I am relatively new to SQL. I have a script that used to run very quickly (<0.5 seconds) but runs very slowly (>120 seconds) if I add one change - and I can't see why this change makes such a difference. Any help would be hugely appreciated!
This is the script and it runs quickly if I do NOT include "tt2.bulk_cnt
" in line 26:
with bulksum1 as
(
select t1.membercode,
t1.schemecode,
t1.transdate
from mina_raw2 t1
where t1.transactiontype in ('RSP','SP','UNTV','ASTR','CN','TVIN','UCON','TRAS')
group by t1.membercode,
t1.schemecode,
t1.transdate
),
bulksum2 as
(
select t1.schemecode,
t1.transdate,
count(*) as bulk_cnt
from bulksum1 t1
group by t1.schemecode,
t1.transdate
having count(*) >= 10
),
results as
(
select t1.*, tt2.bulk_cnt
from mina_raw2 t1
inner join bulksum2 tt2
on t1.schemecode = tt2.schemecode and t1.transdate = tt2.transdate
where t1.transactiontype in ('RSP','SP','UNTV','ASTR','CN','TVIN','UCON','TRAS')
)
select * from results
EDIT: I apologise for not putting enough detail in here previously - although I can use basic SQL code, I am a complete novice when it comes to databases.
Database: Oracle (I'm not sure which version, sorry)
Execution plans:
QUICK query:
Plan hash value: 1712123489
---------------------------------------------
| Id | Operation | Name |
---------------------------------------------
| 0 | SELECT STATEMENT | |
| 1 | HASH JOIN | |
| 2 | VIEW | |
| 3 | FILTER | |
| 4 | HASH GROUP BY | |
| 5 | VIEW | VM_NWVW_0 |
| 6 | HASH GROUP BY | |
| 7 | TABLE ACCESS FULL| MINA_RAW2 |
| 8 | TABLE ACCESS FULL | MINA_RAW2 |
---------------------------------------------
SLOW query:
Plan hash value: 1298175315
--------------------------------------------
| Id | Operation | Name |
--------------------------------------------
| 0 | SELECT STATEMENT | |
| 1 | FILTER | |
| 2 | HASH GROUP BY | |
| 3 | HASH JOIN | |
| 4 | VIEW | VM_NWVW_0 |
| 5 | HASH GROUP BY | |
| 6 | TABLE ACCESS FULL| MINA_RAW2 |
| 7 | TABLE ACCESS FULL | MINA_RAW2 |
--------------------------------------------
A few observations, and then some things to do:
1) More information is needed. In particular, how many rows are there in the MINA_RAW2 table, what indexes exist on this table, and when was the last time it was analyzed? To determine the answers to these questions, run:
SELECT COUNT(*) FROM MINA_RAW2;
SELECT TABLE_NAME, LAST_ANALYZED, NUM_ROWS
FROM USER_TABLES
WHERE TABLE_NAME = 'MINA_RAW2';
From looking at the plan output it looks like the database is doing two FULL SCANs on MINA_RAW2 - it would be nice if this could be reduced to no more than one, and hopefully none. It's always tough to tell without very detailed information about the data in the table, but at first blush it appears that an index on TRANSACTIONTYPE might be helpful. If such an index doesn't exist you might want to consider adding it.
2) Assuming that the statistics are out-of-date (as in, old, nonexistent, or a significant amount of data (> 10%) has been added, deleted, or updated since the last analysis) run the following:
BEGIN
DBMS_STATS.GATHER_TABLE_STATS(owner => 'YOUR-SCHEMA-NAME',
table_name => 'MINA_RAW2');
END;
substituting the correct schema name for "YOUR-SCHEMA-NAME" above. Remember to capitalize the schema name! If you don't know if you should or shouldn't gather statistics, err on the side of caution and do it. It shouldn't take much time.
