Python Performance Monitoring

"Scout allows us to deploy with confidence. Their APM is both easily configurable & allows us to get the most insight into our applications."

Solve memory bloat and n+1 queries in Python

Scout's Application Performance Monitoring (APM) tool pinpoints and prioritizes performance and stability issues in Python applications, such as N+1 database queries and memory bloat. With Scout's tracing logic developers can detect the exact line of code causing the performance abnormality, and with detailed backtraces you can fix the issue before customers ever notice. Scout provides actionable insights by automatically calculating memory increase, prioritizing query data by potential time saved, and tracking object allocations to minimize overhead.

Instantly report key Python performance metrics with details on every web endpoint and background job

Scout's integrated dashboard provides performance KPIs in one place

  • Response Time

  • Throughput

  • Web transactions

  • Error Rate

  • Deploy tracking

  • Memory Usage

  • Detailed Tracing

  • Slow Query Insights

Leverage Database Query Monitoring for Python apps

Scout's database query monitoring feature helps you identify slow and repeatedly expensive database queries within your Python application. The database feature specifically collects metrics around the database and concisely visualizes only the relevant insights you need to understand how your database performs in relation to your app. Connect transaction traces to their corresponding calls to provide better insights into what the query is and where it occurred with the database monitoring feature.

Track user-specific issues with custom context for your Python Application

Custom context delivers greater understanding into how and which customer(s) were impacted by a specific query and which developer wrote the code. Establishing custom context enables you to define different values to better understand why a particular query was slow. Scout also offers a customizable dashboard of custom context parameters and connect them to your slowest endpoint traces to optimize performance based on user experience.

Autoinstrumentation for popular Python libraries

Scout is the only leading APM that offers autoinstrumentation for many popular Python libraries to provide deeper insights into exactly how long your code took to run. Scout's Autoinstruments feature enables developers to stop writing custom instrumentation for controller code that wouldn't be instrumented out of the box. Autoinstruments provide developers with the custom code insights instead of lumping that data under Controllers, like many other platforms.

Python FAQs

  1. What frameworks does Scout Python Monitoring support?

  2. What kind of alerting options do I get with Scout?

    Scout provides alerting options such as Github, Slack, weekly digest emails and more. For a full list visit our alerting options, visit this section of our docs page.

  3. What kind of integrations does Scout support?

    Scout supports integrations with PagerDuty, Rollbar, Sentry, Zapier, Github, plus others. Click here for a comprehensive list of our integrations.

  4. What's Scout overhead like?

    Scout's monitoring agent uses just a portion of the resources of existing app monitoring agents while providing higher-fidelity trace details.

  5. What is the installation process like?

    You can start collecting insights in just 3 easy steps. Simply install your framework's package/bundle, set a configuration key, and deploy.

  6. Is it safe to run Scout alongside my current APM library?


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