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Alternatives to `EXPLAIN ANALYZE` for queries that won't complete
I have a large and complex PostgreSQL SELECT query that I would like to make faster. EXPLAIN suggests it should run quickly, with the worst parts being scans of a few thousand rows. When run, it does not complete in any reasonable amount of time (if statement_timeout is set to infinite, it eventually still gives up, complaining about having exceeded temporary file size limits, suggesting something is loading way more data than expected).
Usually, this would suggest to me that EXPLAIN's estimates are horribly inaccurate in some way, and I would try EXPLAIN ANALYZE to see what's really happening. But since this particular query is so bad I can't run it at all, I also can't run it with EXPLAIN ANALYZE.
What other tools are at my disposal for this sort of situation? Can I ask PostgreSQL for some sort of partial or time-limited EXPLAIN ANALYZE, as in "run this for five minutes, then stop and tell me what you spent those five minutes doing"? If I start commenting out bits of the query until it goes fast again, can I rely on the results being accurate, or does PostgreSQL's optimizer work more globally than that?
(Query itself omitted because I've run into this situation a few times, and would like general strategies rather than an answer for this specific query.)
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Note: I have limited experience with PostgreSQL, but extensive experience working with SQL Server, so not everything below might apply to PostgreSQL.
I have a large and complex PostgreSQL query that I would like to make faster. (..) When run, it does not complete in any reasonable amount of time
I would assume we are talking about a SELECT statement. One quick change to try would be using a LIMIT and see if the query ends for a small amount of returned rows.
However, I think that the real issue is that the query became large and complex. This should be broken into multiple statements with the help of temporary tables. This can also be encapsulated in a stored procedure.
The code structure can look like the following:
- create the temporary table (i.e. empty, contains the output structure)
- minimally populate the temporary table, for example having only a few columns populated with values (the rest remain NULL)
- add UPDATE statements to deal with the rest of the columns. Define as many UPDATEs as are needed to have a good enough performance
Another advantage of this approach is readability (smaller queries) and maintainability (e.g. easier to change when a column is added as this affects a small query).
Other things to consider:
- historical data - if the query deals with historical data aggregates, these can be precomputed in some persisted tables
- indexes - consider adding covering indexes
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You can try to break up the query into CTEs, and then see if any of the individual CTEs are unusually slow.
I am guessing the query is not just one select, but probably has subqueries, window functions, aggregations, joins and so on. All of these can be split into CTEs pretty easily (if you have questions about specific syntax, like "how do I move aggregation to a CTE" that's worth a separate question). Some code editors can even do that refactor automatically.
Binary search is a good approach here. Start by splitting off half your query into a CTE, and the rest as the final query. See if the CTE is much faster. If so, then extract another half of the remaining query as a second CTE. If not, then extract half of the CTE instead. If you repeat this it should eventually identify the specific part that is slowing things down.
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