Re: MergeJoin beats HashJoin in the case of multiple hash clauses
On 9/11/23 10:04, Lepikhov Andrei wrote: > > > On Mon, Sep 11, 2023, at 11:51 AM, Andy Fan wrote: >> Hi, >> >> On Thu, Jun 15, 2023 at 4:30 PM Andrey Lepikhov >> wrote: >>> Hi, all. >>> >>> Some of my clients use JOIN's with three - four clauses. Quite >>> frequently, I see complaints on unreasonable switch of JOIN algorithm to >>> Merge Join instead of Hash Join. Quick research have shown one weak >>> place - estimation of an average bucket size in final_cost_hashjoin (see >>> q2.sql in attachment) with very conservative strategy. >>> Unlike estimation of groups, here we use smallest ndistinct value across >>> all buckets instead of multiplying them (or trying to make multivariate >>> analysis). >>> It works fine for the case of one clause. But if we have many clauses, >>> and if each has high value of ndistinct, we will overestimate average >>> size of a bucket and, as a result, prefer to use Merge Join. As the >>> example in attachment shows, it leads to worse plan than possible, >>> sometimes drastically worse. >>> I assume, this is done with fear of functional dependencies between hash >>> clause components. But as for me, here we should go the same way, as >>> estimation of groups. >> Yes, this analysis is correct - final_cost_hashjoin assumes the clauses may be correlated (not necessarily by functional dependencies, just that the overall ndistinct is not a simple product of per-column ndistincts). And it even says so in the comment before calculating bucket size: * Determine bucketsize fraction and MCV frequency for the inner * relation. We use the smallest bucketsize or MCV frequency estimated * for any individual hashclause; this is undoubtedly conservative. I'm sure this may lead to inflated cost for "good" cases (where the actual bucket size really is a product), which may push the optimizer to use the less efficient/slower join method. Unfortunately, AFAICS the patch simply assumes the extreme in the opposite direction - it assumes each clause splits the bucket for each distinct value in the column. Which works great when it's true, but surely it'd have issues when the columns are correlated? I think this deserves more discussion, i.e. what happens if the assumptions do not hold? We know what happens for the conservative approach, but what's the worst thing that would happen for the optimistic one? I doubt e can simply switch from the conservative approach to the optimistic one. Yes, it'll make some queries faster, but for other queries it likely causes problems and slowdowns. IMHO the only principled way forward is to get a better ndistinct estimate (which this implicitly does), perhaps by using extended statistics. I haven't tried, but I guess it'd need to extract the clauses for the inner side, and call estimate_num_groups() on it. This however reminds me we don't use extended statistics for join clauses at all. Which means that even with accurate extended statistics, we can still get stuff like this for multiple join clauses: Hash Join (cost=1317.00..2386.00 rows=200 width=24) (actual time=85.781..8574.784 rows=800 loops=1) This is unrelated to the issue discussed here, of course, as it won't affect join method selection for that join. But it certainly will affect all estimates/costs above that join, which can be pretty disastrous. regards -- Tomas Vondra EnterpriseDB: http://www.enterprisedb.com The Enterprise PostgreSQL Company
Re: MergeJoin beats HashJoin in the case of multiple hash clauses
On Mon, Sep 11, 2023, at 11:51 AM, Andy Fan wrote: > Hi, > > On Thu, Jun 15, 2023 at 4:30 PM Andrey Lepikhov > wrote: >> Hi, all. >> >> Some of my clients use JOIN's with three - four clauses. Quite >> frequently, I see complaints on unreasonable switch of JOIN algorithm to >> Merge Join instead of Hash Join. Quick research have shown one weak >> place - estimation of an average bucket size in final_cost_hashjoin (see >> q2.sql in attachment) with very conservative strategy. >> Unlike estimation of groups, here we use smallest ndistinct value across >> all buckets instead of multiplying them (or trying to make multivariate >> analysis). >> It works fine for the case of one clause. But if we have many clauses, >> and if each has high value of ndistinct, we will overestimate average >> size of a bucket and, as a result, prefer to use Merge Join. As the >> example in attachment shows, it leads to worse plan than possible, >> sometimes drastically worse. >> I