Apache Solr vs. MongoDB

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Solr
Score 6.9 out of 10
N/A
Apache Solr is an open-source enterprise search server.N/A
MongoDB
Score 8.0 out of 10
N/A
MongoDB is an open source document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.
$0.10
million reads
Pricing
Apache SolrMongoDB
Editions & Modules
No answers on this topic
Shared
$0
per month
Serverless
$0.10million reads
million reads
Dedicated
$57
per month
Offerings
Pricing Offerings
Apache SolrMongoDB
Free Trial
NoYes
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional DetailsFully managed, global cloud database on AWS, Azure, and GCP
More Pricing Information
Community Pulse
Apache SolrMongoDB
Considered Both Products
Apache Solr
Chose Apache Solr
Some people on my team tried MondoDB and had several problems (don't remember which ones).

Elasticsearch would be a good choice but we didn't have it in our minds when we made the choice.
MongoDB

No answer on this topic

Top Pros
Top Cons
Features
Apache SolrMongoDB
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
Apache Solr
-
Ratings
MongoDB
9.1
38 Ratings
4% above category average
Performance00 Ratings9.038 Ratings
Availability00 Ratings9.738 Ratings
Concurrency00 Ratings8.638 Ratings
Security00 Ratings8.638 Ratings
Scalability00 Ratings9.438 Ratings
Data model flexibility00 Ratings9.138 Ratings
Deployment model flexibility00 Ratings9.137 Ratings
Best Alternatives
Apache SolrMongoDB
Small Businesses
Algolia
Algolia
Score 8.7 out of 10
IBM Cloudant
IBM Cloudant
Score 9.5 out of 10
Medium-sized Companies
Guru
Guru
Score 8.9 out of 10
IBM Cloudant
IBM Cloudant
Score 9.5 out of 10
Enterprises
Guru
Guru
Score 8.9 out of 10
IBM Cloudant
IBM Cloudant
Score 9.5 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SolrMongoDB
Likelihood to Recommend
9.0
(10 ratings)
9.4
(78 ratings)
Likelihood to Renew
-
(0 ratings)
10.0
(67 ratings)
Usability
-
(0 ratings)
9.0
(14 ratings)
Availability
-
(0 ratings)
9.0
(1 ratings)
Support Rating
-
(0 ratings)
9.6
(13 ratings)
Implementation Rating
-
(0 ratings)
8.4
(2 ratings)
User Testimonials
Apache SolrMongoDB
Likelihood to Recommend
Apache
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.
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MongoDB
If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.
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Pros
Apache
  • 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.
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MongoDB
  • Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
  • You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
  • Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.
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Cons
Apache
  • 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.
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MongoDB
  • An aggregate pipeline can be a bit overwhelming as a newcomer.
  • There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
  • Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.
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Likelihood to Renew
Apache
No answers on this topic
MongoDB
I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.
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Usability
Apache
No answers on this topic
MongoDB
NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.
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Support Rating
Apache
No answers on this topic
MongoDB
Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global support.
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Implementation Rating
Apache
No answers on this topic
MongoDB
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.
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Alternatives Considered
Apache
Apache Solr is a ready-to-use product addressing specific use cases such as keyword searches from a huge set of data documents.
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MongoDB
We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.
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Return on Investment
Apache
  • Improved response time in e-commerce websites.
  • Developer's job is easier with Apache Solr in use.
  • Customization in filtering and sorting is possible.
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MongoDB
  • Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
  • You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB
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ScreenShots

MongoDB Screenshots

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