https://github.com/dmonroy/aiometrics

Generate metrics from AsyncIO applications
https://github.com/dmonroy/aiometrics

asyncio metrics newrelic

Last synced: about 1 month ago
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Generate metrics from AsyncIO applications

• Host: GitHub
• URL: https://github.com/dmonroy/aiometrics
• Owner: dmonroy
• License: mit
• Created: 2016-06-19T04:12:27.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2017-02-09T17:08:54.000Z (over 7 years ago)
• Last Synced: 2024-07-21T23:07:56.026Z (about 2 months ago)
• Topics: asyncio, metrics, newrelic
• Language: Python
• Size: 7.81 KB
• Stars: 16
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• starred-awesome - aiometrics - Generate metrics from AsyncIO applications (Python)

README

        
# aiometrics

Generate metrics from asyncio applications

## How to install

As any other python package, use pip or include it in the `install_requires` section of your setup.py

```

pip install aiometrics

```

## Sample

Sample application included below makes usage of the default StdoutDriver to print the metrics into the stdout.

```

# sample.py

import asyncio

import datetime

import random

import aiometrics

@aiometrics.trace

@asyncio.coroutine

def echo_delay():

    ms = random.randrange(0, 5000)

    t = ms/1000

    yield from asyncio.sleep(t)

    print(datetime.datetime.now())

@asyncio.coroutine

def main():

    while True:

        yield from echo_delay()

if __name__ == '__main__':

    loop = asyncio.get_event_loop()

    asyncio.ensure_future(main())

    loop.run_forever()

    loop.close()

```

Run this sample with the following command:

```python sample.py```

Here is the sample output:

```

2016-06-20 17:56:03.969607

...

2016-06-20 17:57:03.993648

{"instance": {"hostname": "dmo.local", "id": "acf9842e-c127-4844-8026-d7c518076ac2"}, "traces": {"__main__:echo_delay": {"2016-06-20T23:56:00+00": {"count": 24, "avg": 2510.3998333333334, "min": 229.113, "max": 4986.661}}}}

```

This generates a _per-minute_ report listing metrics for every coroutine function decorated with `@aiometrics.trace`. Metrics include (per minute):

- Total number of executions

- Minimum execution time

- Maximum execution time

- Average execution time