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The existing unit test is updated to assert that.","shortMessageHtmlLink":"fix(txn): discard empty transactions on CommitWith (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2010810310\" data-permission-text=\"Title is private\" data-url=\"https://github.com/dgraph-io/badger/issues/2031\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/dgraph-io/badger/pull/2031/hovercard\" href=\"https://github.com/dgraph-io/badger/pull/2031\">#2031</a>)"}},{"before":null,"after":"103900b0e2a4dff0b922cd0cfaf53f2152012186","ref":"refs/heads/dependabot/go_modules/google.golang.org/grpc-1.56.3","pushedAt":"2023-10-25T21:20:58.000Z","pushType":"branch_creation","commitsCount":0,"pusher":{"login":"dependabot[bot]","name":null,"path":"/apps/dependabot","primaryAvatarUrl":"https://avatars.githubusercontent.com/in/29110?s=80&v=4"},"commit":{"message":"chore(deps): bump google.golang.org/grpc from 1.20.1 to 1.56.3\n\nBumps [google.golang.org/grpc](https://github.com/grpc/grpc-go) from 1.20.1 to 1.56.3.\n- [Release notes](https://github.com/grpc/grpc-go/releases)\n- [Commits](https://github.com/grpc/grpc-go/compare/v1.20.1...v1.56.3)\n\n---\nupdated-dependencies:\n- dependency-name: google.golang.org/grpc\n  dependency-type: indirect\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>","shortMessageHtmlLink":"chore(deps): bump google.golang.org/grpc from 1.20.1 to 1.56.3"}},{"before":"b84bc01e234c7b9fd8ea1ddcef633786b2239489","after":"fb1b009595813ae6dabd11628281568c8537f7fd","ref":"refs/heads/main","pushedAt":"2023-10-13T07:44:11.000Z","pushType":"pr_merge","commitsCount":1,"pusher":{"login":"mangalaman93","name":"Aman Mangal","path":"/mangalaman93","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/2542340?s=80&v=4"},"commit":{"message":"optimize allocations by using pre allocated buffer for priorities (#2006)\n\n## Problem\r\nBadger allocates a lot of objects over time. I created a simple\r\nreproducer and measured allocations after 10 minutes of running it.\r\n\r\n```\r\n(pprof) top\r\nShowing nodes accounting for 267006, 99.54% of 268253 total\r\nDropped 71 nodes (cum <= 1341)\r\nShowing top 10 nodes out of 14\r\n      flat  flat%   sum%        cum   cum%\r\n    155255 57.88% 57.88%     155255 57.88%  github.com/dgraph-io/badger/v4.(*levelsController).pickCompactLevels.func1 (inline)\r\n     65539 24.43% 82.31%      65539 24.43%  github.com/dgraph-io/badger/v4.(*levelsController).levelTargets\r\n     43691 16.29% 98.60%     264485 98.60%  github.com/dgraph-io/badger/v4.(*levelsController).pickCompactLevels\r\n      2521  0.94% 99.54%       2521  0.94%  os.(*File).Stat\r\n         0     0% 99.54%     264485 98.60%  github.com/dgraph-io/badger/v4.(*levelsController).runCompactor\r\n         0     0% 99.54%     264485 98.60%  github.com/dgraph-io/badger/v4.(*levelsController).runCompactor.func3\r\n         0     0% 99.54%       2521  0.94%  github.com/dgraph-io/badger/v4.(*logFile).open\r\n         0     0% 99.54%       2521  0.94%  github.com/dgraph-io/badger/v4.(*valueLog).open\r\n         0     0% 99.54%       2528  0.94%  github.com/dgraph-io/badger/v4.Open\r\n         0     0% 99.54%       2521  0.94%  github.com/dgraph-io/ristretto/z.OpenMmapFile\r\n(pprof) sample_index=alloc_space\r\n(pprof) top\r\nShowing nodes accounting for 238.72MB, 98.59% of 242.14MB total\r\nDropped 51 nodes (cum <= 1.21MB)\r\nShowing top 10 nodes out of 34\r\n      flat  flat%   sum%        cum   cum%\r\n  166.41MB 68.72% 68.72%   166.41MB 68.72%  github.com/dgraph-io/badger/v4/skl.newArena (inline)\r\n   59.04MB 24.38% 93.10%    59.04MB 24.38%  github.com/dgraph-io/badger/v4.