Apache Lucene vs. Apache Solr

Apache Lucene is a perfect text search implementation where the heap space usage needs to be kept to its minimal. It also enables search based on various search fields and most importantly the search and index process can happen simultaneously. The only scenario where it might be less appropriate would be when the index size grows too big. We have witnessed few scalable issues where the search would take a while when the index size is too large.

Incentivized

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.

Incentivized

• We found Apache Lucene to be extremely performant in querying large amounts of data and retrieving the correct files based on the metadata provided.
• The online community offers great support for the product. Even though it is an open source tool, it is not difficult to find help online for it.
• When we were creating a proof of concept application, we found that the software worked just as well, while being run locally on a resource-limited PC.

Incentivized

• 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.

Incentivized

• User interface for setup and maintenance would be helpful.
• Easier cloud/cluster setup.
• Better, centralized documentation.

Incentivized

• 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.

The search and index performance of [Apache] Lucene is excellent and the quality of results is good, if not better. For implementing it with small scale applications it is a no brainer, Lucene is the best and most cost effective solution. Learning curve is not too steep either.

Incentivized

Apache Solr is a ready-to-use product addressing specific use cases such as keyword searches from a huge set of data documents.

Incentivized

• Being an open source project we did not have to pay any licensing fees for using Apache Lucene. It has greatly improved our search functionality in our web apps.

Incentivized

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

Incentivized