assume, this is done with fear of functional dependencies between hash >> clause components. But as for me, here we should go the same way, as >> estimation of groups. > > I can reproduce the visitation you want to improve and verify the patch > can do it expectedly. I think this is a right thing to do. > >> The attached patch shows a sketch of the solution. > > I understand that this is a sketch of the solution, but the below > changes still > make me confused. > > + if (innerbucketsize > virtualbuckets) > + innerbucketsize = 1.0 / virtualbuckets; > > innerbucketsize is a fraction of rows in all the rows, so it is between > 0.0 and 1.0. > and virtualbuckets is the number of buckets in total (when considered > the mutli > batchs), how is it possible for 'innerbucketsize > virtualbuckets' ? > Am > I missing something? You are right here. I've made a mistake here. Changed diff is in attachment. -- Regards, Andrei Lepikhov fix_bucketsize_v2.diff Description: Binary data
Re: MergeJoin beats HashJoin in the case of multiple hash clauses
Hi, On Thu, Jun 15, 2023 at 4:30 PM Andrey Lepikhov wrote: > Hi, all. > > Some of my clients use JOIN's with three - four clauses. Quite > frequently, I see complaints on unreasonable switch of JOIN algorithm to > Merge Join instead of Hash Join. Quick research have shown one weak > place - estimation of an average bucket size in final_cost_hashjoin (see > q2.sql in attachment) with very conservative strategy. > Unlike estimation of groups, here we use smallest ndistinct value across > all buckets instead of multiplying them (or trying to make multivariate > analysis). > It works fine for the case of one clause. But if we have many clauses, > and if each has high value of ndistinct, we will overestimate average > size of a bucket and, as a result, prefer to use Merge Join. As the > example in attachment shows, it leads to worse plan than possible, > sometimes drastically worse. > I assume, this is done with fear of functional dependencies between hash > clause components. But as for me, here we should go the same way, as > estimation of groups. > I can reproduce the visitation you want to improve and verify the patch can do it expectedly. I think this is a right thing to do. > The attached patch shows a sketch of the solution. > I understand that this is a sketch of the solution, but the below changes still make me confused. + if (innerbucketsize > virtualbuckets) + innerbucketsize = 1.0 / virtualbuckets; innerbucketsize is a fraction of rows in all the rows, so it is between 0.0 and 1.0. and virtualbuckets is the number of buckets in total (when considered the mutli batchs), how is it possible for 'innerbucketsize > virtualbuckets' ? Am I missing something? -- Best Regards Andy Fan
Re: MergeJoin beats HashJoin in the case of multiple hash clauses
Does anyone else have an opinion on this patch? It looks promising. --- On Wed, Jun 28, 2023 at 04:53:06PM +0300, Alena Rybakina wrote: > Hi! > > On 15.06.2023 11:30, Andrey Lepikhov wrote: > > Hi, all. > > Some of my clients use JOIN's with three - four clauses. Quite frequently, > I see complaints on unreasonable switch of JOIN algorithm to Merge Join > instead of Hash Join. Quick research have shown one weak place - > estimation > of an average bucket size in final_cost_hashjoin (see q2.sql in > attachment) > with very conservative strategy. > Unlike estimation of groups, here we use smallest ndistinct value across > all buckets instead of multiplying them (or trying to make multivariate > analysis). > It works fine for the case of one clause. But if we have many clauses, and > if each has high value of ndistinct, we will overestimate average size of > a > bucket and, as a result, prefer to use Merge Join. As the example in > attachment shows, it leads to worse plan than possible, sometimes > drastically worse. > I assume, this is done with fear of functional dependencies between hash > clause components. But as for me, here we should go the same way, as > estimation of groups. > The attached patch shows a sketch of the solution. > > > This problem is very important. > > Honestly, I'm still learning your code and looking for cases on which cases > your patch can affect for the worse or for the better. But I have already > found > something that seemed interesting to me. I have found several other > interesting > cases where your patch can solve some problem in order to choose a more > correct > plan, but in focus on memory consumption. > > To make it easier to