How to Set Up Performance Monitoring in Python

Overview

Users notice when your Python app slows down — even by a few hundred milliseconds. Performance monitoring quantifies the problem so you can fix it.

Bugsly provides a first-class Python integration for performance monitoring that takes minutes to set up and immediately starts delivering value.

Installation and Setup

Start by adding Bugsly to your project:

pip install bugsly-sdk

Next, initialize the SDK in your application:

import bugsly_sdk

bugsly_sdk.init(
    dsn="YOUR_BUGSLY_DSN",
    traces_sample_rate=1.0,
    profiles_sample_rate=1.0,
)

How It Works

Once initialized, the Bugsly Python SDK automatically instruments your application. Here's what happens behind the scenes:

• Automatic error capture: Unhandled exceptions and promise rejections are caught and reported with full stack traces
• Context enrichment: Each event includes environment data, request details, and user context where available
• Intelligent grouping: Similar errors are grouped together so you see issues, not individual events
• Performance data: Transaction traces help you understand the performance impact of errors

Customizing Your Integration

For Python projects, we recommend enabling these additional features after the basic setup:

1. Source maps (if applicable) — upload source maps during your build so Bugsly can display readable stack traces
2. User feedback — prompt users who encounter errors for additional context
3. Custom tags — add business-specific metadata to events for filtering and analysis

Bugsly's dashboard will become your team's go-to tool for understanding what's happening in your Python production environment.

Next Steps

With performance monitoring in place, set up transaction-level alerts for endpoints that exceed your response time targets. Use Bugsly's performance dashboard to track web vitals and identify the slowest transactions. Enable profiling to get function-level performance data for your most critical code paths.