Common performance optimization techniques include database indexing, caching, using asynchronous requests, optimizing query performance, and minimizing HTTP requests.

Optimizing the performance of Python web applications is critical for delivering a smooth user experience and ensuring scalability as traffic increases. There are several common techniques that developers can employ to enhance application performance. One fundamental technique is database indexing, which improves query performance by allowing the database to locate data more efficiently. Proper indexing can significantly reduce the time it takes to retrieve records from large tables. Caching is another essential optimization strategy; by storing frequently accessed data in memory, applications can reduce the load on the database and decrease response times. Implementing asynchronous requests allows the application to handle multiple tasks concurrently, improving overall responsiveness, particularly for I/O-bound operations. Additionally, optimizing database queries by avoiding N+1 queries and using efficient joins can lead to better performance. Reducing the number of HTTP requests made by the client, such as combining CSS and JavaScript files or using image sprites, can also enhance load times. Finally, leveraging Content Delivery Networks (CDNs) to serve static files can reduce latency and improve resource delivery speed. By adopting these optimization techniques, developers can create faster, more efficient Python web applications that can handle higher traffic levels without compromising performance.