evaluate, I added a hook to your patch that makes it > easier to switch to your or the original way of estimating the size of baskets > (diff_estimate.diff). > > Here are other cases where your fix improves the query plan. > > > First of all, I changed the way creation of tables are created to look at the > behavior of the query plan in terms of planning and execution time: > > DROP TABLE IF EXISTS a,b CASCADE; > CREATE TABLE a AS > SELECT ((3*gs) % 300) AS x, ((3*gs+1) % 300) AS y, ((3*gs+2) % 300) AS z > FROM generate_series(1,1e5) AS gs; > CREATE TABLE b AS > SELECT gs % 90 AS x, gs % 49 AS y, gs %100 AS z, 'abc' || gs AS payload > FROM generate_series(1,1e5) AS gs; > ANALYZE a,b; > > SET enable_cost_size = 'on'; > EXPLAIN ANALYZE > SELECT * FROM a,b > WHERE a.x=b.x AND a.y=b.y AND a.z=b.z; > > SET enable_cost_size = 'off'; > EXPLAIN ANALYZE > SELECT * FROM a,b > WHERE a.x=b.x AND a.y=b.y AND a.z=b.z; > > > QUERY PLAN > --- > Hash Join (actual time=200.872..200.879 rows=0 loops=1) > Hash Cond: ((b.x = a.x) AND (b.y = a.y) AND (b.z = a.z)) > -> Seq Scan on b (actual time=0.029..15.946 rows=10 loops=1) > -> Hash (actual time=97.645..97.649 rows=10 loops=1) > Buckets: 131072 Batches: 1 Memory Usage: 5612kB > -> Seq Scan on a (actual time=0.024..17.153 rows=10 loops=1) > Planning Time: 2.910 ms > Execution Time: 201.949 ms > (8 rows) > > SET > QUERY PLAN > --- > Merge Join (actual time=687.415..687.416 rows=0 loops=1) > Merge Cond: ((b.y = a.y) AND (b.x = a.x) AND (b.z = a.z)) > -> Sort (actual time=462.022..536.716 rows=10 loops=1) > Sort Key: b.y, b.x, b.z > Sort Method: external merge Disk: 3328kB > -> Seq Scan on b (actual time=0.017..12.326 rows=10 loops=1) > -> Sort (actual time=111.295..113.196 rows=16001 loops=1) > Sort Key: a.y, a.x, a.z > Sort Method: external sort Disk: 2840kB > -> Seq Scan on a (actual time=0.020..10.129 rows=10 loops=1) > Planning Time: 0.752 ms > Execution Time: 688.829 ms > (12 rows) > > Secondly, I found another case that is not related to the fact that the > planner > would prefer to choose merge join rather than hash join, but we have the > opportunity to see that the plan has become better due to the consumption of > less memory, and also takes less planning time. > > Here, with the same query, the planning time was reduced by 5 times, and the > number of buckets by 128 times, therefore, memory consumption also decreased: > > DROP TABLE IF EXISTS a,b CASCADE; > > CREATE TABLE a AS > SELECT ((3*gs) % 300) AS x, ((3*gs+1) % 300) AS y, ((3*gs+2) % 300) AS z > FROM generate_series(1,600) AS gs; > CREATE TABLE b AS > SELECT gs % 90 AS x, gs % 49 AS y, gs %100 AS z, 'abc' || gs AS payload > FROM generate_series(1,1e5) AS gs; >
Re: MergeJoin beats HashJoin in the case of multiple hash clauses
Hi! On 15.06.2023 11:30, Andrey Lepikhov wrote: Hi, all. Some of my clients use JOIN's with three - four clauses. Quite frequently, I see complaints on unreasonable switch of JOIN algorithm to Merge Join instead of Hash Join. Quick research have shown one weak place - estimation of an average bucket size in final_cost_hashjoin (see q2.sql in attachment) with very conservative strategy. Unlike estimation of groups, here we use smallest ndistinct value across all buckets instead of multiplying them (or trying to make multivariate analysis). It works fine for the case of one clause. But if we have many clauses, and if each has high value of ndistinct, we will overestimate average size of a bucket and, as a result, prefer to use Merge Join. As the example in attachment shows, it leads to worse plan than possible, sometimes drastically worse. I assume, this is done with fear of functional dependencies between hash clause components. But as for me, here we should go the same way, as estimation of groups. The attached patch shows a sketch of the solution. This problem is very important. Honestly, I'm still learning your code and looking for cases on which cases your patch can affect for the worse or for the better. But I have already found something that seemed interesting to me. I have found several other interesting cases where your patch can solve some problem in order to choose a more correct plan, but in focus on memory consumption. To make it easier to evaluate, I added a hook to your patch that makes it easier to switch to your or the original way of estimating the size of baskets (diff_estimate.diff). Here are other cases where your fix improves the query plan. First of all, I changed the way creation of tables