TrustRadius Insights for Apache Solr are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
Fast Performance: Many reviewers have praised the platform for its fast performance. They have found it impressive and appreciated the ability to rapidly grow their environments to meet expanding business needs.
Flexibility of Solr: Users have mentioned that Solr is highly flexible and can be customized to meet specific business needs. They have been able to make Solr bend to their requirements, which they found advantageous.
Useful Functionality: Reviewers have emphasized the usefulness of Solr's faceted navigation and field collapsing/grouping functionalities. These features allow them to filter and obtain quick results for their websites, resulting in good and efficient outcomes for their customers.
Loading Reviews List....
Apache Solr Reviews
3 Reviews
Professional, Scientific, and Technical ServicesInformation Technology & Services3
Search is temporarily unavailable. Filters are still applied.
I am an e-commerce website developer in my organization and our application uses mainly Apache Solr for the display, sorting, and managing of products on our website. With the Apache Solr in use products sorting, filtering with ranges, fetching filtered results, and display of products on list pages is possible. With a simple query, we can fetch desired results, and the query in Apache Solr is easy to learn.
Pros
Sorting on products for display.
Fetching filtered results.
Creating ranges, facets as per requirement.
Querying in Apache Solr is easy to learn.
Cons
Training material should be easily available.
Features with examples for developers to use.
Better documentation.
Likelihood to Recommend
Apache Solr is very useful in e-commerce websites. Apache Solr can be used for major features like sorting and filtering on websites. Querying in Apache Solr is easy. With the use of the Apache Solr website's response, time can be improved as results fetched using Apache Solr are faster compared to the database.
VU
Verified User
Team Lead in Information Technology (10,001+ employees)
I worked as a CTO for a pure play company in real estate activity. We had to design and to build five websites for the customers of real estate agencies. We manage about 2 millions classifieds. This area is highly competitive. An the same time, we doubled our unique number of users. So we (the people I managed and myself) decided to use a NoSQL database for our search engine. Our choice went to Apache Solr 4. This DB redesign was done at the same time as a PHP code redesign with Zend Framework. All our five websites were redesigned over a period of 2.5 years. We did a proof of concept with Apache Solr when we needed to redesign our registered customers searches (match 500k searches with 2M classifieds).
Pros
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)
Cons
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.
Likelihood to Recommend
It is well suited for classified search and filtering, and high volume data matching.
We use Apache Solr in different cases. The most usual is for index and searching, and it do it great! The accuracy is impressive. The other case is for caching some information and keep it worm. This case is very helpfull when some information do not need constant update and can be retrived from get requests.
Pros
Search indexes
Information caching
Databse rebuild
Cons
Memory consumption
Read and write operations
Load balancing for multiples sources
Likelihood to Recommend
For index search itens in a database, it is the best! The accuracy is fantastic. For caching information, it is good as well, but there are better and most appropriated softwares to do that.
