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feat(python): add cross-cutting gate scenarios for 3.14/3.15
Add paired gil_contention, uvloop, and gevent scenarios for integration shapes on the migration gate path. Gate grows from 20 to 26 scenarios.
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‎README.md‎

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**Inputs:**
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- `dd_trace_py_commit_sha` — commit to test (required)
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- `test_scenarios` — regexp passed to `TEST_SCENARIOS` (dd-trace-py passes the [16-scenario 3.14/3.15 migration gate](scenarios/python_downstream_gate/README.md); the downstream workflow default alone is `python.*`)
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- `test_scenarios` — regexp passed to `TEST_SCENARIOS` (dd-trace-py passes the [22-scenario 3.14/3.15 migration gate](scenarios/python_downstream_gate/README.md); the downstream workflow default alone is `python.*`)
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## Creating new tests
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‎scenarios/python_downstream_gate/README.md‎

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# Python downstream gate (dd-trace-py)
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Sixteen prof-correctness scenarios exercise the profiling stack on **3.14
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Twenty-two prof-correctness scenarios exercise the profiling stack on **3.14
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(baseline)** and **3.15 (candidate)** for the same workloads. They are the
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default set when dd-trace-py triggers downstream CI on profiling changes.
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| asyncio (task labels) | `python_asyncio_3.14` | `python_asyncio_3.15` |
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| native-cpu | `python_native_cpu_3.14` | `python_native_cpu_3.15` |
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| deep-stack | `python_deep_stack_3.14` | `python_deep_stack_3.15` |
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| gil-contention | `python_gil_contention_3.14` | `python_gil_contention_3.15` |
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| uvloop | `python_uvloop_3.14` | `python_uvloop_3.15` |
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| gevent | `python_gevent_3.14` | `python_gevent_3.15` |
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| exceptions | `python_exceptions_3.14` | `python_exceptions_3.15` |
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| async-gen | `python_async_gen_3.14` | `python_async_gen_3.15` |
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| lock | `python_lock_3.14` | `python_lock_3.15` |
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## Profiler coverage
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| Family | Profile type asserted |
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|--------|----------------------|
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| cpu (stack) | `cpu-time` + `thread name` |
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| alloc (memory) | `alloc-space` + `alloc-samples` |
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| asyncio | `wall-time` + `task name` |
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| native-cpu | `cpu-time` + `wall-time` (C-extension frames) |
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| deep-stack | `cpu-time` (400-frame unwinding) |
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| exceptions | `exception-samples` + `exception type` |
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| async-gen | `wall-time` |
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| lock | `lock-acquire` + `lock-release` + `lock name` |
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Feature-specific pairs (mem_domain, live_heap) and cross-cutting integration
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scenarios land in follow-up PRs.
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## Default downstream regexp
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```
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python_(cpu|alloc|asyncio|native_cpu|deep_stack|exceptions|async_gen|lock)_3\.(14|15)
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python_(cpu|alloc|asyncio|native_cpu|deep_stack|gil_contention|uvloop|gevent|exceptions|async_gen|lock)_3\.(14|15)
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```
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## Local run
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```sh
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export DDTRACE_INSTALL_URL="https://dd-trace-py-builds.s3.amazonaws.com/<commit-sha>/install.sh"
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TEST_SCENARIOS='python_(cpu|alloc|asyncio|native_cpu|deep_stack|exceptions|async_gen|lock)_3\.(14|15)' go test -v -run TestScenarios
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TEST_SCENARIOS='python_(cpu|alloc|asyncio|native_cpu|deep_stack|gil_contention|uvloop|gevent|exceptions|async_gen|lock)_3\.(14|15)' go test -v -run TestScenarios
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```
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Feature-specific pairs (`mem_domain`, `live_heap`) are separate PRs stacked on
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`vlad/gate-infra`.
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## Further reading
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- prof-correctness downstream wiring: [README](../../README.md#downstream-from-dd-trace-py)

‎scenarios/python_gevent_3.14/Dockerfile‎

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ARG BASE_IMAGE="prof-python-3.14"
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FROM $BASE_IMAGE
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COPY ./scenarios/python_gevent_3.14/main.py \
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./scenarios/python_gevent_3.14/requirements.txt \
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/app/
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WORKDIR /app
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RUN pip install -r requirements.txt
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ENV EXECUTION_TIME_SEC=2
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ENV DD_PROFILING_ENABLED=true
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CMD ddtrace-run python main.py

