Mysql: Join vs. sub-query ~ Tech Blog

Friday 2 November 2018

Mysql: Join vs. sub-query

I am an old-school MySQL user and have always preferred JOIN over sub-query. But nowadays everyone uses sub-query and I hate it, I don't know why.

I lack the theoretical knowledge to judge for myself if there is any difference. Is a sub-query as good as a JOIN and therefore there is nothing to worry about?

Answers

Taken from the manual (13.2.10.11 Rewriting Subqueries as Joins):

A LEFT [OUTER] JOIN can be faster than an equivalent subquery because the server might be able to optimize it better—a fact that is not specific to MySQL Server alone.

So subqueries can be slower than LEFT [OUTER] JOINS, but in my opinion their strength is slightly higher readability.

In most cases JOINs are faster than sub-queries and it is very rare for a sub-query to be faster.

In JOINs RDBMS can create an execution plan that is better for your query and can predict what data should be loaded to be processed and save time, unlike the sub-query where it will run all the queries and load all their data to do the processing.

The good thing in sub-queries is that they are more readable than JOINs: that's why most new SQL people prefer them; it is the easy way; but when it comes to performance, JOINS are better in most cases even though they are not hard to read too.

First of all, to compare the two first you should distinguish queries with subqueries to:

a class of subqueries that always have corresponding equivalent query written with joins
a class of subqueries that can not be rewritten using joins

For the first class of queries a good RDBMS will see joins and subqueries as equivalent and will produce same query plans.

These days even mysql does that.

Still, sometimes it does not, but this does not mean that joins will always win - I had cases when using subqueries in mysql improved performance. (For example if there is something preventing mysql planner to correctly estimate the cost and if the planner doesn't see the join-variant and subquery-variant as same then subqueries can outperform the joins by forcing a certain path).

Conclusion is that you should test your queries for both join and subquery variants if you want to be sure which one will perform better.

For the second class the comparison makes no sense as those queries can not be rewritten using joins and in these cases subqueries are natural way to do the required tasks and you should not discriminate against them.

I think what has been under-emphasized in the cited answers is the issue of duplicates and problematic results that may arise from specific (use) cases.

(although Marcelo Cantos does mention it)

I will cite the example from Stanford's Lagunita courses on SQL.

Student Table

+------+--------+------+--------+
| sID  | sName  | GPA  | sizeHS |
+------+--------+------+--------+
|  123 | Amy    |  3.9 |   1000 |
|  234 | Bob    |  3.6 |   1500 |
|  345 | Craig  |  3.5 |    500 |
|  456 | Doris  |  3.9 |   1000 |
|  567 | Edward |  2.9 |   2000 |
|  678 | Fay    |  3.8 |    200 |
|  789 | Gary   |  3.4 |    800 |
|  987 | Helen  |  3.7 |    800 |
|  876 | Irene  |  3.9 |    400 |
|  765 | Jay    |  2.9 |   1500 |
|  654 | Amy    |  3.9 |   1000 |
|  543 | Craig  |  3.4 |   2000 |
+------+--------+------+--------+

Apply Table

(applications made to specific universities and majors)

+------+----------+----------------+----------+
| sID  | cName    | major          | decision |
+------+----------+----------------+----------+
|  123 | Stanford | CS             | Y        |
|  123 | Stanford | EE             | N        |
|  123 | Berkeley | CS             | Y        |
|  123 | Cornell  | EE             | Y        |
|  234 | Berkeley | biology        | N        |
|  345 | MIT      | bioengineering | Y        |
|  345 | Cornell  | bioengineering | N        |
|  345 | Cornell  | CS             | Y        |
|  345 | Cornell  | EE             | N        |
|  678 | Stanford | history        | Y        |
|  987 | Stanford | CS             | Y        |
|  987 | Berkeley | CS             | Y        |
|  876 | Stanford | CS             | N        |
|  876 | MIT      | biology        | Y        |
|  876 | MIT      | marine biology | N        |
|  765 | Stanford | history        | Y        |
|  765 | Cornell  | history        | N        |
|  765 | Cornell  | psychology     | Y        |
|  543 | MIT      | CS             | N        |
+------+----------+----------------+----------+

