This query takes 6+ minutes to run:
SELECT null as primary_id, t211.appln_id as appln_id, t212.pat_publn_id, t212.citn_id, citn_origin, cited_pat_publn_id, cited_appln_id, pat_citn_seq_nr, relevant_claim,
t212.cited_npl_publn_id, npl_citn_seq_nr, citn_gener_auth, npl_biblio, npl_type, publn_nr_original, t215.citn_categ, t215.CITN_REPLENISHED, npl_author,
npl_title1, npl_title2, npl_editor, npl_volume, npl_issue, npl_publn_date, npl_publn_end_date, npl_publisher, npl_page_first, npl_page_last, npl_abstract_nr, npl_doi, npl_isbn, npl_issn, online_availability, online_classification, online_search_date
FROM
tls211_pat_publn as t211
LEFT JOIN tls212_citation as t212 on t211.pat_publn_id = t212.pat_publn_id
LEFT JOIN tls214_npl_publn as t214 on t214.npl_publn_id = t212.cited_npl_publn_id
LEFT JOIN tls215_citn_categ as t215 on t215.pat_publn_id = t212.pat_publn_id and t215.citn_id = t212.citn_id
where t211.pat_publn_id=777;
This query takes 0 seconds to run:
SELECT null as primary_id, t211.appln_id as appln_id, t212.pat_publn_id, t212.citn_id, citn_origin, cited_pat_publn_id, cited_appln_id, pat_citn_seq_nr, relevant_claim,
t212.cited_npl_publn_id, npl_citn_seq_nr, citn_gener_auth, npl_biblio, npl_type, publn_nr_original, t215.citn_categ, t215.CITN_REPLENISHED, npl_author,
npl_title1, npl_title2, npl_editor, npl_volume, npl_issue, npl_publn_date, npl_publn_end_date, npl_publisher, npl_page_first, npl_page_last, npl_abstract_nr, npl_doi, npl_isbn, npl_issn, online_availability, online_classification, online_search_date
FROM
tls211_pat_publn as t211
LEFT JOIN tls212_citation as t212 on t211.pat_publn_id = t212.pat_publn_id
LEFT JOIN tls214_npl_publn as t214 on t214.npl_publn_id = t212.cited_npl_publn_id
LEFT JOIN tls215_citn_categ as t215 on t215.pat_publn_id = t212.pat_publn_id
where t211.pat_publn_id=777;
The only difference is adding: and t215.citn_id = t212.citn_id at the end of the last LEFT JOIN.
Both t212 and t215 have indexes for citn_id.
I've run "explain" on this an absurd number of times. Here's the explain on the slow one:
+----+-------------+-------+------------+--------+-------------------------+-------------+---------+-------------------------------------------+------+----------+-------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+-------+------------+--------+-------------------------+-------------+---------+-------------------------------------------+------+----------+-------------+
| 1 | SIMPLE | t211 | NULL | const | PRIMARY | PRIMARY | 4 | const | 1 | 100.00 | NULL |
| 1 | SIMPLE | t212 | NULL | ref | PRIMARY,ppi | ppi | 4 | const | 7 | 100.00 | NULL |
| 1 | SIMPLE | t214 | NULL | eq_ref | PRIMARY | PRIMARY | 4 | patstat_2019_fall.t212.cited_npl_publn_id | 1 | 100.00 | NULL |
| 1 | SIMPLE | t215 | NULL | ref | PRIMARY,idx_citn_id,ppi | idx_citn_id | 2 | patstat_2019_fall.t212.citn_id | 11 | 100.00 | Using where |
+----+-------------+-------+------------+--------+-------------------------+-------------+---------+-------------------------------------------+------+----------+-------------+
4 rows in set, 1 warning (0.00 sec)
Here's the explain on the fast one:
+----+-------------+-------+------------+--------+---------------+---------+---------+-------------------------------------------+------+----------+-------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+-------+------------+--------+---------------+---------+---------+-------------------------------------------+------+----------+-------+
| 1 | SIMPLE | t211 | NULL | const | PRIMARY | PRIMARY | 4 | const | 1 | 100.00 | NULL |
| 1 | SIMPLE | t212 | NULL | ref | PRIMARY,ppi | ppi | 4 | const | 7 | 100.00 | NULL |
| 1 | SIMPLE | t214 | NULL | eq_ref | PRIMARY | PRIMARY | 4 | patstat_2019_fall.t212.cited_npl_publn_id | 1 | 100.00 | NULL |
| 1 | SIMPLE | t215 | NULL | ref | PRIMARY,ppi | ppi | 4 | patstat_2019_fall.t212.pat_publn_id | 47 | 100.00 | NULL |
+----+-------------+-------+------------+--------+---------------+---------+---------+-------------------------------------------+------+----------+-------+
4 rows in set, 1 warning (0.02 sec)
I see that the slow one says, "Using where" in the Extra column. This sounds ominous, but says only 11 rows.
