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[86.49.253.141]) by smtp.gmail.com with ESMTPSA id b13sm14722946edw.50.2020.05.24.19.10.47 (version=TLS1_3 cipher=TLS_AES_256_GCM_SHA384 bits=256/256); Sun, 24 May 2020 19:10:47 -0700 (PDT) Date: Mon, 25 May 2020 04:10:45 +0200 From: Tomas Vondra To: Jeff Davis Cc: pgsql-hackers@postgresql.org Subject: Re: Trouble with hashagg spill I/O pattern and costing Message-ID: <20200525021045.dilgcsmgiu4l5jpa@development> References: <20200519151202.u2p2gpiawoaznsv2@development> <88dd39d782178539d7ee46f98b98579940e2a12e.camel@j-davis.com> <20200519175320.tl4qm6le5dbg2pcf@development> <20200521191318.gw3xjivrxracvzmg@development> <8619437df796903e64c8479e299d13ebacb5fbe0.camel@j-davis.com> <20200521214122.6opovye4mnbqczlc@development> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="udm7ijhvs3sjubzk" Content-Disposition: inline In-Reply-To: <20200521214122.6opovye4mnbqczlc@development> List-Id: List-Help: List-Subscribe: List-Post: List-Owner: List-Archive: Precedence: bulk --udm7ijhvs3sjubzk Content-Type: text/plain; charset=us-ascii; format=flowed Content-Disposition: inline On Thu, May 21, 2020 at 11:41:22PM +0200, Tomas Vondra wrote: >On Thu, May 21, 2020 at 02:16:37PM -0700, Jeff Davis wrote: >>On Thu, 2020-05-21 at 21:13 +0200, Tomas Vondra wrote: >>>2) We could make it self-tuning, by increasing the number of blocks >>>we pre-allocate. So every time we exhaust the range, we double the >>>number of blocks (with a reasonable maximum, like 1024 or so). Or we >>>might just increment it by 32, or something. >> >>Attached a new version that uses the doubling behavior, and cleans it >>up a bit. It also returns the unused prealloc blocks back to lts- >>freeBlocks when the tape is rewound for reading. >> > >Ah, the returning is a nice idea, that should limit the overhead quite a >bit, I think. > >>>IIUC the danger of pre-allocating blocks is that we might not fill >>>them, >>>resulting in temp file much larger than necessary. It might be >>>harmless >>>on some (most?) current filesystems that don't actually allocate >>>space >>>for blocks that are never written, but it also confuses our >>>accounting >>>of temporary file sizes. So we should try to limit that, and growing >>>the >>>number of pre-allocated blocks over time seems reasonable. >> >>There's another danger here: it doesn't matter how well the filesystem >>deals with sparse writes, because ltsWriteBlock fills in the holes with >>zeros anyway. That's potentially a significant amount of wasted IO >>effort if we aren't careful. >> > >True. I'll give it a try on both machines and report some numbers. Might >take a couple of days. > OK, so I do have some numbers to share. I think there's a clear conclusion that the two patches are a huge improvement, but there's also something fishy about planning of parallel queries. Firstly, I have two machines that I used for testing: 1) small one: i5-2500k (4 cores), 8GB RAM, SSD RAID for data, SSD for temporary tablespace, using TPC-H 32GB data set 2) big one: 2x xeon e5-2620v3 (8 cores), 64GB RAM, NVME SSD for data, temporary tablespace on SATA RAID0 (3 x 7.2k), using TPC-H 75GB serial queries (no parallelism) =============================== Results with parallel query disabled on the two machines look like this: 1) small one (SSD) algorithm master prealloc tlist prealloc-tlist -------------------------------------------------- hash 1365 437 368 213 sort 226 214 224 215 The sort row simply means "enable_hashagg = off" and AFAIK the patches should not have a lot of influence here - the prealloc does, but it's fairly negligible. It's not always exactly on part, I've seen cases where hash or sort were a bit faster (probably depending on work_mem), but I think we can ignore that for now. 2) big one (SATA) algorithm master tlist prealloc prealloc+tlist -------------------------------------------------- hash 25534 5120 2402 540 sort 460 460 465 485 The effect is even more pronounced, thanks to poor handling of random I/O by the SATA RAID device. It's not exactly on par with sort, but it's close enough ... parallel queries ================ And now the fun begins ... 