(*levelsController).pickCompactLevels.func1 (inline)\r\n       4MB  1.65% 94.75%        4MB  1.65%  github.com/dgraph-io/ristretto/z.Calloc (inline)\r\n       4MB  1.65% 96.41%        4MB  1.65%  github.com/dgraph-io/badger/v4.(*levelsController).levelTargets\r\n    3.01MB  1.24% 97.65%     3.01MB  1.24%  github.com/google/flatbuffers/go.NewBuilder (inline)\r\n    1.27MB  0.52% 98.17%     1.27MB  0.52%  github.com/dgraph-io/ristretto.newCmRow\r\n       1MB  0.41% 98.59%    64.04MB 26.45%  github.com/dgraph-io/badger/v4.(*levelsController).pickCompactLevels\r\n         0     0% 98.59%     7.01MB  2.89%  github.com/dgraph-io/badger/v4.(*DB).flushMemtable\r\n         0     0% 98.59%     7.01MB  2.89%  github.com/dgraph-io/badger/v4.(*DB).handleMemTableFlush\r\n         0     0% 98.59%    83.20MB 34.36%  github.com/dgraph-io/badger/v4.(*DB).newMemTable\r\n```\r\n\r\nWe see that pickCompactLevels makes a pretty high number of allocations\r\ndue to appending to slice over and over again:\r\n```\r\n(pprof) list pickCompactLevels\r\nTotal: 268253\r\nROUTINE ======================== github.com/dgraph-io/badger/v4.(*levelsController).pickCompactLevels in /Users/deff/go/pkg/mod/github.com/dgraph-io/badger/v4@v4.2.0/levels.go\r\n     43691     264485 (flat, cum) 98.60% of Total\r\n         .          .    539:func (s *levelsController) pickCompactLevels() (prios []compactionPriority) {\r\n         .      65539    540:\tt := s.levelTargets()\r\n         .          .    541:\taddPriority := func(level int, score float64) {\r\n         .          .    542:\t\tpri := compactionPriority{\r\n         .          .    543:\t\t\tlevel:    level,\r\n         .          .    544:\t\t\tscore:    score,\r\n         .          .    545:\t\t\tadjusted: score,\r\n         .          .    546:\t\t\tt:        t,\r\n         .          .    547:\t\t}\r\n         .          .    548:\t\tprios = append(prios, pri)\r\n         .          .    549:\t}\r\n         .          .    550:\r\n         .          .    551:\t// Add L0 priority based on the number of tables.\r\n         .      42134    552:\taddPriority(0, float64(s.levels[0].numTables())/float64(s.kv.opt.NumLevelZeroTables))\r\n         .          .    553:\r\n         .          .    554:\t// All other levels use size to calculate priority.\r\n         .          .    555:\tfor i := 1; i < len(s.levels); i++ {\r\n         .          .    556:\t\t// Don't consider those tables that are already being compacted right now.\r\n         .          .    557:\t\tdelSize := s.cstatus.delSize(i)\r\n         .          .    558:\r\n         .          .    559:\t\tl := s.levels[i]\r\n         .          .    560:\t\tsz := l.getTotalSize() - delSize\r\n         .     113121    561:\t\taddPriority(i, float64(sz)/float64(t.targetSz[i]))\r\n         .          .    562:\t}\r\n         .          .    563:\ty.AssertTrue(len(prios) == len(s.levels))\r\n         .          .    564:\r\n         .          .    565:\t// The following code is borrowed from PebbleDB and results in healthier LSM tree structure.\r\n         .          .    566:\t// If Li-1 has score > 1.0, then we'll divide Li-1 score by Li. If Li score is >= 1.0, then Li-1\r\n         .          .    567:\t// score is reduced, which means we'll prioritize the compaction of lower levels (L5, L4 and so\r\n         .          .    568:\t// on) over the higher levels (L0, L1 and so on). On the other hand, if Li score is < 1.0, then\r\n         .          .    569:\t// we'll increase the priority of Li-1.\r\n         .          .    570:\t// Overall what this means is, if the bottom level is already overflowing, then de-prioritize\r\n         .          .    571:\t// compaction of the above level. If the bottom level is not full, then increase the priority of\r\n         .          .    572:\t// above level.\r\n         .          .    573:\tvar prevLevel int\r\n         .          .    574:\tfor level := t.baseLevel; level < len(s.levels); level++ {\r\n         .          .    575:\t\tif prios[prevLevel].adjusted >= 1 {\r\n         .          .    576:\t\t\t// Avoid absurdly large scores by placing a floor on the score that we'll\r\n         .          .    577:\t\t\t// adjust a level by. The value of 0.01 was chosen somewhat arbitrarily\r\n         .          .    578:\t\t\tconst minScore = 0.01\r\n         .          .    579:\t\t\tif prios[level].score >= minScore {\r\n         .          .    580:\t\t\t\tprios[prevLevel].adjusted /= prios[level].adjusted\r\n         .          .    581:\t\t\t} else {\r\n         .          .    582:\t\t\t\tprios[prevLevel].adjusted /= minScore\r\n         .          .    583:\t\t\t}\r\n         .          .    584:\t\t}\r\n         .          .    585:\t\tprevLevel = level\r\n         .          .    586:\t}\r\n         .          .    587:\r\n         .          .    588:\t// Pick all the levels whose original score is >= 1.0, irrespective of their adjusted score.\r\n         .          .    589:\t// We'll still sort them by their adjusted score below. Having both these scores allows us to\r\n         .          .    590:\t// make better decisions about compacting L0. If we see a score >= 1.0, we can do L0->L0\r\n         .          .    591:\t// compactions. If the adjusted score >= 1.0, then we can do L0->Lbase compactions.\r\n         .          .    592:\tout := prios[:0]\r\n         .          .    593:\tfor _, p := range prios[:len(prios)-1] {\r\n         .          .    594:\t\tif p.score >= 1.0 {\r\n         .          .    595:\t\t\tout = append(out, p)\r\n         .          .    596:\t\t}\r\n         .          .    597:\t}\r\n         .          .    598:\tprios = out\r\n         .          .    599:\r\n         .          .    600:\t// Sort by the adjusted score.\r\n     43691      43691    601:\tsort.Slice(prios, func(i, j int) bool {\r\n         .          .    602:\t\treturn prios[i].adjusted > prios[j].adjusted\r\n         .          .    603:\t})\r\n         .          .    604:\treturn prios\r\n         .          .    605:}\r\n         .          .    606:\r\nROUTINE ======================== github.com/dgraph-io/badger/v4.(*levelsController).pickCompactLevels.func1 in /Users/deff/go/pkg/mod/github.com/dgraph-io/badger/v4@v4.2.0/levels.go\r\n    155255     155255 (flat, cum) 57.88% of Total\r\n         .          .    541:\taddPriority := func(level int, score float64) {\r\n         .          .    542:\t\tpri := compactionPriority{\r\n         .          .    543:\t\t\tlevel:    level,\r\n         .          .    544:\t\t\tscore:    score,\r\n         .          .    545:\t\t\tadjusted: score,\r\n         .          .    546:\t\t\tt:        t,\r\n         .          .    547:\t\t}\r\n    155255     155255    548:\t\tprios = append(prios, pri)\r\n         .          .    549:\t}\r\n         .          .    550:\r\n         .          .    551:\t// Add L0 priority based on the number of tables.\r\n         .          .    552:\taddPriority(0, float64(s.levels[0].numTables())/float64(s.kv.opt.NumLevelZeroTables))\r\n         .          .    553:\r\n```\r\n\r\n <!--\r\n Please add a description with these things:\r\n 1. Explain the problem by providing a good description.\r\n 2. If it fixes any GitHub issues, say \"Fixes #GitHubIssue\".\r\n 3. If it corresponds to a Jira issue, say \"Fixes DGRAPH-###\".\r\n4. If this is a breaking change, please prefix `[Breaking]` in the\r\ntitle. In the description, please put a note with exactly who these\r\nchanges are breaking for.\r\n -->\r\n\r\n## Solution\r\nI suggest two optimizations:\r\n1. Pre-allocate `prios` capacity according to numbers of `s.levels`\r\n2. Reuse `prios` memory in compaction process, thanks to one-threaded\r\nlogic of compactor\r\n\r\nResults after optimization (10 min run of reproducer):\r\n```\r\n(pprof) top\r\nShowing nodes accounting for 165466, 99.84% of 165735 total\r\nDropped 27 nodes (cum <= 828)\r\nShowing top 10 nodes out of 48\r\n      flat  flat%   sum%        cum   cum%\r\n     40962 24.72% 24.72%      40962 24.72%  github.com/dgraph-io/badger/v4.(*levelsController).levelTargets\r\n     32768 19.77% 44.49%      32768 19.77%  github.com/dgraph-io/badger/v4/skl.(*Arena).putNode\r\n     32768 19.77% 64.26%      32768 19.77%  github.com/dgraph-io/badger/v4/y.KeyWithTs (inline)\r\n     21845 13.18% 77.44%      62807 37.90%  github.com/dgraph-io/badger/v4.(*levelsController).pickCompactLevels\r\n     21845 13.18% 90.62%      21845 13.18%  github.com/dgraph-io/badger/v4.(*logFile).encodeEntry\r\n      8192  4.94% 95.56%       8192  4.94%  github.com/dgraph-io/badger/v4/table.(*Builder).addHelper\r\n      4681  2.82% 98.39%       4681  2.82%  regexp/syntax.(*Regexp).Simplify\r\n      2341  1.41% 99.80%       2341  1.41%  runtime/pprof.allFrames\r\n        64 0.039% 99.84%      32832 19.81%  github.com/dgraph-io/badger/v4.(*Txn).commitAndSend\r\n         0     0% 99.84%      32832 19.81%  github.com/dgraph-io/badger/v4.(*DB).Update\r\n(pprof) sample_index=alloc_space\r\n(pprof) top\r\nShowing nodes accounting for 180.47MB, 97.79% of 184.54MB total\r\nDropped 22 nodes (cum <= 0.92MB)\r\nShowing top 10 nodes out of 53\r\n      flat  flat%   sum%        cum   cum%\r\n  166.41MB 90.17% 90.17%   166.41MB 90.17%  github.com/dgraph-io/badger/v4/skl.newArena\r\n       4MB  2.17% 92.34%        4MB  2.17%  github.com/dgraph-io/ristretto/z.Calloc\r\n    3.01MB  1.63% 93.97%     3.01MB  1.63%  github.com/google/flatbuffers/go.NewBuilder\r\n    2.50MB  1.35% 95.32%     2.50MB  1.35%  github.com/dgraph-io/badger/v4.(*levelsController).levelTargets\r\n    1.76MB  0.96% 96.28%     2.97MB  1.61%  compress/flate.NewWriter (inline)\r\n    1.16MB  0.63% 96.91%     1.16MB  0.63%  github.com/dgraph-io/ristretto/z.(*Bloom).Size\r\n    0.64MB  0.34% 97.25%     1.20MB  0.65%  compress/flate.(*compressor).init\r\n    0.50MB  0.27% 97.52%        1MB  0.54%  github.com/dgraph-io/badger/v4.(*Txn).commitAndSend\r\n    0.50MB  0.27% 97.79%        3MB  1.63%  github.com/dgraph-io/badger/v4.(*levelsController).pickCompactLevels\r\n         0     0% 97.79%     2.97MB  1.61%  compress/gzip.(*Writer).Write\r\n```\r\n\r\nAnd inside pickCompactLevels:\r\n```\r\nROUTINE ======================== github.com/dgraph-io/badger/v4.