Solr Review

The Natural Language Search capability is helpful and intuitive for our users

What is our primary use case?

Our primary use case is to enable content search for the enterprise.

How has it helped my organization?

Our users are now able to find ETFs and their documents that are scattered across different repositories. It is proven because we have a consistent 80% of users who are using the application every month.

What is most valuable?

The most valuable feature is the ability to perform a natural language search. That is helpful because users tend to use natural language when doing research.

What needs improvement?

The performance for this solution, in terms of queries, could be improved.

Improvements with the backend and capability could be made so that it is easier for the engineers to maintain it. 

For how long have I used the solution?

Three years.

What do I think about the stability of the solution?

This solution is very stable. It has very few operational hiccups.

What do I think about the scalability of the solution?

It is a highly scalable solution. It is a distributed system that can scale infinitely, horizontally.

Everybody uses the system. Eighty percent of the users in the whole enterprise, from management to ground level staff, use it at different times of the week, and at different times of the year. We do not have plans to increase our usage at this point, as it is at a comfortable level.

How are customer service and technical support?

Technical support is pretty good. There are also internet-based community groups who are knowledgeable with respect to this solution. 

Which solution did I use previously and why did I switch?

We did use a previous solution, but we switched in order to cut costs and have more flexibility over the solution.

How was the initial setup?

The initial setup is complex because this is a distributed system, and you have to make sure that every individual node is aware of every other node in existence. This search engine has a large capacity, so you need to make sure that there is enough buffer space.

We took one month to deploy and perform a fresh setup. Our strategy was to start with a local data center, before venturing into cross data center replicas.

A staff size of two to four people is suitable for deploying and maintaining the solution, depending upon the scale. They would set up the solution and put monitoring in place for the indexing jobs, as well as design the schema so that the data can feed well.

What about the implementation team?

We used a service provider to assist with the migration and setup.

What's my experience with pricing, setup cost, and licensing?

The only costs in addition to the standard licensing fees are related to the hardware, depending on whether it is cloud-based, or on-premise.

What other advice do I have?

My advice to anyone interested in implementing this solution is to make sure that somebody who is experienced with this product is on the team. It is best to get mistakes out of the way early.

This is an infinitely scalable product with state-of-the-art technology, and the value of Natural Language Search is tremendous.

I would rate this solution an eight out of ten.

Which version of this solution are you currently using?

**Disclosure: I am a real user, and this review is based on my own experience and opinions.
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