A method to improve full-text search performance of MongoDB

B-Tree based text indexes used in MongoDB are slow compared to different structures such as inverted indexes. In this study, it has been shown that the full-text search speed can be increased significantly by indexing a structure in which each different word in the text is included only once. The Multi-Stream Word-Based Compression Algorithm (MWCA), developed in our previous work, stores word dictionaries and data in different streams. While adding the documents to a MongoDB collection, they were encoded with MWCA and separated into six different streams. Each stream was stored in a different field, and three of them containing unique words were used when creating a text index. In this way, the index could be created in a shorter time and took up less space. It was also seen that Snappy and Zlib block compression methods used by MongoDB reached higher compression ratios on data encoded with MWCA. Search tests on text indexes created on collections using different compression options shows that our method provides 19 to 146 times speed increase and 34% to 40% less memory usage. Tests on regex searches that do not use the text index also shows that the MWCA model provides 7 to 13 times speed increase and 29% to 34% less memory usage.