Research on Storage and Query Optimization Algorithm of Distributed Database for Big Data

This paper deeply discusses the storage and query optimization algorithm of distributed database for big data. Firstly, the importance of distributed database storage optimization is analyzed, and key technologies such as column storage technology, data backup and consistent hash algorithm are introduced. Then, the distributed database query optimization algorithm is mainly studied, and an innovative algorithm combining semi-join, direct join and hash partition equijoin strategy is proposed, and the query plan is dynamically adjusted by introducing an intelligent prediction model to maximize the query performance. The experimental results show that the algorithm has significantly improved the storage efficiency and query performance, which proves its effectiveness and application prospect in the big data environment. Future research will focus on further improving the adaptability and generalization ability of the algorithm to cope with more complex data scenarios and query requirements.