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Feature freeze is >> still quite a long way from release. I do expect that the most likely >> time that someone would find issues with this change would be during >> beta or RC, as that's when people would give PostgreSQL production >> workloads to try out. During the dev cycle, I expect it's *mostly* >> just hackers giving the database toy workloads in a very targeted way >> to something specific that they're hacking on. Anyway, now since >> you've added the GUCs to control the weights, there's a way for users >> to have some influence, so at least there's an escape hatch. > > Thanks for chiming in. Hey Nathan, et. al., I'll chime in too! Apologies in advance for the le= ngth of the message. I need to learn how to be more concise... First off, thanks for looking into this! I think your work is a signifi= cant improvement over where we are today and should be in v19. Put simp= ly, I think that the v12 patch is no worse than pg_class order we use to= day and in many cases much better. That said, I think we should tweak i= t a bit more while we're at it. >> I think the GUCs are probably a good idea. I expect the most likely >> change that people might want to make would be to raise the priority >> of analyze over vacuum since that's often much faster to complete. We >> know that some workloads are very sensitive to outdated statistics. >>=20 >> On the other hand, we shouldn't be taking adding 5 new autovacuum GUCs >> lightly as there are already so many. If we are going to come up with >> something better than this in a few years then it could be better to >> wait to reduce the pain of having to remove GUCs in the future. I >> don't personally have any better ideas, so I'd rather see it go in >> than not. > > Yeah, adding these GUCs feels a bit like etching in stone, but if folks > want configurability, and nobody has better ideas, I'm not sure what e= lse > to do. I'm late in the review process. I know David Rowley proposed the unified= scoring approach that became the foundation of this patch, and I think = that's a great direction. However, I'm concerned that the patch's defaul= t scoring weights don't give XID-age urgency sufficient priority over de= ad-tuple urgency. The weight GUCs (autovacuum_vacuum_score_weight, etc.)= can address this, but they max at 1.0, meaning you can only reduce dead= -tuple priority, not increase XID priority. >> I didn't look at the patch in detail, but noticed that you might want >> to add a "See Section N.N.N for more information." to the new GUCs to >> link to the text you've added on how they're used. > > Good idea. I've added that. > >> Do you think it's worth making the call to >> list_sort(tables_to_process, TableToProcessComparator) conditional on >> a variable set like: sort_required |=3D (score !=3D 0.0);? I recall t= hat >> someone had concerns that the actual sort itself could add overhead. > > I don't think it's necessary. I tested sorting 1M and 10M tables with > randomly generated scores (on my MacBook, with assertions enabled). T= he > former took ~150 milliseconds, and the latter took ~1770 milliseconds.= I > suspect there are far bigger problems to worry about if you have anywh= ere > near that many tables. > > --=20 > nathan > > Attachments: > * v12-0001-autovacuum-scheduling-improvements.patch I decided to model before/after behavior using discrete-event simulation= . With a bit of LLM help, we no have autovacuum_simulation.py and autova= cuum_simulation_fixed.py to compare all three approaches (Before/v12/Pro= posed small fix to v12) across 20 runs with randomized OID assignments. Results from the models are at the end of this email. They simulate the= before/after/fixed behavior of the v12 autovacuum scheduling patch and = a suggested fix (I'll get to why in a minute). They test a contention scenario: 100 tables (5 already past freeze_max_a= ge, 15 approaching it at staggered rates, and 80 high-churn tables const= antly exceeding their dead-tuple vacuum thresholds) competing for 3 auto= vacuum workers over 7 days. I'm sure there are other scenarios that we = could test, this felt representational enough to me based on what I've s= een. With the first test, the "Before" scheduler processes tables in OID orde= r; the "After" scheduler uses the patch's urgency scoring with all defau= lt GUC values. The model of the v12 patch produces a clear system-wide improvement: the= number of tables simultaneously past freeze_max_age peaks at roughly 20= under score-based scheduling versus 80+ under OID order. This happens b= ecause high-churn