chore(ci): adding datadog-lambda-python CI to Gitlab#15475
chore(ci): adding datadog-lambda-python CI to Gitlab#15475rithikanarayan merged 13 commits intomainfrom
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Bootstrap import analysisComparison of import times between this PR and base. SummaryThe average import time from this PR is: 244 ± 1 ms. The average import time from base is: 246 ± 1 ms. The import time difference between this PR and base is: -2.6 ± 0.06 ms. Import time breakdownThe following import paths have shrunk:
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Performance SLOsComparing candidate rithika.narayan/APMSVLS-284-lambda-ci (469dac2) with baseline main (7e53632) 📈 Performance Regressions (3 suites)📈 iastaspects - 118/118✅ add_aspectTime: ✅ 18.193µs (SLO: <20.000µs -9.0%) vs baseline: 📈 +21.9% Memory: ✅ 42.998MB (SLO: <43.250MB 🟡 -0.6%) vs baseline: +5.1% ✅ add_inplace_aspectTime: ✅ 14.889µs (SLO: <20.000µs 📉 -25.6%) vs baseline: -0.6% Memory: ✅ 42.802MB (SLO: <43.250MB 🟡 -1.0%) vs baseline: +4.8% ✅ add_inplace_noaspectTime: ✅ 0.341µs (SLO: <10.000µs 📉 -96.6%) vs baseline: +2.0% Memory: ✅ 42.762MB (SLO: <43.000MB 🟡 -0.6%) vs baseline: +4.8% ✅ add_noaspectTime: ✅ 0.545µs (SLO: <10.000µs 📉 -94.5%) vs baseline: -0.9% Memory: ✅ 42.782MB (SLO: <43.000MB 🟡 -0.5%) vs baseline: +4.7% ✅ bytearray_aspectTime: ✅ 17.874µs (SLO: <30.000µs 📉 -40.4%) vs baseline: -0.6% Memory: ✅ 42.841MB (SLO: <43.500MB 🟡 -1.5%) vs baseline: +4.3% ✅ bytearray_extend_aspectTime: ✅ 23.751µs (SLO: <30.000µs 📉 -20.8%) vs baseline: +0.2% Memory: ✅ 42.861MB (SLO: <43.500MB 🟡 -1.5%) vs baseline: +4.9% ✅ bytearray_extend_noaspectTime: ✅ 2.631µs (SLO: <10.000µs 📉 -73.7%) vs baseline: +0.1% Memory: ✅ 42.625MB (SLO: <43.500MB -2.0%) vs baseline: +4.7% ✅ bytearray_noaspectTime: ✅ 1.466µs (SLO: <10.000µs 📉 -85.3%) vs baseline: +1.1% Memory: ✅ 42.684MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +4.9% ✅ bytes_aspectTime: ✅ 16.666µs (SLO: <20.000µs 📉 -16.7%) vs baseline: -0.2% Memory: ✅ 42.821MB (SLO: <43.000MB 🟡 -0.4%) vs baseline: +4.5% ✅ bytes_noaspectTime: ✅ 1.386µs (SLO: <10.000µs 📉 -86.1%) vs baseline: -1.7% Memory: ✅ 42.684MB (SLO: <43.000MB 🟡 -0.7%) vs baseline: +4.8% ✅ bytesio_aspectTime: ✅ 54.971µs (SLO: <70.000µs 📉 -21.5%) vs baseline: ~same Memory: ✅ 42.880MB (SLO: <43.500MB 🟡 -1.4%) vs baseline: +4.7% ✅ bytesio_noaspectTime: ✅ 3.208µs (SLO: <10.000µs 📉 -67.9%) vs baseline: -0.7% Memory: ✅ 42.841MB (SLO: <43.500MB 🟡 -1.5%) vs baseline: +5.3% ✅ capitalize_aspectTime: ✅ 14.630µs (SLO: <20.000µs 📉 -26.8%) vs baseline: +0.2% Memory: ✅ 42.703MB (SLO: <43.000MB 🟡 -0.7%) vs baseline: +4.3% ✅ capitalize_noaspectTime: ✅ 2.565µs (SLO: <10.000µs 📉 -74.3%) vs baseline: +0.3% Memory: ✅ 42.723MB (SLO: <43.000MB 🟡 -0.6%) vs baseline: +4.5% ✅ casefold_aspectTime: ✅ 14.612µs (SLO: <20.000µs 📉 -26.9%) vs baseline: -0.3% Memory: ✅ 42.900MB (SLO: <43.000MB 🟡 -0.2%) vs baseline: +4.5% ✅ casefold_noaspectTime: ✅ 3.085µs (SLO: <10.000µs 📉 -69.2%) vs baseline: -0.8% Memory: ✅ 42.684MB (SLO: <43.000MB 🟡 -0.7%) vs baseline: +5.1% ✅ decode_aspectTime: ✅ 15.529µs (SLO: <30.000µs 📉 -48.2%) vs baseline: +0.2% Memory: ✅ 42.979MB (SLO: <43.500MB 🟡 -1.2%) vs baseline: +4.6% ✅ decode_noaspectTime: ✅ 1.589µs (SLO: <10.000µs 📉 -84.1%) vs baseline: -0.8% Memory: ✅ 42.802MB (SLO: <43.500MB 🟡 -1.6%) vs baseline: +5.2% ✅ encode_aspectTime: ✅ 18.181µs (SLO: <30.000µs 📉 -39.4%) vs baseline: 📈 +22.6% Memory: ✅ 42.861MB (SLO: <43.500MB 🟡 -1.5%) vs baseline: +4.3% ✅ encode_noaspectTime: ✅ 1.472µs (SLO: <10.000µs 📉 -85.3%) vs baseline: ~same Memory: ✅ 42.743MB (SLO: <43.000MB 🟡 -0.6%) vs baseline: +5.1% ✅ format_aspectTime: ✅ 171.564µs (SLO: <200.000µs 📉 -14.2%) vs baseline: +0.3% Memory: ✅ 42.880MB (SLO: <43.250MB 🟡 -0.9%) vs baseline: +4.3% ✅ format_map_aspectTime: ✅ 191.267µs (SLO: <200.000µs -4.4%) vs baseline: ~same Memory: ✅ 42.802MB (SLO: <43.500MB 🟡 -1.6%) vs baseline: +4.3% ✅ format_map_noaspectTime: ✅ 3.738µs (SLO: <10.000µs 📉 -62.6%) vs baseline: ~same Memory: ✅ 42.723MB (SLO: <43.250MB 🟡 -1.2%) vs baseline: +5.1% ✅ format_noaspectTime: ✅ 3.153µs (SLO: <10.000µs 📉 -68.5%) vs baseline: -1.3% Memory: ✅ 42.821MB (SLO: <43.250MB 🟡 -1.0%) vs baseline: +5.0% ✅ index_aspectTime: ✅ 15.226µs (SLO: <20.000µs 📉 -23.9%) vs baseline: -0.6% Memory: ✅ 42.762MB (SLO: <43.250MB 🟡 -1.1%) vs baseline: +4.8% ✅ index_noaspectTime: ✅ 0.462µs (SLO: <10.000µs 📉 -95.4%) vs baseline: ~same Memory: ✅ 42.546MB (SLO: <43.000MB 🟡 -1.1%) vs baseline: +4.2% ✅ join_aspectTime: ✅ 16.956µs (SLO: <20.000µs 📉 -15.2%) vs baseline: +0.2% Memory: ✅ 42.762MB (SLO: <43.500MB 🟡 -1.7%) vs baseline: +4.8% ✅ join_noaspectTime: ✅ 1.494µs (SLO: <10.000µs 📉 -85.1%) vs baseline: -0.3% Memory: ✅ 42.664MB (SLO: <43.250MB 🟡 -1.4%) vs baseline: +4.5% ✅ ljust_aspectTime: ✅ 20.791µs (SLO: <30.000µs 📉 -30.7%) vs baseline: -0.6% Memory: ✅ 42.821MB (SLO: <43.250MB 🟡 -1.0%) vs baseline: +4.4% ✅ ljust_noaspectTime: ✅ 2.668µs (SLO: <10.000µs 📉 -73.3%) vs baseline: ~same Memory: ✅ 42.566MB (SLO: <43.250MB 