‎scenarios/python_gevent_3.14/expected_profile.json‎

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{
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"test_name": "python_gevent_3.14",
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"scale_by_duration": false,
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"pprof-regex": "",
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"stacks": [
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{
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"profile-type": "wall-time",
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"pprof-regex": "",
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"stack-content": [
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{
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"regular_expression": "^run$",
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"value": 2000000000,
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"error_margin": 25,
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"labels": [
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{
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"key": "task name",
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"values": [
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"Hub"
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],
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"values_regex": ""
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}
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]
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}
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]
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},
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{
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"profile-type": "wall-time",
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"pprof-regex": "",
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"stack-content": [
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{
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"regular_expression": ".*target.*",
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"value": 1000000000,
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"error_margin": 25,
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"labels": [
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{
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"key": "task name",
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"values": [
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"Greenlet-0"
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],
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"values_regex": ""
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}
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]
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}
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]
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},
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{
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"profile-type": "wall-time",
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"pprof-regex": "",
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"stack-content": [
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{
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"regular_expression": ".*target.*",
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"value": 1000000000,
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"error_margin": 25,
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"labels": [
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{
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"key": "task name",
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"values": [
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"Greenlet-1"
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],
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"values_regex": ""
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}
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]
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}
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]
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},
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{
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"profile-type": "wall-time",
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"pprof-regex": "",
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"stack-content": [
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{
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"regular_expression": ".*target.*",
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"value": 1000000000,
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"error_margin": 25,
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"labels": [
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{
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"key": "task name",
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"values": [
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"Greenlet-2"
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],
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"values_regex": ""
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}
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]
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}
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]
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},
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{
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"profile-type": "wall-time",
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"pprof-regex": "",
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"stack-content": [
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{
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"regular_expression": ".*target.*",
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"value": 1000000000,
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"error_margin": 25,
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"labels": [
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{
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"key": "task name",
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"values": [
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"Greenlet-3"
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],
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"values_regex": ""
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}
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]
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}
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]
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},
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{
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"profile-type": "wall-time",
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"pprof-regex": "",
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"stack-content": [
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{
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"regular_expression": ".*target.*",
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"value": 1000000000,
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"error_margin": 25,
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"labels": [
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{
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"key": "task name",
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"values": [
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"Greenlet-4"
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],
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"values_regex": ""
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}
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]
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}
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]
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},
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{
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"profile-type": "wall-time",
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"pprof-regex": "",
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"stack-content": [
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{
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"regular_expression": ".*target.*",
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"value": 1000000000,
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"error_margin": 25,
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"labels": [
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{
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"key": "task name",
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"values": [
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"Greenlet-5"
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],
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"values_regex": ""
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}
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]
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}
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]
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},
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{
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"profile-type": "wall-time",
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"pprof-regex": "",
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"stack-content": [
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{
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"regular_expression": ".*target.*",
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"value": 1000000000,
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"error_margin": 25,
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"labels": [
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{
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"key": "task name",
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"values": [
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"Greenlet-6"
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],
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"values_regex": ""
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}
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]
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}
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]
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},
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{
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"profile-type": "wall-time",
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"pprof-regex": "",
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"stack-content": [
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{
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"regular_expression": ".*target.*",
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"value": 1000000000,
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"error_margin": 25,
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"labels": [
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{
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"key": "task name",
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"values": [
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"Greenlet-7"
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],
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"values_regex": ""
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}
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]
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}
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]
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},
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{
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"profile-type": "wall-time",
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"pprof-regex": "",
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"stack-content": [
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{
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"regular_expression": ".*target.*",
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"value": 1000000000,
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"error_margin": 25,
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"labels": [
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{
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"key": "task name",
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"values": [
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"Greenlet-8"
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],
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"values_regex": ""
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}
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]
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}
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]
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},
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{
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"profile-type": "wall-time",
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"pprof-regex": "",
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"stack-content": [
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{
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"regular_expression": ".*target.*",
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"value": 1000000000,
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"error_margin": 25,
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"labels": [
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{
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"key": "task name",
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"values": [
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"Greenlet-9"
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],
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"values_regex": ""
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}
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]
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}
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]
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}
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]
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}

‎scenarios/python_gevent_3.14/main.py‎

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from gevent import monkey
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monkey.patch_all()
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import os # noqa: E402
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import time # noqa: E402
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from threading import Thread # noqa: E402
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def target(n: int) -> None:
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# Do actual work instead of just sleeping so profiler can capture it
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end_time = time.monotonic() + n
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count = 0
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while time.monotonic() < end_time:
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count += 1
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if count % 1000 == 0:
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# Yield to gevent
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time.sleep(0.01)
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if __name__ == "__main__":
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EXECUTION_TIME_SEC = int(os.environ.get("EXECUTION_TIME_SEC", "2"))
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threads = [Thread(target=target, args=(EXECUTION_TIME_SEC / 2,)) for _ in range(10)]
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for thread in threads:
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thread.start()
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target(EXECUTION_TIME_SEC)
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for thread in threads:
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thread.join()

‎scenarios/python_gevent_3.14/requirements.txt‎

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ddtrace
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gevent

‎scenarios/python_gevent_3.15/Dockerfile‎

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ARG BASE_IMAGE="prof-python-3.15"
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FROM $BASE_IMAGE
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COPY ./scenarios/python_gevent_3.15/main.py /app/main.py
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WORKDIR /app
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ENV EXECUTION_TIME_SEC=2
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ENV DD_PROFILING_ENABLED=true
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CMD ddtrace-run python main.py

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