tables earn high dead-tuple scores, get vacuumed frequ= ently, and their relfrozenxid ages reset as a side effect - preventing t= hem from ever reaching freeze_max_age. Under OID order, high-OID active = tables are *starved* (for no other reason than they were created later t= han their counterparts!) and their XID ages grow needlessly into the dan= ger zone. The v12 patch also correctly prioritizes the most dangerous fo= rce-vacuum tables first (those closest to failsafe age), whereas OID ord= er's success with any particular table is coincidental. But there's a downside in v12, the simulation reveals a scoring-scale co= ncern under default weights. Active tables accumulate dead-tuple scores = of 18=E2=80=9370+ within minutes of their last vacuum, while force-vacuu= m tables that have just crossed freeze_max_age carry XID scores of only = 1.0=E2=80=934.0 (age/freeze_max_age). The exponential boost doesn't acti= vate until failsafe age (1.6B), which is 8=C3=97 the freeze_max_age thre= shold. In the gap between 200M and 1.6B, force-vacuum tables are consist= ently outranked by ordinary dead-tuple work. In the tested scenario this= meant only 2 of 20 at-risk tables were actually vacuumed by the score-b= ased scheduler (versus 5 by OID due to luck), and average per-table wrap= around exposure was 26% worse. One possible remedy within the current design would be to either raise t= he default autovacuum_freeze_score_weight or apply a floor multiplier wh= en force_vacuum is true. For example, ensuring any force-vacuum score i= s at least as large as the maximum non-force-vacuum score in the current= candidate set. Alternatively, the weight GUC range could be expanded ab= ove 1.0 to allow DBAs to explicitly boost XID-age priority. The existing= 0.0=E2=80=931.0 range only allows reducing component priority, which ma= kes it difficult to express "wraparound prevention is more important tha= n bloat control. Tiered sorting using the existing wraparound flag might be the simplest = and safest fix. While the thread discussed using scoring with exponentia= l boost to prioritize wraparound tables, I think a two-tier approach (wr= aparound tier vs routine tier) would be more robust. We already treat wr= aparound as non-negotiable (force_vacuum bypasses av_enabled, triggers e= mergency behavior). The scoring system should reflect this by making wra= paround a hard priority tier, not a score component competing with bloat= cleanup. Instead of sorting solely by score, sort first by wraparound, then by sc= ore within each tier. The score computation stays exactly as-is; it's on= ly used for relative ordering among force_vacuum tables and among non-fo= rce-vacuum tables. Something like: /* * Comparison function for sorting TableToProcess candidates. * * Tables at risk of wraparound are always processed before routine * maintenance work. Within each tier, tables are sorted by descending * urgency score. */ static int TableToProcessComparator(const ListCell *a, const ListCell *b) { TableToProcess *ta =3D (TableToProcess *) lfirst(a); TableToProcess *tb =3D (TableToProcess *) lfirst(b); /* Wraparound prevention always takes priority */ if (ta->wraparound && !tb->wraparound) return -1; if (!ta->wraparound && tb->wraparound) return 1; /* Within same tier, highest score first */ if (ta->score > tb->score) return -1; if (ta->score < tb->score) return 1; return 0; } The simulation code, workload generator, and visualizations are attached= (in "foo_tgz" because my last attempt at this email was stuck in the mo= deration queue). I'd welcome feedback on whether the scenario is represe= ntative. The autovacuum_simulation_fixed.py includes the proposed fix an= d also runs a number of iteration where it randomized the table Oids so = as to remove any dependency on ordering that may be implied in our curre= nt algorithm (the before, or v18 algorithm). The proposed addition to your fix is in the v20260318 patch attached. best. -greg $ ./autovacuum_simulation.py =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D AUTOVACUUM SCHEDULING SIMULATION (v12 patch) Accurate model of score-based prioritization =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D PostgreSQL config: autovacuum_freeze_max_age =3D 200,000,000 vacuum_failsafe_age =3D 1,600,000,000 autovacuum_vacuum_threshold =3D 50 autovacuum_vacuum_scale_factor=3D 0.2 autovacuum_max_workers =3D 3 Simulation: 7 days, 60s steps, seed=3D42 Generating tables... 