexes a table has, use SHOW INDEX FROM tbl_name.

  • key (JSON name: key)

    The key column indicates the key (index) that MySQL actually decided to use. If MySQL decides to use one of the possible_keys indexes to look up rows, that index is listed as the key value.

    It is possible that key names an index that is not present in the possible_keys value. This can happen if none of the possible_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 because InnoDB stores the primary key value with each secondary index. If key is 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_keys column, use FORCE INDEX, USE INDEX, or IGNORE INDEX in your query. See Section 8.9.3, “Index Hints”.

    For MyISAM and NDB tables, running ANALYZE TABLE helps the optimizer choose better indexes. For NDB tables, this also improves performance of distributed pushed-down joins. For MyISAM tables, myisamchk --analyze does the same as ANALYZE TABLE. See 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 of key_len enables you to determine how many parts of a multiple-part key MySQL actually uses. If the key column says NULL, the key_len column also says NULL.

    Due to the key storage format, the key length is one greater for a column that can be NULL than for a NOT NULL column.

  • ref (JSON name: 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.

    If the value is func, the value used is the result of some function. To see which function, use SHOW WARNINGS following EXPLAIN EXTENDED to see the extended EXPLAIN output. The function might actually be an operator such as an arithmetic operator.

  • rows (JSON name: rows)

    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 are 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 and rows × filtered shows the number of rows that are joined with the following table. For example, if rows is 1000 and filtered is 50.00 (50%), the number of rows to be joined with the following table is 1000 × 50% = 500. This column is displayed if you use EXPLAIN EXTENDED.

  • Extra (JSON name: none)

    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 the message property.

EXPLAIN Join Types

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:

  • 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 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 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;

  • fulltext

    The join is performed using a FULLTEXT index.

  • 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 8.2.1.12, “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 8.2.1.3, “Index Merge Optimization”.

  • unique_subquery

    This type replaces eq_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 nonunique 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, LIKE, 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

    The index join type is the same as ALL, 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 says Using index. An index-only scan usually is faster than ALL 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 the Extra column.

    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 enable row retrieval from the table based on constant values or column values from earlier tables.

EXPLAIN Extra Information

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 'table' pushed join@1 (JSON: message 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 the ndb_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 tbl_name, the table was empty.

  • Deleting all rows (JSON property: message)

    For DELETE, some storage engines (such as MyISAM) support a handler method that removes all table rows in a simple and fast way. This Extra 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(tbl_name) (JSON property: 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 (and system) tables and notice that the WHERE clause is always false.

  • LooseScan(m..n) (JSON property: message)

    The semijoin LooseScan strategy is used. m and n 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 or UPDATE, the optimizer found nothing to delete or update after partition pruning. It is similar in meaning to Impossible WHERE for SELECT statements.

  • No tables used (JSON property: message)

    The query has no FROM clause, or has a FROM DUAL clause.

    For INSERT or REPLACE statements, EXPLAIN displays this value when there is no SELECT part. For example, it appears for EXPLAIN INSERT INTO t VALUES(10) because that is equivalent to EXPLAIN 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 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) (JSON property: 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 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 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 value N is a bitmask value that indicates which indexes are candidates. For example, a value of 0x19 (binary 11001) means that indexes 1, 4, and 5 are considered.

  • Scanned N databases (JSON property: message)

    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 of N can be 0, 1, or all.

  • 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 and MIN(c2) can be retrieved by reading one row from a different index. That is, for each column c1 and c2, 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 with c1 <= 10 must be scanned to find the minimum c2 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 minimum c2 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 not InnoDB), this Extra value can occur for COUNT(*) queries for which the WHERE clause is missing or always true and there is no GROUP 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 tbl_name, no rows satisfy the condition for a UNIQUE index or PRIMARY 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 8.2.1.13, “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 when Using index is absent from the Extra column. This is the case if type is index and key is PRIMARY.

  • 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 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 8.2.1.14, “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 the EXPLAIN output are buffered, and the matching rows are fetched in batches from the table represented by the line in which Using join buffer appears.

    In JSON-formatted output, the value of using_join_buffer is always either one of Block Nested Loop or Batched 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 and ORDER 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 the Extra value is not Using where and the table join type is ALL or index.

    Using where has no direct counterpart in JSON-formatted output; the attached_condition property contains any WHERE 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”.

EXPLAIN Output Interpretation

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.

    TableColumnData 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.

    TableIndex
    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 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”.

 

posted on 2023-06-02 08:16  zno2  阅读(15)  评论(0)    收藏  举报

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