test(profiling): unflake some tests#15797
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Performance SLOsComparing candidate kowalski/test-profiling-unflake-some-tests (1afc46c) with baseline main (0e20665) 📈 Performance Regressions (3 suites)📈 iastaspects - 118/118✅ add_aspectTime: ✅ 17.969µs (SLO: <20.000µs 📉 -10.2%) vs baseline: 📈 +21.0% Memory: ✅ 42.664MB (SLO: <43.250MB 🟡 -1.4%) vs baseline: +5.1% ✅ add_inplace_aspectTime: ✅ 14.901µs (SLO: <20.000µs 📉 -25.5%) vs baseline: -0.4% Memory: ✅ 42.605MB (SLO: <43.250MB 🟡 -1.5%) vs baseline: +5.0% ✅ add_inplace_noaspectTime: ✅ 0.340µs (SLO: <10.000µs 📉 -96.6%) vs baseline: +0.8% Memory: ✅ 42.664MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +5.1% ✅ add_noaspectTime: ✅ 0.551µs (SLO: <10.000µs 📉 -94.5%) vs baseline: +0.9% Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +4.9% ✅ bytearray_aspectTime: ✅ 17.914µs (SLO: <30.000µs 📉 -40.3%) vs baseline: -0.4% Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +5.1% ✅ bytearray_extend_aspectTime: ✅ 23.647µs (SLO: <30.000µs 📉 -21.2%) vs baseline: -0.6% Memory: ✅ 42.664MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +5.1% ✅ bytearray_extend_noaspectTime: ✅ 2.749µs (SLO: <10.000µs 📉 -72.5%) vs baseline: -0.1% Memory: ✅ 42.585MB (SLO: <43.500MB -2.1%) vs baseline: +4.8% ✅ bytearray_noaspectTime: ✅ 1.453µs (SLO: <10.000µs 📉 -85.5%) vs baseline: -0.3% Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +5.2% ✅ bytes_aspectTime: ✅ 16.550µs (SLO: <20.000µs 📉 -17.3%) vs baseline: -0.8% Memory: ✅ 42.723MB (SLO: <43.500MB 🟡 -1.8%) vs baseline: +5.1% ✅ bytes_noaspectTime: ✅ 1.415µs (SLO: <10.000µs 📉 -85.8%) vs baseline: -1.3% Memory: ✅ 42.684MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +5.2% ✅ bytesio_aspectTime: ✅ 55.292µs (SLO: <70.000µs 📉 -21.0%) vs baseline: -0.8% Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +5.0% ✅ bytesio_noaspectTime: ✅ 3.282µs (SLO: <10.000µs 📉 -67.2%) vs baseline: +0.2% Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +5.0% ✅ capitalize_aspectTime: ✅ 14.567µs (SLO: <20.000µs 📉 -27.2%) vs baseline: -1.0% Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +4.8% ✅ capitalize_noaspectTime: ✅ 2.543µs (SLO: <10.000µs 📉 -74.6%) vs baseline: -0.9% Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +5.1% ✅ casefold_aspectTime: ✅ 14.600µs (SLO: <20.000µs 📉 -27.0%) vs baseline: -0.6% Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +5.1% ✅ casefold_noaspectTime: ✅ 3.162µs (SLO: <10.000µs 📉 -68.4%) vs baseline: ~same Memory: ✅ 42.585MB (SLO: <43.500MB -2.1%) vs baseline: +4.7% ✅ decode_aspectTime: ✅ 15.554µs (SLO: <30.000µs 📉 -48.2%) vs baseline: -0.5% Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +4.7% ✅ decode_noaspectTime: ✅ 1.593µs (SLO: <10.000µs 📉 -84.1%) vs baseline: -1.2% Memory: ✅ 