Let's try to find the GPA scores for students that have applied to CS major (regardless of the university)

Using a subquery:

select GPA from Student where sID in (select sID from Apply where major = 'CS');

+------+
| GPA  |
+------+
|  3.9 |
|  3.5 |
|  3.7 |
|  3.9 |
|  3.4 |
+------+

The average value for this resultset is:

select avg(GPA) from Student where sID in (select sID from Apply where major = 'CS');

+--------------------+
| avg(GPA)           |
+--------------------+
| 3.6800000000000006 |
+--------------------+

Using a join:

select GPA from Student, Apply where Student.sID = Apply.sID and Apply.major = 'CS';

+------+
| GPA  |
+------+
|  3.9 |
|  3.9 |
|  3.5 |
|  3.7 |
|  3.7 |
|  3.9 |
|  3.4 |
+------+

average value for this resultset:

select avg(GPA) from Student, Apply where Student.sID = Apply.sID and Apply.major = 'CS';

+-------------------+
| avg(GPA)          |
+-------------------+
| 3.714285714285714 |
+-------------------+

It is obvious that the second attempt yields misleading results in our use case, given that it counts duplicates for the computation of the average value. It is also evident that usage of distinct with the join - based statement will not eliminate the problem, given that it will erroneously keep one out of three occurrences of the 3.9 score. The correct case is to account for TWO (2) occurrences of the 3.9 score given that we actually have TWO (2) students with that score that comply with our query criteria.

It seems that in some cases a sub-query is the safest way to go, besides any performance issues.

MySQL version: 5.5.28-0ubuntu0.12.04.2-log

I was also under the impression that JOIN is always better than a sub-query in MySQL, but EXPLAIN is a better way to make a judgment. Here is an example where sub queries work better than JOINs.

Here is my query with 3 sub-queries:

EXPLAIN SELECT vrl.list_id,vrl.ontology_id,vrl.position,l.name AS list_name, vrlih.position AS previous_position, vrl.moved_date 
FROM `vote-ranked-listory` vrl 
INNER JOIN lists l ON l.list_id = vrl.list_id 
INNER JOIN `vote-ranked-list-item-history` vrlih ON vrl.list_id = vrlih.list_id AND vrl.ontology_id=vrlih.ontology_id AND vrlih.type='PREVIOUS_POSITION' 
INNER JOIN list_burial_state lbs ON lbs.list_id = vrl.list_id AND lbs.burial_score < 0.5 
WHERE vrl.position <= 15 AND l.status='ACTIVE' AND l.is_public=1 AND vrl.ontology_id < 1000000000 
 AND (SELECT list_id FROM list_tag WHERE list_id=l.list_id AND tag_id=43) IS NULL 
 AND (SELECT list_id FROM list_tag WHERE list_id=l.list_id AND tag_id=55) IS NULL 
 AND (SELECT list_id FROM list_tag WHERE list_id=l.list_id AND tag_id=246403) IS NOT NULL 
ORDER BY vrl.moved_date DESC LIMIT 200;

EXPLAIN shows:

