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* Teach planner and executor about monotonic window funcsDavid Rowley2022-04-08
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Window functions such as row_number() always return a value higher than the previously returned value for tuples in any given window partition. Traditionally queries such as; SELECT * FROM ( SELECT *, row_number() over (order by c) rn FROM t ) t WHERE rn <= 10; were executed fairly inefficiently. Neither the query planner nor the executor knew that once rn made it to 11 that nothing further would match the outer query's WHERE clause. It would blindly continue until all tuples were exhausted from the subquery. Here we implement means to make the above execute more efficiently. This is done by way of adding a pg_proc.prosupport function to various of the built-in window functions and adding supporting code to allow the support function to inform the planner if the window function is monotonically increasing, monotonically decreasing, both or neither. The planner is then able to make use of that information and possibly allow the executor to short-circuit execution by way of adding a "run condition" to the WindowAgg to allow it to determine if some of its execution work can be skipped. This "run condition" is not like a normal filter. These run conditions are only built using quals comparing values to monotonic window functions. For monotonic increasing functions, quals making use of the btree operators for <, <= and = can be used (assuming the window function column is on the left). You can see here that once such a condition becomes false that a monotonic increasing function could never make it subsequently true again. For monotonically decreasing functions the >, >= and = btree operators for the given type can be used for run conditions. The best-case situation for this is when there is a single WindowAgg node without a PARTITION BY clause. Here when the run condition becomes false the WindowAgg node can simply return NULL. No more tuples will ever match the run condition. It's a little more complex when there is a PARTITION BY clause. In this case, we cannot return NULL as we must still process other partitions. To speed this case up we pull tuples from the outer plan to check if they're from the same partition and simply discard them if they are. When we find a tuple belonging to another partition we start processing as normal again until the run condition becomes false or we run out of tuples to process. When there are multiple WindowAgg nodes to evaluate then this complicates the situation. For intermediate WindowAggs we must ensure we always return all tuples to the calling node. Any filtering done could lead to incorrect results in WindowAgg nodes above. For all intermediate nodes, we can still save some work when the run condition becomes false. We've no need to evaluate the WindowFuncs anymore. Other WindowAgg nodes cannot reference the value of these and these tuples will not appear in the final result anyway. The savings here are small in comparison to what can be saved in the top-level WingowAgg, but still worthwhile. Intermediate WindowAgg nodes never filter out tuples, but here we change WindowAgg so that the top-level WindowAgg filters out tuples that don't match the intermediate WindowAgg node's run condition. Such filters appear in the "Filter" clause in EXPLAIN for the top-level WindowAgg node. Here we add prosupport functions to allow the above to work for; row_number(), rank(), dense_rank(), count(*) and count(expr). It appears technically possible to do the same for min() and max(), however, it seems unlikely to be useful enough, so that's not done here. Bump catversion Author: David Rowley Reviewed-by: Andy Fan, Zhihong Yu Discussion: https://postgr.es/m/CAApHDvqvp3At8++yF8ij06sdcoo1S_b2YoaT9D4Nf+MObzsrLQ@mail.gmail.com
* Update copyright for 2022Bruce Momjian2022-01-07
| | | | Backpatch-through: 10
* Update copyright for 2021Bruce Momjian2021-01-02
| | | | Backpatch-through: 9.5
* Update copyrights for 2020Bruce Momjian2020-01-01
| | | | Backpatch-through: update all files in master, backpatch legal files through 9.4
* Allow extensions to generate lossy index conditions.Tom Lane2019-02-11
| | | | | | | | | | | | | | | | | | | | | | | | For a long time, indxpath.c has had the ability to extract derived (lossy) index conditions from certain operators such as LIKE. For just as long, it's been obvious that we really ought to make that capability available to extensions. This commit finally accomplishes that, by adding another API for planner support functions that lets them create derived index conditions for their functions. As proof of concept, the hardwired "special index operator" code formerly present in indxpath.c is pushed out to planner support functions attached to LIKE and other relevant operators. A weak spot in this design is that an extension needs to know OIDs for the operators, datatypes, and opfamilies involved in the transformation it wants to make. The core-code prototypes use hard-wired OID references but extensions don't have that option for their own operators etc. It's usually possible to look up the required info, but that may be slow and inconvenient. However, improving that situation is a separate task. I want to do some additional refactorization around selfuncs.c, but that also seems like a separate task. Discussion: https://postgr.es/m/15193.1548028093@sss.pgh.pa.us
* Build out the planner support function infrastructure.Tom Lane2019-02-09
| | | | | | | | | | | | | | | | | | | | | | | | Add support function requests for estimating the selectivity, cost, and number of result rows (if a SRF) of the target function. The lack of a way to estimate selectivity of a boolean-returning function in WHERE has been a recognized deficiency of the planner since Berkeley days. This commit finally fixes it. In addition, non-constant estimates of cost and number of output rows are now possible. We still fall back to looking at procost and prorows if the support function doesn't service the request, of course. To make concrete use of the possibility of estimating output rowcount for SRFs, this commit adds support functions for array_unnest(anyarray) and the integer variants of generate_series; the lack of plausible rowcount estimates for those, even when it's obvious to a human, has been a repeated subject of complaints. Obviously, much more could now be done in this line, but I'm mostly just trying to get the infrastructure in place. Discussion: https://postgr.es/m/15193.1548028093@sss.pgh.pa.us
* Create the infrastructure for planner support functions.Tom Lane2019-02-09
Rename/repurpose pg_proc.protransform as "prosupport". The idea is still that it names an internal function that provides knowledge to the planner about the behavior of the function it's attached to; but redesign the API specification so that it's not limited to doing just one thing, but can support an extensible set of requests. The original purpose of simplifying a function call is handled by the first request type to be invented, SupportRequestSimplify. Adjust all the existing transform functions to handle this API, and rename them fron "xxx_transform" to "xxx_support" to reflect the potential generalization of what they do. (Since we never previously provided any way for extensions to add transform functions, this change doesn't create an API break for them.) Also add DDL and pg_dump support for attaching a support function to a user-defined function. Unfortunately, DDL access has to be restricted to superusers, at least for now; but seeing that support functions will pretty much have to be written in C, that limitation is just theoretical. (This support is untested in this patch, but a follow-on patch will add cases that exercise it.) Discussion: https://postgr.es/m/15193.1548028093@sss.pgh.pa.us