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title: Best Last9 posts — August 2024 | daily.dev
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# Best of Last9 — August 2024

1. 1  
[](https://daily.dev/posts/python-logging-best-practices-the-ultimate-guide-0n7ibkudu "Python Logging Best Practices: The Ultimate Guide")  
Article  
![Avatar of last9](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/0542036d401f4cb1885680f1984fb880)Last9 · 2y  
Python Logging Best Practices: The Ultimate Guide  
Logging is a crucial aspect of Python development for tracking application behavior and troubleshooting. This guide delves into setting up logging, avoiding common pitfalls, and using advanced techniques to better handle logs in various project sizes. It emphasizes the importance of good logging practices to grasp application flow, debug effectively, get notifications of potential issues, and gain insights into user behavior and application performance. Key strategies include using the logging module efficiently, configuring loggers, handlers, formatters, and incorporating best practices for performance and security.  
49
2. 2  
[](https://daily.dev/posts/top-observability-best-practices-for-microservices-in-2024-5p7bemq0m "Top Observability Best Practices for Microservices in 2024")  
Article  
![Avatar of last9](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/0542036d401f4cb1885680f1984fb880)Last9 · 2y  
Top Observability Best Practices for Microservices in 2024  
Microservices architecture provides benefits such as agility, scalability, and flexibility but also introduces complexity that can be managed with the right observability tools. Key best practices, such as standardization, data retention, anomaly detection, root cause analysis, and ongoing optimization, are crucial for effective monitoring and management. Challenges include handling data volume, latency in data processing, skillset requirements, and cost management. Advanced techniques like distributed context propagation and intelligent sampling in tracing, centralized error management, and optimizing telemetry data can enhance observability and system reliability.  
32

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