3) Re-try your existing query after updating the table statistics. I think there's a fair chance that having up-to-date statistics in the database will solve your issues. If not:
4) This query is doing a GROUP BY on the results of a GROUP BY. This doesn't appear to be necessary as the initial GROUP BY doesn't do any grouping - instead, it appears this is being done to get the unique combinations of MEMBERCODE, SCHEMECODE, and TRANSDATE so that the count of the members by scheme and date can be determined. I think the whole query can be simplified to:
WITH cteWORKING_TRANS AS (SELECT *
FROM MINA_RAW2
WHERE TRANSACTIONTYPE IN ('RSP','SP','UNTV',
'ASTR','CN','TVIN',
'UCON','TRAS')),
cteBULKSUM AS (SELECT a.SCHEMECODE,
a.TRANSDATE,
COUNT(*) AS BULK_CNT
FROM (SELECT DISTINCT MEMBERCODE,
SCHEMECODE,
TRANSDATE
FROM cteWORKING_TRANS) a
GROUP BY a.SCHEMECODE,
a.TRANSDATE)
SELECT t.*, b.BULK_CNT
FROM cteWORKING_TRANS t
INNER JOIN cteBULKSUM b
ON b.SCHEMECODE = t.SCHEMECODE AND
b.TRANSDATE = t.TRANSDATE
I managed to remove an unnecessary subquery, but this syntax with distinct inside count may not work outside of PostgreSQL or may not be the desired result. I know I've certainly used it there.
select t1.*, tt2.bulk_cnt
from mina_raw2 t1
inner join (select t2.schemecode,
t2.transdate,
count(DISTINCT membercode) as bulk_cnt
from mina_raw2 t2
where t2.transactiontype in ('RSP','SP','UNTV','ASTR','CN','TVIN','UCON','TRAS')
group by t2.schemecode,
t2.transdate
having count(DISTINCT membercode) >= 10) tt2
on t1.schemecode = tt2.schemecode and t1.transdate = tt2.transdate
where t1.transactiontype in ('RSP','SP','UNTV','ASTR','CN','TVIN','UCON','TRAS')
When you use those with queries, instead of subqueries when you don't need to, you're kneecapping the query optimizer.
For an assignment I have to write several SQL queries for a database stored in a PostgreSQL server running PostgreSQL 9.3.0. However, I find myself blocked with last query. The database models a reservation system for an opera house. The query is about associating the a spectator the other spectators that assist to the same events every time.
The model looks like this:
Reservations table
id_res | create_date | tickets_presented | id_show | id_spectator | price | category
-------+---------------------+---------------------+---------+--------------+-------+----------
1 | 2015-08-05 17:45:03 | | 1 | 1 | 195 | 1
2 | 2014-03-15 14:51:08 | 2014-11-30 14:17:00 | 11 | 1 | 150 | 2
Spectators table
id_spectator | last_name | first_name | email | create_time | age
---------------+------------+------------+----------------------------------------+---------------------+-----
1 | gonzalez | colin | colin.gonzalez#gmail.com | 2014-03-15 14:21:30 | 22
2 | bequet | camille | bequet.camille#gmail.com | 2014-12-10 15:22:31 | 22
Shows table
id_show | name | kind | presentation_date | start_time | end_time | id_season | capacity_cat1 | capacity_cat2 | capacity_cat3 | price_cat1 | price_cat2 | price_cat3
---------+------------------------+--------+-------------------+------------+----------+-----------+---------------+---------------+---------------+------------+------------+------------
1 | madama butterfly | opera | 2015-09-05 | 19:30:00 | 21:30:00 | 2 | 315 | 630 | 945 | 195 | 150 | 100
2 | don giovanni | opera | 2015-09-12 | 19:30:00 | 21:45:00 | 2 | 315 | 630 | 945 | 195 | 150 | 100
So far I've started by writing a query to get the id of the spectator and the date of the show he's attending to, the query looks like this.
SELECT Reservations.id_spectator, Shows.presentation_date
FROM Reservations
LEFT JOIN Shows ON Reservations.id_show = Shows.id_show;
Could someone help me understand better the problem and hint me towards finding a solution. Thanks in advance.
So the result I'm expecting should be something like this
id_spectator | other_id_spectators
-------------+--------------------
1| 2,3
Meaning that every time spectator with id 1 went to a show, spectators 2 and 3 did too.
Note based on comments: Wanted to make clear that this answer may be of limited use as it was answered in the context of SQL-Server (tag was present at the time)
There is probably a better way to do it, but you could do it with the 'stuff 'function. The only drawback here is that, since your ids are ints, placing a comma between values will involve a work around (would need to be a string). Below is the method I can think of using a work around.
