Pypprof HTTP Profiling Endpoints

0.0.1 · maintenance · verified Thu Apr 16

pypprof (version 0.0.1) adds HTTP-based endpoints to Python applications for collecting CPU and heap profiles, similar to Go's `net/http/pprof`. It leverages `zprofile` and `mprofile` under the hood for profile collection. The last release was in 2019, indicating the library is in a maintenance state rather than active development.

Common errors

```
go tool pprof: no such file or directory
```
cause The `go tool pprof` command is part of the Go programming language toolchain and is required to interactively view the profiles generated by `pypprof`. This error indicates that Go is not installed or not in your system's PATH.

fix Install the Go programming language (go.dev/doc/install) and ensure its binaries are accessible in your system's PATH. This will enable you to use `go tool pprof` for visualizing profiles.
```
ImportError: cannot import name 'start_pprof_server' from 'pypprof.net_http'
```
cause This error typically occurs if `pypprof` is not installed correctly, or if there is a typo in the import statement.

fix First, ensure `pypprof` is installed by running `pip install pypprof`. If it is, double-check the import statement for any typos, it should be `from pypprof.net_http import start_pprof_server`.
```
mprofile.exceptions.MProfileException: failed to install hooks: mprofile only works on linux/amd64
```
cause This error directly indicates that you are trying to use `mprofile` (and thus memory profiling in `pypprof`) on an unsupported architecture, such as `linux/arm64`.

fix To fix this, you must run `pypprof` on a `linux/amd64` machine. If that's not possible, you will need to disable memory profiling or use an alternative profiling tool that supports your system's architecture.

Warnings

deprecated The `pypprof` library (version 0.0.1) was last released in October 2019 and is classified as 'Pre-Alpha' on PyPI. It is not actively maintained, and users might encounter compatibility issues with newer Python versions or modern application frameworks. Consider more actively developed profiling solutions for new projects.
Fix: Evaluate alternative, actively maintained profiling libraries like `memory_profiler`, `cProfile`, `pyinstrument`, or framework-specific wrappers like `flask-pypprof` for Flask applications.
gotcha Memory profiling functionality, which relies on the `mprofile` dependency, is only fully supported by default for Python >= 3.4. For Python versions between 2.7 and 3.3, memory profiling requires manually patching your Python installation and installing `mprofile`.
Fix: For Python < 3.4, refer to the `mprofile` documentation for manual installation and patching instructions. For newer Python versions, `mprofile` should be a more straightforward dependency.
gotcha The `mprofile` dependency, essential for memory profiling in `pypprof`, is known to only work on `linux/amd64` architectures. It does not support `linux/arm64` or other platforms, which can lead to failures when attempting memory profiling on unsupported systems.
Fix: Ensure your application runs on a `linux/amd64` environment if memory profiling is critical. Alternatively, disable memory profiling or explore other profiling tools that support your target architecture.

Install

pip install pypprof Install pypprof

Imports

start_pprof_server

from pypprof.net_http import start_pprof_server

Quickstart

This quickstart demonstrates how to integrate `pypprof` into a simple Python application to expose profiling endpoints. After running, you can access CPU and heap profiles via `go tool pprof` (requires Go installed) or `curl` against the exposed `/debug/pprof/` endpoints. A background thread continuously performs a CPU-intensive task to generate data for profiling.

from pypprof.net_http import start_pprof_server
import time
import threading

def my_heavy_computation():
    total = 0
    for i in range(10000000):
        total += i * i
    return total

def background_task():
    while True:
        print(f"Performing heavy computation: {my_heavy_computation()}")
        time.sleep(1)

if __name__ == "__main__":
    # Start the pprof server on port 8081
    print("Starting pypprof server on port 8081...")
    start_pprof_server(port=8081)
    print("pypprof server started. Access profiles at http://localhost:8081/debug/pprof/")
    print("To fetch a CPU profile: go tool pprof -http=:8088 :8081/debug/pprof/profile")
    print("To fetch a heap profile: go tool pprof :8081/debug/pprof/heap")

    # Run a background task to generate some CPU load for profiling
    worker_thread = threading.Thread(target=background_task, daemon=True)
    worker_thread.start()

    try:
        while True:
            time.sleep(10) # Keep the main thread alive
    except KeyboardInterrupt:
        print("Exiting.")

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