Apache Solr is an open-source enterprise search server.
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Constructor.io Search
Score 7.1 out of 10
Enterprise companies (1,001+ employees)
Constructor Search promises to improve conversions and revenue from onsite and in-app search, using search science and artificial intelligence, Constructor's cloud-based search-as-a-service solution uses natural language processing, machine learning-enhanced results ranking, collaborative personalization, and merchant controls to power enterprise-grade onsite and in-app search. Whether search results are optimized for relevance, revenue, conversions, conversations — or all of the…
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Apache Solr
Constructor.io Search
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Apache Solr
Constructor.io Search
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Apache Solr
Constructor.io Search
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Apache Solr
Constructor.io Search
Small Businesses
Yext
Score 8.9 out of 10
Bloomreach - The Agentic Platform for Personalization
Very effective for end-user searching applications and for generating search results. Also very well suited to those looking for high reliability and performance. If [you're doing] fuzzy searching or if you are working on a smaller end-user application or an internal application that does not require high performance and flexible/adapting searching then it may not be necessary to use Solr.
Constructor.io Search takes all of the guesswork out of maintaining a search engine. As merchandisers or product teams, we have educated guesses at how search relevance should work but the customer is always king. We can't always predict the ways in which consumers will search or what their intent is. That's why the behavioral-driven approach that Constructor.io employs works so well. It means that merchandisers can focus on their sales and promotional responsibilities, instead of wasting time and bandwidth on base-level relevance questions.
Faceted navigation and field collapsing/grouping : filtering and quick results were what we needed for our websites. Our customers needed to have this functionalities for good and efficient results.
We tested them with our customers' registered searches (they received all new goods matching with their registered searches by emails and/or mobile push). Results were incredible by comparison with our old system (old MySQL requests).
Note : we didn't put all our data in Solr. Just what we need for searching uses. Other data stayed in our MySQL database.
Auto-suggest : our old auto-suggest wasn't performing well. With Apache Solr, our new one was worked really well ! The suggestions came quickly and suggestions were good.
We also extended auto-suggestion with geo-spatial data and it worked well.
Hit highlighting : we used this functionality and we didn't have problem and nasty surprise.
Keep all data status during data upgrading (see next details for improvements)
It takes some time to deploy and currectly maintein it. And also, to learn how to use and integrate in the enviroment as well. Once you get theses steps done, it usability is very simple, and almost of the time it don't require no further attention on it. Even for maintence, if you deploy it on a cluster mode, it is very reliable and easy to take one host down.
We switched from search indexes stored in MySQL to soar and it's made a world of difference for our growing businesses. The relational databases are very poor for handling the complex data searches require and Solr delivered all the tools we need to get the performance our end users are demanding.
It's enabled us to deliver fast, relevant search results on our new website. The site is still in beta and being actively developed so our complete ROI is still unknown.
It integrates very well with Drupal so it has saved us from having to develop a custom solution.