5 critical =E2=80=94 already past freeze_max_age 15 aging =E2=80=94 approaching freeze_max_age 80 active =E2=80=94 high dead-tuple rate BEFORE simulation (catalog OID order): [OID order ] 0% [OID order ] 20% [OID order ] 40% [OID order ] 60% [OID order ] 80% [OID order ] 100% AFTER simulation (score-based priority): [Score-based] 0% [Score-based] 20% [Score-based] 40% [Score-based] 60% [Score-based] 80% [Score-based] 100% =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D RESULTS: Exposure time (minutes at risk before vacuum) Table OID Crossed Before After Change ------------------------------------------------------------------------= ------ critical_0 16465 day 0.0 10080m 10080m +0% critical_1 16398 day 0.0 14m 10080m -71900% critical_2 16387 day 0.0 4m 10080m -251900% critical_3 16478 day 0.0 10080m 4m +100% critical_4 16419 day 0.0 10080m 4m +100% aging_0 16415 day 0.3 9648m 9648m +0% aging_1 16412 day 0.7 9114m 9114m +0% aging_7 16459 day 0.7 9027m 9027m +0% aging_6 16395 day 0.8 9m 8982m -99700% aging_10 16481 day 0.9 8721m 8721m +0% aging_11 16472 day 0.9 8721m 8721m +0% aging_2 16401 day 1.0 415m 8579m -1967% aging_3 16397 day 1.0 12m 8578m -71383% aging_4 16470 day 1.1 8499m 8499m +0% aging_12 16411 day 1.1 8483m 8483m +0% aging_8 16438 day 1.2 8296m 8296m +0% aging_5 16453 day 1.3 8178m 8178m +0% aging_14 16448 day 2.1 7101m 7101m +0% aging_9 16388 day 2.2 4m 6979m -174375% aging_13 16413 day 2.2 6959m 6959m +0% ------------------------------------------------------------------------= ------ AVERAGE 6172m 7806m -26% MAXIMUM 10080m 10080m +0% =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D Generating visualization... =E2=9C=93 output/autovacuum_scheduling_impact.png Done. ------------------------------------------------------- $ ./autovacuum_simulation_fixed.py =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D THREE-WAY AUTOVACUUM SCHEDULING COMPARISON Before (OID) vs v12 Patch (Score) vs Proposed Fix (Tiered) =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Config: 3 workers, 7-day sim, 60s steps, 20 runs Tables: 5 critical + 15 aging + 80 active =3D 100 freeze_max_age =3D 200,000,000 Estimated runtime: 3-8 minutes Run OID avg Score avg Tiered avg ------------------------------------------- 1 7222m 7892m 4m 2 7642m 7892m 4m 3 7961m 7892m 4m 4 6333m 7892m 4m 5 8110m 7892m 4m 6 6359m 7892m 4m 7 6629m 7892m 4m 8 8526m 7892m 4m 9 6385m 7892m 4m 10 6813m 7892m 4m 11 8588m 7892m 4m 12 7261m 7892m 4m 13 6682m 7892m 4m 14 8035m 7892m 4m 15 5667m 7892m 4m 16 7595m 7892m 4m 17 6394m 7892m 4m 18 6686m 7892m 4m 19 7819m 7892m 4m 20 7178m 7892m 4m =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D AGGREGATE RESULTS =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Avg exposure per run (minutes): OID : 7194 =C2=B1 816 (min=3D5667, max=3D8588) Score : 7892 =C2=B1 0 (min=3D7892, max=3D7892) Tiered : 4 =C2=B1 0 (min=3D4, max=3D4) Peak concurrent force_vacuum tables: OID : 82 =C2=B1 3 (min=3D79, max=3D88) Score : 20 =C2=B1 0 (min=3D20, max=3D20) Tiered : 5 =C2=B1 0 (min=3D5, max=3D5) Pairwise wins (lower avg exposure =3D better): Score beats OID: 5/20 loses: 15/20 ties: 0/20 Tiered beats OID: 20/20 loses: 0/20 ties: 0/20 Tiered beats Score: 20/20 loses: 0/20 ties: 0/20 Variance (std dev of avg exposure across runs): OID : 816 min Score : 0 min Tiered : 0 min Per-table mean exposure (minutes): Table OID Score Tiered -------------------------------------------------------------- critical_0 7564=C2=B14470 10080=C2=B10 7=C2=B10 critical_1 8577=C2=B13671 10080=C2=B10 7=C2=B10 critical_2 9073=C2=B13099 8938=C2=B10 3=C2=B10 critical_3 8581=C2=B13661 4=C2=B10 4=C2=B10 critical_4 9167=C2=B12826 3=C2=B10 3=C2=B10 aging_0 7253=C2=B14256 9648=C2=B10 4=C2=B10 aging_1 7324=C2=B13675 9114=C2=B10 4=C2=B10 aging_2 6865=C2=B13517 8579=C2=B10 5=C2=B10 aging_3 7253=C2=B13714 8851=C2=B10 4=C2=B10 aging_4 6018=C2=B14014 8579=C2=B10 4=C2=B10 aging_5 6856=C2=B12951 8064=C2=B10 4=C2=B10 aging_6 6799=C2=B13482 8496=C2=B10 3=C2=B10 aging_7 5765=C2=B14318 8853=C2=B10 4=C2=B10 aging_8 7091=C2=B13227 8644=C2=B10 4=C2=B10 aging_9 6358=C2=B12738 7479=C2=B10 4=C2=B10 aging_10 7106=C2=B13620 8870=C2=B10 4=C2=B10 aging_11 7175=C2=B13673 8965=C2=B10 4=C2=B10 aging_12 6499=C2=B13299 8106=C2=B10 4=C2=B10 aging_13 6080=C2=B13108 7595=C2=B10 4=C2=B10 aging_14 6479=C2=B13913 8891=C2=B10 4=C2=B10 =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Completed in 108 seconds (1.8 minutes) =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Generating visualization... =E2=9C=93 output/three_way_comparison.png --1753e1e59aa4463a8a4de85acca8b4cc Content-Disposition: attachment; filename*0="v20260318-0001-autovacuum-scheduling-improvements.patch" 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