(*levelsController).pickCompactLevels in /Users/deff/dev/work/badger/levels.go\r\n     21845      62807 (flat, cum) 37.90% of Total\r\n         .          .    544:func (s *levelsController) pickCompactLevels(prios []compactionPriority) []compactionPriority {\r\n         .      40962    545:\tt := s.levelTargets()\r\n         .          .    546:\taddPriority := func(level int, score float64) {\r\n         .          .    547:\t\tpri := compactionPriority{\r\n         .          .    548:\t\t\tlevel:    level,\r\n         .          .    549:\t\t\tscore:    score,\r\n         .          .    550:\t\t\tadjusted: score,\r\n         .          .    551:\t\t\tt:        t,\r\n         .          .    552:\t\t}\r\n         .          .    553:\t\tprios = append(prios, pri)\r\n         .          .    554:\t}\r\n         .          .    555:\r\n         .          .    556:\tif cap(prios) < len(s.levels) {\r\n         .          .    557:\t\tprios = make([]compactionPriority, 0, len(s.levels))\r\n         .          .    558:\t}\r\n         .          .    559:\tprios = prios[:0]\r\n         .          .    560:\r\n         .          .    561:\t// Add L0 priority based on the number of tables.\r\n         .          .    562:\taddPriority(0, float64(s.levels[0].numTables())/float64(s.kv.opt.NumLevelZeroTables))\r\n         .          .    563:\r\n         .          .    564:\t// All other levels use size to calculate priority.\r\n         .          .    565:\tfor i := 1; i < len(s.levels); i++ {\r\n         .          .    566:\t\t// Don't consider those tables that are already being compacted right now.\r\n         .          .    567:\t\tdelSize := s.cstatus.delSize(i)\r\n         .          .    568:\r\n         .          .    569:\t\tl := s.levels[i]\r\n         .          .    570:\t\tsz := l.getTotalSize() - delSize\r\n         .          .    571:\t\taddPriority(i, float64(sz)/float64(t.targetSz[i]))\r\n         .          .    572:\t}\r\n         .          .    573:\ty.AssertTrue(len(prios) == len(s.levels))\r\n         .          .    574:\r\n         .          .    575:\t// The following code is borrowed from PebbleDB and results in healthier LSM tree structure.\r\n         .          .    576:\t// If Li-1 has score > 1.0, then we'll divide Li-1 score by Li. If Li score is >= 1.0, then Li-1\r\n         .          .    577:\t// score is reduced, which means we'll prioritize the compaction of lower levels (L5, L4 and so\r\n         .          .    578:\t// on) over the higher levels (L0, L1 and so on). On the other hand, if Li score is < 1.0, then\r\n         .          .    579:\t// we'll increase the priority of Li-1.\r\n         .          .    580:\t// Overall what this means is, if the bottom level is already overflowing, then de-prioritize\r\n         .          .    581:\t// compaction of the above level. If the bottom level is not full, then increase the priority of\r\n         .          .    582:\t// above level.\r\n         .          .    583:\tvar prevLevel int\r\n         .          .    584:\tfor level := t.baseLevel; level < len(s.levels); level++ {\r\n         .          .    585:\t\tif prios[prevLevel].adjusted >= 1 {\r\n         .          .    586:\t\t\t// Avoid absurdly large scores by placing a floor on the score that we'll\r\n         .          .    587:\t\t\t// adjust a level by. The value of 0.01 was chosen somewhat arbitrarily\r\n         .          .    588:\t\t\tconst minScore = 0.01\r\n         .          .    589:\t\t\tif prios[level].score >= minScore {\r\n         .          .    590:\t\t\t\tprios[prevLevel].adjusted /= prios[level].adjusted\r\n         .          .    591:\t\t\t} else {\r\n         .          .    592:\t\t\t\tprios[prevLevel].adjusted /= minScore\r\n         .          .    593:\t\t\t}\r\n         .          .    594:\t\t}\r\n         .          .    595:\t\tprevLevel = level\r\n         .          .    596:\t}\r\n         .          .    597:\r\n         .          .    598:\t// Pick all the levels whose original score is >= 1.0, irrespective of their adjusted score.\r\n         .          .    599:\t// We'll still sort them by their adjusted score below. Having both these scores allows us to\r\n         .          .    600:\t// make better decisions about compacting L0. If we see a score >= 1.0, we can do L0->L0\r\n         .          .    601:\t// compactions. If the adjusted score >= 1.0, then we can do L0->Lbase compactions.\r\n         .          .    602:\tout := prios[:0]\r\n         .          .    603:\tfor _, p := range prios[:len(prios)-1] {\r\n         .          .    604:\t\tif p.score >= 1.0 {\r\n         .          .    605:\t\t\tout = append(out, p)\r\n         .          .    606:\t\t}\r\n         .          .    607:\t}\r\n         .          .    608:\tprios = out\r\n         .          .    609:\r\n         .          .    610:\t// Sort by the adjusted score.\r\n     21845      21845    611:\tsort.Slice(prios, func(i, j int) bool {\r\n         .          .    612:\t\treturn prios[i].adjusted > prios[j].adjusted\r\n         .          .    613:\t})\r\n         .          .    614:\treturn prios\r\n         .          .    615:}\r\n         .          .    616:\r\n```\r\n <!--\r\n Please add a description with these things:\r\n 1. Explain the solution to make it easier to review the PR.\r\n4. Make it easier for the reviewer by describing complex sections with\r\ncomments.\r\n -->\r\n\r\n## Profile from real project\r\nBoth profiles are measured after 30 minutes from application start\r\n\r\n### Before optimization:\r\n```\r\n(pprof) top\r\nShowing nodes accounting for 621.02MB, 85.32% of 727.90MB total\r\nDropped 550 nodes (cum <= 3.64MB)\r\nShowing top 10 nodes out of 146\r\n      flat  flat%   sum%        cum   cum%\r\n  380.72MB 52.30% 52.30%   380.72MB 52.30%  github.com/dgraph-io/badger/v3.(*levelsController).pickCompactLevels.func1\r\n  104.01MB 14.29% 66.59%   104.01MB 14.29%  github.com/dgraph-io/badger/v3/skl.newArena\r\n   33.27MB  4.57% 71.16%    33.27MB  4.57%  github.com/dgraph-io/ristretto.newCmRow\r\n   27.05MB  3.72% 74.88%    27.05MB  3.72%  github.com/dgraph-io/badger/v3/y.SafeCopy\r\n   23.50MB  3.23% 78.11%    23.50MB  3.23%  github.com/dgraph-io/badger/v3.(*levelsController).levelTargets\r\n   18.31MB  2.52% 80.62%    18.31MB  2.52%  github.com/dgraph-io/ristretto/z.(*Bloom).Size\r\n   18.02MB  2.48% 83.10%    18.02MB  2.48%  github.com/dgraph-io/badger/v3/y.(*Slice).Resize\r\n       8MB  1.10% 84.20%   412.23MB 56.63%  github.com/dgraph-io/badger/v3.(*levelsController).pickCompactLevels\r\n    4.12MB  0.57% 84.77%     8.13MB  1.12%  github.com/blevesearch/vellum.(*FSTIterator).next\r\n       4MB  0.55% 85.32%        4MB  0.55%  github.com/blevesearch/vellum.