Solr Reviews

We use the solution in the telecommunications industry.

One of the best aspects of the solution is the indexing. It's already indexed to all the fields in the category. We don't need to spend so much extra effort to do the indexing. It's great.

The solution's grammar and syntax should be easier.

It does take a little bit of effort to use and understand the solution. It would help us a lot if the solution offered up more documentation or tutorials to help with training or troubleshooting.

MongoDB can realize more complex operations than Solr can. Solr should add some more complex operations to the database to at least bring it up to MondoDB's level of functionality. It would make it more competitive. 

There might be some compatibility issues between the data types within Solr. This needs to be improved.

Solr has a schema that we have to load the schema as an HTML, or SML file. This usually needs to be done by our engineers. It should be easier to do without needing too much technical background.

We've been using the solution for more than one and a half years.

The stability is pretty good. however, I wouldn't say that it is as stable as MongoDB. Mongo is much more reliable.

I'm unsure about the scalability of the solution. We have ten people in our team, and out of those, four people actually work on it. They're mostly data engineers. I'm not sure if we plan to increase usage or not in the future.

I haven't really dealt with technical support. I've mostly just gone through online tutorials to get help. I find those lacking, however. They need work so that they would actually be more useful for people.

We also use MongoDB. Solr is better than MongoDB because the Solr already indexes to all in their categorical field. We find that Solr also sometimes offers better performance. However, we do find that MongoDB is better for more complex operations.

I'm unsure about the initial setup. Our data engineer handled the implementation. I didn't participate in the process, so I can't speak to how straightforward or complex it was.

Our data engineer handled the process for us. It was handled in house.

We're just customers. We don't have a business relationship with the company.

I'd rate the solution seven out of ten.

It has improved our search ranking, relevancy, search performance, and user retention.

• Sharding data
• Faceting
• Hit Highlighting
• Parent-child Block Join and Grouping
• Multi-mode platform

SolrCloud stability, indexing and commit speed, and real-time indexing need improvement.

• Encountered issues with both master-slave and SolrCloud.
• Indexing and serving traffic from same collection has very poor performance.
• Some components are slow for searching.

With increased sharding, performance degrades. Merger, when present, is a bottle-neck. Peer-to-peer sync has issues in SolrCloud when index is incrementally updated.

Online content is available, but release cycle is relatively slow. Since it's open-source, there is no dedicated support to reach out to when there is a P1 issue.

No previous solution.

Complex in making system scalable and performant enough.

Elasticsearch.

One of the best search platforms available today.

We use Solr to index over 600k documents. It's very fast and has been flexible to use. The speed of indexing individual documents has been great. The most valuable feature has been the powerful search syntax that we use to find documents with specific attributes.

We now have a much more powerful way to search our set of documents and the open source price is right.

Memory utilization could be better but it is an industrial strength tool so some overhead is to be expected.

4 years

N/A - use books and online forums for support.

Doing this again I would look at search as a service solution. The overhead cost of running Solr on a cloud server and the additional storage should be evaluated against something like elasticsearch. Rackspace has not had a search service, but if they did we would consider it.