Apache Solr vs Inbenta Enterprise Search

• 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.

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• Fairly easy to use interface.
• Quick to onboard new users, both in terms of access as well as training.
• The NLP-based semantic search feature is quite impressive.

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• 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.

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• It is a fairly new tool in the market, so not much market feedback exists for this. This made us apprehensive in using it.
• The documentation and online forums are bare minimum.
• Quite buggy when working with multiple sources.

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• Improved response time in e-commerce websites.
• Developer's job is easier with Apache Solr in use.
• Customization in filtering and sorting is possible.

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• Enabled our developers to quickly deploy changes to the search model.
• NLP-powered semantic search feedback provides good actionable insights.

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