42.664MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +5.0% ✅ encode_aspectTime: ✅ 17.977µs (SLO: <30.000µs 📉 -40.1%) vs baseline: 📈 +21.4% Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +5.1% ✅ encode_noaspectTime: ✅ 1.443µs (SLO: <10.000µs 📉 -85.6%) vs baseline: -4.1% Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +4.9% ✅ format_aspectTime: ✅ 171.352µs (SLO: <200.000µs 📉 -14.3%) vs baseline: ~same Memory: ✅ 42.802MB (SLO: <43.250MB 🟡 -1.0%) vs baseline: +5.3% ✅ format_map_aspectTime: ✅ 191.183µs (SLO: <200.000µs -4.4%) vs baseline: +0.1% Memory: ✅ 42.861MB (SLO: <43.500MB 🟡 -1.5%) vs baseline: +5.1% ✅ format_map_noaspectTime: ✅ 3.816µs (SLO: <10.000µs 📉 -61.8%) vs baseline: +0.9% Memory: ✅ 42.605MB (SLO: <43.250MB 🟡 -1.5%) vs baseline: +4.9% ✅ format_noaspectTime: ✅ 3.113µs (SLO: <10.000µs 📉 -68.9%) vs baseline: -1.0% Memory: ✅ 42.585MB (SLO: <43.250MB 🟡 -1.5%) vs baseline: +4.9% ✅ index_aspectTime: ✅ 15.208µs (SLO: <20.000µs 📉 -24.0%) vs baseline: -0.6% Memory: ✅ 42.703MB (SLO: <43.250MB 🟡 -1.3%) vs baseline: +5.1% ✅ index_noaspectTime: ✅ 0.464µs (SLO: <10.000µs 📉 -95.4%) vs baseline: +0.5% Memory: ✅ 42.526MB (SLO: <43.500MB -2.2%) vs baseline: +4.8% ✅ join_aspectTime: ✅ 16.926µs (SLO: <20.000µs 📉 -15.4%) vs baseline: -1.2% Memory: ✅ 42.703MB (SLO: <43.500MB 🟡 -1.8%) vs baseline: +4.9% ✅ join_noaspectTime: ✅ 1.542µs (SLO: <10.000µs 📉 -84.6%) vs baseline: -0.3% Memory: ✅ 42.605MB (SLO: <43.250MB 🟡 -1.5%) vs baseline: +4.9% ✅ ljust_aspectTime: ✅ 20.701µs (SLO: <30.000µs 📉 -31.0%) vs baseline: -0.7% Memory: ✅ 42.684MB (SLO: <43.250MB 🟡 -1.3%) vs baseline: +4.5% ✅ ljust_noaspectTime: ✅ 2.654µs (SLO: <10.000µs 📉 -73.5%) vs baseline: -2.4% Memory: ✅ 42.684MB (SLO: <43.250MB 🟡 -1.3%) vs baseline: +5.1% ✅ lower_aspectTime: ✅ 17.766µs (SLO: <30.000µs 📉 -40.8%) vs baseline: -1.8% Memory: ✅ 42.566MB (SLO: <43.500MB -2.1%) vs baseline: +4.8% ✅ lower_noaspectTime: ✅ 2.406µs (SLO: <10.000µs 📉 -75.9%) vs baseline: -0.3% Memory: ✅ 42.585MB (SLO: <43.250MB 🟡 -1.5%) vs baseline: +5.0% ✅ lstrip_aspectTime: ✅ 17.586µs (SLO: <30.000µs 📉 -41.4%) vs baseline: -0.5% Memory: ✅ 42.684MB (SLO: <43.250MB 🟡 -1.3%) vs baseline: +5.0% ✅ lstrip_noaspectTime: ✅ 1.847µs (SLO: <10.000µs 📉 -81.5%) vs baseline: -0.4% Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +4.7% ✅ modulo_aspectTime: ✅ 166.424µs (SLO: <200.000µs 📉 -16.8%) vs baseline: -0.2% Memory: ✅ 42.802MB (SLO: <43.500MB 🟡 -1.6%) vs baseline: +5.0% ✅ modulo_aspect_for_bytearray_bytearrayTime: ✅ 180.138µs (SLO: <200.000µs -9.9%) vs baseline: +3.2% Memory: ✅ 42.703MB (SLO: <43.500MB 🟡 -1.8%) vs baseline: +5.1% ✅ modulo_aspect_for_bytesTime: ✅ 168.623µs (SLO: <200.000µs 📉 -15.7%) vs baseline: -0.3% Memory: ✅ 42.861MB (SLO: <43.500MB 🟡 -1.5%) vs baseline: +4.9% ✅ modulo_aspect_for_bytes_bytearrayTime: ✅ 171.765µs (SLO: <200.000µs 📉 -14.1%) vs