performance improvements

[fan-turintech]

The code changes focus on improving performance, readability, and adhering to Python best practices. Key modifications include:

1. Optimizing Redis operations by using pipelines to batch multiple commands, reducing network overhead and improving efficiency.
2. Replacing traditional loops with list comprehensions and generator expressions for more concise and potentially faster code execution.
3. Updating string formatting to use f-strings, enhancing readability and performance.
4. Refactoring data structures, such as changing a dictionary to a list, to better suit the needs of the application and improve data access patterns.
5. Implementing more specific exception handling to catch and process errors more precisely.
6. Simplifying complex conditional statements and loops to make the code more maintainable and easier to understand.

These changes are expected to enhance the overall performance of the application,
particularly when dealing with large datasets or high-traffic scenarios. The improved code
readability and adherence to Python best practices will also make the codebase easier to
maintain and extend in the future. While the core functionality remains largely
unchanged, these optimizations should result in a more efficient and robust application.

The performance improvements are measured as:
Runtime: 1.92%
Memory Usage: 0.70%

The performance was measured on a 8 X Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz machine.