(*decoderV1).stateAt\r\n```\r\n### After optimization:\r\n```\r\nType: alloc_space\r\nTime: Sep 11, 2023 at 5:50pm (+05)\r\nEntering interactive mode (type \"help\" for commands, \"o\" for options)\r\n(pprof) top\r\nShowing nodes accounting for 262.17MB, 66.88% of 391.99MB total\r\nDropped 453 nodes (cum <= 1.96MB)\r\nShowing top 10 nodes out of 290\r\n      flat  flat%   sum%        cum   cum%\r\n  104.01MB 26.53% 26.53%   104.01MB 26.53%  github.com/dgraph-io/badger/v3/skl.newArena\r\n   33.91MB  8.65% 35.18%    33.91MB  8.65%  github.com/dgraph-io/ristretto.newCmRow\r\n   28.50MB  7.27% 42.45%    28.50MB  7.27%  github.com/dgraph-io/badger/v3.(*levelsController).levelTargets\r\n   26.52MB  6.77% 49.22%    26.52MB  6.77%  github.com/dgraph-io/badger/v3/y.(*Slice).Resize\r\n   25.03MB  6.38% 55.61%    25.03MB  6.38%  github.com/dgraph-io/badger/v3/y.SafeCopy\r\n   17.16MB  4.38% 59.98%    17.16MB  4.38%  github.com/dgraph-io/ristretto/z.(*Bloom).Size\r\n    7.12MB  1.82% 61.80%     9.12MB  2.33%  github.com/anyproto/go-chash.(*cHash).addMembers\r\n    6.72MB  1.72% 63.51%    12.22MB  3.12%  github.com/blevesearch/vellum.(*FSTIterator).next\r\n    6.71MB  1.71% 65.22%     6.71MB  1.71%  bytes.growSlice\r\n    6.50MB  1.66% 66.88%     6.50MB  1.66%  github.com/blevesearch/vellum.(*builderNodePool).Get\r\n```\r\n# Reproducer\r\n```\r\npackage main\r\n\r\nimport (\r\n\t\"fmt\"\r\n\t\"os\"\r\n\t\"github.com/dgraph-io/badger/v4\"\r\n\t_ \"net/http/pprof\"\r\n\t\"net/http\"\r\n\t\"log\"\r\n)\r\n\r\nfunc generateItems(db *badger.DB, n int) error {\r\n\treturn db.Update(func(txn *badger.Txn) error {\r\n\t\tfor i := 0; i < n; i++ {\r\n\t\t\terr := txn.Set([]byte(fmt.Sprintf(\"key-%d\", i)), []byte(fmt.Sprintf(\"value-%d\", i)))\r\n\t\t\tif err != nil {\r\n\t\t\t\treturn err\r\n\t\t\t}\r\n\t\t}\r\n\t\treturn nil\r\n\t})\r\n}\r\n\r\nfunc run() error {\r\n\tforever := make(chan struct{})\r\n\r\n\tdb, err := badger.Open(badger.DefaultOptions(\"./tmp\"))\r\n\tif err != nil {\r\n\t\treturn fmt.Errorf(\"open badger: %w\", err)\r\n\t}\r\n\terr = generateItems(db, 1000)\r\n\tif err != nil {\r\n\t\treturn fmt.Errorf(\"generate items: %w\", err)\r\n\t}\r\n\r\n\tgo func() {\r\n\t\tlog.Println(http.ListenAndServe(\"localhost:9000\", nil))\r\n\t}()\r\n\r\n\t<-forever\r\n\treturn nil\r\n}\r\n\r\nfunc main() {\r\n\terr := run()\r\n\tif err != nil {\r\n\t\tfmt.Fprintln(os.Stderr, err)\r\n\t\tos.Exit(1)\r\n\t}\r\n}\r\n```","shortMessageHtmlLink":"optimize allocations by using pre allocated buffer for priorities (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1894062274\" data-permission-text=\"Title is private\" data-url=\"https://github.com/dgraph-io/badger/issues/2006\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/dgraph-io/badger/pull/2006/hovercard\" href=\"https://github.com/dgraph-io/badger/pull/2006\">#2006</a>"}},{"before":"1493d7f87211fd060b1c82d9a2f6274474db6e96","after":null,"ref":"refs/heads/dependabot/go_modules/golang.org/x/net-0.17.0","pushedAt":"2023-10-13T07:37:57.000Z","pushType":"branch_deletion","commitsCount":0,"pusher":{"login":"mangalaman93","name":"Aman Mangal","path":"/mangalaman93","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/2542340?s=80&v=4"}},{"before":"1741e474446b4eb053b1a959ea8d31d9d5cf6948","after":"b84bc01e234c7b9fd8ea1ddcef633786b2239489","ref":"refs/heads/main","pushedAt":"2023-10-13T07:37:56.000Z","pushType":"pr_merge","commitsCount":1,"pusher":{"login":"mangalaman93","name":"Aman Mangal","path":"/mangalaman93","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/2542340?s=80&v=4"},"commit":{"message":"chore(deps): bump golang.org/x/net from 0.7.0 to 0.17.0 (#2017)","shortMessageHtmlLink":"chore(deps): bump golang.org/x/net from 0.7.0 to 0.17.0 (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1938920175\" data-permission-text=\"Title