1) small one (SSD, max_parallel_workers_per_gather = 2) algorithm master tlist prealloc prealloc+tlist -------------------------------------------------- hash 693 390 177 128 sort 103 99 101 99 This looks pretty nice - the patches have the expected effect, it got faster than with just a single CPU etc. 2) big one (SATA, max_parallel_workers_per_gather = 16) algorithm master tlist prealloc prealloc+tlist -------------------------------------------------- hash ? 25000 ? 3132 sort 248 234 216 200 Well, not that nice :-( The hash queries take so much time that I've decided not to wait for them and the two numbers are actually just estimates (after processing just a couple of logical tapes). Plus it actually gets slower than with serial execution, so what's the problem here? Especially considering it worked OK on the small machine? At first I thought it's something about SSD vs. SATA, but it seems to be more about how we construct the plans, because the plans between the two machines are very different. And it seems to be depend by the number of workers per gather - for low number of workers the plan looks like this (the plans are attached in plans.txt in case the formatting gets broken by your client): QUERY PLAN --------------------------------------------------------------------------------------------------------------- Limit -> Aggregate -> Hash Join Hash Cond: (part.p_partkey = lineitem_1.l_partkey) Join Filter: (lineitem.l_quantity < ((0.2 * avg(lineitem_1.l_quantity)))) -> Gather Workers Planned: 2 -> Nested Loop -> Parallel Seq Scan on part Filter: ((p_brand = 'Brand#22'::bpchar) AND (p_container = 'LG BOX'::bpchar)) -> Index Scan using idx_lineitem_part_supp on lineitem Index Cond: (l_partkey = part.p_partkey) -> Hash -> Finalize HashAggregate Group Key: lineitem_1.l_partkey -> Gather Workers Planned: 2 -> Partial HashAggregate Group Key: lineitem_1.l_partkey -> Parallel Seq Scan on lineitem lineitem_1 (20 rows) but then if I crank the number of workers up, it switches to this: QUERY PLAN --------------------------------------------------------------------------------------------------------------------- Limit -> Finalize Aggregate -> Gather Workers Planned: 5 -> Partial Aggregate -> Nested Loop Join Filter: (part.p_partkey = lineitem.l_partkey) -> Hash Join Hash Cond: (part.p_partkey = lineitem_1.l_partkey) -> Parallel Seq Scan on part Filter: ((p_brand = 'Brand#22'::bpchar) AND (p_container = 'LG BOX'::bpchar)) -> Hash -> HashAggregate Group Key: lineitem_1.l_partkey -> Seq Scan on lineitem lineitem_1 -> Index Scan using idx_lineitem_part_supp on lineitem Index Cond: (l_partkey = lineitem_1.l_partkey) Filter: (l_quantity < ((0.2 * avg(lineitem_1.l_quantity)))) (18 rows) Notice that in the first plan, the hashagg is on top of parallel-aware path - so each workers builds hashagg only on a subset of data, and also spills only a fraction of the input rows (so that all workers combined spill rouhly the "whole" table). In the second plan, the hashagg is on the non-partitioned side of the join, so each workers builds a hash aggregate on the *whole* set of input rows. Which means that (a) we need much more disk space for temp files, making it unlikely to fit into page cache and (b) there's a lot of contention for I/O, making it much more random. Now, I haven't seen the second plan with sort-based aggregation, no matter how I set the number of workers it always looks like this: QUERY PLAN --------------------------------------------------------------------------------------------------------------------- Limit -> Aggregate -> Merge Join Merge Cond: (lineitem_1.l_partkey = part.p_partkey) Join Filter: (lineitem.l_quantity < ((0.2 * avg(lineitem_1.l_quantity)))) -> Finalize GroupAggregate Group Key: lineitem_1.l_partkey -> Gather Merge Workers Planned: 8 -> Partial GroupAggregate Group Key: lineitem_1.l_partkey -> Sort Sort Key: lineitem_1.l_partkey -> Parallel Seq Scan on lineitem lineitem_1 -> Materialize -> Gather Merge Workers Planned: 6 -> Nested Loop -> Parallel Index Scan using part_pkey on part Filter: ((p_brand = 'Brand#22'::bpchar) AND (p_container = 'LG BOX'::bpchar)) -> Index Scan using idx_lineitem_part_supp on lineitem Index Cond: (l_partkey = part.p_partkey) (22 rows) How come we don't have the same issue here? Is there something in the optimizer that prevents us from creating the "silly" plans with groupagg, and we should do the same thing for hashagg? regards -- Tomas Vondra http://www.2ndQuadrant.com PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services --udm7ijhvs3sjubzk Content-Type: text/plain; charset=us-ascii Content-Disposition: attachment; filename="plans.txt" QUERY PLAN --------------------------------------------------------------------------------------------------------------- Limit -> Aggregate -> Hash Join Hash Cond: (part.p_partkey = lineitem_1.l_partkey) Join Filter: (lineitem.l_quantity < ((0.2 * avg(lineitem_1.l_quantity)))) -> Gather Workers Planned: 2 -> Nested Loop -> Parallel Seq Scan on part Filter: ((p_brand = 'Brand#22'::bpchar) AND (p_container = 'LG BOX'::bpchar)) -> Index Scan using idx_lineitem_part_supp on lineitem Index Cond: (l_partkey = part.p_partkey) -> Hash -> Finalize HashAggregate Group Key: lineitem_1.l_partkey -> Gather Workers Planned: 2 -> Partial HashAggregate Group Key: lineitem_1.l_partkey -> Parallel Seq Scan on lineitem lineitem_1 (20 rows) QUERY PLAN --------------------------------------------------------------------------------------------------------------------- Limit -> Finalize Aggregate -> Gather Workers Planned: 5 -> Partial Aggregate -> Nested Loop Join Filter: (part.p_partkey = lineitem.l_partkey) -> Hash Join Hash Cond: (part.p_partkey = lineitem_1.l_partkey) -> Parallel Seq Scan on part Filter: ((p_brand = 'Brand#22'::bpchar) AND (p_container = 'LG BOX'::bpchar)) -> Hash -> HashAggregate Group Key: lineitem_1.l_partkey -> Seq Scan on lineitem lineitem_1 -> Index Scan using idx_lineitem_part_supp on lineitem Index Cond: (l_partkey = lineitem_1.l_partkey) Filter: (l_quantity < ((0.2 * avg(lineitem_1.l_quantity)))) (18 rows) QUERY PLAN --------------------------------------------------------------------------------------------------------------------- Limit -> Aggregate -> Merge Join Merge Cond: (lineitem_1.l_partkey = part.p_partkey) Join Filter: (lineitem.l_quantity < ((0.2 * avg(lineitem_1.l_quantity)))) -> Finalize GroupAggregate Group Key: lineitem_1.l_partkey -> Gather Merge Workers Planned: 8 -> Partial GroupAggregate Group Key: lineitem_1.l_partkey -> Sort Sort Key: lineitem_1.l_partkey -> Parallel Seq Scan on lineitem lineitem_1 -> Materialize -> Gather Merge Workers Planned: 6 -> Nested Loop -> Parallel Index Scan using part_pkey on part Filter: ((p_brand = 'Brand#22'::bpchar) AND (p_container = 'LG BOX'::bpchar)) -> Index Scan using idx_lineitem_part_supp on lineitem Index Cond: (l_partkey = part.p_partkey) (22 rows) QUERY PLAN ----------------------------------------------------------------------------------------------------------------------------------- Limit (cost=9323493.08..9323493.10 rows=1 width=32) -> Aggregate (cost=9323493.08..9323493.10 rows=1 width=32) -> Hash Join (cost=8690894.07..9323348.15 rows=57973 width=8) Hash Cond: (part.p_partkey = lineitem_1.l_partkey) Join Filter: (lineitem.l_quantity < ((0.2 * avg(lineitem_1.l_quantity)))) -> Gather (cost=1000.57..587448.17 rows=201706 