Solr 9.2.0 and Elasticsearch 8.8 are both open-source search engines that offer a wide range of features and capabilities. However, there are some key differences between the two platforms.
Solr is a monolithic search engine, while Elasticsearch is a distributed system. This means that Solr runs as a single process, while Elasticsearch can be scaled out to multiple nodes.
Solr is generally faster than Elasticsearch for static data, such as e-commerce product catalogs. This is because Solr uses caching and other optimizations that are not available in Elasticsearch. However, Elasticsearch is better suited for timeseries data, such as log files.
Solr and Elasticsearch offer a similar set of features, but there are some differences. For example, Solr has a more mature faceting framework, while Elasticsearch has a more mature machine learning framework.
Solr is a good choice for applications that require high performance for static data. Elasticsearch is a good choice for applications that require scalability and flexibility for timeseries data.
The following table summarizes the key differences between Solr 9.2.0 and Elasticsearch 8.8:
|Faster for static data
|Faster for timeseries data
|Mature faceting framework
|Mature machine learning framework
|High-performance static data applications
|Scalable and flexible timeseries data applications
Ultimately, the best choice for you will depend on your specific needs and requirements. If you are not sure which platform is right for you, I recommend doing some benchmarking and testing to see which one performs better for your specific workload