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add Benchmark (pytest) benchmark result for 52adc10
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github-action-benchmark committed Dec 28, 2023
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180 changes: 179 additions & 1 deletion dev/bench/data.js
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window.BENCHMARK_DATA = {
"lastUpdate": 1703762678908,
"lastUpdate": 1703762716670,
"repoUrl": "https://github.com/mlrun/storey",
"entries": {
"Benchmark": [
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"extra": "mean: 6.081240122399981 sec\nrounds: 5"
}
]
},
{
"commit": {
"author": {
"email": "[email protected]",
"name": "Gal Topper",
"username": "gtopper"
},
"committer": {
"email": "[email protected]",
"name": "GitHub",
"username": "web-flow"
},
"distinct": true,
"id": "52adc10f5910af3b6dd55b416420ada73c31d4cb",
"message": "Fix reuse of various steps (#482)\n\n* Fix `StreamTarget` and `KafkaTarget` init on flow reuse\r\n\r\n[ML-5351](https://jira.iguazeng.com/browse/ML-5351)\r\n\r\n* Fix `AsyncEmitSource` reuse\r\n\r\n* Add regression test\r\n\r\n* Move table creation to _init for proper step reuse\r\n\r\n* Proper SyncEmitSource init in case of flow reuse (not a fix)\r\n\r\n* Add test, overhaul closeables\r\n\r\n* Revert \"Add test, overhaul closeables\"\r\n\r\nThis reverts commit da272ddd06e28727954aeaec9a45a8fa91446ed1.\r\n\r\n* Revert changes related to table init",
"timestamp": "2023-12-28T19:21:27+08:00",
"tree_id": "3bfdbeb6ba0d82ce524632d438fc2947d396f79e",
"url": "https://github.com/mlrun/storey/commit/52adc10f5910af3b6dd55b416420ada73c31d4cb"
},
"date": 1703762714108,
"tool": "pytest",
"benches": [
{
"name": "bench/bench_flow.py::test_simple_flow_n_events[0]",
"value": 1279.7794411839816,
"unit": "iter/sec",
"range": "stddev: 0.00007142325204323292",
"extra": "mean: 781.3846416182893 usec\nrounds: 519"
},
{
"name": "bench/bench_flow.py::test_simple_flow_n_events[1]",
"value": 1121.3663408621944,
"unit": "iter/sec",
"range": "stddev: 0.00005238085287930083",
"extra": "mean: 891.7692314815882 usec\nrounds: 972"
},
{
"name": "bench/bench_flow.py::test_simple_flow_n_events[1000]",
"value": 5.957424911382867,
"unit": "iter/sec",
"range": "stddev: 0.013735495717944027",
"extra": "mean: 167.8577598333296 msec\nrounds: 6"
},
{
"name": "bench/bench_flow.py::test_simple_flow_n_events[5000]",
"value": 1.1242597311519205,
"unit": "iter/sec",
"range": "stddev: 0.018585040840587003",
"extra": "mean: 889.4741777999968 msec\nrounds: 5"
},
{
"name": "bench/bench_flow.py::test_simple_async_flow_n_events[0]",
"value": 4420.8542283756715,
"unit": "iter/sec",
"range": "stddev: 0.0004089948249001402",
"extra": "mean: 226.20062737681 usec\nrounds: 2893"
},
{
"name": "bench/bench_flow.py::test_simple_async_flow_n_events[1]",
"value": 3923.2224163650617,
"unit": "iter/sec",
"range": "stddev: 0.000030070863230318164",
"extra": "mean: 254.89250770710026 usec\nrounds: 3114"
},
{
"name": "bench/bench_flow.py::test_simple_async_flow_n_events[1000]",
"value": 68.45513967233498,
"unit": "iter/sec",
"range": "stddev: 0.00012943065558130808",
"extra": "mean: 14.608106926471343 msec\nrounds: 68"
},
{
