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I have this entry in slow query log:

# User@Host: user[host] @  [ip]
# Thread_id: 1514428  Schema: db  Last_errno: 0  Killed: 0
# Query_time: 2.795454  Lock_time: 0.000116  Rows_sent: 15  Rows_examined: 65207  Rows_affected: 0  Rows_read: 65207
# Bytsent: 26618
SET timestamp=1407511874;

select off.*,translated_title,translated_description 
from ephpb2b_products off  USE INDEX(id_viewed)  
  INNER JOIN ephpb2b_members mem 
    ON  off.uid = 
  Left Join ephpb2b_product_language_new pol 
    ON = pol.offer_id                                         
    and pol.language='en'
where off.approved=1 
order by off.viewed  
LIMIT 15; 

When I explain this query, it's absolutely fine.

mysql> explain select off.*,translated_title,translated_description from ephpb2b_products off  USE INDEX(id_viewed)  INNER JOIN ephpb2b_members mem ON off.uid = Left Join ephpb2b_product_language_new pol ON = pol.offer_id and pol.language='en' where off.approved=1 order by off.viewed  LIMIT 15;

| id | select_type | table | type   | possible_keys           | key         | key_len | ref                       | rows | Extra       |
|  1 | SIMPLE      | off   | index  | NULL                    | id_viewed   | 4       | NULL                      |    3 | Using where |
|  1 | SIMPLE      | mem   | eq_ref | PRIMARY                 | PRIMARY     | 4       | |    1 | Using index |
|  1 | SIMPLE      | pol   | ref    | offer_id,id_language | offer_id | 5       |  |    4 |             |
3 rows in set (0.17 sec)

How do I optimize this query? Why does explain show 3 rows and slow query log says it examined 65207 rows.

share|improve this question

In order to answer this question, you must understand what the rows column on explain means, and the difference between calculations based on statistics and post-execution statistics.

When you run explain, the rows column will tell you, for each table access, how many rows will be examined by using the intended filter. There are two ways to calculate that: either an index dive (that usually should give you exact results) or by using approximate statistics that each engine stores independently -up to 5.6- for each table. While the first method is preferred when it can be used (simple filters on a single indexed column), in many cases, only an approximation could be used -otherwise, the query optimiser would take as much time as the query execution itself.

In any case, the rows are the calculated rows to be read (not to be returned) per table access. Even if it were exact (and many times has differences with several order of magnitudes, but still is good enough for the optimiser), it does not predict the real number of rows accessed throughout a join. For example, if you join table A (reading exactly X rows) and table B (reading exactly Y rows), in the order A -> B, the real number of rows read will be: X + # of rows returned by A (<=X) multiplied by Y, as standard mysql only supports nested loop joins.

The slow log, like the handler statistics, or other profiling mechanisms tell you the real number of rows processed and sent, because those statistics are gathered after the execution, thus exact.

Regarding your particular case, EXPLAIN is to blame because it shows that only 3 rows would be scanned for the first access, when in reality it may be doing a full index scan (as it is using the key only for sorting), which later gets multiplied for each join performed. Do not trust explain. You can use:

-- Execute your query here
SHOW STATUS like 'Hand%';

To check the actual number and kind (PK access, ref, index scan, table scan) of row operations. I would use it to test each table access individually.

For more specific help, we would need the table structure of each table and the approximate selectivity of each filter condition.

share|improve this answer
+1 for Do not trust explain. InnoDB stats are always guesses. Even if setting innodb_stats_on_metadata to 0 can stabilize the explain plan, it will make the plan use stale stats more often. Explaining calculations based on statistics and post-execution statistics is brilliant. – RolandoMySQLDBA Aug 8 '14 at 16:35
Great answer! +1 for explaining index dive vs statistics. – Morgan Tocker Aug 8 '14 at 16:48
@rolandomysqldba independently of the engine, MySQL will use an index dive in many cases, and much better with the 5.6 tuning:… Also, persistent statistics usually makes them more stable, which is generally a good thing. – jynus Aug 8 '14 at 17:37

+1 to @junus for the explanations regarding EXPLAIN, the slow query log and rows examined.

Regarding "How to optimize the query":

Assuming there is an explicit foreign key relationship between the products and the members table, the join between them:

ephpb2b_products off  
  INNER JOIN ephpb2b_members mem 
    ON  off.uid = 

can be converted to a LEFT JOIN. The FK will assure that the two queries are 100% equivalent. Having that in mind, and that the where and order clauses:

WHERE off.approved=1
ORDER BY off.viewed  

uses only columns from the base table (products), i.e. the table in the "left" part in the FROM clause, we can use a subquery to limit the rows first and the join the other two tables, a technique I call
"first limit, then join":

    ( SELECT p.*                                 -- first limit
      FROM ephpb2b_products AS p
      WHERE p.approved=1
      ORDER BY p.viewed  
      LIMIT 15  
    ) AS off 
  LEFT JOIN                                      -- then join
    ephpb2b_members AS mem 
      ON  off.uid = 
    ephpb2b_product_language_new AS pol 
      ON  pol.language = 'en'                                         
      AND pol.offer_id =   
    off.viewed ;                         -- no LIMIT required here 

With an index on ephpb2b_products (approved, viewed), the subquery will be quite efficient. The rest of the execution plan will not matter as only 15 rows will be involved (and I assume you have indexes in the joining columns).

An additional index on phpb2b_product_language_new (language, offer_id) may also improve further the efficiency (but not blindly add this, test first. The above index and rewriting may be just enough to improve speed drastically.)

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up vote 0 down vote accepted

Thank you everyone for your insights. I solved this issue by splitting this query in two different queries. First I queried for ids, then just passed those ids to other table for info.

select id from ephpb2b_products off INNER JOIN ephpb2b_members mem 
    ON  off.uid = 
where off.approved=1 
order by off.viewed  


select * from ephpb2b_product_language_new where offer_id IN ({ids from lasts query})

This performs much better and doesn't act weird.

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