My slow query log says:
Query_time: 269.658475 Lock_time: 0.000000 Rows_sent: 9 Rows_examined: 113106063
That's a lot of rows examined... but I have failed to understand WHY.
I'm confident this is obvious to someone, but I'm at the end of my rope.
Help?!
Only one index can be used for each join. If you have separate indexes for pat_publn_id and citn_id, it has to pick one of them to use, and then scan for the other column.
Since you're joining on two columns, to get the most effective indexing you need a multi-column index containing both columns.
ALTER TABLE tls212_citation ADD INDEX (pat_publn_id, citn_id);
ALTER TABLE tls215_citn_categ ADD INDEX (pat_publn_id, citn_id);
Does citn_id has single row entry in both t212 & t215? Or t212 has single but t215 has multiple entry. The second one cause speed issue. Even query can stop working.
You need to look on this, whether both table has single entry.
If multiple entry then left join won't work. You need to format the SQL in other way.
Related
I am having issue with mysql query.
SELECT Count(*) AS aggregate
FROM (SELECT Group_concat(gateways.public_name) AS client_gateways,
`clients`.`id`,
`clients`.`name`,
`clients`.`status`,
`clients`.`api_key`,
`clients`.`user_name`,
`clients`.`psp_id`,
`clients`.`suspend`,
`clients`.`secret_key`,
`clients`.`created_at`,
`companies`.`name` AS `company_name`,
`mid_groups_mid`.`mid_id`,
`mid_groups_mid`.`mid_group_id`,
`mid_groups`.`id` AS `group_id`,
`mid_groups`.`user_id`,
`mids`.`mid_group_id` AS `id_of_mid`
FROM `clients`
LEFT JOIN `client_site_gateways`
ON `clients`.`id` = `client_site_gateways`.`client_id`
LEFT JOIN `gateways`
ON `client_site_gateways`.`gateway_id` = `gateways`.`id`
LEFT JOIN `client_broker`
ON `client_broker`.`client_id` = `clients`.`id`
LEFT JOIN `mid_groups`
ON `mid_groups`.`user_id` = `clients`.`psp_id`
LEFT JOIN `mid_groups_mid`
ON `mid_groups_mid`.`mid_group_id` = `mid_groups`.`id`
LEFT JOIN `mids`
ON `mids`.`mid_group_id` = `mid_groups_mid`.`mid_group_id`
INNER JOIN `companies`
ON `companies`.`id` = `clients`.`company_id`
WHERE `is_corp` = 0
AND `clients`.`suspend` = '0'
AND ( `clients`.`company_id` = 1 )
AND `clients`.`deleted_at` IS NULL
GROUP BY `clients`.`id`,
`clients`.`name`,
`clients`.`status`,
`clients`.`api_key`,
`clients`.`suspend`,
`clients`.`secret_key`,
`clients`.`created_at`,
`companies`.`name`,
`clients`.`user_name`,
`clients`.`psp_id`,
`mid_groups_mid`.`mid_id`,
`mid_groups_mid`.`mid_group_id`,
`mid_groups`.`id`,
`mid_groups`.`user_id`,
`mids`.`mid_group_id`) count_row_table
all table have few hundreds records. here is explain query result
+------+-------------+----------------------+--------+-------------------------------------+-------------------------------------+---------+----------------------------------------------+------------+-------------------------------------------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+------+-------------+----------------------+--------+-------------------------------------+-------------------------------------+---------+----------------------------------------------+------------+-------------------------------------------------+