+----+--------------------+----------+--------+-----------------------------------------------------+--------------+---------+-------------------------------------------------+------+--------------------------+
| id | select_type        | table    | type   | possible_keys                                       | key          | key_len | ref                                             | rows | Extra                    |
+----+--------------------+----------+--------+-----------------------------------------------------+--------------+---------+-------------------------------------------------+------+--------------------------+
|  1 | PRIMARY            | vrl      | index  | PRIMARY                                             | moved_date   | 8       | NULL                                            |  200 | Using where              |
|  1 | PRIMARY            | l        | eq_ref | PRIMARY,status,ispublic,idx_lookup,is_public_status | PRIMARY      | 4       | ranker.vrl.list_id                              |    1 | Using where              |
|  1 | PRIMARY            | vrlih    | eq_ref | PRIMARY                                             | PRIMARY      | 9       | ranker.vrl.list_id,ranker.vrl.ontology_id,const |    1 | Using where              |
|  1 | PRIMARY            | lbs      | eq_ref | PRIMARY,idx_list_burial_state,burial_score          | PRIMARY      | 4       | ranker.vrl.list_id                              |    1 | Using where              |
|  4 | DEPENDENT SUBQUERY | list_tag | ref    | list_tag_key,list_id,tag_id                         | list_tag_key | 9       | ranker.l.list_id,const                          |    1 | Using where; Using index |
|  3 | DEPENDENT SUBQUERY | list_tag | ref    | list_tag_key,list_id,tag_id                         | list_tag_key | 9       | ranker.l.list_id,const                          |    1 | Using where; Using index |
|  2 | DEPENDENT SUBQUERY | list_tag | ref    | list_tag_key,list_id,tag_id                         | list_tag_key | 9       | ranker.l.list_id,const                          |    1 | Using where; Using index |
+----+--------------------+----------+--------+-----------------------------------------------------+--------------+---------+-------------------------------------------------+------+--------------------------+

The same query with JOINs is:

EXPLAIN SELECT vrl.list_id,vrl.ontology_id,vrl.position,l.name AS list_name, vrlih.position AS previous_position, vrl.moved_date 
FROM `vote-ranked-listory` vrl 
INNER JOIN lists l ON l.list_id = vrl.list_id 
INNER JOIN `vote-ranked-list-item-history` vrlih ON vrl.list_id = vrlih.list_id AND vrl.ontology_id=vrlih.ontology_id AND vrlih.type='PREVIOUS_POSITION' 
INNER JOIN list_burial_state lbs ON lbs.list_id = vrl.list_id AND lbs.burial_score < 0.5 
LEFT JOIN list_tag lt1 ON lt1.list_id = vrl.list_id AND lt1.tag_id = 43 
LEFT JOIN list_tag lt2 ON lt2.list_id = vrl.list_id AND lt2.tag_id = 55 
INNER JOIN list_tag lt3 ON lt3.list_id = vrl.list_id AND lt3.tag_id = 246403 
WHERE vrl.position <= 15 AND l.status='ACTIVE' AND l.is_public=1 AND vrl.ontology_id < 1000000000 
AND lt1.list_id IS NULL AND lt2.tag_id IS NULL 
ORDER BY vrl.moved_date DESC LIMIT 200;

and the output is:

+----+-------------+-------+--------+-----------------------------------------------------+--------------+---------+---------------------------------------------+------+----------------------------------------------+
| id | select_type | table | type   | possible_keys                                       | key          | key_len | ref                                         | rows | Extra                                        |
+----+-------------+-------+--------+-----------------------------------------------------+--------------+---------+---------------------------------------------+------+----------------------------------------------+
|  1 | SIMPLE      | lt3   | ref    | list_tag_key,list_id,tag_id                         | tag_id       | 5       | const                                       | 2386 | Using where; Using temporary; Using filesort |
|  1 | SIMPLE      | l     | eq_ref | PRIMARY,status,ispublic,idx_lookup,is_public_status | PRIMARY      | 4       | ranker.lt3.list_id                          |    1 | Using where                                  |
|  1 | SIMPLE      | vrlih | ref    | PRIMARY                                             | PRIMARY      | 4       | ranker.lt3.list_id                          |  103 | Using where                                  |
|  1 | SIMPLE      | vrl   | ref    | PRIMARY                                             | PRIMARY      | 8       | ranker.lt3.list_id,ranker.vrlih.ontology_id |   65 | Using where                                  |
|  1 | SIMPLE      | lt1   | ref    | list_tag_key,list_id,tag_id                         | list_tag_key | 9       | ranker.lt3.list_id,const                    |    1 | Using where; Using index; Not exists         |
|  1 | SIMPLE      | lbs   | eq_ref | PRIMARY,idx_list_burial_state,burial_score          | PRIMARY      | 4       | ranker.vrl.list_id                          |    1 | Using where                                  |
|  1 | SIMPLE      | lt2   | ref    | list_tag_key,list_id,tag_id                         | list_tag_key | 9       | ranker.lt3.list_id,const                    |    1 | Using where; Using index                     |
+----+-------------+-------+--------+-----------------------------------------------------+--------------+---------+---------------------------------------------+------+----------------------------------------------+

A comparison of the rows column tells the difference and the query with JOINs is using Using temporary; Using filesort.

