Apache Solr is an open-source enterprise search server.
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Glean
Score 9.2 out of 10
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Glean is an AI-powered workplace search tool that supports search across all of a company's apps, centralizing company knowledge and helping employees to more quickly find what they need, with 100+ connectors.
Solr spins up nicely and works effectively for small enterprise environments providing helpful mechanisms for fuzzy searches and facetted searching. For larger enterprises with complex business solutions you'll find the need to hire an expert Solr engineer to optimize the powerful platform to your needs. Internationalization is tricky with Solr and many hosting solutions may limit you to a latin character set.
Based on my experience, Glean is well suited for scenarios where you need to search through large amounts of information in a structured format, such as Confluence pages, knowledge bases, or other similar repositories. Glean's search algorithms make it easy to find the exact information you need, even if it's buried deep in a document or spread across multiple pages. On the other hand, Glean may be less appropriate for scenarios where you need to search through unstructured data, such as messages. While Glean can still be useful in these cases, its effectiveness may be limited.
Easy to get started with Apache Solr. Whether it is tackling a setup issue or trying to learn some of the more advanced features, there are plenty of resources to help you out and get you going.
Performance. Apache Solr allows for a lot of custom tuning (if needed) and provides great out of the box performance for searching on large data sets.
Maintenance. After setting up Solr in a production environment there are plenty of tools provided to help you maintain and update your application. Apache Solr comes with great fault tolerance built in and has proven to be very reliable.
These examples are due to the way we use Apache Solr. I think we have had the same problems with other NoSQL databases (but perhaps not the same solution). High data volumes of data and a lot of users were the causes.
We have lot of classifications and lot of data for each classification. This gave us several problems:
First: We couldn't keep all our data in Solr. Then we have all data in our MySQL DB and searching data in Solr. So we need to be sure to update and match the 2 databases in the same time.
Second: We needed several load balanced Solr databases.
Third: We needed to update all the databases and keep old data status.
If I don't speak about problems due to our lack of experience, the main Solr problem came from frequency of updates vs validation of several database. We encountered several locks due to this (our ops team didn't want to use real clustering, so all DB weren't updated). Problem messages were not always clear and we several days to understand the problems.
