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Qian","path":"/wqian94","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/1192680?s=80&v=4"},"commit":{"message":"WIP","shortMessageHtmlLink":"WIP"}},{"before":"9e238c885f87118fe60f5d0dfff18e2680fe53ac","after":null,"ref":"refs/heads/gh-readonly-queue/master/pr-1221-65e1858730d4cd43882fa61b62f60847a8ee61b1","pushedAt":"2024-05-20T20:01:30.000Z","pushType":"branch_deletion","commitsCount":0,"pusher":{"login":"github-merge-queue[bot]","name":null,"path":"/apps/github-merge-queue","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/9919?s=80&v=4"}},{"before":"65e1858730d4cd43882fa61b62f60847a8ee61b1","after":"9e238c885f87118fe60f5d0dfff18e2680fe53ac","ref":"refs/heads/master","pushedAt":"2024-05-20T20:01:30.000Z","pushType":"merge_queue_merge","commitsCount":1,"pusher":{"login":"github-merge-queue[bot]","name":null,"path":"/apps/github-merge-queue","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/9919?s=80&v=4"},"commit":{"message":"Perf 5459  compare disable sbe plan cache (#1221)\n\n<!---\nThanks for submitting a PR to the Genny repo. Please include the\nfollowing fields (if relevant) prior to submitting your PR.\n--->\n\n**Jira Ticket:** [PERF-5459](https://jira.mongodb.org/browse/PERF-5459)\n\n### Whats Changed\n\nI've added two new Genny benchmarks:\n* $group on an empty collection, intended to show a case where the time\ntaken by stage-builders matters.\n* $eq with a varying array, intended to show a case where the Classic\nplan cache has a much better hit rate than the SBE plan cache.\n\nI've spot checked it locally to make sure it does what we want. I\nchecked the number of plan cache entries in each scenario:\n* empty $group\n* master: 5 cache entries (one per query shape -- there are no\nparameters varying here)\n    * draft PR: 0 cache entries (because there are no indexes)\n* $eq array\n    * master: 1000 cache entries (one per parameter value)\n* draft PR: 1 cache entry (showing Classic's auto-parameterization\nworking well)\n\nI also looked at the histograms locally, just to spot check. (Don't\nbelieve the actual numbers, because I didn't build with `--release`.)\nThings I noticed are:\n* empty $group\n    * master\n        * The median latency is negative in many cases??\n* There's consistently one outlier per query shape. (Maybe this is the\nfirst request, which does the query planning?)\n* Despite the SBE plan cache, the average latency does increase with the\nsize of the query text.\n    * draft PR\n* For each query shape the average latency is higher than on master.\n* There's still consistently one outlier. (It can't be that only the\nfirst request did query planning, because there are 0 cache entries.)\n* $eq array\n    * master\n* There's still one outlier. (It can't be due to planning, because there\nare 1000 cache entries. It must be some other kind of warm-up.)\n        * Very roughly, one query took 7ms and the rest took 5ms.\n    * draft PR\n* Now there are two \"outliers\": 7ms and 5ms, while the rest took 2ms.\n* (We know there's only one cache entry, and IIRC it would require\nplanning twice to become active. So the 7ms and 5ms must be the two\nrequests that did the planning, which makes sense because the 5ms\nmatches the typical value on master, which is planning every time.)\n\nThe histograms are all generated with `genny_venv/bin/python\nsrc/workloads/contrib/analysis/test_result_summary.py -m timers.dur -a\n\".*agg.*\"`.\n\n\n<!---\nHigh level explanation of changes\n--->\n\n### Patch Testing Results\n\nI've started two individual patch builds:\n* new benchmark on draft pr:\nhttps://spruce.mongodb.com/version/66452b0162b06200078e3f27/tasks?sorts=STATUS%3AASC%3BBASE_STATUS%3ADESC\n* new benchmarks on base commit:\nhttps://spruce.mongodb.com/version/66452b3ae0a31d00071c6ea4/tasks?sorts=STATUS%3AASC%3BBASE_STATUS%3ADESC\n\nJust to make sure it basically works, and that I've selected the right\ntasks. If that works I'll do a multipatch to get more trustworthy\nnumbers, or I'll address any PR feedback first.\n\n\n<!---\nLinting YAML files\n\nIf you update/add any YAML files under the workload and/or resmoke\ndirectory or\nthe evergreen file, please lint them first: src/lamplib/src/genny/tasks/\n\ncd <genny_repo_root> # replace `<genny_repo_root>` with the actual\ndirectory\n./run-genny -v lint-yaml --format\n\nNote: it will report 'invalid config: no such rule: \"anchors\"' if the\nyamllint\nin the genny venv is too old. To fix this issue, please `rm -r\ngenny_venv` and\nthen re-run the aforementioned lint command.\n\n--->\n\n\n### Histograms I ran locally\n\n\nempty $group, master:\n```\nGroupQuery01Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.3135  \n                median    : -0.1903 \n                mode      : 0.3056  \n                stddev    : 0.02719 \n                [min, max]: ['0.3024', '1.108']\n                histogram:\n                        [0,0): ************************************************************...  (992)\n                        [0,0): ******   (6)\n                        [0,0):  (0)\n                        [0,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1]: *        (1)\n\n\nGroupQuery04Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.3363  \n                median    : 0.3329  \n                mode      : 0.3317  \n                stddev    : 0.02574 \n                [min, max]: ['0.3265', '1.079']\n                histogram:\n                        [0,0): ************************************************************...  (993)\n                        [0,0): ****     (4)\n                        [0,0): *        (1)\n                        [0,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1]: *        (1)\n\n\nGroupQuery08Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.3856  \n                median    : -0.118  \n                mode      : 0.3832  \n                stddev    : 0.02769 \n                [min, max]: ['0.3747', '1.215']\n                histogram:\n                        [0,0): ************************************************************...  (995)\n                        [0,0): ***      (3)\n                        [0,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1]: *        (1)\n\n\nGroupQuery16Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.4898  \n                median    : -0.01565\n                mode      : 0.4814  \n                stddev    : 0.04695 \n                [min, max]: ['0.4755', '1.899']\n                histogram:\n                        [0,1): ************************************************************...  (995)\n                        [1,1): ***      (3)\n                        [1,1):  (0)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2]: *        (1)\n\n\nGroupQuery32Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.6548  \n                median    : 0.1489  \n                mode      : 0.6443  \n                stddev    : 0.05987 \n                [min, max]: ['0.6386', '2.507']\n                histogram:\n                        [1,1): ************************************************************...  (997)\n                        [1,1): **       (2)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,3]: *        (1)\n```\n\nempty $group, draft PR:\n```\nGroupQuery01Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.4015  \n                median    : -0.1025 \n                mode      : 0.3966  \n                stddev    : 0.01979 \n                [min, max]: ['0.3888', '0.8884']\n                histogram:\n                        [0,0): ************************************************************...  (966)\n                        [0,0): **************************       (26)\n                        [0,0): ****     (4)\n                        [0,1): *        (1)\n                        [1,1): *        (1)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1]: *        (1)\n\n\nGroupQuery04Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.5456  \n                median    : 0.04166 \n                mode      : 0.5391  \n                stddev    : 0.01639 \n                [min, max]: ['0.5319', '0.9104']\n                histogram:\n                        [1,1): ************************************************************...  (878)\n                        [1,1): ************************************************************...  (114)\n                        [1,1): ****     (4)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1]: *        (1)\n\n\nGroupQuery08Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.7421  \n                median    : 0.2377  \n                mode      : 0.7292  \n                stddev    : 0.01712 \n                [min, max]: ['0.7244', '1.074']\n                histogram:\n                        [1,1): ************************************************************...  (741)\n                        [1,1): ************************************************************...  (235)\n                        [1,1): **************   (14)\n                        [1,1): ******   (6)\n                        [1,1):  (0)\n                        [1,1): ***      (3)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1]: *        (1)\n\n\nGroupQuery16Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 1.130   \n                median    : 0.6253  \n                mode      : 1.128   \n                stddev    : 0.02557 \n                [min, max]: ['1.097', '1.510']\n                histogram:\n                        [1,1): ************************************************************...  (485)\n                        [1,1): ************************************************************...  (397)\n                        [1,1): ************************************************************...  (84)\n                        [1,1): **************************       (26)\n                        [1,1): **       (2)\n                        [1,1): ***      (3)\n                        [1,1):  (0)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,2]: *        (1)\n\n\nGroupQuery32Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 1.917   \n                median    : 1.406   \n                mode      : 1.927   \n                stddev    : 0.06044 \n                [min, max]: ['1.828', '2.360']\n                histogram:\n                        [2,2): ************************************************************...  (155)\n                        [2,2): ************************************************************...  (289)\n                        [2,2): ************************************************************...  (266)\n                        [2,2): ************************************************************...  (158)\n                        [2,2): *******************************************************  (55)\n                        [2,2): *************************************    (37)\n                        [2,2): **************** (16)\n                        [2,2): *****    (5)\n                        [2,2): ******** (8)\n                        [2,2): ******   (6)\n                        [2,2): ***      (3)\n                        [2,2): *        (1)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2]: *        (1)\n```\n\n$eq array, master:\n```\nMatchQuery02Indexes.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 5.116   \n                median    : 4.616   \n                mode      : 5.101   \n                stddev    : 0.09576 \n                [min, max]: ['4.913', '7.244']\n                histogram:\n                        [5,5): ************************************************************...  (234)\n                        [5,5): ************************************************************...  (719)\n                        [5,5): *********************************************    (45)\n                        [5,6): *        (1)\n                        [6,6):  (0)\n                        [6,6):  (0)\n                        [6,6):  (0)\n                        [6,6):  (0)\n                        [6,6):  (0)\n                        [6,6):  (0)\n                        [6,7):  (0)\n                        [7,7):  (0)\n                        [7,7):  (0)\n                        [7,7):  (0)\n                        [7,7]: *        (1)\n```\n\n$eq array, draft PR:\n```\nMatchQuery02Indexes.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 1.771   \n                median    : 1.751   \n                mode      : 1.817   \n                stddev    : 0.2151  \n                [min, max]: ['1.648', '7.319']\n                histogram:\n                        [2,2): ************************************************************...  (997)\n                        [2,2): *        (1)\n                        [2,3):  (0)\n                        [3,3):  (0)\n                        [3,4):  (0)\n                        [4,4):  (0)\n                        [4,4):  (0)\n                        [4,5):  (0)\n                        [5,5):  (0)\n                        [5,5): *        (1)\n                        [5,6):  (0)\n                        [6,6):  (0)\n                        [6,7):  (0)\n                        [7,7):  (0)\n                        [7,7]: *        (1)\n```\n\n\n<!---\nIf applicable, link a patch test showing code changes running\nsuccessfully\n--->\n\n<!---\n### Workload Submission form\n\nIf applicable, only required if there is a new workload being added. The\nform is [here].\n\n[here]:\nhttps://docs.google.com/forms/d/e/1FAIpQLSf1oh23Ddo1khjLZMqZ9DHbRdObnpeH20PSXTBuXVg-7mxxTA/viewform\n\n### Merging and Linting\n\nWe currently have a manual linting stage required if you're\nadding/modifying a workload YAML document. Evergreen will verify that\nthis has been run.\n\n```bash\n./run-genny generate-docs\n```\n\nOnce (1) your PR is approved by a CODEOWNER and (2) all your CI tests\npass, you can initiate a merge by clicking \"Add to merge queue\".\nThis kicks your PR into the [Github Merge Queue]. Github will\nautomatically squash-merge your commit after checking that Evergreen's\nCI tasks have returned a successful status.