SELECT [id_spectator], [id_show]
, STUFF((SELECT ',' + CAST(A.[id_spectator] as NVARCHAR(10))
FROM reservations A
Where A.[id_show]=B.[id_show] AND a.[id_spectator] != b.[id_spectator] FOR XML PATH('')),1,1,'') As [other_id_spectators]
From reservations B
Group By [id_spectator], [id_show]
This will show you all other spectators that attended the same shows.
Meaning that every time spectator with id 1 went to a show, spectators 2 and 3 did too.
In other words, you want a list of ...
all spectators that have seen all the shows that a given spectator has seen (and possibly more than the given one)
This is a special case of relational division. We have assembled an arsenal of basic techniques here:
How to filter SQL results in a has-many-through relation
It is special because the list of shows each spectator has to have attended is dynamically determined by the given prime spectator.
Assuming that (d_spectator, id_show) is unique in reservations, which has not been clarified.
A UNIQUE constraint on those two columns (in that order) also provides the most important index.
For best performance in query 2 and 3 below also create an index with leading id_show.
1. Brute force
The primitive approach would be to form a sorted array of shows the given user has seen and compare the same array of others:
SELECT 1 AS id_spectator, array_agg(sub.id_spectator) AS id_other_spectators
FROM (
SELECT id_spectator
FROM reservations r
WHERE id_spectator <> 1
GROUP BY 1
HAVING array_agg(id_show ORDER BY id_show)
#> (SELECT array_agg(id_show ORDER BY id_show)
FROM reservations
WHERE id_spectator = 1)
) sub;
But this is potentially very expensive for big tables. The whole table hast to be processes, and in a rather expensive way, too.
2. Smarter
Use a CTE to determine relevant shows, then only consider those
WITH shows AS ( -- all shows of id 1; 1 row per show
SELECT id_spectator, id_show
FROM reservations
WHERE id_spectator = 1 -- your prime spectator here
)
SELECT sub.id_spectator, array_agg(sub.other) AS id_other_spectators
FROM (
SELECT s.id_spectator, r.id_spectator AS other
FROM shows s
JOIN reservations r USING (id_show)
WHERE r.id_spectator <> s.id_spectator
GROUP BY 1,2
HAVING count(*) = (SELECT count(*) FROM shows)
) sub
GROUP BY 1;
#> is the "contains2 operator for arrays - so we get all spectators that have at least seen the same shows.
Faster than 1. because only relevant shows are considered.
3. Real smart
To also exclude spectators that are not going to qualify early from the query, use a recursive CTE:
WITH RECURSIVE shows AS ( -- produces exactly 1 row
SELECT id_spectator, array_agg(id_show) AS shows, count(*) AS ct
FROM reservations
WHERE id_spectator = 1 -- your prime spectator here
GROUP BY 1
)
, cte AS (
SELECT r.id_spectator, 1 AS idx
FROM shows s
JOIN reservations r ON r.id_show = s.shows[1]
WHERE r.id_spectator <> s.id_spectator
UNION ALL
SELECT r.id_spectator, idx + 1
FROM cte c
JOIN reservations r USING (id_spectator)
JOIN shows s ON s.shows[c.idx + 1] = r.id_show
)
SELECT s.id_spectator, array_agg(c.id_spectator) AS id_other_spectators
FROM shows s
JOIN cte c ON c.idx = s.ct -- has an entry for every show
GROUP BY 1;
Note that the first CTE is non-recursive. Only the second part is recursive (iterative really).
This should be fastest for small selections from big tables. Row that don't qualify are excluded early. the two indices I mentioned are essential.
SQL Fiddle demonstrating all three.
It sounds like you have one half of the total question--determining which id_shows a particular id_spectator attended.
What you want to ask yourself is how you can determine which id_spectators attended an id_show, given an id_show. Once you have that, combine the two answers to get the full result.
So the final answer I got, looks like this :
SELECT id_spectator, id_show,(
SELECT string_agg(to_char(A.id_spectator, '999'), ',')
FROM Reservations A
WHERE A.id_show=B.id_show
) AS other_id_spectators
FROM Reservations B
GROUP By id_spectator, id_show
ORDER BY id_spectator ASC;
Which prints something like this:
id_spectator | id_show | other_id_spectators
-------------+---------+---------------------
1 | 1 | 1, 2, 9
1 | 14 | 1, 2
Which suits my needs, however if you have any improvements to offer, please share :) Thanks again everybody!
i am joining two tables: accn_demographics and accn_payments. The relationship between the two tables is one to many between accn_demographics.accn_id and accn_payments.accn_id
My question is when I am summing the PAID_AMT and COPAY_AMT, I am getting double/triple/quadrouple the number that I should be getting.