baseline: ~same Memory: ✅ 42.782MB (SLO: <43.500MB 🟡 -1.7%) vs baseline: +5.1% ✅ modulo_noaspectTime: ✅ 3.646µs (SLO: <10.000µs 📉 -63.5%) vs baseline: -1.5% Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +4.9% ✅ replace_aspectTime: ✅ 211.435µs (SLO: <300.000µs 📉 -29.5%) vs baseline: ~same Memory: ✅ 42.625MB (SLO: <44.000MB -3.1%) vs baseline: +4.8% ✅ replace_noaspectTime: ✅ 2.934µs (SLO: <10.000µs 📉 -70.7%) vs baseline: +0.8% Memory: ✅ 42.703MB (SLO: <43.500MB 🟡 -1.8%) vs baseline: +5.1% ✅ repr_aspectTime: ✅ 1.431µs (SLO: <10.000µs 📉 -85.7%) vs baseline: +0.7% Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +4.9% ✅ repr_noaspectTime: ✅ 0.525µs (SLO: <10.000µs 📉 -94.7%) vs baseline: ~same Memory: ✅ 42.743MB (SLO: <43.500MB 🟡 -1.7%) vs baseline: +5.1% ✅ rstrip_aspectTime: ✅ 19.019µs (SLO: <30.000µs 📉 -36.6%) vs baseline: +0.1% Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +4.9% ✅ rstrip_noaspectTime: ✅ 1.923µs (SLO: <10.000µs 📉 -80.8%) vs baseline: -0.2% Memory: ✅ 42.664MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +5.1% ✅ slice_aspectTime: ✅ 15.760µs (SLO: <20.000µs 📉 -21.2%) vs baseline: -1.1% Memory: ✅ 42.605MB (SLO: <43.500MB -2.1%) vs baseline: +4.8% ✅ slice_noaspectTime: ✅ 0.601µs (SLO: <10.000µs 📉 -94.0%) vs baseline: +0.4% Memory: ✅ 42.664MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +5.0% ✅ stringio_aspectTime: ✅ 53.889µs (SLO: <80.000µs 📉 -32.6%) vs baseline: -1.0% Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +5.0% ✅ stringio_noaspectTime: ✅ 3.615µs (SLO: <10.000µs 📉 -63.8%) vs baseline: ~same Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +4.8% ✅ strip_aspectTime: ✅ 17.452µs (SLO: <20.000µs 📉 -12.7%) vs baseline: -1.6% Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +5.1% ✅ strip_noaspectTime: ✅ 1.841µs (SLO: <10.000µs 📉 -81.6%) vs baseline: -1.6% Memory: ✅ 42.723MB (SLO: <43.500MB 🟡 -1.8%) vs baseline: +5.3% ✅ swapcase_aspectTime: ✅ 18.393µs (SLO: <30.000µs 📉 -38.7%) vs baseline: -1.4% Memory: ✅ 42.664MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +5.2% ✅ swapcase_noaspectTime: ✅ 2.811µs (SLO: <10.000µs 📉 -71.9%) vs baseline: +0.8% Memory: ✅ 42.684MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +5.1% ✅ title_aspectTime: ✅ 18.141µs (SLO: <30.000µs 📉 -39.5%) vs baseline: -1.1% Memory: ✅ 42.684MB (SLO: <43.000MB 🟡 -0.7%) vs baseline: +5.1% ✅ title_noaspectTime: ✅ 2.620µs (SLO: <10.000µs 📉 -73.8%) vs baseline: -1.9% Memory: ✅ 42.585MB (SLO: <43.500MB -2.1%) vs baseline: +4.8% ✅ translate_aspectTime: ✅ 24.138µs (SLO: <30.000µs 📉 -19.5%) vs baseline: 📈 +16.8% Memory: ✅ 42.664MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +5.0% ✅ translate_noaspectTime: ✅ 4.289µs (SLO: <10.000µs 📉 -57.1%) vs baseline: -0.7% Memory: ✅ 42.644MB (SLO: <43.500MB 🟡 -2.0%) vs baseline: +5.1% ✅ upper_aspectTime: ✅ 17.744µs (SLO: <30.000µs 📉 -40.9%) vs baseline: -1.7% Memory: ✅ 42.566MB (SLO: <43.500MB -2.1%) vs baseline: +4.8% ✅ upper_noaspectTime: ✅ 2.400µs (SLO: <10.000µs 📉 -76.0%) vs baseline: -0.9% Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +4.9% 📈 iastaspectsospath - 24/24✅ ospathbasename_aspectTime: ✅ 5.125µs (SLO: <10.000µs 📉 -48.7%) vs baseline: 📈 +19.5% Memory: ✅ 42.526MB (SLO: <43.500MB -2.2%) vs baseline: +5.0% ✅ ospathbasename_noaspectTime: ✅ 4.236µs (SLO: <10.000µs 📉 -57.6%) vs baseline: -2.4% Memory: ✅ 42.566MB (SLO: <43.500MB -2.1%) vs baseline: +4.8% ✅ ospathjoin_aspectTime: ✅ 6.217µs (SLO: <10.000µs 📉 -37.8%) vs baseline: -1.6% Memory: ✅ 42.723MB (SLO: <43.500MB 🟡 -1.8%) vs baseline: +5.3% ✅ ospathjoin_noaspectTime: ✅ 6.354µs (SLO: <10.000µs 📉 -36.5%) vs baseline: -0.2% Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +5.1% ✅ ospathnormcase_aspectTime: ✅ 3.544µs (SLO: <10.000µs 📉 -64.6%) vs baseline: -1.2% Memory: ✅ 42.605MB (SLO: <43.500MB -2.1%) vs baseline: +5.1% ✅ ospathnormcase_noaspectTime: ✅ 3.581µs (SLO: <10.000µs 📉 -64.2%) vs baseline: -2.8% Memory: ✅ 42.585MB (SLO: <43.500MB -2.1%) vs baseline: +4.9% ✅ ospathsplit_aspectTime: ✅ 4.912µs (SLO: <10.000µs 📉 -50.9%) vs baseline: -0.7% Memory: ✅ 42.546MB (SLO: <43.500MB -2.2%) vs baseline: +5.1% ✅ ospathsplit_noaspectTime: ✅ 4.996µs (SLO: <10.000µs 📉 -50.0%) vs baseline: -0.1% Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +5.2% ✅ ospathsplitdrive_aspectTime: ✅ 3.776µs (SLO: <10.000µs 📉 -62.2%) vs baseline: ~same Memory: ✅ 42.507MB (SLO: <43.500MB -2.3%) vs baseline: +4.8% ✅ ospathsplitdrive_noaspectTime: ✅ 0.741µs (SLO: <10.000µs 📉 -92.6%) vs baseline: -0.8% Memory: ✅ 42.664MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +5.3% ✅ ospathsplitext_aspectTime: ✅ 4.605µs (SLO: <10.000µs 📉 -54.0%) vs baseline: -0.9% Memory: ✅ 42.546MB (SLO: <43.500MB -2.2%) vs baseline: +4.9% ✅ ospathsplitext_noaspectTime: ✅ 4.582µs (SLO: <10.000µs 📉 -54.2%) vs baseline: -1.9% Memory: ✅ 42.526MB (SLO: <43.500MB -2.2%) vs baseline: +5.0% 📈 telemetryaddmetric - 30/30✅ 1-count-metric-1-timesTime: ✅ 3.411µs (SLO: <20.000µs 📉 -82.9%) vs baseline: 📈 +14.6% Memory: ✅ 34.957MB (SLO: <35.500MB 🟡 -1.5%) vs baseline: +5.2% ✅ 1-count-metrics-100-timesTime: ✅ 199.813µs (SLO: <220.000µs -9.2%) vs baseline: +0.3% Memory: ✅ 34.859MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +4.8% ✅ 1-distribution-metric-1-timesTime: ✅ 3.360µs (SLO: <20.000µs 📉 -83.2%) vs baseline: +0.6% Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.7% ✅ 1-distribution-metrics-100-timesTime: ✅ 214.073µs (SLO: <230.000µs -6.9%) vs baseline: -0.4% Memory: ✅ 34.878MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +4.9% ✅ 1-gauge-metric-1-timesTime: ✅ 2.163µs (SLO: <20.000µs 📉 -89.2%) vs baseline: -0.7% Memory: ✅ 34.898MB (SLO: <35.500MB 🟡 -1.7%) vs baseline: +4.5% ✅ 1-gauge-metrics-100-timesTime: ✅ 136.462µs (SLO: <150.000µs -9.0%) vs baseline: -0.3% Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.9% ✅ 1-rate-metric-1-timesTime: ✅ 3.169µs (SLO: <20.000µs 📉 -84.2%) vs baseline: +0.9% Memory: ✅ 34.859MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +5.3% ✅ 1-rate-metrics-100-timesTime: ✅ 213.733µs (SLO: <250.000µs 📉 -14.5%) vs baseline: +0.7% Memory: ✅ 34.898MB (SLO: <35.500MB 🟡 -1.7%) vs baseline: +4.6% ✅ 100-count-metrics-100-timesTime: ✅ 19.990ms (SLO: <22.000ms -9.1%) vs baseline: +0.5% Memory: ✅ 34.918MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +4.7% ✅ 100-distribution-metrics-100-timesTime: ✅ 2.210ms (SLO: <2.550ms 📉 -13.3%) vs baseline: -1.1% Memory: ✅ 34.839MB (SLO: <35.500MB 🟡 -1.9%) vs baseline: +5.0% ✅ 100-gauge-metrics-100-timesTime: ✅ 1.410ms (SLO: <1.550ms -9.0%) vs baseline: +1.1% Memory: ✅ 34.898MB (SLO: <35.500MB 🟡 -1.7%) vs baseline: +5.0% ✅ 100-rate-metrics-100-timesTime: ✅ 2.175ms (SLO: <2.550ms 📉 -14.7%) vs baseline: -0.3% Memory: ✅ 34.898MB (SLO: <35.500MB 🟡 -1.7%) vs baseline: +4.8% ✅ flush-1-metricTime: ✅ 4.506µs (SLO: <20.000µs 📉 -77.5%) vs baseline: -1.5% Memory: ✅ 35.291MB (SLO: <35.500MB 🟡 -0.6%) vs baseline: +5.2% ✅ flush-100-metricsTime: ✅ 173.922µs (SLO: <250.000µs 📉 -30.4%) vs baseline: ~same Memory: ✅ 35.330MB (SLO: <35.500MB 🟡 -0.5%) vs baseline: +5.3% ✅ flush-1000-metricsTime: ✅ 2.172ms (SLO: <2.500ms 📉 -13.1%) vs baseline: -0.2% Memory: ✅ 36.117MB (SLO: <36.500MB 🟡 -1.0%) vs baseline: +5.4% 🟡 Near SLO Breach (16 suites)🟡 coreapiscenario - 10/10 (1 unstable)
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## Description This improves typing in a test file. Related - #15797
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## Description This improves typing in a test file. Related - #15797
Can you please expand why? Also, great variable naming, I had to look it up: penultimate: last but one in a series of things; second last. Unsure why it helps the test tho, lol. |
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@vlad-scherbich addressed your comments, please take another look 🙇 |
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The backport to To backport manually, run these commands in your terminal: # Fetch latest updates from GitHub
git fetch
# Create a new working tree
git worktree add .worktrees/backport-4.0 4.0
# Navigate to the new working tree
cd .worktrees/backport-4.0
# Create a new branch
git switch --create backport-15797-to-4.0
# Cherry-pick the merged commit of this pull request and resolve the conflicts
git cherry-pick -x --mainline 1 4e2e136b1a9e73ec2865978d10ba942d70596af5
# Push it to GitHub
git push --set-upstream origin backport-15797-to-4.0
# Go back to the original working tree
cd ../..
