Assistance with Profiling and Monitoring Microservices in Azure

Question

Hello,

I'm building my project using microservices, with components developed in both Python and Java. I need to effectively capture key performance metrics such as response time, CPU usage, memory consumption, and other relevant metrics for both API and UI components.

1. Profiling Tools:
   • What is the most suitable profiling tool for capturing these metrics in a mixed-language environment (Python and Java)?
   • Are there any free tools recommended for this purpose?
2. Integration with Azure:
   • Once the metrics are collected, can they be seamlessly integrated into Azure for visualization and analysis?
   • Could you outline the recommended approach for sending profile data to Azure and creating meaningful visualizations using dashboards and workbooks? Your guidance on this would be greatly appreciated.
   Thank you!

Answer 1

Hello Geetha Vani Gowri,

Welcome to the Microsoft Q&A and thank you for posting your questions here.

Problem

I understand that you are in need of suitable Profiling and Monitoring Microservices tools in Azure, and also how the metrics can be integrated with Azure.

Solution

As you have asked about the Profiling tools, you need to be aware that Java and Python have different ecosystem:

For Java components, consider using VisualVM, async-profiler, or JDK Flight Recorder (JFR). These open-source profilers offer insights into CPU usage, memory consumption, and other performance metrics. https://www.architect.io/blog/2022-08-30/microservices-top-10-testing-monitoring/.

In Python, you can use tools like Pyinstrument or the built-in cProfile for profiling. These tools help identify bottlenecks and performance issues. https://signoz.io/blog/distributed-tracing-tools/

Tracing tools:

Distributed tracing tools like Jaeger can help capture transactions across microservices. Jaeger provides spans and traces to understand the flow of requests and identify bottlenecks. Also, consider using SigNoz, an open-source distributed tracing tool that supports Python, Java, Ruby, and JavaScript. It records exceptions and allows easy alert setup. https://github.com/jabrena/101-profiling-microservices

Integration with Azure:

Once you've collected metrics, you can integrate them into Azure for visualization and analysis.

Below is the recommended approach:

• Use Azure Monitor to collect telemetry data from your microservices. Set up Application Insights for your Java and Python components.
• Add the Application Insights SDK to your codebase. For Java, use the Application Insights Java SDK. For Python, use the Application Insights Python SDK.
• Instrument your code to send custom metrics (e.g., response time, CPU usage) to Application Insights.
• Create dashboards and workbooks in Azure Monitor to visualize the collected data. You can create custom charts, tables, and visualizations.
• Set up alerts based on thresholds (e.g., high CPU usage, slow response time).
• Use Log Analytics to query and analyze logs from your microservices.
• If you need programmatic access to the data, use the Application Insights REST API.

I hope this is helpful! Do not hesitate to let me know if you have any other questions.

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Best Regards,

Sina Salam