Abstract: With the development of manufacturing industry, various industries generate a large amount of engineering data during the manufacturing process, the data retrieval requirements of the modern engineering field requires that the corresponding results can be retrieved quickly and accurately through keywords. The retrieval of engineering data can be achieved by using ElasticSearch, but there is still space for optimization in terms of its performance. In order to solve this problem, based on the in-depth study of the underlying theory of ElasticSearch, the index creation, index fragmentation and index segment merging of ElasticSearch are optimized. Firstly, the ElasticSearch tokenizer is modified and a custom dictionary is configured. Secondly, an index sharding strategy based on the performance of the cluster node and the size of the index data is proposed. Finally, the timing of index segment merging based on node performance is optimized. Through the experiments based on the retrieval of subway engineering data, the experimental results show that the improvement method can indeed improve the data writing and query performance of ElasticSearch.

Key words: ElasticSearch full-text search engine, index, shard, segment merge, performance optimization