Duplicate Weedout Strategy

DuplicateWeedout is an execution strategy for Semi-join subqueries.

The idea

The idea is to run the semi-join (a query with uses WHERE X IN (SELECT Y FROM ...)) as if it were a regular inner join, and then eliminate the duplicate record combinations using a temporary table.

Suppose, you have a query where you're looking for countries which have more than 33% percent of their population in one big city:

SELECT * 
FROM Country 
WHERE 
   Country.code IN (SELECT City.Country
                    FROM City 
                    WHERE 
                      City.Population > 0.33 * Country.Population AND 
                      City.Population > 1*1000*1000);

First, we run a regular inner join between the City and Country tables:

duplicate-weedout-inner-join

The Inner join produces duplicates. We have Germany three times, because it has three big cities. Now, lets put DuplicateWeedout into the picture:

duplicate-weedout-diagram

Here one can see that a temporary table with a primary key was used to avoid producing multiple records with 'Germany'.

DuplicateWeedout in action

The Start temporary and End temporary from the last diagram are shown in the EXPLAIN output:

EXPLAIN SELECT * FROM Country WHERE Country.code IN 
  (select City.Country from City where City.Population > 0.33 * Country.Population 
   AND City.Population > 1*1000*1000)\G
*************************** 1. row ***************************
           id: 1
  select_type: PRIMARY
        TABLE: City
         type: RANGE
possible_keys: Population,Country
          KEY: Population
      key_len: 4
          ref: NULL
         ROWS: 238
        Extra: USING INDEX CONDITION; Start temporary
*************************** 2. row ***************************
           id: 1
  select_type: PRIMARY
        TABLE: Country
         type: eq_ref
possible_keys: PRIMARY
          KEY: PRIMARY
      key_len: 3
          ref: world.City.Country
         ROWS: 1
        Extra: USING WHERE; End temporary
2 rows in set (0.00 sec)

This query will read 238 rows from the City table, and for each of them will make a primary key lookup in the Country table, which gives another 238 rows. This gives a total of 476 rows, and you need to add 238 lookups in the temporary table (which are typically much cheaper since the temporary table is in-memory).

If we run the same query with semi-join optimizations disabled, we'll get:

EXPLAIN SELECT * FROM Country WHERE Country.code IN 
  (select City.Country from City where City.Population > 0.33 * Country.Population 
    AND City.Population > 1*1000*1000)\G
*************************** 1. row ***************************
           id: 1
  select_type: PRIMARY
        TABLE: Country
         type: ALL
possible_keys: NULL
          KEY: NULL
      key_len: NULL
          ref: NULL
         ROWS: 239
        Extra: USING WHERE
*************************** 2. row ***************************
           id: 2
  select_type: DEPENDENT SUBQUERY
        TABLE: City
         type: index_subquery
possible_keys: Population,Country
          KEY: Country
      key_len: 3
          ref: func
         ROWS: 18
        Extra: USING WHERE
2 rows in set (0.00 sec)

This plan will read (239 + 239*18) = 4541 rows, which is much slower.

Factsheet

• DuplicateWeedout is shown as "Start temporary/End temporary" in EXPLAIN.

• The strategy can handle correlated subqueries.

• But it cannot be applied if the subquery has meaningful GROUP BY and/or aggregate functions.

• DuplicateWeedout allows the optimizer to freely mix a subquery's tables and the parent select's tables.

• There is no separate @@optimizer_switch flag for DuplicateWeedout. The strategy can be disabled by switching off all semi-join optimizations with SET @@optimizer_switch='optimizer_semijoin=off' command.

See Also

• Subquery Optimizations Map

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