\n\n[Github Merge Queue]:\nhttps://docs.devprod.prod.corp.mongodb.com/evergreen/Project-Configuration/Merge-Queue\n\n--->","shortMessageHtmlLink":"Perf 5459 compare disable sbe plan cache (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2298952578\" data-permission-text=\"Title is private\" data-url=\"https://github.com/mongodb/genny/issues/1221\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/mongodb/genny/pull/1221/hovercard\" href=\"https://github.com/mongodb/genny/pull/1221\">#1221</a>)"}},{"before":null,"after":"9e238c885f87118fe60f5d0dfff18e2680fe53ac","ref":"refs/heads/gh-readonly-queue/master/pr-1221-65e1858730d4cd43882fa61b62f60847a8ee61b1","pushedAt":"2024-05-20T18:21:37.000Z","pushType":"branch_creation","commitsCount":0,"pusher":{"login":"github-merge-queue[bot]","name":null,"path":"/apps/github-merge-queue","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/9919?s=80&v=4"},"commit":{"message":"Perf 5459  compare disable sbe plan cache (#1221)\n\n<!---\nThanks for submitting a PR to the Genny repo. Please include the\nfollowing fields (if relevant) prior to submitting your PR.\n--->\n\n**Jira Ticket:** [PERF-5459](https://jira.mongodb.org/browse/PERF-5459)\n\n### Whats Changed\n\nI've added two new Genny benchmarks:\n* $group on an empty collection, intended to show a case where the time\ntaken by stage-builders matters.\n* $eq with a varying array, intended to show a case where the Classic\nplan cache has a much better hit rate than the SBE plan cache.\n\nI've spot checked it locally to make sure it does what we want. I\nchecked the number of plan cache entries in each scenario:\n* empty $group\n* master: 5 cache entries (one per query shape -- there are no\nparameters varying here)\n    * draft PR: 0 cache entries (because there are no indexes)\n* $eq array\n    * master: 1000 cache entries (one per parameter value)\n* draft PR: 1 cache entry (showing Classic's auto-parameterization\nworking well)\n\nI also looked at the histograms locally, just to spot check. (Don't\nbelieve the actual numbers, because I didn't build with `--release`.)\nThings I noticed are:\n* empty $group\n    * master\n        * The median latency is negative in many cases??\n* There's consistently one outlier per query shape. (Maybe this is the\nfirst request, which does the query planning?)\n* Despite the SBE plan cache, the average latency does increase with the\nsize of the query text.\n    * draft PR\n* For each query shape the average latency is higher than on master.\n* There's still consistently one outlier. (It can't be that only the\nfirst request did query planning, because there are 0 cache entries.)\n* $eq array\n    * master\n* There's still one outlier. (It can't be due to planning, because there\nare 1000 cache entries. It must be some other kind of warm-up.)\n        * Very roughly, one query took 7ms and the rest took 5ms.\n    * draft PR\n* Now there are two \"outliers\": 7ms and 5ms, while the rest took 2ms.\n* (We know there's only one cache entry, and IIRC it would require\nplanning twice to become active. So the 7ms and 5ms must be the two\nrequests that did the planning, which makes sense because the 5ms\nmatches the typical value on master, which is planning every time.)\n\nThe histograms are all generated with `genny_venv/bin/python\nsrc/workloads/contrib/analysis/test_result_summary.py -m timers.dur -a\n\".*agg.*\"`.\n\n\n<!---\nHigh level explanation of changes\n--->\n\n### Patch Testing Results\n\nI've started two individual patch builds:\n* new benchmark on draft pr:\nhttps://spruce.mongodb.com/version/66452b0162b06200078e3f27/tasks?sorts=STATUS%3AASC%3BBASE_STATUS%3ADESC\n* new benchmarks on base commit:\nhttps://spruce.mongodb.com/version/66452b3ae0a31d00071c6ea4/tasks?sorts=STATUS%3AASC%3BBASE_STATUS%3ADESC\n\nJust to make sure it basically works, and that I've selected the right\ntasks. If that works I'll do a multipatch to get more trustworthy\nnumbers, or I'll address any PR feedback first.\n\n\n<!---\nLinting YAML files\n\nIf you update/add any YAML files under the workload and/or resmoke\ndirectory or\nthe evergreen file, please lint them first: src/lamplib/src/genny/tasks/\n\ncd <genny_repo_root> # replace `<genny_repo_root>` with the actual\ndirectory\n./run-genny -v lint-yaml --format\n\nNote: it will report 'invalid config: no such rule: \"anchors\"' if the\nyamllint\nin the genny venv is too old. To fix this issue, please `rm -r\ngenny_venv` and\nthen re-run the aforementioned lint command.\n\n--->\n\n\n### Histograms I ran locally\n\n\nempty $group, master:\n```\nGroupQuery01Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.3135  \n                median    : -0.1903 \n                mode      : 0.3056  \n                stddev    : 0.02719 \n                [min, max]: ['0.3024', '1.108']\n                histogram:\n                        [0,0): ************************************************************...  (992)\n                        [0,0): ******   (6)\n                        [0,0):  (0)\n                        [0,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1]: *        (1)\n\n\nGroupQuery04Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.3363  \n                median    : 0.3329  \n                mode      : 0.3317  \n                stddev    : 0.02574 \n                [min, max]: ['0.3265', '1.079']\n                histogram:\n                        [0,0): ************************************************************...  (993)\n                        [0,0): ****     (4)\n                        [0,0): *        (1)\n                        [0,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1]: *        (1)\n\n\nGroupQuery08Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.3856  \n                median    : -0.118  \n                mode      : 0.3832  \n                stddev    : 0.02769 \n                [min, max]: ['0.3747', '1.215']\n                histogram:\n                        [0,0): ************************************************************...  (995)\n                        [0,0): ***      (3)\n                        [0,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1]: *        (1)\n\n\nGroupQuery16Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.4898  \n                median    : -0.01565\n                mode      : 0.4814  \n                stddev    : 0.04695 \n                [min, max]: ['0.4755', '1.899']\n                histogram:\n                        [0,1): ************************************************************...  (995)\n                        [1,1): ***      (3)\n                        [1,1):  (0)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2]: *        (1)\n\n\nGroupQuery32Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.6548  \n                median    : 0.1489  \n                mode      : 0.6443  \n                stddev    : 0.05987 \n                [min, max]: ['0.6386', '2.507']\n                histogram:\n                        [1,1): ************************************************************...  (997)\n                        [1,1): **       (2)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,3]: *        (1)\n```\n\nempty $group, draft PR:\n```\nGroupQuery01Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.4015  \n                median    : -0.1025 \n                mode      : 0.3966  \n                stddev    : 0.01979 \n                [min, max]: ['0.3888', '0.8884']\n                histogram:\n                        [0,0): ************************************************************...  (966)\n                        [0,0): **************************       (26)\n                        [0,0): ****     (4)\n                        [0,1): *        (1)\n                        [1,1): *        (1)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1]: *        (1)\n\n\nGroupQuery04Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.5456  \n                median    : 0.04166 \n                mode      : 0.5391  \n                stddev    : 0.01639 \n                [min, max]: ['0.5319', '0.9104']\n                histogram:\n                        [1,1): ************************************************************...  (878)\n                        [1,1): ************************************************************...  (114)\n                        [1,1): ****     (4)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1]: *        (1)\n\n\nGroupQuery08Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 0.7421  \n                median    : 0.2377  \n                mode      : 0.7292  \n                stddev    : 0.01712 \n                [min, max]: ['0.7244', '1.074']\n                histogram:\n                        [1,1): ************************************************************...  (741)\n                        [1,1): ************************************************************...  (235)\n                        [1,1): **************   (14)\n                        [1,1): ******   (6)\n                        [1,1):  (0)\n                        [1,1): ***      (3)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1]: *        (1)\n\n\nGroupQuery16Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 1.130   \n                median    : 0.6253  \n                mode      : 1.128   \n                stddev    : 0.02557 \n                [min, max]: ['1.097', '1.510']\n                histogram:\n                        [1,1): ************************************************************...  (485)\n                        [1,1): ************************************************************...  (397)\n                        [1,1): ************************************************************...  (84)\n                        [1,1): **************************       (26)\n                        [1,1): **       (2)\n                        [1,1): ***      (3)\n                        [1,1):  (0)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1): *        (1)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,1):  (0)\n                        [1,2]: *        (1)\n\n\nGroupQuery32Accum.