PROB🧪RKA

Python 3 library to write simple asynchronous health checks (probes).

Overview

Probirka is a lightweight and flexible Python library for implementing asynchronous health checks in your applications. It provides a simple yet powerful API for monitoring the health of various components and services, making it ideal for microservices architectures, containerized applications, and distributed systems.

Installation

Install Probirka using pip:

pip install probirka

Quick Start

Here is a simple example of how to use Probirka to create health checks using decorators:

import asyncio
from dataclasses import asdict
from pprint import pprint
from probirka import Probirka

# Create a Probirka instance
probirka = Probirka()

# Add some custom information
probirka.add_info("version", "1.0.0")
probirka.add_info("environment", "production")

# Define health checks using decorators
@probirka.add(name="database")  # This probe will always run
async def check_database():
    # Simulate a database check
    await asyncio.sleep(1)
    return True

@probirka.add(groups=["cache"])  # This probe will only run when cache group is requested
async def check_cache():
    # Simulate a cache check
    await asyncio.sleep(1)
    return False  # Simulate a failed check

@probirka.add(groups=["external"])  # This probe will only run when external group is requested
def check_external_service():
    # Synchronous check example
    return True

async def main():
    print("-"*64)
    # Run only required probes (without groups)
    basic_results = await probirka.run()
    print("Basic check results:")
    print()
    pprint(asdict(basic_results))

    print("-"*64)
    # Run only cache group probes
    cache_results = await probirka.run(with_groups="cache", skip_required=True)
    print("Cache check results:")
    print()
    pprint(asdict(cache_results))

    print("-"*64)
    # Run required probes + multiple groups
    full_results = await probirka.run(with_groups=["cache", "external"])
    print("Full check results:")
    print()
    pprint(asdict(full_results))

if __name__ == "__main__":
    asyncio.run(main())

Output:

----------------------------------------------------------------
Basic check results:

{'checks': [{'cached': None,
             'elapsed': datetime.timedelta(seconds=1, microseconds=1430),
             'error': None,
             'info': None,
             'name': 'database',
             'ok': True,
             'started_at': datetime.datetime(2025, 4, 2, 9, 41, 53, 417942)}],
 'elapsed': datetime.timedelta(seconds=1, microseconds=1601),
 'info': {'environment': 'production', 'version': '1.0.0'},
 'ok': True,
 'started_at': datetime.datetime(2025, 4, 2, 9, 41, 53, 417898)}
----------------------------------------------------------------
Cache check results:

{'checks': [{'cached': None,
             'elapsed': datetime.timedelta(seconds=1, microseconds=1468),
             'error': None,
             'info': None,
             'name': 'check_cache',
             'ok': False,
             'started_at': datetime.datetime(2025, 4, 2, 9, 41, 54, 420261)}],
 'elapsed': datetime.timedelta(seconds=1, microseconds=1776),
 'info': {'environment': 'production', 'version': '1.0.0'},
 'ok': False,
 'started_at': datetime.datetime(2025, 4, 2, 9, 41, 54, 420133)}
----------------------------------------------------------------
Full check results:

{'checks': [{'cached': None,
             'elapsed': datetime.timedelta(seconds=1, microseconds=1570),
             'error': None,
             'info': None,
             'name': 'database',
             'ok': True,
             'started_at': datetime.datetime(2025, 4, 2, 9, 41, 55, 423083)},
            {'cached': None,
             'elapsed': datetime.timedelta(seconds=1, microseconds=1597),
             'error': None,
             'info': None,
             'name': 'check_cache',
             'ok': False,
             'started_at': datetime.datetime(2025, 4, 2, 9, 41, 55, 423173)},
            {'cached': None,
             'elapsed': datetime.timedelta(microseconds=25),
             'error': None,
             'info': None,
             'name': 'check_external_service',
             'ok': True,
             'started_at': datetime.datetime(2025, 4, 2, 9, 41, 55, 423245)}],
 'elapsed': datetime.timedelta(seconds=1, microseconds=2136),
 'info': {'environment': 'production', 'version': '1.0.0'},
 'ok': False,
 'started_at': datetime.datetime(2025, 4, 2, 9, 41, 55, 422905)}

