CODEWITHSUNDEEP
sqlmysqlquery-optimization
andersonbd1
andersonbd1

Reputation: 5386

optimize mysql count query

Is there a way to optimize this further or should I just be satisfied that it takes 9 seconds to count 11M rows ?

devuser@xcmst > mysql --user=user --password=pass -D marctoxctransformation -e "desc record_updates"                                                                    
+--------------+----------+------+-----+---------+-------+
| Field        | Type     | Null | Key | Default | Extra |
+--------------+----------+------+-----+---------+-------+
| record_id    | int(11)  | YES  | MUL | NULL    |       | 
| date_updated | datetime | YES  | MUL | NULL    |       | 
+--------------+----------+------+-----+---------+-------+
devuser@xcmst > date; mysql --user=user --password=pass -D marctoxctransformation -e "select count(*) from record_updates where date_updated > '2009-10-11 15:33:22' "; date                         
Thu Dec  9 11:13:17 EST 2010
+----------+
| count(*) |
+----------+
| 11772117 | 
+----------+
Thu Dec  9 11:13:26 EST 2010
devuser@xcmst > mysql --user=user --password=pass -D marctoxctransformation -e "explain select count(*) from record_updates where date_updated > '2009-10-11 15:33:22' "      
+----+-------------+----------------+-------+--------------------------------------------------------+--------------------------------------------------------+---------+------+----------+--------------------------+
| id | select_type | table          | type  | possible_keys                                          | key                                                    | key_len | ref  | rows     | Extra                    |
+----+-------------+----------------+-------+--------------------------------------------------------+--------------------------------------------------------+---------+------+----------+--------------------------+
|  1 | SIMPLE      | record_updates | index | idx_marctoxctransformation_record_updates_date_updated | idx_marctoxctransformation_record_updates_date_updated | 9       | NULL | 11772117 | Using where; Using index | 
+----+-------------+----------------+-------+--------------------------------------------------------+--------------------------------------------------------+---------+------+----------+--------------------------+
devuser@xcmst > mysql --user=user --password=pass -D marctoxctransformation -e "show keys from record_updates"
+----------------+------------+--------------------------------------------------------+--------------+--------------+-----------+-------------+----------+--------+------+------------+---------+
| Table          | Non_unique | Key_name                                               | Seq_in_index | Column_name  | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment |
+----------------+------------+--------------------------------------------------------+--------------+--------------+-----------+-------------+----------+--------+------+------------+---------+
| record_updates |          1 | idx_marctoxctransformation_record_updates_date_updated |            1 | date_updated | A         |        2416 |     NULL | NULL   | YES  | BTREE      |         | 
| record_updates |          1 | idx_marctoxctransformation_record_updates_record_id    |            1 | record_id    | A         |    11772117 |     NULL | NULL   | YES  | BTREE      |         | 
+----------------+------------+--------------------------------------------------------+--------------+--------------+-----------+-------------+----------+--------+------+------------+---------+

Upvotes: 16

Views: 23280

Answers (10)

Brent Baisley
Brent Baisley

Reputation: 12721

If mysql has to count 11M rows, there really isn't much of a way to speed up a simple count. At least not to get it to a sub 1 second speed. You should rethink how you do your count. A few ideas:

  1. Add an auto increment field to the table. It looks you wouldn't delete from the table, so you can use simple math to find the record count. Select the min auto increment number for the initial earlier date and the max for the latter date and subtract one from the other to get the record count. For example: 

    SELECT min(incr_id) min_id FROM record_updates WHERE date_updated BETWEEN '2009-10-11 15:33:22' AND '2009-10-12 23:59:59';
SELECT max(incr_id) max_id FROM record_updates WHERE date_updated > DATE_SUB(NOW(), INTERVAL 2 DAY);`

  2. Create another table summarizing the record count for each day. Then you can query that table for the total records. There would only be 365 records for each year. If you need to get down to more fine grained times, query the summary table for full days and the current table for just the record count for the start and end days. Then add them all together.

If the data isn't changing, which it doesn't seem like it is, then summary tables will be easy to maintain and update. They will significantly speed things up.

Upvotes: 29

Jeff Ferland
Jeff Ferland

Reputation: 18282

MySQL doesn't "optimize" count(*) queries in InnoDB because of versioning. Every item in the index has to be iterated over and checked to make sure that the version is correct for display (e.g., not an open commit). Since any of your data can be modified across the database, ranged selects and caching won't work. However, you possibly can get by using triggers. There are two methods to this madness.

This first method risks slowing down your transactions since none of them can truly run in parallel: use after insert and after delete triggers to increment / decrement a counter table. Second trick: use those insert / delete triggers to call a stored procedure which feeds into an external program which similarly adjusts values up and down, or acts upon a non-transactional table. Beware that in the event of a rollback, this will result in inaccurate numbers.

If you don't need an exact numbers, check out this query:

select table_rows from information_schema.tables
where table_name = 'foo';

Example difference: count(*): 1876668, table_rows: 1899004. The table_rows value is an estimation, and you'll get a different number every time even if you database doesn't change.

For my own curiosity: do you need exact numbers that are updated every second? IF so, why?