| 1 | PRIMARY | <derived2> | ALL | NULL | NULL | NULL | NULL | 2849642280 | |
| 2 | DERIVED | companies | const | PRIMARY | PRIMARY | 4 | const | 1 | Using temporary; Using filesort |
| 2 | DERIVED | clients | ref | clients_company_id_foreign | clients_company_id_foreign | 4 | const | 543 | Using where |
| 2 | DERIVED | client_site_gateways | ref | client_id | client_id | 4 | knox_staging.clients.id | 5 | |
| 2 | DERIVED | gateways | eq_ref | PRIMARY | PRIMARY | 4 | knox_staging.client_site_gateways.gateway_id | 1 | Using where |
| 2 | DERIVED | client_broker | ALL | NULL | NULL | NULL | NULL | 6 | Using where; Using join buffer (flat, BNL join) |
| 2 | DERIVED | mid_groups | ref | mid_groups_user_id_foreign | mid_groups_user_id_foreign | 4 | knox_staging.clients.psp_id | 1 | Using where; Using index |
| 2 | DERIVED | mid_groups_mid | ref | mid_groups_mid_mid_group_id_foreign | mid_groups_mid_mid_group_id_foreign | 8 | knox_staging.mid_groups.id | 433 | Using where |
| 2 | DERIVED | mids | ref | mids_mid_group_id_foreign | mids_mid_group_id_foreign | 9 | knox_staging.mid_groups_mid.mid_group_id | 404 | Using where; Using index |
+------+-------------+----------------------+--------+-------------------------------------+-------------------------------------+---------+----------------------------------------------+------------+-------------------------------------------------+
in explain results what is causing to have 2849642280 row. while tables have only few hundreds records. all tables have proper indexing.
what i am thinking causing storage full is tmp table with above records. i tried to scale storage upto 60GB database size is few MBs. all storage filled up as soon as i run above query. i am not sure what causing left join to filter 2849642280 rows
The problem is probably the "aggregate." If the only thing you need is the count of records, you should write a new query which gets that count.
I cant find explanation of MySQL behavior. Examples are below.
Fields name and name_full are text type and field price_steps is json type. None of them has index.
SELECT
name,
name_full,
price_steps
FROM
`lots`
WHERE
EXISTS (
SELECT
*
FROM
`categories`
INNER JOIN `category_lot` ON `categories`.`id` = `category_lot`.`category_id`
WHERE
`lots`.`id` = `category_lot`.`lot_id`
AND `category_id` IN (25)
)
ORDER BY
`created_at` DESC
LIMIT 31 OFFSET 0
MySQL throws error: [Err] 1038 - Out of sort memory, consider increasing server sort buffer size.
Ok, let it be.
Then I added extra field into select part.
SUBSTRING(`name_full`, 1, 200000000000) as name_full2
and query runs successfully (Why? Extra field should lead to extra memory allocation, isnt it?).
Then I decided to make query heavier and replace string
AND `category_id` IN (25)
with
AND `category_id` IN (1,2,3,4,5,6,7,8,9,10, 25)
and the query also finishes successfully.
Count of rows with category = 25 is only about 250, but with categories in (1,2,3,4,5,6,7,8,9,10, 25) is about 40000 rows. This must lead to extra memory demands, but mysql doesnt throw error. Why?
Any explanation to this paradox? Thanks in advance!
