Apache Solr vs. MongoDB

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.

Incentivized

MongoDB [is] great at storing JSON data grouped into "collections". In this format, you can store any JSON documents and conveniently categorize them by collections. The JSON document contained in MongoDB is called binary JSON or BSON and, like any other document in this format, is unstructured. Therefore, unlike traditional DBMS, any kind of data can be stored in collections, and this flexibility is combined with the horizontal scalability of the database. It should be noted that MongoDB does not have links between documents and “collections” (this is partially compensated by the Database Reference - links in the DBMS, but this does not completely solve the problem). As a result, a situation arises in which there is a certain set of data that is not related to other information in the database, and there is no way to combine data from different documents. In SQL systems, this would be an elementary task.

• 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)

• Easy to learn. When I picked up MongoDB for the first time, I had little background in database management or modeling. If you have a background in javascript (and JSON)... then you can figure out how to use MongoDB pretty fast.
• Fast performance.
• It's relatively easy to set up in certain environments because there are lots of ready-made solutions out there.
• There's a lot of support in the existing ecosystem for it —, especially in the node.js realm.
• Query syntax is pretty simple to grasp and utilize.
• Aggregate functions are powerful.
• Scaling options.
• Documentation is quite good and versioned for each release.

Incentivized

• Ease of use -- this is not always the easier platform to configure.
• Developer Community -- There are not a whole lot of folks out there that understand this stuff so finding talent is difficult.
• Drupal 8 -- We've been frustrated with slow development on APIs for Drupal 8 integration.

Incentivized

• I love the idea of Map-Reduce native support in MongoDB. Admittedly I have not used it as much as I would like -- it always seems to trip me up.
• Recent additions to the aggregation queries have helped reduce (no pun intended) my need to better wield the weapon that is Map-Reduce.

Incentivized

MongoDB is one of the most famous non-relational databases in the world, there are famous active projects that use this database. I think that the same company that develops the database gives you the online induction totally free is something that really is very positive. Accounts with a first-class support to be able to relate the correct implementation of the database, in addition to teaching you the best practices to optimize your projects, I believe that with this decision it is more than obvious which is the best decision at the time of seeing with which database to work.

Incentivized

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.

Incentivized

It is one of the reasons why we prefer it to store documents in a JSON-style format, to access the desired document very quickly regardless of its size, to be readable by human eyes, and to be easily scalable and manageable.

Incentivized

I have reached multiple times to the MongoDB community for the help and they have provided each and easy solution for every problem. Over the internet and on stack overflow many people responds over the challenges. Now this tool is very much used in every company and projects so internally many people are there to give a support.

Incentivized

While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.

Incentivized

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.

Incentivized

The environment I work in is somewhat unique in that we use both MySQL and MongoDB. However, each is used for specific purposes that the other is not well suited for. MongoDB is not a relational database like MySQL, so it serves as the perfect place to dump key bits of data for quick retrieval later. This is something we can't easily do with MySQL. On this smaller database, MongoDB also lets us retrieve data more quickly with its fast and efficient querying.

Incentivized

• 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.

Incentivized

• We can make more open and flexible systems due to its easy adaptation to new evolutions in web applications.
• In the latest versions it offers support for different transactions and we could carry out real tests related to the concurrency of the application.
• MongoDB allows you to have distributed clusters, which improves the speed of the queries by reducing the latency that exists between the database cluster and the service that executes the query.

Incentivized