# Delete the working tree
git worktree remove .worktrees/backport-4.0Then, create a pull request where the |
## Description This makes some tests less flaky. Previously, we would sometimes parse an empty Profile (0 samples in it) and make the test fail. The fix is to, when this happens, ignore the failure and parse/use the previous Profile instead (which we expect to ALWAYS be non-empty). - [See in Flaky Management](https://app.datadoghq.com/ci/test/flaky?query=%40test.codeowners%3A%2Aprofiling-python%2A%20flaky_test_state%3Aquarantined%20%40test.suite%3Atest_main.py&sort=-pipelines_failed&viewMode=flaky) - [See failures on this branch](https://app.datadoghq.com/ci/test/runs?query=test_level%3Atest%20%40test.codeowners%3A%2Aprofiling-python%2A%20%40test.suite%3Atest_main.py%20status%3Aerror%20%40git.branch%3Akowalski%2Ftest-profiling-unflake-some-tests&agg_m=count&agg_m_source=base&agg_t=count&fromUser=false&index=citest&start=1766388450558&end=1766993250558&paused=false) Related - #15796 (cherry picked from commit 4e2e136)
## Description This makes some tests less flaky. Previously, we would sometimes parse an empty Profile (0 samples in it) and make the test fail. The fix is to, when this happens, ignore the failure and parse/use the previous Profile instead (which we expect to ALWAYS be non-empty). - [See in Flaky Management](https://app.datadoghq.com/ci/test/flaky?query=%40test.codeowners%3A%2Aprofiling-python%2A%20flaky_test_state%3Aquarantined%20%40test.suite%3Atest_main.py&sort=-pipelines_failed&viewMode=flaky) - [See failures on this branch](https://app.datadoghq.com/ci/test/runs?query=test_level%3Atest%20%40test.codeowners%3A%2Aprofiling-python%2A%20%40test.suite%3Atest_main.py%20status%3Aerror%20%40git.branch%3Akowalski%2Ftest-profiling-unflake-some-tests&agg_m=count&agg_m_source=base&agg_t=count&fromUser=false&index=citest&start=1766388450558&end=1766993250558&paused=false) Related - #15796 (cherry picked from commit 4e2e136)
## Description This makes some tests less flaky. Previously, we would sometimes parse an empty Profile (0 samples in it) and make the test fail. The fix is to, when this happens, ignore the failure and parse/use the previous Profile instead (which we expect to ALWAYS be non-empty). - [See in Flaky Management](https://app.datadoghq.com/ci/test/flaky?query=%40test.codeowners%3A%2Aprofiling-python%2A%20flaky_test_state%3Aquarantined%20%40test.suite%3Atest_main.py&sort=-pipelines_failed&viewMode=flaky) - [See failures on this branch](https://app.datadoghq.com/ci/test/runs?query=test_level%3Atest%20%40test.codeowners%3A%2Aprofiling-python%2A%20%40test.suite%3Atest_main.py%20status%3Aerror%20%40git.branch%3Akowalski%2Ftest-profiling-unflake-some-tests&agg_m=count&agg_m_source=base&agg_t=count&fromUser=false&index=citest&start=1766388450558&end=1766993250558&paused=false) Related - #15796 (cherry picked from commit 4e2e136)
## Description This makes some tests less flaky. Previously, we would sometimes parse an empty Profile (0 samples in it) and make the test fail. The fix is to, when this happens, ignore the failure and parse/use the previous Profile instead (which we expect to ALWAYS be non-empty). - [See in Flaky Management](https://app.datadoghq.com/ci/test/flaky?query=%40test.codeowners%3A%2Aprofiling-python%2A%20flaky_test_state%3Aquarantined%20%40test.suite%3Atest_main.py&sort=-pipelines_failed&viewMode=flaky) - [See failures on this branch](https://app.datadoghq.com/ci/test/runs?query=test_level%3Atest%20%40test.codeowners%3A%2Aprofiling-python%2A%20%40test.suite%3Atest_main.py%20status%3Aerror%20%40git.branch%3Akowalski%2Ftest-profiling-unflake-some-tests&agg_m=count&agg_m_source=base&agg_t=count&fromUser=false&index=citest&start=1766388450558&end=1766993250558&paused=false) Related - #15796 (cherry picked from commit 4e2e136)
Backport 4e2e136 from #15797 to 4.1. ## Description This makes some tests less flaky. Previously, we would sometimes parse an