Is there an obvious problem with my join condition?
select sum(p.paid_amt) as SumPaidAmount
, sum(p.copay_amt) as SumCoPay
, p.pmt_date
, d.load_Date
, p.ACCN_ID
from accn_payments p
join
(
select distinct load_date, accn_id
from accn_demographics
) d
on p.ACCN_ID=d.ACCN_ID
where p.POSTED='Y'
and p.pmt_date between '20120701' and '20120731'
group by p.pmt_date, d.load_Date,p.ACCN_ID
order by 3 desc
thanks so much for your guidance.
You need to do the summation in a subquery:
select sum(p.SumPaidAmount) as SumPaidAmount, sum(p.SumCoPay) as SumCoPay,
p.pmt_date, d.load_Date, p.ACCN_ID
from (select accn_id, p.pmt_date, sum(paid_amt) as SumPaidAmt,
sum(copay_amt) as SumCoPay
from accn_payments p
where p.POSTED='Y' and
p.pmt_date between '20120701' and '20120731'
group by accn_id, pmt_date
) p join
(select distinct load_date, accn_id from accn_demographics) d
on p.ACCN_ID=d.ACCN_ID
group by p.pmt_date, d.load_Date,p.ACCN_ID
order by 3 desc
Question: do you really intend for pmt_date to be in the final results? It looks like you want to remove it from both the outer SELECT and the subquery.
The only thing I can see if that (select distinct load_date, accn_id from accn_demographics) might return several matches. Look at your data and run a separate query
select distinct load_date, accn_id from accn_demographics WHERE accn_id=SomeID
where SomeID is one of the result accounts that is returning double/triple values. That should pinpoint your problem.
Yes, but it's not so obvious for beginners. What happens is that for every accn_payments record, you're matching on ONLY the accn_id, which means if there are multiple records in accn_demographics for that particular accn_id, then you will get duplicate accn_payment records due to the join. Is there another limiting field on accn_demographics to join back to the payments?
Ultimately, think of it this way:
accn_payments (p):
accn_id | paid_amt | copay_amt | ...
----------------------------------------------------
1 | 100.00 | 20.00 | ...
accn_demographics (d):
accn_id | load_date | ...
------------------------------------
1 | 2012/01/01 | ...
1 | 2012/03/05 | ...
1 | 2012/06/23 | ...
After joining, your results will look like this:
p.accn_id | p.paid_amt | p.copay_amt | p... | d.accn_id | d.load_date | d...
----------------------------------------------------------------------------
1 | 100.00 | 20.00 | .... | 1 | 2012/01/01 | ....
1 | 100.00 | 20.00 | .... | 1 | 2012/03/05 | ....
1 | 100.00 | 20.00 | .... | 1 | 2012/06/21 | ....
As you can see, the same row from accn_payments gets replicated for every matching accn_demographics record, since you specified only the accn_id column to be the join criteria. It can't limit the results any further, so it the DB engine says "Hey, look, this p record matches for all these d records, this must be what he was asking for!" Obviously not what was intended, as when you sum on the p.paid_amt and p.copay_amt, it performs a sum for ALL ROWS (even though they are duplicated).
Ultimately, see if you can limit the join criteria for accn_demographics even further (by some date, perhaps), that way you limit the number of duplicate payment records during the join.
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 is there nothing to worry about?
Sub-queries are the logically correct way to solve problems of the form, "Get facts from A, conditional on facts from B". In such instances, it makes more logical sense to stick B in a sub-query than to do a join. It is also safer, in a practical sense, since you don't have to be cautious about getting duplicated facts from A due to multiple matches against B.
Practically speaking, however, the answer usually comes down to performance. Some optimisers suck lemons when given a join vs a sub-query, and some suck lemons the other way, and this is optimiser-specific, DBMS-version-specific and query-specific.
Historically, explicit joins usually win, hence the established wisdom that joins are better, but optimisers are getting better all the time, and so I prefer to write queries first in a logically coherent way, and then restructure if performance constraints warrant this.
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.
Taken from the MySQL 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] JOIN, but in my opinion their strength is slightly higher readability.
