aiopromql 0.1.1

Minimalist, Pythonic Prometheus HTTP client that supports both async and sync usage

aiopromql

aiopromql is a minimalist Prometheus HTTP client for Python that supports both synchronous and asynchronous querying. It provides a clean, Pythonic model layer for Prometheus query responses and convenient helpers for mapping metrics into structured time series.

---

🚀 Features

• Sync and async Prometheus client interfaces via httpx
• Pydantic models for Prometheus vector and matrix responses
• Time series utilities and hashable metric keys
• Zero dependencies outside of httpx and pydantic

---

📦 Installation

pip install aiopromql

---

🔧 Basic Usage

Synchronous Query

from aiopromql import PrometheusSync

# Initialize the client
client = PrometheusSync("http://localhost:9090")

# Execute a simple query
resp = client.query('up')
metric_map = resp.to_metric_map()

# Process the results
for labels, series in metric_map.items():
    print(f"Labels: {labels.dict}")
    for point in series:
        print(f"  {point}")

Ranged Query

from datetime import datetime, timedelta, timezone

# Set time range
end = datetime.now(timezone.utc)
start = end - timedelta(hours=1)

# Execute range query
resp = client.query_range('up', start=start, end=end, step='60s')
metric_map = resp.to_metric_map()

# Process results as before
for labels, series in metric_map.items():
    print(f"Labels: {labels.dict}")
    for point in series:
        print(f"  {point}")

Asynchronous Query

import asyncio
from aiopromql import PrometheusAsync

async def main():
    async with PrometheusAsync("http://localhost:9090") as client:
        # Execute multiple queries concurrently
        queries = ['up', 'process_cpu_seconds_total', 'process_resident_memory_bytes']
        tasks = [client.query(q) for q in queries]
        responses = await asyncio.gather(*tasks)

        # Process results
        for query, resp in zip(queries, responses):
            print(f"Results for query: {query}")
            metric_map = resp.to_metric_map()
            for labels, series in metric_map.items():
                print(f"  Labels: {labels.dict}")
                for point in series:
                    print(f"    {point}")

asyncio.run(main())

Asynchronous Ranged Query

from datetime import datetime, timedelta, timezone
from aiopromql import PrometheusAsync

async def get_range_data():
    async with PrometheusAsync("http://localhost:9090") as client:
        end = datetime.now(timezone.utc)
        start = end - timedelta(hours=1)

        resp = await client.query_range('up', start=start, end=end, step='60s')
        return resp.to_metric_map()

# Run the async function
metric_map = asyncio.run(get_range_data())

🚧 Development

For full guidelines on contributing, please see CONTRIBUTING.md.

🤝 Acknowledgments

This project is used in the DECICE — DEVICE-EDGE-CLOUD Intelligent Collaboration framEwork — aiming to bridge HPC cloud and edge orchestration.

Special thanks to the VeNIT Lab and other partners for their support and collaboration.

📄 License

MIT