UPDATE1
EXPLAIN with failing query
mysql> EXPLAIN SELECT name, name_full, price_steps FROM `lots` WHERE EXISTS ( SELECT * FROM `categories` INNER JOIN `category_lot` ON `categories`.`id` = `category_lot`.`category_id` WHERE `lots`.`id` = `category_lot`.`lot_id` AND `category_id` IN(25) ) ORDER BY `created_at` DESC;
+----+-------------+--------------+------------+--------+--------------------------------------------------------------+----------------------------------+---------+---------------------------+------+----------+----------------------------------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+--------------+------------+--------+--------------------------------------------------------------+----------------------------------+---------+---------------------------+------+----------+----------------------------------------------+
| 1 | SIMPLE | categories | NULL | const | PRIMARY | PRIMARY | 8 | const | 1 | 100.00 | Using index; Using temporary; Using filesort |
| 1 | SIMPLE | category_lot | NULL | ref | category_lot_lot_id_foreign,category_lot_category_id_foreign | category_lot_category_id_foreign | 8 | const | 1099 | 100.00 | Start temporary |
| 1 | SIMPLE | lots | NULL | eq_ref | PRIMARY | PRIMARY | 8 | torgs.category_lot.lot_id | 1 | 100.00 | End temporary |
+----+-------------+--------------+------------+--------+--------------------------------------------------------------+----------------------------------+---------+---------------------------+------+----------+----------------------------------------------+
3 rows in set, 2 warnings (0.00 sec)
EXPLAIN with success query (added 4th field)
mysql> EXPLAIN SELECT name, name_full, price_steps,SUBSTRING(`name_full`, 1, 200000000000) as name_full2 FROM `lots` WHERE EXISTS ( SELECT * FROM `categories` INNER JOIN `category_lot` ON `categories`.`id` = `category_lot`.`category_id` WHERE `lots`.`id` = `category_lot`.`lot_id` AND `category_id` IN(25) ) ORDER BY `created_at` DESC;
+----+-------------+--------------+------------+--------+--------------------------------------------------------------+----------------------------------+---------+---------------------------+------+----------+----------------------------------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+--------------+------------+--------+--------------------------------------------------------------+----------------------------------+---------+---------------------------+------+----------+----------------------------------------------+
| 1 | SIMPLE | categories | NULL | const | PRIMARY | PRIMARY | 8 | const | 1 | 100.00 | Using index; Using temporary; Using filesort |
| 1 | SIMPLE | category_lot | NULL | ref | category_lot_lot_id_foreign,category_lot_category_id_foreign | category_lot_category_id_foreign | 8 | const | 1099 | 100.00 | Start temporary |
| 1 | SIMPLE | lots | NULL | eq_ref | PRIMARY | PRIMARY | 8 | torgs.category_lot.lot_id | 1 | 100.00 | End temporary |
+----+-------------+--------------+------------+--------+--------------------------------------------------------------+----------------------------------+---------+---------------------------+------+----------+----------------------------------------------+
3 rows in set, 2 warnings (0.00 sec)
EXPLAIN with success query (added 4th field and category_id in (1,2,3,4,5,6,7,8,9,10,25))
mysql> EXPLAIN SELECT name, name_full, price_steps, SUBSTRING(`name_full`, 1, 200000000000) as name_full2 FROM `lots` WHERE EXISTS ( SELECT * FROM `categories` INNER JOIN `category_lot` ON `categories`.`id` = `category_lot`.`category_id` WHERE `lots`.`id` = `category_lot`.`lot_id` AND `category_id` IN(1,2,3,4,5,6,7,8,9,10,25) ) ORDER BY `created_at` DESC;
+----+--------------+--------------+------------+--------+--------------------------------------------------------------+----------------------------------+---------+---------------------+------+----------+---------------------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+--------------+--------------+------------+--------+--------------------------------------------------------------+----------------------------------+---------+---------------------+------+----------+---------------------------------+
| 1 | SIMPLE | <subquery2> | NULL | ALL | NULL | NULL | NULL | NULL | NULL | 100.00 | Using temporary; Using filesort |
| 1 | SIMPLE | lots | NULL | eq_ref | PRIMARY | PRIMARY | 8 | <subquery2>.lot_id | 1 | 100.00 | NULL |
