pyquerytracker 0.1.1

A lightweight, decorator-based query performance tracking library for Python applications. Monitor and analyze database query performance with ease.

🐍 pyquerytracker

pyquerytracker is a lightweight Python utility to track and analyze database query performance using simple decorators. It enables developers to gain visibility into SQL execution time, log metadata, and export insights in JSON format — with optional FastAPI integration and scheduled reporting.

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🚀 Features

• ✅ Easy-to-use decorator to track function execution (e.g., SQL queries)
• ✅ Capture runtime, function name, args, return values, and more

TODO Features

• ✅ Export logs to JSON or CSV
• ✅ FastAPI integration to expose tracked metrics via REST API
• ✅ Schedule periodic exports using APScheduler
• ✅ Plug-and-play with any Python database client (SQLAlchemy, psycopg2, etc.)
• ✅ Modular and extensible design

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📦 Installation

pip install pyquerytracker

🔧 Configuration

import logging
from pyquerytracker.config import configure

configure(
    slow_log_threshold_ms=200,     # Log queries slower than 200ms
    slow_log_level=logging.DEBUG   # Use DEBUG level for slow logs
)

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⚙️ Usage

Basic Usage

import time
from pyquerytracker import TrackQuery

@TrackQuery()
def run_query():
    time.sleep(0.3)  # Simulate SQL execution
    return "SELECT * FROM users;"

run_query()

Output:

2025-06-14 14:23:00,123 - pyquerytracker - INFO - Function run_query executed successfully in 305.12ms

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🧩 Async Support

Use the same decorator with async functions or class methods:

import asyncio
from pyquerytracker import TrackQuery

@TrackQuery()
async def fetch_data():
    await asyncio.sleep(0.2)
    return "fetched"

class MyService:
    @TrackQuery()
    async def do_work(self, x, y):
        await asyncio.sleep(0.1)
        return x + y

asyncio.run(fetch_data())

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🌐 Run the FastAPI Server

To view tracked query logs via REST, WebSocket, or a Web-based dashboard, start the built-in FastAPI server:

uvicorn pyquerytracker.api:app --reload

• ⚠️ If your project or file structure is different, replace pyquerytracker.api with your own module path, like <your_project_name>.<your_server(file)_name>.

• Open docs at http://localhost:8000/docs

• Query Dashboard UI: http://localhost:8000/dashboard

• REST endpoint: GET /queries

• WebSocket stream: ws://localhost:8000/ws

Then run your tracked functions in another terminal or script:

@TrackQuery()
def insert_query():
    time.sleep(0.4)
    return "INSERT INTO users ..."

You’ll see logs live on the server via API/WebSocket.

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📤 Export Logs

Enable exporting to CSV or JSON by setting config:

from pyquerytracker.config import configure

configure(
    export_type="json",
    export_path="./query_logs.json"
)

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Let us know how you’re using pyquerytracker and feel free to contribute!