Python application monitoring

Monitor, Troubleshoot, and Optimize Python App Performance with Atatus. Detect performance bottlenecks swiftly, and resolve issues with detailed insights. Fine-tune resource consumption to ensure your Python app operates efficiently under all conditions.

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Python Monitoring Made Simple.

Python application monitoring offers deep visibility into application performance, memory usage, and function execution times, helping you proactively detect performance issues. Atatus allows you to correlate metrics and logs for quick troubleshooting, ensuring your Python applications stay performant even during peak load times.

See Everything with Clear App Insights

  • Monitor every layer of your Python application, from APIs to backend services, in real-time.
  • Track resource usage such as CPU, memory, and execution time to get a complete picture of system health.
  • Visualize key metrics for individual processes, helping you spot performance issues quickly.
  • Gain real-time insights into external dependencies, such as third-party APIs and databases, to maintain seamless integrations and optimize workflows.
Python Insights

Spot and Fix Performance Issues Before They Escalate

  • Receive real-time alerts based on custom thresholds for resource consumption, memory leaks, and error rates.
  • Automatically detect bottlenecks in Python performance using anomaly detection.
  • Correlate logs and metrics to quickly troubleshoot and resolve issues, minimizing downtime and improving response times.
  • Use distributed tracing to follow requests across your services, pinpointing latency and performance issues affecting the end-user experience.
Python Performance

Keep Apps Fast and Reliable, Even Under Pressure

  • Monitor Python app scalability by tracking performance during high traffic or heavy workloads.
  • Ensure smooth operation with insights into load balancing and autoscaling to prevent server overload.
  • Track critical metrics like memory usage, garbage collection, and execution time to optimize resource utilization.
  • Detect and resolve issues with asynchronous tasks and long-running processes that could impact the performance of your Python application.
Python Metrics

Start fixing issues impacting your users right now

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Awesome Support

Best APM Monitoring tool

"Atatus customer service is just amazing. I had before New Relic and Stackify and I can honestly say that Atatus compared to those two is the leader! Leader in pricing and user interface and ability to drill down to the problem."

— S Herman Kiefus, DevOps Admin, Compass

We've Got Your Stack Covered!

Boost Framework Performance

Boost Framework Performance

Gain insights into your performance, enhancing transaction flow and speeding up error resolution.

Trace Every Request Instantly

Trace Every Request Instantly

Visualize end-to-end traces across your stack, ensuring that you catch every error, performance issue, or bottleneck before it affects users.

Identify Slow Queries Instantly

Identify Slow Queries Instantly

Pinpoint and resolve slow database queries and eliminate performance bottlenecks impacting your application's responsiveness, leading to faster response times

Stay Alert to Vulnerabilities

Stay Alert to Vulnerabilities

Get alerted to potential library vulnerabilities, preventing security risks before they affect your customers or compliance.

Simplify Logs, Troubleshoot Faster

Simplify Logs, Troubleshoot Faster

Centralize all your logs in one place, and quickly identify the root cause of issues using advanced filtering, pattern detection, and log pipelines.

Custom Metrics That Matter

Custom Metrics That Matter

Set up and track custom metrics that align with your app's KPIs to ensure you're monitoring exactly what matters most for your success.

Quick Request Analysis Anytime

Quick Request Analysis Anytime

Explore request-level analysis, including stdout APM logs, to understand execution times, bottlenecks, and areas that need optimization.

Align APM with Server Metrics

Align APM with Server Metrics

Correlate your app’s APM metrics with server health to get a complete picture of your application’s performance and infrastructure dependencies.

Actionable Alerts

Actionable Alerts

Receive real-time alerts for app performance degradations and critical issues. Take immediate action to prevent downtime and optimize user experiences.

FAQ on Python APM and Performance Monitoring

How do I improve Python performance?

To improve Python performance:

  1. Identify bottlenecks using Python APM tools to track memory leaks, slow database queries, or inefficient code execution.
  2. Optimize long-running tasks and reduce unnecessary computations.
  3. Use caching mechanisms like Redis to speed up recurring operations.
  4. Continuously monitor Python applications to detect and resolve performance issues early.
What metrics should be monitored in Python performance monitoring?

Key metrics for monitoring Python performance include:

  1. Garbage collection:Tracks the frequency and duration of garbage collection processes.
  2. Memory usage: Tracks heap and overall memory consumption in your application.
  3. Function execution times: Monitors the duration of function calls to detect slow operations.
  4. API response times: Measures how fast your Python app processes incoming requests.
  5. Error rates: Detects how frequently errors occur in your application.
Can I set up alerts for performance issues in my Python application?

Yes, Atatus allows you to configure custom alerts for performance issues in your Python application. Set up alerts based on key metrics like memory usage, error rates, response times, and function execution times, so you're notified when performance degrades.

What are the benefits of Python APM?

The benefits of Python application performance monitoring (APM) include:

  1. Real-time insights: Immediate visibility into the health of your application.
  2. Quick issue resolution: Detect and fix performance problems quickly.
  3. Resource management: Track CPU and memory usage to avoid resource exhaustion.
  4. Improved user experience: Ensure your Python app runs smoothly, even under high loads.
Does Atatus support distributed tracing for Python applications?

Yes, Atatus supports distributed tracing for Python applications, allowing you to trace requests across microservices and external dependencies. This gives you a complete view of how requests flow through your system, making it easier to identify and resolve performance issues.