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 1.917   \n                median    : 1.406   \n                mode      : 1.927   \n                stddev    : 0.06044 \n                [min, max]: ['1.828', '2.360']\n                histogram:\n                        [2,2): ************************************************************...  (155)\n                        [2,2): ************************************************************...  (289)\n                        [2,2): ************************************************************...  (266)\n                        [2,2): ************************************************************...  (158)\n                        [2,2): *******************************************************  (55)\n                        [2,2): *************************************    (37)\n                        [2,2): **************** (16)\n                        [2,2): *****    (5)\n                        [2,2): ******** (8)\n                        [2,2): ******   (6)\n                        [2,2): ***      (3)\n                        [2,2): *        (1)\n                        [2,2):  (0)\n                        [2,2):  (0)\n                        [2,2]: *        (1)\n```\n\n$eq array, master:\n```\nMatchQuery02Indexes.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 5.116   \n                median    : 4.616   \n                mode      : 5.101   \n                stddev    : 0.09576 \n                [min, max]: ['4.913', '7.244']\n                histogram:\n                        [5,5): ************************************************************...  (234)\n                        [5,5): ************************************************************...  (719)\n                        [5,5): *********************************************    (45)\n                        [5,6): *        (1)\n                        [6,6):  (0)\n                        [6,6):  (0)\n                        [6,6):  (0)\n                        [6,6):  (0)\n                        [6,6):  (0)\n                        [6,6):  (0)\n                        [6,7):  (0)\n                        [7,7):  (0)\n                        [7,7):  (0)\n                        [7,7):  (0)\n                        [7,7]: *        (1)\n```\n\n$eq array, draft PR:\n```\nMatchQuery02Indexes.aggregate summary:\n        timers.dur (measured in nanoseconds, displayed in milliseconds):\n                count     : 1000    \n                average   : 1.771   \n                median    : 1.751   \n                mode      : 1.817   \n                stddev    : 0.2151  \n                [min, max]: ['1.648', '7.319']\n                histogram:\n                        [2,2): ************************************************************...  (997)\n                        [2,2): *        (1)\n                        [2,3):  (0)\n                        [3,3):  (0)\n                        [3,4):  (0)\n                        [4,4):  (0)\n                        [4,4):  (0)\n                        [4,5):  (0)\n                        [5,5):  (0)\n                        [5,5): *        (1)\n                        [5,6):  (0)\n                        [6,6):  (0)\n                        [6,7):  (0)\n                        [7,7):  (0)\n                        [7,7]: *        (1)\n```\n\n\n<!---\nIf applicable, link a patch test showing code changes running\nsuccessfully\n--->\n\n<!---\n### Workload Submission form\n\nIf applicable, only required if there is a new workload being added. The\nform is [here].\n\n[here]:\nhttps://docs.google.com/forms/d/e/1FAIpQLSf1oh23Ddo1khjLZMqZ9DHbRdObnpeH20PSXTBuXVg-7mxxTA/viewform\n\n### Merging and Linting\n\nWe currently have a manual linting stage required if you're\nadding/modifying a workload YAML document. Evergreen will verify that\nthis has been run.\n\n```bash\n./run-genny generate-docs\n```\n\nOnce (1) your PR is approved by a CODEOWNER and (2) all your CI tests\npass, you can initiate a merge by clicking \"Add to merge queue\".\nThis kicks your PR into the [Github Merge Queue]. Github will\nautomatically squash-merge your commit after checking that Evergreen's\nCI tasks have returned a successful status.\n\n[Github Merge Queue]:\nhttps://docs.devprod.prod.corp.mongodb.com/evergreen/Project-Configuration/Merge-Queue\n\n--->","shortMessageHtmlLink":"Perf 5459 compare disable sbe plan cache (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2298952578\" data-permission-text=\"Title is private\" data-url=\"https://github.com/mongodb/genny/issues/1221\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/mongodb/genny/pull/1221/hovercard\" href=\"https://github.com/mongodb/genny/pull/1221\">#1221</a>)"}}],"hasNextPage":true,"hasPreviousPage":false,"activityType":"all","actor":null,"timePeriod":"all","sort":"DESC","perPage":30,"cursor":"djE6ks8AAAAEWVpzgQA","startCursor":null,"endCursor":null}},"title":"Activity · mongodb/genny"}