empty Profile (0 samples in it) and make the test fail. The fix is to, when this happens, ignore the failure and parse/use the previous Profile instead (which we expect to ALWAYS be non-empty). - [See in Flaky Management](https://app.datadoghq.com/ci/test/flaky?query=%40test.codeowners%3A%2Aprofiling-python%2A%20flaky_test_state%3Aquarantined%20%40test.suite%3Atest_main.py&sort=-pipelines_failed&viewMode=flaky) - [See failures on this branch](https://app.datadoghq.com/ci/test/runs?query=test_level%3Atest%20%40test.codeowners%3A%2Aprofiling-python%2A%20%40test.suite%3Atest_main.py%20status%3Aerror%20%40git.branch%3Akowalski%2Ftest-profiling-unflake-some-tests&agg_m=count&agg_m_source=base&agg_t=count&fromUser=false&index=citest&start=1766388450558&end=1766993250558&paused=false) Related - #15796 Co-authored-by: Thomas Kowalski <thomas.kowalski@datadoghq.com>
## Description This makes some tests less flaky. Previously, we would sometimes parse an empty Profile (0 samples in it) and make the test fail. The fix is to, when this happens, ignore the failure and parse/use the previous Profile instead (which we expect to ALWAYS be non-empty). - [See in Flaky Management](https://app.datadoghq.com/ci/test/flaky?query=%40test.codeowners%3A%2Aprofiling-python%2A%20flaky_test_state%3Aquarantined%20%40test.suite%3Atest_main.py&sort=-pipelines_failed&viewMode=flaky) - [See failures on this branch](https://app.datadoghq.com/ci/test/runs?query=test_level%3Atest%20%40test.codeowners%3A%2Aprofiling-python%2A%20%40test.suite%3Atest_main.py%20status%3Aerror%20%40git.branch%3Akowalski%2Ftest-profiling-unflake-some-tests&agg_m=count&agg_m_source=base&agg_t=count&fromUser=false&index=citest&start=1766388450558&end=1766993250558&paused=false) Related - #15796 (cherry picked from commit 4e2e136)
## Description This improves typing in a test file. Related - DataDog#15797
## Description This makes some tests less flaky. Previously, we would sometimes parse an empty Profile (0 samples in it) and make the test fail. The fix is to, when this happens, ignore the failure and parse/use the previous Profile instead (which we expect to ALWAYS be non-empty). - [See in Flaky Management](https://app.datadoghq.com/ci/test/flaky?query=%40test.codeowners%3A%2Aprofiling-python%2A%20flaky_test_state%3Aquarantined%20%40test.suite%3Atest_main.py&sort=-pipelines_failed&viewMode=flaky) - [See failures on this branch](https://app.datadoghq.com/ci/test/runs?query=test_level%3Atest%20%40test.codeowners%3A%2Aprofiling-python%2A%20%40test.suite%3Atest_main.py%20status%3Aerror%20%40git.branch%3Akowalski%2Ftest-profiling-unflake-some-tests&agg_m=count&agg_m_source=base&agg_t=count&fromUser=false&index=citest&start=1766388450558&end=1766993250558&paused=false) Related - DataDog#15796
## Description This makes some tests less flaky. Previously, we would sometimes parse an empty Profile (0 samples in it) and make the test fail. The fix is to, when this happens, ignore the failure and parse/use the previous Profile instead (which we expect to ALWAYS be non-empty). - [See in Flaky Management](https://app.datadoghq.com/ci/test/flaky?query=%40test.codeowners%3A%2Aprofiling-python%2A%20flaky_test_state%3Aquarantined%20%40test.suite%3Atest_main.py&sort=-pipelines_failed&viewMode=flaky) - [See failures on this branch](https://app.datadoghq.com/ci/test/runs?query=test_level%3Atest%20%40test.codeowners%3A%2Aprofiling-python%2A%20%40test.suite%3Atest_main.py%20status%3Aerror%20%40git.branch%3Akowalski%2Ftest-profiling-unflake-some-tests&agg_m=count&agg_m_source=base&agg_t=count&fromUser=false&index=citest&start=1766388450558&end=1766993250558&paused=false) Related - #15796

Description
This makes some tests less flaky. Previously, we would sometimes parse an empty Profile (0 samples in it) and make the test fail. The fix is to, when this happens, ignore the failure and parse/use the previous Profile instead (which we expect to ALWAYS be non-empty).
Related