width=21) Workers Planned: 2 -> Nested Loop (cost=0.57..566277.57 rows=84044 width=21) -> Parallel Seq Scan on part (cost=0.00..171069.18 rows=2802 width=4) Filter: ((p_brand = 'Brand#22'::bpchar) AND (p_container = 'LG BOX'::bpchar)) -> Index Scan using idx_lineitem_part_supp on lineitem (cost=0.57..140.70 rows=35 width=17) Index Cond: (l_partkey = part.p_partkey) -> Hash (cost=8577801.28..8577801.28 rows=5518338 width=36) -> Finalize HashAggregate (cost=8353618.79..8522617.90 rows=5518338 width=36) Group Key: lineitem_1.l_partkey Planned Partitions: 16 -> Gather (cost=6362701.20..7925947.60 rows=11036676 width=36) Workers Planned: 2 -> Partial HashAggregate (cost=6361701.20..6821280.00 rows=5518338 width=36) Group Key: lineitem_1.l_partkey Planned Partitions: 16 -> Parallel Seq Scan on lineitem lineitem_1 (cost=0.00..4399328.93 rows=79994793 width=9) (22 rows) QUERY PLAN --------------------------------------------------------------------------------------------------------------------------------- Limit (cost=11757514.10..11757514.11 rows=1 width=32) -> Finalize Aggregate (cost=11757514.10..11757514.11 rows=1 width=32) -> Gather (cost=11757513.56..11757514.07 rows=5 width=32) Workers Planned: 5 -> Partial Aggregate (cost=11756513.56..11756513.57 rows=1 width=32) -> Nested Loop (cost=11416439.73..11756484.57 rows=11595 width=8) Join Filter: (part.p_partkey = lineitem.l_partkey) -> Hash Join (cost=11416439.16..11609836.77 rows=1160 width=40) Hash Cond: (part.p_partkey = lineitem_1.l_partkey) -> Parallel Seq Scan on part (cost=0.00..150269.09 rows=1345 width=4) Filter: ((p_brand = 'Brand#22'::bpchar) AND (p_container = 'LG BOX'::bpchar)) -> Hash (cost=11304346.93..11304346.93 rows=5518338 width=36) -> HashAggregate (cost=10228949.50..11249163.55 rows=5518338 width=36) Group Key: lineitem_1.l_partkey Planned Partitions: 16 -> Seq Scan on lineitem lineitem_1 (cost=0.00..5519256.04 rows=191987504 width=9) -> Index Scan using idx_lineitem_part_supp on lineitem (cost=0.57..126.27 rows=12 width=17) Index Cond: (l_partkey = lineitem_1.l_partkey) Filter: (l_quantity < ((0.2 * avg(lineitem_1.l_quantity)))) (19 rows) QUERY PLAN ----------------------------------------------------------------------------------------------------------------------------------- Limit (cost=13805808.43..13805808.44 rows=1 width=32) -> Aggregate (cost=13805808.43..13805808.44 rows=1 width=32) -> Merge Join (cost=7193277.61..13805663.49 rows=57973 width=8) Merge Cond: (lineitem_1.l_partkey = part.p_partkey) Join Filter: (lineitem.l_quantity < ((0.2 * avg(lineitem_1.l_quantity)))) -> Finalize GroupAggregate (cost=7192276.51..13300598.75 rows=5518338 width=36) Group Key: lineitem_1.l_partkey -> Gather Merge (cost=7192276.51..12886723.40 rows=44146704 width=36) Workers Planned: 8 -> Partial GroupAggregate (cost=7191276.37..7440243.88 rows=5518338 width=36) Group Key: lineitem_1.l_partkey -> Sort (cost=7191276.37..7251272.46 rows=23998438 width=9) Sort Key: lineitem_1.l_partkey -> Parallel Seq Scan on lineitem lineitem_1 (cost=0.00..3839365.38 rows=23998438 width=9) -> Materialize (cost=1001.10..433407.27 rows=201706 width=21) -> Gather Merge (cost=1001.10..432903.01 rows=201706 width=21) Workers Planned: 6 -> Nested Loop (cost=1.00..407388.21 rows=33618 width=21) -> Parallel Index Scan using part_pkey on part (cost=0.43..249276.65 rows=1121 width=4) Filter: ((p_brand = 'Brand#22'::bpchar) AND (p_container = 'LG BOX'::bpchar)) -> Index Scan using idx_lineitem_part_supp on lineitem (cost=0.57..140.70 rows=35 width=17) Index Cond: (l_partkey = part.p_partkey) (22 rows) --udm7ijhvs3sjubzk Content-Type: image/png Content-Disposition: attachment; filename="sata-parallel.png" Content-Transfer-Encoding: base64 iVBORw0KGgoAAAANSUhEUgAAAwAAAAHgCAYAAAASWgolAABOz0lEQVR4nO3dCZgU1fX38TPA 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