Optimization Method for Storing Massive Small Files in Multi-modal Medical Data

Hadoop distributed file system (HDFS) is used for the storage and management of large files, while storing and computing a large number of small files consume a lot of NameNode memory usage and access time. Therefore, the small file problem becomes an important factor that restricts HDFS performance. Aiming at the problem of massive small files in multi-modal medical data, a small file storage method based on two-layer hash coding and HBase is proposed to optimize the storage of massive small files on HDFS. When merging small files, an expandable hash function is utilized to build an index file bucket to expand the index file dynamically as needed and realize the file append function. To read the file in O(1) time complexity and improve the efficiency of file search, the MWHC hash function is used to store the position of the index information of each file in the index file. There is no need to read the index information of all files, only need to read the index information of the corresponding bucket. To meet the storage needs of multi-modal medical data, HBase is used to store the index information and set the identification column to identify different modal medical data, which is convenient for storage and management of different modal data and improves file reading speed. To further optimize storage performance, the LRU-based metadata prefetching mechanism is established, and the LZ4 compression algorithm is utilized to compress the merged files. The experiment compares file access performance and NameNode memory usage. The results show that compared with the original HDFS, HAR, MapFile, TypeStorage, and HPF small file storage methods, the proposed algorithm has a shorter file access time, which can improve the overall performance of HDFS when processing massive small files in multi-modal medical data. © 2023 Chinese Academy of Sciences. All rights reserved.