Python profiling¶

DataKit Python profiling supports dd-trace-py and py-spy.

Requirements¶

Install DataKit and enable profile input.

Use dd-trace-py¶

• Install dd-trace-py library

pip3 install ddtrace

• Profiling by attaching into the target process

DD_PROFILING_ENABLED=true \
DD_ENV=dev \
DD_SERVICE=my-web-app \
DD_VERSION=1.0.3 \
DD_TRACE_AGENT_URL=http://127.0.0.1:9529 \
ddtrace-run python app.py

• Profiling by writing code

import time
import ddtrace
from ddtrace.profiling import Profiler

ddtrace.tracer.configure(
     https=False,
     hostname="localhost",
     port="9529",
)

prof = Profiler()
prof.start(True, True)

# your code here ...
# while True:
#     time.sleep(1)

There is no need to add ddtrace-run command

DD_ENV=testing DD_SERVICE=python-profiling-manual DD_VERSION=1.2.3 python3 app.py

View Profile¶

After a minute or two, you can visualize your profiles on the APM -> Profile .

Use py-spy¶

py-spyis a non-invasive Python performance metric sampling tool provided by the open source community, which has the advantages of running independently and having low impact on target program load By default, py-spy will output sampling data in different formats to a local file based on the specified parameters. To simplify the integration of py-spy and DataKit, Observation Cloud provides a branch version [py-spy-for-datakit]（https://github.com/GuanceCloud/py-spy-for-datakit）{: target="_Blank"}, with little modifications made to the original version, supporting automatic profiling send data to DataKit.

• Installation

pip install is recommend way.

pip3 install py-spy-for-datakit

besides, Github Release page provides pre compiled versions of some mainstream platforms, which you can also download and install using PIP. Below is Linux x86_64 platform as an example (other platforms is similar), let's introduce the installation steps of the pre compiled version:

# download binary
curl -SL https://github.com/GuanceCloud/py-spy-for-datakit/releases/download/v0.3.15/py_spy_for_datakit-0.3.15-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl -O

# use pip to install
pip3 install --force-reinstall --no-index --find-links . py-spy-for-datakit

# confirm successful installation
py-spy-for-datakit help

if your machine has rust and cargo installed, you can use cargo to install it.

cargo install py-spy-for-datakit

• Usage

py-spy-for-datakit has added the datakit command to the original subcommand of py-spy, specifically used to send sampling data to DataKit. You can type py-spy-for-datakit help datakit for usage help:

py-spy-for-datakit can analyze the currently running program by using the --pid <PID> or -p <PID> parameters to pass the process PID of the running Python program to py-spy-for-datakit.

Imaging your target process PID is 12345, and Datakit is listening at 127.0.0.1:9529:

py-spy-for-datakit datakit \
  --host 127.0.0.1 \
  --port 9529 \
  --service <your-service-name> \
  --env testing \
  --version v0.1 \
  --duration 60 \
  --pid 12345

If needed, please add sudo prefix.

py-spy-for-datakit also supports direct startup commands with Python projects, so there is no need to specify a process PID. At the same time, data sampling will be performed when the program starts, and the running commands are similar:

py-spy-for-datakit datakit \
  --host 127.0.0.1 \
  --port 9529 \
  --service your-service-name \
  --env testing \
  --version v0.1 \
  -d 60 \
  -- python3 server.py  # There is a blank in front of python3

After a minute or two, you can visualize your profiles on the profile.