Optimization Implementation and Performance Analysis of Divide-and-Conquer Algorithm Based on Python in Big Data Sorting and Retrieval

This paper offers an in-depth look at divide-and-conquer algorithms, especially in big data sorting and retrieval, with a particular focus on how these techniques are implemented in Python. As data sizes grow and become more complex, having scalable algorithms that are both efficient and flexible is more important than ever. Divide-and-conquer approaches are naturally suited for this task because they break down problems into smaller parts, making it easier to run tasks in parallel or across distributed systemssomething that's incredibly useful in big data projects. In our review, we explore recent Python implementations of popular sorting algorithms like merge sort and quicksort. We compare how they perform when using multiprocessing or hybrid methods. While Python makes development straightforward and flexible, it also comes with some challengesthings like the Global Interpreter Lock (GIL), recursion limits, and the overhead of managing inter-process communication can impact performance. Our findings indicate that merge sort tends to perform better than quicksort when it comes to using Pythons parallel processing capabilities. Besides, tools like PySpark or Dask can help overcome certain language-specific obstacles, making large-scale data processing more manageable. Overall, this review provides practical guidance for researchers and engineers aiming to strike a good balance between algorithm design and efficient implementation in systems that handle massive amounts of data.