Note that this is only possible in the case of SQL functions. The ld function has been replaced with the underlying log function. The interesting point here can be found in the WHERE clause. Now let’s see what happens in a real query:įunction Scan on generate_series x (cost=0.00.0.18 rows=1 width=4)įilter: (log('2'::numeric, (x)::numeric) = '1000'::numeric) Let’s create a function to calculate a logarithm:ĭemo=# CREATE OR REPLACE FUNCTION ld(int)Ģ^10 = 1024. The goal is to reduce function calls as much as possible and thus speed up the query. ![]() One more important technique is the idea of function inlining. PostgreSQL query optimizer: Function inlining That’s why you should try to make sure that the filter is on the right side, and not on the column you might want to index. ![]() What we see here is that PostgreSQL does not transform the expression to “x = 8” in this case. Why is that important? In case “x” is indexed (assuming it is a table), we can easily look up 8 in the index.įunction Scan on generate_series x (cost=0.00.0.15 rows=1 width=4) What the system does is to “fold” the constant and instead do “x = 8”. What you can see here is that we add a filter to the query: x = 7 + 1. Let’s see what happens during this process:įunction Scan on generate_series x (cost=0.00.0.13 rows=1 width=4) PostgreSQL constant foldingĬonstant folding is one of the more simplistic and easier things to describe. There is a lot more going on, but it makes sense to take a look at the most basic things in order to gain a good understanding of the process. Note that the techniques listed here are in no way complete. So let’s take a tour through the PostgreSQL optimizer and get an overview of some of the most important techniques the optimizer uses to speed up queries. For many people, the workings of the optimizer itself remain a mystery, so we have decided to give users some insight into what is really going on behind the scenes. Just like any advanced relational database, PostgreSQL uses a cost-based query optimizer that tries to turn your SQL queries into something efficient that executes in as little time as possible. "resulting_condition": "((`e`.`uneven_val` between 3) and multiple equal(`c`.`id`, `g`.`category_id`) and multiple equal(`g`.`id`, `e`.Anti-join exists from_collapse_limit optimizer performance postgresql query volatile "transformation": "trivial_condition_removal", ![]() "transformation": "constant_propagation", "resulting_condition": "((`e`.`uneven_val` between 3) and multiple equal(`c`.`id`, `g`.`category_id`) and multiple equal(`g`.`id`, `e`.`group_id`))" "transformation": "equality_propagation", Doing so takes control away from the optimizer and I generally think it's a lot smarter than I am. This was my conclusion as well, but I very much dislike forcing an access plan unless I am certain there is a bug or I am working with a known edge case. > current plan useful, e.g., idx_entry_group_on_category_id) > (or to make the query ignore indexes that makes the > Hence, I think your best bet is to add STRAIGHT_JOIN Please note that the data I tested against has changed slightly, as described in the bug report I posted. I was able (after some initial mistakes) to create this and I will post it below. I'm glad you pointed this tool out to me. > here, it would be nice if you could provide > In order to understand what really goes wrong It is entirely possible that it is a red herring, though. The only reason I thought this distribution might be the cause is that it seemed to make the problem easier to reproduce. > the actual distance in the B-tree between the > the size of the range will be estimated based on ![]() > pre-collected statistics should not matter since > Wrt the uneven distribution of uneven_val, the > accurate statistics could come up with a more > However, I cannot exclude that 5.0 through less I just don't follow your meaning regarding "50% of real value". > just 50% of real value for InnoDB tables.) > (Given that pre-5.7 versions of MySQL all show the rows columns of the EXPLAIN is as expected.
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