is private\" data-url=\"https://github.com/dgraph-io/badger/issues/2017\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/dgraph-io/badger/pull/2017/hovercard\" href=\"https://github.com/dgraph-io/badger/pull/2017\">#2017</a>)"}},{"before":"27724c4dbe8eeef057407cf418ca833cdf7f40d8","after":"1741e474446b4eb053b1a959ea8d31d9d5cf6948","ref":"refs/heads/main","pushedAt":"2023-10-13T07:37:15.000Z","pushType":"pr_merge","commitsCount":1,"pusher":{"login":"mangalaman93","name":"Aman Mangal","path":"/mangalaman93","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/2542340?s=80&v=4"},"commit":{"message":"fix resource consumption on empty write transaction (#2018)\n\n## Problem\r\nIf a write transaction is opened without doing any updates, e.g. \r\n```\r\ntxn := db.NewTransaction(true)\r\ntxn.Commit()\r\n```\r\nthe read mark is never marked done, because `txn.Discard` has not been\r\ncalled and neither has the commit logic because of the early return in\r\n`Commit()`:\r\n```\r\nif len(txn.pendingWrites) == 0 {\r\n    return nil // Nothing to do.\r\n}\r\n```\r\nThis causes unbounded storage growth until the service is restarted. The\r\nwatermark process in `y/watermark.go` never receives a mark for that\r\ntransaction, and so never updates the `DoneUntil`. This value is used in\r\n`levels.go` for compaction to determine the `discardTS` value, and since\r\nit never updates, compaction can never occur.\r\n\r\n## Solution\r\nThe solution here is to call `txn.Discard` in the case when there are no\r\nwrites on the transaction. This marks the read as done and allows the\r\nwatermark process to properly update.","shortMessageHtmlLink":"fix resource consumption on empty write transaction (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1940673979\" data-permission-text=\"Title is private\" data-url=\"https://github.com/dgraph-io/badger/issues/2018\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/dgraph-io/badger/pull/2018/hovercard\" href=\"https://github.com/dgraph-io/badger/pull/2018\">#2018</a>)"}},{"before":null,"after":"1493d7f87211fd060b1c82d9a2f6274474db6e96","ref":"refs/heads/dependabot/go_modules/golang.org/x/net-0.17.0","pushedAt":"2023-10-11T23:35:31.000Z","pushType":"branch_creation","commitsCount":0,"pusher":{"login":"dependabot[bot]","name":null,"path":"/apps/dependabot","primaryAvatarUrl":"https://avatars.githubusercontent.com/in/29110?s=80&v=4"},"commit":{"message":"chore(deps): bump golang.org/x/net from 0.7.0 to 0.17.0\n\nBumps [golang.org/x/net](https://github.com/golang/net) from 0.7.0 to 0.17.0.\n- [Commits](https://github.com/golang/net/compare/v0.7.0...v0.17.0)\n\n---\nupdated-dependencies:\n- dependency-name: golang.org/x/net\n  dependency-type: direct:production\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>","shortMessageHtmlLink":"chore(deps): bump golang.org/x/net from 0.7.0 to 0.17.0"}},{"before":"a5635e18d3232a468928f565c667783aa7a011ba","after":null,"ref":"refs/heads/joshua/ghost-jobs","pushedAt":"2023-09-29T21:35:17.000Z","pushType":"branch_deletion","commitsCount":0,"pusher":{"login":"joshua-goldstein","name":"Joshua Goldstein","path":"/joshua-goldstein","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/92491720?s=80&v=4"}}],"hasNextPage":true,"hasPreviousPage":false,"activityType":"all","actor":null,"timePeriod":"all","sort":"DESC","perPage":30,"cursor":"djE6ks8AAAAEQQXHGQA","startCursor":null,"endCursor":null}},"title":"Activity · dgraph-io/badger"}