Performance difference: condition placed at INNER JOIN vs WHERE clause

Question

Say I have a table order as

id | clientid | type | amount | itemid | date
---|----------|------|--------|--------|-----------
23 | 258      | B    | 150    | 14     | 2012-04-03
24 | 258      | S    | 69     | 14     | 2012-04-03
25 | 301      | S    | 10     | 20     | 2012-04-03
26 | 327      | B    | 54     | 156    | 2012-04-04

• clientid is a foreign-key back to the client table
• itemid is a foreign key back to an item table
• type is only B or S
• amount is an integer

and a table processed as

id | orderid | processed | date
---|---------|-----------|---------
41 | 23      | true      | 2012-04-03
42 | 24      | true      | 2012-04-03
43 | 25      | false     | <NULL>
44 | 26      | true      | 2012-04-05     

I need to get all the rows from order that for the same clientid on the same date have opposing type values. Keep in mind type can only have one of two values - B or S. In the example above this would be rows 23 and 24.

The other constraint is that the corresponding row in processed must be true for the orderid.

My query so far

SELECT c1.clientid,
       c1.date,
       c1.type,
       c1.itemid,
       c1.amount,
       c2.date,
       c2.type,
       c2.itemid,
       c2.amount

FROM   order c1
INNER JOIN order c2 ON c1.itemid    =  c2.itemid AND
                       c1.date      =  c2.date   AND
                       c1.clientid  =  c2.clientid AND
                       c1.type     <>  c2.type AND
                       c1.id        <  c2.id

INNER JOIN processed p1 ON p1.orderid   =  c1.id AND
                         p1.processed =  true
INNER JOIN processed p2 ON p2.orderid   =  c2.id AND
                         p2.processed =  true

QUESTION: Keeping the processed = true as part of the join clause is slowing the query down. If I move it to the WHERE clause then the performance is much better. This has piqued my interest and I'd like to know why.

The primary keys and respective foreign key columns are indexed while the value columns (value, processed etc) aren't.

Disclaimer: I have inherited this DB structure and the performance difference is roughly 6 seconds.

Solution

The reason that you're seeing a difference is due to the execution plan that the planner is putting together, this is obviously different depending on the query (arguably, it should be optimising the 2 queries to be the same and this may be a bug). This means that the planner thinks it has to work in a particular way to get to the result in each statement.

When you do it within the JOIN, the planner will probably have to select from the table, filter by the "True" part, then join the result sets. I would imagine this is a large table, and therefore a lot of data to look through, and it can't use the indexes as efficiently.

I suspect that if you do it in a WHERE clause, the planner is choosing a route that is more efficient (ie. either index based, or pre filtered dataset).

You could probably make the join work as fast (if not faster) by adding an index on the two columns (not sure if included columns and multiple column indexes are supported on Postgres yet).

In short, the planner is the problem it is choosing 2 different routes to get to the result sets, and one of those is not as efficient as the other. It's impossible for us to know what the reasons are without the full table information and the EXPLAIN ANALYZE information.

If you want specifics on why your specific query is doing this, you'll need to provide more information. However the reason is the planner choosing different routes.

Additional Reading Material:

http://www.postgresql.org/docs/current/static/explicit-joins.html

Just skimmed, seems that the postgres planner doesn't re-order joins to optimise it. try changing the order of the joins in your statement to see if you then get the same performance... just a thought.

Comments

[Insectatorious]

Right....makes sense...the trouble is I've simplified the tables and their respective structures to post this question..I'll try and get the explain analyse

[ypercubeᵀᴹ]

You do not force the query planner by putting conditions in the ON or the WHERE clause. A decent optimizer/query planner should be able to identify both versions as equivalent (when they are) and choose from various execution plans.

[Cade Roux]

@ypercube Optimizer would normally push them down in as low as possible to reduce the cardinality as soon as possible, but obviously that is not good when it results in a table op instead of an index op. And then perhaps it's not smart enough to pull it up and use it later when the working set is smaller. What's most interesting is that the optimizer doesn't push around the clauses in the WHERE version to be the same.

[ypercubeᵀᴹ]

@CadeRoux: Yeah but I think Postgres is mature enough to do that. What may confuse the optimizer is that it has to join 4 tables (so quite a lot of plans there) and only a few indexes. If there were useful indexes, I think it would choose same plans in both cases.

[Martin]

Maybe "Force" isn't the right word, however, the concept is correct. Maybe "Tell" is the word, but this is meant to be descriptive to people who are not familiar with planners. By doing what he's doing (JOIN vs WHERE) the planner is taking another path, and therefore there is a difference in performance.

[ypercubeᵀᴹ]

"Force" is not the right word and neither is "have to". It's not "Tell" either. An SQL statement/query does not tell the DBMS how to do something, it tells what to do. If one wants to give hints of force the optimizer to do things a specific way, there are ways too (that vary from DBMS to DBMS). But the query has no such hints.

[ypercubeᵀᴹ]

@Martin: What you are right, is that the optimizer is choosing different (execution) paths/plans.

[Martin]

We could go into technical detail about how planners work, however, that is way beyond the scope of the question. The answer gives the OP, and someone not familar with planners, the ability to understand why it is happening without providing meaningless information.

[Martin]

@ypercube: I've updated the answer to move away from those words, I think that covers the question that was asked, do you agree?