"name": "bench/bench_flow.py::test_simple_async_flow_n_events[5000]",
"value": 13.889373515037148,
"unit": "iter/sec",
"range": "stddev: 0.0002842115246879553",
"extra": "mean: 71.99748778570633 msec\nrounds: 14"
},
{
"name": "bench/bench_flow.py::test_complete_flow_n_events[0]",
"value": 1321.1981995401948,
"unit": "iter/sec",
"range": "stddev: 0.00006151424948681571",
"extra": "mean: 756.888709315545 usec\nrounds: 891"
},
{
"name": "bench/bench_flow.py::test_complete_flow_n_events[1]",
"value": 1036.4551748594224,
"unit": "iter/sec",
"range": "stddev: 0.00005363101966002164",
"extra": "mean: 964.827060789805 usec\nrounds: 987"
},
{
"name": "bench/bench_flow.py::test_complete_flow_n_events[1000]",
"value": 5.514368041148498,
"unit": "iter/sec",
"range": "stddev: 0.0103044256837085",
"extra": "mean: 181.34444283333076 msec\nrounds: 6"
},
{
"name": "bench/bench_flow.py::test_complete_flow_n_events[5000]",
"value": 1.0036481453882187,
"unit": "iter/sec",
"range": "stddev: 0.033699492120382896",
"extra": "mean: 996.3651152000011 msec\nrounds: 5"
},
{
"name": "bench/bench_flow.py::test_aggregate_by_key_n_events[0]",
"value": 1172.1139392310713,
"unit": "iter/sec",
"range": "stddev: 0.00009956605711716828",
"extra": "mean: 853.1593785635027 usec\nrounds: 877"
},
{
"name": "bench/bench_flow.py::test_aggregate_by_key_n_events[1]",
"value": 375.95353186236065,
"unit": "iter/sec",
"range": "stddev: 0.008796897034903875",
"extra": "mean: 2.6599031934779305 msec\nrounds: 460"
},
{
"name": "bench/bench_flow.py::test_aggregate_by_key_n_events[1000]",
"value": 4.173048715241871,
"unit": "iter/sec",
"range": "stddev: 0.010228825512041894",
"extra": "mean: 239.63295619999485 msec\nrounds: 5"
},
{
"name": "bench/bench_flow.py::test_aggregate_by_key_n_events[5000]",
"value": 0.7962255774956779,
"unit": "iter/sec",
"range": "stddev: 0.05647634468742982",
"extra": "mean: 1.2559254918000022 sec\nrounds: 5"
},
{
"name": "bench/bench_flow.py::test_batch_n_events[0]",
"value": 1352.2474487331936,
"unit": "iter/sec",
"range": "stddev: 0.00004734055896465717",
"extra": "mean: 739.5096222491051 usec\nrounds: 818"
},
{
"name": "bench/bench_flow.py::test_batch_n_events[1]",
"value": 1023.7067757272164,
"unit": "iter/sec",
"range": "stddev: 0.00006307893372566869",
"extra": "mean: 976.8422205563936 usec\nrounds: 934"
},
{
"name": "bench/bench_flow.py::test_batch_n_events[1000]",
"value": 6.06252035258418,
"unit": "iter/sec",
"range": "stddev: 0.010062318040122248",
"extra": "mean: 164.94789985715184 msec\nrounds: 7"
},
{
"name": "bench/bench_flow.py::test_batch_n_events[5000]",
"value": 1.1265894017631843,
"unit": "iter/sec",
"range": "stddev: 0.00500107774792404",
"extra": "mean: 887.6348370000073 msec\nrounds: 5"
},
{
"name": "bench/bench_flow.py::test_aggregate_df_86420_events",
"value": 0.1198706449968189,
"unit": "iter/sec",
"range": "stddev: 0.0923802572849715",
"extra": "mean: 8.342326013400008 sec\nrounds: 5"
},
{
"name": "bench/bench_flow.py::test_aggregate_df_86420_events_basic",
"value": 0.16342066619301746,
"unit": "iter/sec",
"range": "stddev: 0.21951904822991483",
"extra": "mean: 6.119177110800001 sec\nrounds: 5"
}
]
}
]
}
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