| 2 | MATERIALIZED | categories | NULL | range | PRIMARY | PRIMARY | 8 | NULL | 11 | 100.00 | Using where; Using index |
| 2 | MATERIALIZED | category_lot | NULL | ref | category_lot_lot_id_foreign,category_lot_category_id_foreign | category_lot_category_id_foreign | 8 | torgs.categories.id | 1883 | 100.00 | NULL |
+----+--------------+--------------+------------+--------+--------------------------------------------------------------+----------------------------------+---------+---------------------+------+----------+---------------------------------+
4 rows in set, 2 warnings (0.00 sec)
UPDATE2
+-------+------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Level | Code | Message |
+-------+------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Note | 1276 | Field or reference 'torgs.lots.id' of SELECT #2 was resolved in SELECT #1 |
| Note | 1003 | /* select#1 */ select `torgs`.`lots`.`name` AS `name`,`torgs`.`lots`.`name_full` AS `name_full`,`torgs`.`lots`.`price_steps` AS `price_steps`,substr(`torgs`.`lots`.`name_full`,1,200000000000) AS `name_full2` from `torgs`.`lots` semi join (`torgs`.`categories` join `torgs`.`category_lot`) where ((`torgs`.`category_lot`.`category_id` = `torgs`.`categories`.`id`) and (`torgs`.`lots`.`id` = `<subquery2>`.`lot_id`) and (`torgs`.`categories`.`id` in (1,2,3,4,5,6,7,8,9,10,25))) order by `torgs`.`lots`.`created_at` desc |
+-------+------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
2 rows in set (0.00 sec)
Query
SELECT SQL_NO_CACHE contacts.id,
contacts.date_modified contacts__date_modified
FROM contacts
INNER JOIN
(SELECT tst.team_set_id
FROM team_sets_teams tst
INNER JOIN team_memberships team_membershipscontacts ON (team_membershipscontacts.team_id = tst.team_id)
AND (team_membershipscontacts.user_id = '5daa2e92-c347-11e9-afc5-525400a80916')
AND (team_membershipscontacts.deleted = 0)
GROUP BY tst.team_set_id) contacts_tf ON contacts_tf.team_set_id = contacts.team_set_id
LEFT JOIN contacts_cstm contacts_cstm ON contacts_cstm.id_c = contacts.id
WHERE contacts.deleted = 0
ORDER BY contacts.date_modified DESC,
contacts.id DESC
LIMIT 21;
Takes extremely long (2 minutes on 2M records). I cant change this query, since it is system generated
This is it's explain:
+----+-------------+--------------------------+------------+--------+-------------------------------------------------------------------------------------------------------+----------------------------+---------+-------------------------------------------+---------+----------+---------------------------------------------------------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+--------------------------+------------+--------+-------------------------------------------------------------------------------------------------------+----------------------------+---------+-------------------------------------------+---------+----------+---------------------------------------------------------------------+
| 1 | PRIMARY | contacts | NULL | ref | idx_contacts_tmst_id,idx_del_date_modified,idx_contacts_del_last,idx_cont_del_reports,idx_del_id_user | idx_del_date_modified | 2 | const | 1113718 | 100.00 | Using temporary; Using filesort |
| 1 | PRIMARY | <derived3> | NULL | ALL | NULL | NULL | NULL | NULL | 2 | 50.00 | Using where; Using join buffer (Block Nested Loop) |
| 1 | PRIMARY | contacts_cstm | NULL | eq_ref | PRIMARY | PRIMARY | 144 | sugarcrm.contacts.id | 1 | 100.00 | Using index |
| 3 | DERIVED | team_membershipscontacts | NULL | ref | idx_team_membership,idx_teammemb_team_user,idx_del_team_user | idx_team_membership | 145 | const | 2 | 99.36 | Using index condition; Using where; Using temporary; Using filesort |
| 3 | DERIVED | tst | NULL | ref | idx_ud_set_id,idx_ud_team_id,idx_ud_team_set_id,idx_ud_team_id_team_set_id | idx_ud_team_id_team_set_id | 144 | sugarcrm.team_membershipscontacts.team_id | 1 | 100.00 | Using index |
+----+-------------+--------------------------+------------+--------+-------------------------------------------------------------------------------------------------------+----------------------------+---------+-------------------------------------------+---------+----------+---------------------------------------------------------------------+
But when I use force index(idx_del_date_modified) (which is the same index used in explain), the query takes just 0.01s and I get slightly different explain.
