Likelihood to Recommend I would like to mentioned about the scenario where Amazon cloud search was well suited is when we need regular updation of data and the subject matter requires continuous alterations due to the continuous changing environment and talking abut the least appropriate would the times when their is high level of customisation requirements in the project.
Read full review 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.
Read full review Pros Really fast queries Good Reporting Reduce the cost of the server Read full review 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. Read full review Cons First thing I would like to mentioned about its indexing speed, I have noticed while working with large sums of data Second I would like to mentioned about customisation challenge, although it is also its pros but a con as well, as it is a tedious task to customise when time value comes place as well. Thirdly, I do feel that its advanced search queries could be more supported. I have seen some level of lagging when it comes to more advanced search queries given the size of data. Read full review 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. Read full review Alternatives Considered I didn't investigate the best alternatives to CloudSearch, but did help with implementing this feature in our application. But from what i tested and used - Cloudsearch is very fast to get queries. Some negative points can be the time to implement this and some configurations that can be tricky.
Read full review Apache Solr is a ready-to-use product addressing specific use cases such as keyword searches from a huge set of data documents.
Read full review Return on Investment It has directly or indirectly improved the operational effiecncy for sure. Its limitations towards limited customisation requirements makes it a bit negative side of the scenario. Its scalability of the growth offers seamless accommodation of large data sets which is a positive impact although. Read full review Improved response time in e-commerce websites. Developer's job is easier with Apache Solr in use. Customization in filtering and sorting is possible. Read full review ScreenShots