AddSearch is a hosted search tool for any website from the company of the same name in Helsinki. It is presented as fast and able to works on all devices to give users full control. The vendor states users of AddSearch will experience an instant search with a modern and beautiful interface. The system has been built to scale to large amounts of users all across the globe.
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Apache Solr
Score 6.5 out of 10
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Apache Solr is an open-source enterprise search server.
We're very happy with the search results, and even better, the ability to impact the actual results with their weighting and priority tools. The integration was easy enough, but the expectations during the sales process was that the AddSearch team would handle more of the implementation. At the end of the day this wasn't a huge deal due to our internal engineering team, but is a caveat for others. We're happy to see continued development of the product and the infusion of AI in the feature set.
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.
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.
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.
AddSearch was not as customizable as the other solutions we looked at, but it was also seemed to be the easiest to integrate. Ultimately, the integration time took longer than expected, but was still relatively easy. The interface was an easier to use interface than the competitors we looked at. Price was also a factor as AddSearch was a lower cost.