Upvotes: 5

ajreal
ajreal

Reputation: 47321

Since >'2009-10-11 15:33:22' contains most of the records,
I would suggest to do a reverse matching like <'2009-10-11 15:33:22' (mysql work less harder and less rows involved)

select 
  TABLE_ROWS -
  (select count(*) from record_updates where add_date<"2009-10-11 15:33:22") 
from information_schema.tables 
where table_schema = "marctoxctransformation" and table_name="record_updates"

You can combine with programming language (like bash shell)
to make this calculation a bit smarter...
such as do execution plan first to calculate which comparison will use lesser row

From my testing (around 10M records), the normal comparison takes around 3s,
and now cut-down to around 0.25s

Upvotes: 7

Unreason
Unreason

Reputation: 12704

There are a few details I'd like you to clarify (would put into comments on the q, but it is actually easier to remove from here when you update your question).

  1. What is the intended usage of data, insert once and get the counts many times, or your inserts and selects are approx on par?
  2. Do you care about insert/update performance?
  3. What is the engine used for the table? (heck you can do SHOW CREATE TABLE ...)
  4. Do you need the counts to be exact or approximately exact (like 0.1% correct)
  5. Can you use triggers, summary tables, change schema, change RDBMS, etc.. or just add/remove indexes?
  6. Maybe you should explain also what is this table supposed to be? You have record_id with cardinality that matches the number of rows, so is it PK or FK or what is it? Also the cardinality of the date_updated suggests (though not necessarily correct) that it has same values for ~5,000 records on average), so what is that? - it is ok to ask a SQL tuning question with not context, but it is also nice to have some context - especially if redesigning is an option.

In the meantime, I'll suggest you to get this tuning script and check the recommendations it will give you (it's just a general tuning script - but it will inspect your data and stats).

Upvotes: 1

user533832
user533832

Reputation:

If the historical data is not volatile, create a summary table. There are various approaches, the one to choose will depend on how your table is updated, and how often.

For example, assuming old data is rarely/never changed, but recent data is, create a monthly summary table, populated for the previous month at the end of each month (eg insert January's count at the end of February). Once you have your summary table, you can add up the full months and the part months at the beginning and end of the range:

select count(*) 
from record_updates 
where date_updated >= '2009-10-11 15:33:22' and date_updated < '2009-11-01';

select count(*) 
from record_updates 
where date_updated >= '2010-12-00';

select sum(row_count) 
from record_updates_summary 
where date_updated >= '2009-11-01' and date_updated < '2010-12-00';

I've left it split out above for clarity but you can do this in one query:

select ( select count(*)
         from record_updates 
         where date_updated >= '2010-12-00'
               or ( date_updated>='2009-10-11 15:33:22' 
                    and date_updated < '2009-11-01' ) ) +
       ( select count(*) 
         from record_updates 
         where date_updated >= '2010-12-00' );

You can adapt this approach for make the summary table based on whole weeks or whole days.

Upvotes: 3

niallo
niallo

Reputation: 366

It depends on a few things but something like this may work for you

im assuming this count never changes as it is in the past so the result can be cached somehow

count1 = "select count(*) from record_updates where date_updated <= '2009-10-11 15:33:22'"

gives you the total count of records in the table, this is an approximate value in innodb table so BEWARE, depends on engine

count2 = "select table_rows from information_schema.`TABLES` where table_schema = 'marctoxctransformation' and TABLE_NAME = 'record_updates'"

your answer

result = count2 - count1

Upvotes: 1

THE DOCTOR
THE DOCTOR

Reputation: 4555

If you need to return the total table's row count, then there is an alternative to the SELECT COUNT(*) statement which you can use. SELECT COUNT(*) makes a full table scan to return the total table's row count, so it can take a long time. You can use the sysindexes system table instead in this case. There is a ROWS column in the sysindexes table. This column contains the total row count for each table in your database. So, you can use the following select statement instead of SELECT COUNT(*):

SELECT rows FROM sysindexes WHERE id = OBJECT_ID('table_name') AND indid < 2

This can improve the speed of your query.

EDIT: I have discovered that my answer would be correct if you were using a SQL Server database. MySQL databases do not have a sysindexes table.

Upvotes: 1

limc
limc

Reputation: 40170

Instead of doing count(*), try doing count(1), like this:-

select count(1) from record_updates where date_updated > '2009-10-11 15:33:22'

I took a DB2 class before, and I remember the instructor mentioned about doing a count(1) when we just want to count number of rows in the table regardless the data because it is technically faster than count(*). Let me know if it makes a difference.

NOTE: Here's a link you might be interested to read: http://www.mysqlperformanceblog.com/2007/04/10/count-vs-countcol/

Upvotes: 0

a1ex07
a1ex07

Reputation: 37354

There is no primary key in your table. It's possible that in this case it always scans the whole table. Having a primary key is never a bad idea.

Upvotes: 1

Doron
Doron

Reputation: 3256

You should add an index on the 'date_updated' field.

Another thing you can do if you don't mind changing the structure of the table, is to use the timestamp of the date in 'int' instead of 'datetime' format, and it might be even faster. If you decide to do so, the query will be

select count(date_updated) from record_updates where date_updated > 1291911807

Upvotes: 2

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