+----+-------------+--------------------------+------------+--------+----------------------------------------------------------------------------+----------------------------+---------+-------------------------------------------+---------+----------+---------------------------------------------------------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+--------------------------+------------+--------+----------------------------------------------------------------------------+----------------------------+---------+-------------------------------------------+---------+----------+---------------------------------------------------------------------+
| 1 | PRIMARY | contacts | NULL | ref | idx_del_date_modified | idx_del_date_modified | 2 | const | 1113718 | 100.00 | Using where |
| 1 | PRIMARY | <derived2> | NULL | ALL | NULL | NULL | NULL | NULL | 2 | 50.00 | Using where |
| 1 | PRIMARY | contacts_cstm | NULL | eq_ref | PRIMARY | PRIMARY | 144 | sugarcrm.contacts.id | 1 | 100.00 | Using index |
| 2 | DERIVED | team_membershipscontacts | NULL | ref | idx_team_membership,idx_teammemb_team_user,idx_del_team_user | idx_team_membership | 145 | const | 2 | 99.36 | Using index condition; Using where; Using temporary; Using filesort |
| 2 | DERIVED | tst | NULL | ref | idx_ud_set_id,idx_ud_team_id,idx_ud_team_set_id,idx_ud_team_id_team_set_id | idx_ud_team_id_team_set_id | 144 | sugarcrm.team_membershipscontacts.team_id | 1 | 100.00 | Using index |
+----+-------------+--------------------------+------------+--------+----------------------------------------------------------------------------+----------------------------+---------+-------------------------------------------+---------+----------+---------------------------------------------------------------------+
The first query uses temporary table and file sort, but the query with force index uses just where. Shouldn't the queries be the same? Why is the query with force index so much faster - used index is still the same.
According to MySQL manual:
Temporary tables can be created under conditions such as these:
If there is an ORDER BY clause and a different GROUP BY clause, or if
the ORDER BY or GROUP BY contains columns from tables other than the
first table in the join queue, a temporary table is created.
DISTINCT combined with ORDER BY may require a temporary table.
If you use the SQL_SMALL_RESULT option, MySQL uses an in-memory
temporary table, unless the query also contains elements (described
later) that require on-disk storage.
Likely, you have better performance because in MySQL there is the query optimizer component.
If you create index the query optimizer could not use the index column even though the index exists.
Using force index(..) you are forcing MySql to use index, instead.
Please consider a detailed example here.
When I EXPLIAN this:
EXPLIAN SELECT m.*,m.id AS mid FROM movie_category mc
LEFT JOIN movie m ON m.id=mc.movie_id
RIGHT JOIN movie_area ma ON ma.movie_id=mc.movie_id
LEFT JOIN area a ON a.id=ma.area_id
LEFT JOIN category c ON c.id=mc.category_id
WHERE 1 and ma.area_id>0
GROUP BY mid
ORDER BY m.read_count desc LIMIT 0,36;
I got this result:
+----+-------------+-------+------------+--------+-----------------+----------+---------+----------------------+-------+----------+---------------------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+-------+------------+--------+-----------------+----------+---------+----------------------+-------+----------+---------------------------------+
| 1 | SIMPLE | ma | NULL | ALL | NULL | NULL | NULL | NULL | 15545 | 100.00 | Using temporary; Using filesort |
| 1 | SIMPLE | mc | NULL | ref | movie_id | movie_id | 5 | flask.ma.movie_id | 2 | 100.00 | NULL |
| 1 | SIMPLE | m | NULL | eq_ref | PRIMARY,year_id | PRIMARY | 4 | flask.ma.movie_id | 1 | 100.00 | NULL |
| 1 | SIMPLE | a | NULL | eq_ref | PRIMARY | PRIMARY | 4 | flask.ma.area_id | 1 | 100.00 | Using index |
| 1 | SIMPLE | c | NULL | eq_ref | PRIMARY | PRIMARY | 4 | flask.mc.category_id | 1 | 100.00 | Using index |
+----+-------------+-------+------------+--------+-----------------+----------+---------+----------------------+-------+----------+---------------------------------+
5 rows in set, 1 warning (0.00 sec)
How to optimise this query? I really down't know what to do, help me out.