In the year 2010 I would have joined the author of this questions and would have strongly voted for JOIN, but with much more experience (especially in MySQL) I can state: Yes subqueries can be better. I've read multiple answers here; some stated subqueries are faster, but it lacked a good explanation. I hope I can provide one with this (very) late answer:
First of all, let me say the most important: There are different forms of sub-queries
And the second important statement: Size matters
If you use sub-queries, you should be aware of how the DB-Server executes the sub-query. Especially if the sub-query is evaluated once or for every row!
On the other side, a modern DB-Server is able to optimize a lot. In some cases a subquery helps optimizing a query, but a newer version of the DB-Server might make the optimization obsolete.
Sub-queries in Select-Fields
SELECT moo, (SELECT roger FROM wilco WHERE moo = me) AS bar FROM foo
Be aware that a sub-query is executed for every resulting row from foo.
Avoid this if possible; it may drastically slow down your query on huge datasets. However, if the sub-query has no reference to foo it can be optimized by the DB-server as static content and could be evaluated only once.
Sub-queries in the Where-statement
SELECT moo FROM foo WHERE bar = (SELECT roger FROM wilco WHERE moo = me)
If you are lucky, the DB optimizes this internally into a JOIN. If not, your query will become very, very slow on huge datasets because it will execute the sub-query for every row in foo, not just the results like in the select-type.
Sub-queries in the Join-statement
SELECT moo, bar
FROM foo
LEFT JOIN (
SELECT MIN(bar), me FROM wilco GROUP BY me
) ON moo = me
This is interesting. We combine JOIN with a sub-query. And here we get the real strength of sub-queries. Imagine a dataset with millions of rows in wilco but only a few distinct me. Instead of joining against a huge table, we have now a smaller temporary table to join against. This can result in much faster queries depending on database size. You can have the same effect with CREATE TEMPORARY TABLE ... and INSERT INTO ... SELECT ..., which might provide better readability on very complex queries (but can lock datasets in a repeatable read isolation level).
Nested sub-queries
SELECT VARIANCE(moo)
FROM (
SELECT moo, CONCAT(roger, wilco) AS bar
FROM foo
HAVING bar LIKE 'SpaceQ%'
) AS temp_foo
GROUP BY moo
You can nest sub-queries in multiple levels. This can help on huge datasets if you have to group or change the results. Usually the DB-Server creates a temporary table for this, but sometimes you do not need some operations on the whole table, only on the resultset. This might provide a much better performance depending on the size of the table.
Conclusion
Sub-queries are no replacement for a JOIN and you should not use them like this (although possible). In my humble opinion, the correct use of a sub-query is the use as a quick replacement of CREATE TEMPORARY TABLE .... A good sub-query reduces a dataset in a way you cannot accomplish in an ON statement of a JOIN. If a sub-query has one of the keywords GROUP BY or DISTINCT and is preferably not situated in the select fields or the where statement, then it might improve performance a lot.
Use EXPLAIN to see how your database executes the query on your data. There is a huge "it depends" in this answer...
PostgreSQL can rewrite a subquery to a join or a join to a subquery when it thinks one is faster than the other. It all depends on the data, indexes, correlation, amount of data, query, etc.
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.
MSDN Documentation for SQL Server says
Many Transact-SQL statements that include subqueries can be alternatively formulated as joins. Other questions can be posed only with subqueries. In Transact-SQL, there is usually no performance difference between a statement that includes a subquery and a semantically equivalent version that does not. However, in some cases where existence must be checked, a join yields better performance. Otherwise, the nested query must be processed for each result of the outer query to ensure elimination of duplicates. In such cases, a join approach would yield better results.
so if you need something like
select * from t1 where exists select * from t2 where t2.parent=t1.id
try to use join instead. In other cases, it makes no difference.
I say: Creating functions for subqueries eliminate the problem of cluttter and allows you to implement additional logic to subqueries. So I recommend creating functions for subqueries whenever possible.
Clutter in code is a big problem and the industry has been working on avoiding it for decades.
As per my observation like two cases, if a table has less then 100,000 records then the join will work fast.
But in the case that a table has more than 100,000 records then a subquery is best result.