Of course when I run both the queries, the first one is done in 0.02 secs, the second one does not complete even after 1 min, so EXPLAIN explained these queries properly.

If I do not have the INNER JOIN on the list_tag table i.e. if I remove

AND (SELECT list_id FROM list_tag WHERE list_id=l.list_id AND tag_id=246403) IS NOT NULL

from the first query and correspondingly:

INNER JOIN list_tag lt3 ON lt3.list_id = vrl.list_id AND lt3.tag_id = 246403

from the second query, then EXPLAIN returns the same number of rows for both queries and both these queries run equally fast.

Subqueries are generally used to return a single row as an atomic value, though they may be used to compare values against multiple rows with the IN keyword. They are allowed at nearly any meaningful point in a SQL statement, including the target list, the WHERE clause, and so on. A simple sub-query could be used as a search condition. For example, between a pair of tables:

   SELECT title FROM books WHERE author_id = (SELECT id FROM authors WHERE last_name = 'Bar' AND first_name = 'Foo');

Note that using a normal value operator on the results of a sub-query requires that only one field must be returned. If you're interested in checking for the existence of a single value within a set of other values, use IN:

   SELECT title FROM books WHERE author_id IN (SELECT id FROM authors WHERE last_name ~ '^[A-E]');

This is obviously different from say a LEFT-JOIN where you just want to join stuff from table A and B even if the join-condition doesn't find any matching record in table B, etc.

If you're just worried about speed you'll have to check with your database and write a good query and see if there's any significant difference in performance.

Taken directly from essentialsql.com:

Joins and subqueries are both used to combine data from different tables into a single result. They share many similarities and differences.Subqueries can be used to return either a scalar (single) value or a row set; whereas, joins are used to return rows.A common use for a subquery may be to calculate a summary value for use in a query. For instance we can use a subquery to help us obtain all products have a greater than average product price. For example:

SELECT ProductID,
       Name,
       ListPrice,
       (SELECT AVG(ListPrice)
        FROM Production.Product
       ) AS AvgListPrice
FROM Production.Product
WHERE ListPrice > (SELECT AVG(ListPrice)
                   FROM Production.Product
                  )

There are two subqueries in this SELECT statement. The first’s purpose is to display the average list price of all products, the second’s purpose is for filtering out products less than or equal to the average list price. Contrast this with a join whose main purpose of a join is to combine rows from one or more tables based on a match condition. For example we can use a join display product names and models.

SELECT Product.Name,
       ProductModel.Name AS ModelName
FROM Production.product
INNER JOIN Production.ProductModel
ON Product.ProductModelID = ProductModel.ProductModelID

In this statement we’re using an INNER JOIN to match rows from both the Product and ProductModel tables. Notice that the column ProducModel.Name is available for use throughout the query.The combined row set is then available by the select statement for use to display, filter, or group by the columns.This is different than the subquery. There the subquery returns a result, which is immediately used.Note that he join is an integral part of the select statement. It can not stand on its own as a subquery can.

The difference is only seen when the second joining table has significantly more data than the primary table. I had an experience like below...

We had a users table of one hundred thousand entries and their membership data (friendship) about 3 hundred thousand entries. It was a join statement in order to take friends and their data, but with a great delay. But it was working fine where there was only a small amount of data in the membership table. Once we changed it to use a sub-query it worked fine.

But in the mean time the join queries are working with other tables that have fewer entries than the primary table.

So I think the join and sub query statements are working fine and it depends on the data and the situation.

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Friday 2 November 2018