edit:
From the explain result,first line's Extra is "Using temporary; Using filesort",is not good.And second line's Extra and third line's Extra are all NULL, are also not good.
ps:
The query span 0.91 seconds,is very bad.How to add index to prove the query speed.
Improve schema
Without seeing the schemas involved, I will assume you have two many-to-many mapping table (mc, ma) that are inefficiently defined.
Follow the rules here ; you will gain some efficiency.
Get rid of unnecessary tables
category and movie_category are not really used in the query, so remove them from the query.
I assume this is a "generated" query? Then make the generation a little more sophisticated!
MySQL version 5.7.17
When I use JOIN for small tables, MySQL don't allow me to use index on the main table.
Compare:
EXPLAIN SELECT `ko_base_client_orders`.*
FROM `ko_base_client_orders`
LEFT JOIN `ko_base_new_perspe` AS `ko_of`
ON (`ko_of`.`id` = `ko_base_client_orders`.`office`)
ORDER BY `order_time` DESC
LIMIT 10 OFFSET 0;
+----+-------------+-----------------------+------------+-------+---------------+---------+---------+------+-------+----------+-----------------------------------------------------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+-----------------------+------------+-------+---------------+---------+---------+------+-------+----------+-----------------------------------------------------------------+
| 1 | SIMPLE | ko_base_client_orders | NULL | ALL | NULL | NULL | NULL | NULL | 60737 | 100.00 | Using temporary; Using filesort |
| 1 | SIMPLE | ko_of | NULL | index | PRIMARY | PRIMARY | 4 | NULL | 2 | 100.00 | Using where; Using index; Using join buffer (Block Nested Loop) |
+----+-------------+-----------------------+------------+-------+---------------+---------+---------+------+-------+----------+-----------------------------------------------------------------+
2 rows in set, 1 warning (0.00 sec)
and the same query with index hint:
EXPLAIN SELECT `ko_base_client_orders`.*
FROM `ko_base_client_orders`
LEFT JOIN `ko_base_new_perspe` AS `ko_of`
FORCE INDEX FOR JOIN (`PRIMARY`)
ON (`ko_of`.`id` = `ko_base_client_orders`.`office`)
ORDER BY `order_time` DESC
LIMIT 10 OFFSET 0;
+----+-------------+-----------------------+------------+--------+---------------+------------+---------+---------------------------------+------+----------+-------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+-----------------------+------------+--------+---------------+------------+---------+---------------------------------+------+----------+-------------+
| 1 | SIMPLE | ko_base_client_orders | NULL | index | NULL | order_time | 6 | NULL | 10 | 100.00 | NULL |
| 1 | SIMPLE | ko_of | NULL | eq_ref | PRIMARY | PRIMARY | 4 | e3.ko_base_client_orders.office | 1 | 100.00 | Using index |
+----+-------------+-----------------------+------------+--------+---------------+------------+---------+---------------------------------+------+----------+-------------+
In second case MySQL uses key for order_time, but in first example it ignores index and scans the entire table.
I can not predict which table would be small, and which would not (it differs from project to project), so I don't want to use index hints all the time. Is there another solution for this problem?