are created to look at the behavior of the query plan in terms of planning and execution time: DROP TABLE IF EXISTS a,b CASCADE; CREATE TABLE a AS SELECT ((3*gs) % 300) AS x, ((3*gs+1) % 300) AS y, ((3*gs+2) % 300) AS z FROM generate_series(1,1e5) AS gs; CREATE TABLE b AS SELECT gs % 90 AS x, gs % 49 AS y, gs %100 AS z, 'abc' || gs AS payload FROM generate_series(1,1e5) AS gs; ANALYZE a,b; SET enable_cost_size = 'on'; EXPLAIN ANALYZE SELECT * FROM a,b WHERE a.x=b.x AND a.y=b.y AND a.z=b.z; SET enable_cost_size = 'off'; EXPLAIN ANALYZE SELECT * FROM a,b WHERE a.x=b.x AND a.y=b.y AND a.z=b.z; QUERY PLAN --- Hash Join (actual time=200.872..200.879 rows=0 loops=1) Hash Cond: ((b.x = a.x) AND (b.y = a.y) AND (b.z = a.z)) -> Seq Scan on b (actual time=0.029..15.946 rows=10 loops=1) -> Hash (actual time=97.645..97.649 rows=10 loops=1) Buckets: 131072 Batches: 1 Memory Usage: 5612kB -> Seq Scan on a (actual time=0.024..17.153 rows=10 loops=1) Planning Time: 2.910 ms Execution Time: 201.949 ms (8 rows) SET QUERY PLAN --- Merge Join (actual time=687.415..687.416 rows=0 loops=1) Merge Cond: ((b.y = a.y) AND (b.x = a.x) AND (b.z = a.z)) -> Sort (actual time=462.022..536.716 rows=10 loops=1) Sort Key: b.y, b.x, b.z Sort Method: external merge Disk: 3328kB -> Seq Scan on b (actual time=0.017..12.326 rows=10 loops=1) -> Sort (actual time=111.295..113.196 rows=16001 loops=1) Sort Key: a.y, a.x, a.z Sort Method: external sort Disk: 2840kB -> Seq Scan on a (actual time=0.020..10.129 rows=10 loops=1) Planning Time: 0.752 ms Execution Time: 688.829 ms (12 rows) Secondly, I found another case that is not related to the fact that the planner would prefer to choose merge join rather than hash join, but we have the opportunity to see that the plan has become better due to the consumption of less memory, and also takes less planning time. Here, with the same query, the planning time was reduced by 5 times, and the number of buckets by 128 times, therefore, memory consumption also decreased: DROP TABLE IF EXISTS a,b CASCADE; CREATE TABLE a AS SELECT ((3*gs) % 300) AS x, ((3*gs+1) % 300) AS y, ((3*gs+2) % 300) AS z FROM generate_series(1,600) AS gs; CREATE TABLE b AS SELECT gs % 90 AS x, gs % 49 AS y, gs %100 AS z, 'abc' || gs AS payload FROM generate_series(1,1e5) AS gs; ANALYZE a,b; SET enable_cost_size = 'on'; EXPLAIN ANALYZE SELECT * FROM a,b WHERE a.x=b.x AND a.y=b.y AND a.z=b.z; SET enable_cost_size = 'off'; EXPLAIN ANALYZE SELECT * FROM a,b WHERE a.x=b.x AND a.y=b.y AND a.z=b.z; QUERY PLAN Hash Join (cost=20.50..3157.58 rows=8 width=32) (actual time=95.648..95.651 rows=0 loops=1) Hash Cond: ((b.x = (a.x)::numeric) AND (b.y = (a.y)::numeric) AND (b.z = (a.z)::numeric)) -> Seq Scan on b
MergeJoin beats HashJoin in the case of multiple hash clauses
Hi, all. Some of my clients use JOIN's with three - four clauses. Quite frequently, I see complaints on unreasonable switch of JOIN algorithm to Merge Join instead of Hash Join. Quick research have shown one weak place - estimation of an average bucket size in final_cost_hashjoin (see q2.sql in attachment) with very conservative strategy. Unlike estimation of groups, here we use smallest ndistinct value across all buckets instead of multiplying them (or trying to make multivariate analysis). It works fine for the case of one clause. But if we have many clauses, and if each has high value of ndistinct, we will overestimate average size of a bucket and, as a result, prefer to use Merge Join. As the example in attachment shows, it leads to worse plan than possible, sometimes drastically worse. I assume, this is done with fear of functional dependencies between hash clause components. But as for me, here we should go the same way, as estimation of groups. The attached patch shows a sketch of the solution. -- regards, Andrey Lepikhov Postgres Professional q2.sql Description: application/sql diff --git a/src/backend/optimizer/path/costsize.c b/src/backend/optimizer/path/costsize.c index ef475d95a1..26f26d6a40 100644 --- a/src/backend/optimizer/path/costsize.c +++ b/src/backend/optimizer/path/costsize.c @@ -4033,11 +4033,12 @@ final_cost_hashjoin(PlannerInfo *root, HashPath *path, thismcvfreq = restrictinfo->left_mcvfreq; } - if (innerbucketsize > thisbucketsize) - innerbucketsize = thisbucketsize; - if (innermcvfreq > thismcvfreq) - innermcvfreq = thismcvfreq; + innerbucketsize *= thisbucketsize; + innermcvfreq *= thismcvfreq; } + + if (innerbucketsize > virtualbuckets) + innerbucketsize = 1.0 / virtualbuckets; } /*
