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[python_many_threads] Initial commit (#136)
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‎scenarios/python_many_threads_3.11/Dockerfile‎

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ARG BASE_IMAGE="prof-python-3.11"
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FROM $BASE_IMAGE
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COPY ./scenarios/python_many_threads_3.11/main.py \
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./scenarios/python_many_threads_3.11/requirements.txt \
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/app/
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RUN chmod 644 /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="30"
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ENV DD_PROFILING_ENABLED="true"
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# Force reservoir sampling: there are 20 worker threads but at most 5 are
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# sampled per cycle, so the inverse-probability wall-time weighting must
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# reconstruct the correct totals.
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# We could go down to 1 or 2 but it makes the test very flaky. Even with
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# 5/20 sampling and 15% margin, the point is sufficiently proven.
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ENV _DD_PROFILING_STACK_MAX_THREADS="5"
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# Reservoir sampling with two threads results in very noisy wall time for
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# many threads. By reducing the interval, we get more samples and less noise.
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ENV _DD_PROFILING_STACK_ADAPTIVE_SAMPLING_ENABLED=1
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ENV _DD_PROFILING_STACK_ADAPTIVE_SAMPLING_MAX_INTERVAL_US="10000"
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CMD python main.py

‎scenarios/python_many_threads_3.11/README.md‎

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# python_many_threads_3.11
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Verifies that the stack sampler's reservoir sampling produces correct wall-time
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totals when the number of live threads exceeds the per-cycle sampling cap.
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The workload spawns 20 identical CPU-bound `worker` threads that all run for the
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full duration. `_DD_PROFILING_STACK_MAX_THREADS=2` forces the sampler to sample
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only 2 of the ~21 live threads each cycle (Algorithm R reservoir sampling),
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scaling each sampled thread's wall time by `n_total / sample_count`.
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## Expected behavior
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- **wall-time**: the combined wall time attributed to `worker` is ~20 cores
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(one core-second per second per worker). Despite subsampling, the
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inverse-probability weighting must reconstruct this total. This is the only
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scenario that crosses the `max_threads` cap and exercises reservoir sampling.

‎scenarios/python_many_threads_3.11/expected_profile.json‎

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{
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"test_name": "python_many_threads",
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"stacks": [
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{
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"profile-type": "wall-time",
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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-0"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-1"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-2"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-3"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-4"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-5"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-6"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-7"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-8"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-9"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-10"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-11"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-12"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-13"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-14"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-15"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-16"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-17"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-18"]
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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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"stack-content": [
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{
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"regular_expression": ".*worker",
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"value": 1000000000,
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"error_margin": 10,
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"labels": [
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{
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"key": "thread name",
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"values": ["worker-19"]
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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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"scale_by_duration": true
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}

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