Alternatively, you can create custom probes by inheriting from the ProbeBase class:

from probirka import Probirka, ProbeBase
import asyncio

class DatabaseProbe(ProbeBase):
    async def _check(self):
        # Simulate a database check
        await asyncio.sleep(1)
        return True

class CacheProbe(ProbeBase):
    async def _check(self):
        # Simulate a cache check
        await asyncio.sleep(1)
        return False  # Simulate a failed check

async def main():
    probirka = Probirka(probes=[DatabaseProbe(), CacheProbe()])
    probirka.add_info("version", "1.0.0")
    probirka.add_info("environment", "production")
    results = await probirka.run()
    print(results)

if __name__ == "__main__":
    asyncio.run(main())

Advanced Usage

Creating Custom Probes

You can create custom probes by inheriting from the ProbeBase class:

from probirka import ProbeBase
import asyncio

class CustomProbe(ProbeBase):
    def __init__(self, name="CustomProbe"):
        super().__init__(name=name)
        
    async def _check(self):
        # Implement your health check logic here
        return True

Adding Metadata to Probes

You can add metadata to your probes:

from probirka import ProbeBase
import asyncio

class DatabaseProbe(ProbeBase):
    async def _check(self):
        await asyncio.sleep(1)
        self.add_info("connection_pool_size", 10)
        self.add_info("active_connections", 5)
        return True

The added information will be included in the probe results and can be accessed through the info field of each probe result. This is useful for providing additional context about the probe's state or performance metrics.

Grouping Probes

Probes can be organized into required and optional groups. Probes without groups are always executed, while probes with groups are only executed when explicitly requested:

import asyncio
from probirka import Probirka

# Create a Probirka instance
probirka = Probirka()

# Required probe (will always run)
@probirka.add(name="database")
async def check_database():
    await asyncio.sleep(1)
    return True

# Optional probes (will only run when their groups are requested)
@probirka.add(groups=["cache"])
async def check_cache():
    await asyncio.sleep(1)
    return True

@probirka.add(groups=["external"])
async def check_external_service():
    return True

async def main():
    # Run only required probes (database)
    basic_results = await probirka.run()
    print("Basic check results:", basic_results)

    # Run required probes + cache group
    cache_results = await probirka.run(with_groups=["cache"])
    print("Cache check results:", cache_results)

    # Run required probes + multiple groups
    full_results = await probirka.run(with_groups=["cache", "external"])
    print("Full check results:", full_results)

if __name__ == "__main__":
    asyncio.run(main())

Setting Timeouts

You can set timeouts for individual probes:

from probirka import ProbeBase
import asyncio

class SlowProbe(ProbeBase):
    async def _check(self):
        await asyncio.sleep(2)  # This will timeout
        return True

probe = SlowProbe(timeout=1.0)  # 1 second timeout

Caching Results

from typing import Optional
from probirka import Probirka, ProbeBase
import asyncio

# Create a Probirka instance with global caching settings
probirka = Probirka(success_ttl=60, failed_ttl=10)  # Cache successful results for 60s, failed for 10s

# Add a probe with custom caching settings
@probirka.add(success_ttl=300)  # Cache successful results for 5 minutes
async def check_database():
    # Simulate a database check
    await asyncio.sleep(1)
    return True

# Or create a custom probe with caching
class DatabaseProbe(ProbeBase):
    def __init__(self, success_ttl: Optional[int] = None, failed_ttl: Optional[int] = None):
        super().__init__(success_ttl=success_ttl, failed_ttl=failed_ttl)
        
    async def _check(self) -> bool:
        # Simulate a database check
        await asyncio.sleep(1)
        return True

The caching mechanism works as follows:

If success_ttl is set, successful results will be cached for the specified number of seconds
If failed_ttl is set, failed results will be cached for the specified number of seconds
If both are set to None (default), no caching will be performed
Global settings in Probirka instance can be overridden by individual probe settings

Integration Examples

FastAPI Integration

from fastapi import FastAPI
from probirka import Probirka, make_fastapi_endpoint

app = FastAPI()
probirka_instance = Probirka()

# Define some health checks
@probirka_instance.add(name="api")
async def check_api():
    return True


# Create and add the endpoint
fastapi_endpoint = make_fastapi_endpoint(probirka_instance)
app.add_route("/health", fastapi_endpoint)

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

aiohttp Integration

from aiohttp import web
from probirka import Probirka, make_aiohttp_endpoint

app = web.Application()
probirka_instance = Probirka()

# Define some health checks
@probirka_instance.add(name="api")
async def check_api():
    return True

# Create and add the endpoint
aiohttp_endpoint = make_aiohttp_endpoint(probirka_instance)
app.router.add_get('/health', aiohttp_endpoint)

if __name__ == '__main__':
    web.run_app(app)

appKODE / probirka