OPTIMIZING SEARCH FUNCTIONALITY: A PERFORMANCE COMPARISON BETWEEN SOLR AND ELASTICSEARCH
Keywords:
Apache Solr, Elasticsearch, Low-poweredAbstract
The importance of data has never been higher than it is currently of big data since it conceals previously unknown insights. However, extracting information that may be used from massive amounts of data is not only required but also difficult. Developers of data-intensive systems have consequently encountered several obstacles while attempting to carry out data processing and analytics in a range of environments. One of the most crucial parts of big data analytics and processing is full-text search, which helps to unearth crucial data points concealed in enormous datasets. The paper began by comparing full-text search technologies, their features, and technical aspects. Then, it compared Apache Solr and Elasticsearch on three separate datasets, looking at indexing speeds and queries. The topic's importance warranted these measures. Based on our analysis of indexing times recorded on three separate workstations with different hardware specifications, we can conclude that Apache Solr performs better with its default configuration. Apache Solr outperforms Elasticsearch in every single case when it comes to query performance. After reviewing the results, we advise using Apache Solr instead of Elasticsearch on low-powered computers. This study's results offer data-intensive system researchers and developers a thorough comparison and some pointers on how to choose the best fulltext search engine for their needs.
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