🟡 -1.6%) vs baseline: +4.5% ✅ lower_aspectTime: ✅ 17.821µs (SLO: <30.000µs 📉 -40.6%) vs baseline: +0.6% Memory: ✅ 42.998MB (SLO: <43.500MB 🟡 -1.2%) vs baseline: +5.1% ✅ lower_noaspectTime: ✅ 2.375µs (SLO: <10.000µs 📉 -76.2%) vs baseline: -0.6% Memory: ✅ 42.664MB (SLO: <43.250MB 🟡 -1.4%) vs baseline: +5.2% ✅ lstrip_aspectTime: ✅ 17.573µs (SLO: <20.000µs 📉 -12.1%) vs baseline: -0.1% Memory: ✅ 42.939MB (SLO: <43.250MB 🟡 -0.7%) vs baseline: +4.5% ✅ lstrip_noaspectTime: ✅ 1.835µs (SLO: <10.000µs 📉 -81.6%) vs baseline: +0.8% Memory: ✅ 42.585MB (SLO: <43.000MB 🟡 -1.0%) vs baseline: +4.6% ✅ modulo_aspectTime: ✅ 166.643µs (SLO: <200.000µs 📉 -16.7%) vs baseline: +0.2% Memory: ✅ 42.841MB (SLO: <43.500MB 🟡 -1.5%) vs baseline: +4.1% ✅ modulo_aspect_for_bytearray_bytearrayTime: ✅ 180.203µs (SLO: <200.000µs -9.9%) vs baseline: +3.4% Memory: ✅ 42.743MB (SLO: <43.500MB 🟡 -1.7%) vs baseline: +4.5% ✅ modulo_aspect_for_bytesTime: ✅ 168.766µs (SLO: <200.000µs 📉 -15.6%) vs baseline: +0.4% Memory: ✅ 42.782MB (SLO: <43.500MB 🟡 -1.7%) vs baseline: +4.4% ✅ modulo_aspect_for_bytes_bytearrayTime: ✅ 171.298µs (SLO: <200.000µs 📉 -14.4%) vs baseline: ~same Memory: ✅ 42.861MB (SLO: <43.500MB 🟡 -1.5%) vs baseline: +4.7% ✅ modulo_noaspectTime: ✅ 3.670µs (SLO: <10.000µs 📉 -63.3%) vs baseline: -1.3% Memory: ✅ 42.546MB (SLO: <43.000MB 🟡 -1.1%) vs baseline: +4.6% ✅ replace_aspectTime: ✅ 214.627µs (SLO: <300.000µs 📉 -28.5%) vs baseline: +0.1% Memory: ✅ 42.920MB (SLO: <44.000MB -2.5%) vs baseline: +4.5% ✅ replace_noaspectTime: ✅ 5.173µs (SLO: <10.000µs 📉 -48.3%) vs baseline: -0.2% Memory: ✅ 42.703MB (SLO: <43.000MB 🟡 -0.7%) vs baseline: +4.9% ✅ repr_aspectTime: ✅ 1.382µs (SLO: <10.000µs 📉 -86.2%) vs baseline: +0.3% Memory: ✅ 42.664MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +4.8% ✅ repr_noaspectTime: ✅ 0.522µs (SLO: <10.000µs 📉 -94.8%) vs baseline: -0.7% Memory: ✅ 42.664MB (SLO: <43.000MB 🟡 -0.8%) vs baseline: +4.7% ✅ rstrip_aspectTime: ✅ 19.209µs (SLO: <30.000µs 📉 -36.0%) vs baseline: +1.5% Memory: ✅ 42.821MB (SLO: <43.000MB 🟡 -0.4%) vs baseline: +4.3% ✅ rstrip_noaspectTime: ✅ 1.907µs (SLO: <10.000µs 📉 -80.9%) vs baseline: +2.3% Memory: ✅ 42.664MB (SLO: <43.000MB 🟡 -0.8%) vs baseline: +4.9% ✅ slice_aspectTime: ✅ 15.920µs (SLO: <20.000µs 📉 -20.4%) vs baseline: ~same Memory: ✅ 42.743MB (SLO: <43.000MB 🟡 -0.6%) vs baseline: +4.4% ✅ slice_noaspectTime: ✅ 0.601µs (SLO: <10.000µs 📉 -94.0%) vs baseline: +0.6% Memory: ✅ 42.743MB (SLO: <43.000MB 🟡 -0.6%) vs baseline: +5.0% ✅ stringio_aspectTime: ✅ 53.449µs (SLO: <80.000µs 📉 -33.2%) vs baseline: -0.5% Memory: ✅ 42.821MB (SLO: <43.500MB 🟡 -1.6%) vs baseline: +4.4% ✅ stringio_noaspectTime: ✅ 3.521µs (SLO: <10.000µs 📉 -64.8%) vs baseline: -0.2% Memory: ✅ 42.684MB (SLO: <43.500MB 🟡 -1.9%) vs baseline: +4.5% ✅ strip_aspectTime: ✅ 17.585µs (SLO: <20.000µs 📉 -12.1%) vs baseline: -0.3% Memory: ✅ 42.782MB (SLO: <43.000MB 🟡 -0.5%) vs baseline: +4.4% ✅ strip_noaspectTime: ✅ 1.826µs (SLO: <10.000µs 📉 -81.7%) vs baseline: +0.8% Memory: ✅ 42.703MB (SLO: <43.000MB 🟡 -0.7%) vs baseline: +4.6% ✅ swapcase_aspectTime: ✅ 18.396µs (SLO: <30.000µs 📉 -38.7%) vs baseline: +0.4% Memory: ✅ 42.939MB (SLO: <43.000MB 🟡 -0.1%) vs baseline: +4.6% ✅ swapcase_noaspectTime: ✅ 2.732µs (SLO: <10.000µs 📉 -72.7%) vs baseline: -1.2% Memory: ✅ 42.664MB (SLO: <43.000MB 🟡 -0.8%) vs baseline: +4.9% ✅ title_aspectTime: ✅ 18.175µs (SLO: <20.000µs -9.1%) vs baseline: -0.3% Memory: ✅ 42.821MB (SLO: <43.000MB 🟡 -0.4%) vs baseline: +4.4% ✅ title_noaspectTime: ✅ 2.643µs (SLO: <10.000µs 📉 -73.6%) vs baseline: ~same Memory: ✅ 42.625MB (SLO: <43.000MB 🟡 -0.9%) vs baseline: +4.9% ✅ translate_aspectTime: ✅ 24.025µs (SLO: <30.000µs 📉 -19.9%) vs baseline: 📈 +18.1% Memory: ✅ 42.782MB (SLO: <43.000MB 🟡 -0.5%) vs baseline: +4.2% ✅ translate_noaspectTime: ✅ 4.270µs (SLO: <10.000µs 📉 -57.3%) vs baseline: +0.5% Memory: ✅ 42.703MB (SLO: <43.000MB 🟡 -0.7%) vs baseline: +5.1% ✅ upper_aspectTime: ✅ 17.799µs (SLO: <30.000µs 📉 -40.7%) vs baseline: -0.4% Memory: ✅ 42.743MB (SLO: <43.000MB 🟡 -0.6%) vs baseline: +4.3% ✅ upper_noaspectTime: ✅ 2.390µs (SLO: <10.000µs 📉 -76.1%) vs baseline: +0.3% Memory: ✅ 42.802MB (SLO: <43.000MB 🟡 -0.5%) vs baseline: +5.0% 📈 iastaspectsospath - 24/24✅ ospathbasename_aspectTime: ✅ 5.189µs (SLO: <10.000µs 📉 -48.1%) vs baseline: 📈 +22.3% Memory: ✅ 41.484MB (SLO: <43.000MB -3.5%) vs baseline: +5.0% ✅ ospathbasename_noaspectTime: ✅ 4.273µs (SLO: <10.000µs 📉 -57.3%) vs baseline: -1.0% Memory: ✅ 41.386MB (SLO: <43.000MB -3.8%) vs baseline: +4.7% ✅ ospathjoin_aspectTime: ✅ 6.113µs (SLO: <10.000µs 📉 -38.9%) vs baseline: -0.3% Memory: ✅ 41.406MB (SLO: <43.000MB -3.7%) vs baseline: +4.9% ✅ ospathjoin_noaspectTime: ✅ 6.205µs (SLO: <10.000µs 📉 -38.0%) vs baseline: -0.3% Memory: ✅ 41.425MB (SLO: <43.000MB -3.7%) vs baseline: +4.8% ✅ ospathnormcase_aspectTime: ✅ 3.507µs (SLO: <10.000µs 📉 -64.9%) vs baseline: +0.2% Memory: ✅ 41.524MB (SLO: <43.000MB -3.4%) vs baseline: +5.2% ✅ ospathnormcase_noaspectTime: ✅ 3.578µs (SLO: <10.000µs 📉 -64.2%) vs baseline: +0.9% Memory: ✅ 41.406MB (SLO: <43.000MB -3.7%) vs baseline: +4.7% ✅ ospathsplit_aspectTime: ✅ 4.833µs (SLO: <10.000µs 📉 -51.7%) vs baseline: -0.3% Memory: ✅ 41.386MB (SLO: <43.000MB -3.8%) vs baseline: +5.0% ✅ ospathsplit_noaspectTime: ✅ 4.954µs (SLO: <10.000µs 📉 -50.5%) vs baseline: ~same Memory: ✅ 41.465MB (SLO: <43.000MB -3.6%) vs baseline: +5.1% ✅ ospathsplitdrive_aspectTime: ✅ 3.764µs (SLO: <10.000µs 📉 -62.4%) vs baseline: -0.2% Memory: ✅ 41.445MB (SLO: <43.000MB -3.6%) vs baseline: +4.9% ✅ ospathsplitdrive_noaspectTime: ✅ 0.748µs (SLO: <10.000µs 📉 -92.5%) vs baseline: +0.8% Memory: ✅ 41.465MB (SLO: <43.000MB -3.6%) vs baseline: +5.1% ✅ ospathsplitext_aspectTime: ✅ 4.625µs (SLO: <10.000µs 📉 -53.7%) vs baseline: +0.5% Memory: ✅ 41.465MB (SLO: <43.000MB -3.6%) vs baseline: +4.8% ✅ ospathsplitext_noaspectTime: ✅ 4.707µs (SLO: <10.000µs 📉 -52.9%) vs baseline: +0.7% Memory: ✅ 41.406MB (SLO: <43.000MB -3.7%) vs baseline: +4.8% 📈 telemetryaddmetric - 30/30✅ 1-count-metric-1-timesTime: ✅ 3.359µs (SLO: <20.000µs 📉 -83.2%) vs baseline: 📈 +10.2% Memory: ✅ 34.859MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +4.8% ✅ 1-count-metrics-100-timesTime: ✅ 202.275µs (SLO: <220.000µs -8.1%) vs baseline: -1.2% Memory: ✅ 34.859MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +4.7% ✅ 1-distribution-metric-1-timesTime: ✅ 3.348µs (SLO: <20.000µs 📉 -83.3%) vs baseline: -1.0% Memory: ✅ 34.878MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +4.8% ✅ 1-distribution-metrics-100-timesTime: ✅ 218.771µs (SLO: <230.000µs -4.9%) vs baseline: -0.7% Memory: ✅ 34.977MB (SLO: <35.500MB 🟡 -1.5%) vs baseline: +4.3% ✅ 1-gauge-metric-1-timesTime: ✅ 2.177µs (SLO: <20.000µs 📉 -89.1%) vs baseline: -1.3% Memory: ✅ 34.721MB (SLO: <35.500MB -2.2%) vs baseline: +4.3% ✅ 1-gauge-metrics-100-timesTime: ✅ 137.717µs (SLO: <150.000µs -8.2%) vs baseline: +0.7% Memory: ✅ 34.878MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +4.9% ✅ 1-rate-metric-1-timesTime: ✅ 3.146µs (SLO: <20.000µs 📉 -84.3%) vs baseline: -1.6% Memory: ✅ 34.937MB (SLO: <35.500MB 🟡 -1.6%) vs baseline: +5.3% ✅ 1-rate-metrics-100-timesTime: ✅ 215.429µs (SLO: <250.000µs 📉 -13.8%) vs baseline: -1.1% Memory: ✅ 34.819MB (SLO: <35.500MB 🟡 -1.9%) vs baseline: +4.8% ✅ 100-count-metrics-100-timesTime: ✅ 20.212ms (SLO: <22.000ms -8.1%) vs baseline: -0.2% Memory: ✅ 34.898MB (SLO: <35.500MB 🟡 -1.7%) vs baseline: +4.7% ✅ 100-distribution-metrics-100-timesTime: ✅ 2.277ms (SLO: <2.550ms 📉 -10.7%) vs baseline: +0.2% Memory: ✅ 34.760MB (SLO: <35.500MB -2.1%) vs baseline: +3.5% ✅ 100-gauge-metrics-100-timesTime: ✅ 1.411ms (SLO: <1.550ms -9.0%) vs baseline: +0.8% Memory: ✅ 34.800MB (SLO: <35.500MB 🟡 -2.0%) vs baseline: +4.9% ✅ 100-rate-metrics-100-timesTime: ✅ 2.215ms (SLO: <2.550ms 📉 -13.1%) vs baseline: -0.4% Memory: ✅ 34.878MB (SLO: <35.500MB 🟡 -1.8%) vs baseline: +5.0% ✅ flush-1-metricTime: ✅ 4.658µs (SLO: <20.000µs 📉 -76.7%) vs baseline: -0.6% Memory: ✅ 35.154MB (SLO: <35.500MB 🟡 -1.0%) vs baseline: +4.9% ✅ flush-100-metricsTime: ✅ 175.866µs (SLO: <250.000µs 📉 -29.7%) vs baseline: ~same Memory: ✅ 35.212MB (SLO: <35.500MB 🟡 -0.8%) vs baseline: +4.7% ✅ flush-1000-metricsTime: ✅ 2.179ms (SLO: <2.500ms 📉 -12.9%) vs baseline: +0.2% Memory: ✅ 36.078MB (SLO: <36.500MB 🟡 -1.2%) vs baseline: +4.8% 🟡 Near SLO Breach (15 suites)🟡 coreapiscenario - 10/10 (1 unstable)
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brettlangdon
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other than updating the DataDog/datadog-lambda-python PR/updating to target main branch or w.e lgtm!
## Description Adding the unit and integration tests from the datadog-lambda-python repository to this repository's CI. Helps catch any changes to dd-trace-py that would cause issues in datadog-lambda-python before merging/release. <!-- Provide an overview of the change and motivation for the change --> ## Testing Gitlab. Verified that if trigger-serverless-lambda-tests downstream pipeline fails, the whole ddtrace pipeline will fail and the dd-gitlab/default-pipeline job will show as failed in the PR using [this Gitlab pipeline](https://gitlab.ddbuild.io/DataDog/apm-reliability/dd-trace-py/-/pipelines/86791461). <!-- Describe your testing strategy or note what tests are included --> ## Risks <!-- Note any risks associated with this change, or "None" if no risks --> ## Additional Notes [Related PR in datadog-lambda-python](DataDog/datadog-lambda-python#700). <!-- Any other information that would be helpful for reviewers -->
Description
Adding the unit and integration tests from the datadog-lambda-python repository to this repository's CI. Helps catch any changes to dd-trace-py that would cause issues in datadog-lambda-python before merging/release.
Testing
Gitlab.
Verified that if trigger-serverless-lambda-tests downstream pipeline fails, the whole ddtrace pipeline will fail and the dd-gitlab/default-pipeline job will show as failed in the PR using this Gitlab pipeline.
Risks
Additional Notes
Related PR in datadog-lambda-python.