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feat: use ruff-format over black #15300
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feat: use ruff-format over black #15300
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Bootstrap import analysisComparison of import times between this PR and base. SummaryThe average import time from this PR is: 251 ± 3 ms. The average import time from base is: 258 ± 3 ms. The import time difference between this PR and base is: -6.4 ± 0.1 ms. Import time breakdownThe following import paths have shrunk:
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Performance SLOsComparing candidate kowalski/feat-use-ruff-format-over-black (f0c9cb5) with baseline main (30d88d9) 📈 Performance Regressions (1 suite)📈 telemetryaddmetric - 30/30✅ 1-count-metric-1-timesTime: ✅ 3.279µs (SLO: <20.000µs 📉 -83.6%) vs baseline: 📈 +12.7% Memory: ✅ 34.564MB (SLO: <35.500MB -2.6%) vs baseline: +5.0% ✅ 1-count-metrics-100-timesTime: ✅ 199.282µs (SLO: <220.000µs -9.4%) vs baseline: +0.2% Memory: ✅ 34.583MB (SLO: <35.500MB -2.6%) vs baseline: +5.1% ✅ 1-distribution-metric-1-timesTime: ✅ 3.605µs (SLO: <20.000µs 📉 -82.0%) vs baseline: +8.6% Memory: ✅ 34.505MB (SLO: <35.500MB -2.8%) vs baseline: +4.7% ✅ 1-distribution-metrics-100-timesTime: ✅ 214.213µs (SLO: <220.000µs -2.6%) vs baseline: -1.0% Memory: ✅ 34.505MB (SLO: <35.500MB -2.8%) vs baseline: +4.9% ✅ 1-gauge-metric-1-timesTime: ✅ 2.484µs (SLO: <20.000µs 📉 -87.6%) vs baseline: 📈 +13.0% Memory: ✅ 34.603MB (SLO: <35.500MB -2.5%) vs baseline: +5.2% ✅ 1-gauge-metrics-100-timesTime: ✅ 135.818µs (SLO: <150.000µs -9.5%) vs baseline: -0.5% Memory: ✅ 34.465MB (SLO: <35.500MB -2.9%) vs baseline: +4.6% ✅ 1-rate-metric-1-timesTime: ✅ 3.065µs (SLO: <20.000µs 📉 -84.7%) vs baseline: -0.5% Memory: ✅ 34.465MB (SLO: <35.500MB -2.9%) vs baseline: +4.7% ✅ 1-rate-metrics-100-timesTime: ✅ 211.249µs (SLO: <250.000µs 📉 -15.5%) vs baseline: +0.1% Memory: ✅ 34.544MB (SLO: <35.500MB -2.7%) vs baseline: +4.8% ✅ 100-count-metrics-100-timesTime: ✅ 19.810ms (SLO: <22.000ms -10.0%) vs baseline: -1.0% Memory: ✅ 34.485MB (SLO: <35.500MB -2.9%) vs baseline: +4.7% ✅ 100-distribution-metrics-100-timesTime: ✅ 2.274ms (SLO: <2.300ms 🟡 -1.1%) vs baseline: +0.7% Memory: ✅ 34.524MB (SLO: <35.500MB -2.7%) vs baseline: +4.7% ✅ 100-gauge-metrics-100-timesTime: ✅ 1.414ms (SLO: <1.550ms -8.8%) vs baseline: ~same Memory: ✅ 34.564MB (SLO: <35.500MB -2.6%) vs baseline: +5.0% ✅ 100-rate-metrics-100-timesTime: ✅ 2.173ms (SLO: <2.550ms 📉 -14.8%) vs baseline: -0.4% Memory: ✅ 34.583MB (SLO: <35.500MB -2.6%) vs baseline: +5.1% ✅ flush-1-metricTime: ✅ 4.876µs (SLO: <20.000µs 📉 -75.6%) vs baseline: +5.9% Memory: ✅ 34.524MB (SLO: <35.500MB -2.7%) vs baseline: +4.9% ✅ flush-100-metricsTime: ✅ 174.000µs (SLO: <250.000µs 📉 -30.4%) vs baseline: ~same Memory: ✅ 34.485MB (SLO: <35.500MB -2.9%) vs baseline: +5.0% ✅ flush-1000-metricsTime: ✅ 2.106ms (SLO: <2.500ms 📉 -15.7%) vs baseline: -0.4% Memory: ✅ 35.330MB (SLO: <36.500MB -3.2%) vs baseline: +5.0% 🟡 Near SLO Breach (2 suites)🟡 flasksimple - 18/18✅ appsec-getTime: ✅ 4.591ms (SLO: <4.750ms -3.4%) vs baseline: -0.2% Memory: ✅ 63.990MB (SLO: <66.500MB -3.8%) vs baseline: +4.8% ✅ appsec-postTime: ✅ 6.620ms (SLO: <6.750ms 🟡 -1.9%) vs baseline: -0.4% Memory: ✅ 64.384MB (SLO: <66.500MB -3.2%) vs baseline: +4.9% ✅ appsec-telemetryTime: ✅ 4.580ms (SLO: <4.750ms -3.6%) vs baseline: ~same Memory: ✅ 64.046MB (SLO: <66.500MB -3.7%) vs baseline: +4.8% ✅ debuggerTime: ✅ 1.856ms (SLO: <2.000ms -7.2%) vs baseline: -0.2% Memory: ✅ 47.867MB (SLO: <49.500MB -3.3%) vs baseline: +4.7% ✅ iast-getTime: ✅ 1.858ms (SLO: <2.000ms -7.1%) vs baseline: +0.3% Memory: ✅ 44.462MB (SLO: <49.000MB -9.3%) vs baseline: +5.0% ✅ profilerTime: ✅ 1.926ms (SLO: <2.100ms -8.3%) vs baseline: +0.2% Memory: ✅ 48.381MB (SLO: <50.000MB -3.2%) vs baseline: +4.6% ✅ resource-renamingTime: ✅ 3.370ms (SLO: <3.650ms -7.7%) vs baseline: -0.3% Memory: ✅ 54.720MB (SLO: <56.000MB -2.3%) vs baseline: +5.6% ✅ tracerTime: ✅ 3.362ms (SLO: <3.650ms -7.9%) vs baseline: +0.1% Memory: ✅ 54.395MB (SLO: <56.500MB -3.7%) vs baseline: +4.9% ✅ tracer-nativeTime: ✅ 3.360ms (SLO: <3.650ms -8.0%) vs baseline: ~same Memory: ✅ 54.619MB (SLO: <60.000MB -9.0%) vs baseline: +5.4% 🟡 otelspan - 22/22✅ add-eventTime: ✅ 38.802ms (SLO: <47.150ms 📉 -17.7%) vs baseline: +1.0% Memory: ✅ 38.915MB (SLO: <47.000MB 📉 -17.2%) vs baseline: +4.7% ✅ add-metricsTime: ✅ 256.945ms (SLO: <344.800ms 📉 -25.5%) vs baseline: ~same Memory: ✅ 43.214MB (SLO: <47.500MB -9.0%) vs baseline: +4.8% ✅ add-tagsTime: ✅ 318.199ms (SLO: <321.000ms 🟡 -0.9%) vs baseline: +1.7% Memory: ✅ 43.391MB (SLO: <47.500MB -8.6%) vs baseline: +5.2% ✅ get-contextTime: ✅ 78.579ms (SLO: <92.350ms 📉 -14.9%) vs baseline: ~same Memory: ✅ 39.334MB (SLO: <46.500MB 📉 -15.4%) vs baseline: +4.9% ✅ is-recordingTime: ✅ 35.925ms (SLO: <44.500ms 📉 -19.3%) vs baseline: +0.6% Memory: ✅ 38.842MB (SLO: <47.500MB 📉 -18.2%) vs baseline: +4.8% ✅ record-exceptionTime: ✅ 56.799ms (SLO: <67.650ms 📉 -16.0%) vs baseline: -0.2% Memory: ✅ 39.281MB (SLO: <47.000MB 📉 -16.4%) vs baseline: +4.8% ✅ set-statusTime: ✅ 42.358ms (SLO: <50.400ms 📉 -16.0%) vs baseline: -0.3% Memory: ✅ 38.826MB (SLO: <47.000MB 📉 -17.4%) vs baseline: +5.1% ✅ startTime: ✅ 35.469ms (SLO: <43.450ms 📉 -18.4%) vs baseline: -0.1% Memory: ✅ 38.742MB (SLO: <47.000MB 📉 -17.6%) vs baseline: +4.5% ✅ start-finishTime: ✅ 81.974ms (SLO: <88.000ms -6.8%) vs baseline: ~same Memory: ✅ 36.530MB (SLO: <46.500MB 📉 -21.4%) vs baseline: +4.6% ✅ start-finish-telemetryTime: ✅ 83.244ms (SLO: <89.000ms -6.5%) vs baseline: ~same Memory: ✅ 36.510MB (SLO: <46.500MB 📉 -21.5%) vs baseline: +4.4% ✅ update-nameTime: ✅ 37.021ms (SLO: <45.150ms 📉 -18.0%) vs baseline: +0.4% Memory: ✅ 38.955MB (SLO: <47.000MB 📉 -17.1%) vs baseline: +4.8%
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brettlangdon
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minor nit you can ignore if you want, otherwise lgtm
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/gitlab resync-job-status |
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BenchmarksBenchmark execution time: 2025-11-18 15:07:11 Comparing candidate commit f0c9cb5 in PR branch Found 0 performance improvements and 3 performance regressions! Performance is the same for 554 metrics, 3 unstable metrics. scenario:iast_aspects-re_match_aspect
scenario:iastaspects-bytes_aspect
scenario:iastaspects-strip_aspect
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Yun-Kim
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formatting changes look good to team mlobs
Description
This replaces the current
blackformatter withruff format(which is included inruff), which is much faster thanblack.Changes
ruff formatandblackhave some slight differences in how they format stuff, which led to many drive-by changes:ruff– those include theblack/ruffdifferencestest_coveragefiles which fixes some tests that expect specific lines numbers (that have changed following our reformatting).