I have one table that has 500,000 records on that I created below query and its result time is like
SELECT *
FROM crv.workorder_details wd
inner join crv.workorder wr on wr.workorder_id = wd.workorder_id;
Result : 13.3 Seconds
select *
from crv.workorder_details
where workorder_id in (select workorder_id from crv.workorder)
Result : 1.65 Seconds
Run on a very large database from an old Mambo CMS:
SELECT id, alias
FROM
mos_categories
WHERE
id IN (
SELECT
DISTINCT catid
FROM mos_content
);
0 seconds
SELECT
DISTINCT mos_content.catid,
mos_categories.alias
FROM
mos_content, mos_categories
WHERE
mos_content.catid = mos_categories.id;
~3 seconds
An EXPLAIN shows that they examine the exact same number of rows, but one takes 3 seconds and one is near instant. Moral of the story? If performance is important (when isn't it?), try it multiple ways and see which one is fastest.
And...
SELECT
DISTINCT mos_categories.id,
mos_categories.alias
FROM
mos_content, mos_categories
WHERE
mos_content.catid = mos_categories.id;
0 seconds
Again, same results, same number of rows examined. My guess is that DISTINCT mos_content.catid takes far longer to figure out than DISTINCT mos_categories.id does.
A general rule is that joins are faster in most cases (99%).
The more data tables have, the subqueries are slower.
The less data tables have, the subqueries have equivalent speed as joins.
The subqueries are simpler, easier to understand, and easier to read.
Most of the web and app frameworks and their "ORM"s and "Active record"s generate queries with subqueries, because with subqueries are easier to split responsibility, maintain code, etc.
For smaller web sites or apps subqueries are OK, but for larger web sites and apps you will often have to rewrite generated queries to join queries, especial if a query uses many subqueries in the query.
Some people say "some RDBMS can rewrite a subquery to a join or a join to a subquery when it thinks one is faster than the other.", but this statement applies to simple cases, surely not for complicated queries with subqueries which actually cause a problems in performance.
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.
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 have ability to calculate aggregation functions on a fly.
E.g. Find minimal price of the book and get all books which are sold with this price.
1) Using Subqueries:
SELECT titles, price
FROM Books, Orders
WHERE price =
(SELECT MIN(price)
FROM Orders) AND (Books.ID=Orders.ID);
2) using JOINs
SELECT MIN(price)
FROM Orders;
-----------------
2.99
SELECT titles, price
FROM Books b
INNER JOIN Orders o
ON b.ID = o.ID
WHERE o.price = 2.99;
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.
These days, many dbs can optimize subqueries and joins. Thus, you just gotto examine your query using explain and see which one is faster. If there is not much difference in performance, I prefer to use subquery as they are simple and easier to understand.
I am not a relational database expert, so take this with a grain of salt.
The general idea about sub queries vs joins is the path the evaluation of the larger query takes.
In order to perform the larger query, every individual subquery has to be executed first, and then the resultset is stored as a temporary table that the larger query interacts with.
This temporary table is unindexed, so, any comparison requires scanning the whole resultset.
In contrast, when you use a join, all indexes are in use and so, comparison require traversing index trees (or hash tables), which is way less expensive in terms of speed.
Now, what I don't know if newer versions of the most popular relational engines execute the evaluation on reverse, and just load the necessary elements in the temporary table, as an optimization method.
I just thinking about the same problem, but I am using subquery in the FROM part.
I need connect and query from large tables, the "slave" table have 28 million record but the result is only 128 so small result big data! I am using MAX() function on it.
First I am using LEFT JOIN because I think that is the correct way, the mysql can optimalize etc.
Second time just for testing, I rewrite to sub-select against the JOIN.
LEFT JOIN runtime: 1.12s
SUB-SELECT runtime: 0.06s
18 times faster the subselect than the join! Just in the chokito adv. The subselect looks terrible but the result ...
It depends on several factors, including the specific query you're running, the amount of data in your database. Subquery runs the internal queries first and then from the result set again filter out the actual results. Whereas in join runs the and produces the result in one go.
The best strategy is that you should test both the join solution and the subquery solution to get the optimized solution.
If you want to speed up your query using join:
For "inner join/join",
Don't use where condition instead use it in "ON" condition.
Eg:
select id,name from table1 a
join table2 b on a.name=b.name
where id='123'
Try,
select id,name from table1 a
join table2 b on a.name=b.name and a.id='123'
For "Left/Right Join",
Don't use in "ON" condition, Because if you use left/right join it will get all rows for any one table.So, No use of using it in "On". So, Try to use "Where" condition