The 5 most important differences between CouchDB and MongoDB are:
- Queries: CouchDB accepts queries via a RESTful HTTP API, while MongoDB has its own query language.
- CAP theorem: CouchDB prioritizes availability, while MongoDB prioritizes consistency.
- User base: MongoDB has a much larger user base than CouchDB, making it easier to find support and hire employees.
- Pricing: Both CouchDB and MongoDB are free and open-source projects, but likely require a paid fully managed service to deploy in production.
- Reviews: MongoDB seems to have somewhat better reviews than CouchDB.
NoSQL databases have shaken up the world of big data, presenting an appealing alternative to traditional relational databases. If you’re looking to use a NoSQL database, CouchDB and MongoDB are likely among your top choices. But which one is better, CouchDB or MongoDB, and what are the differences between them?
Below, we’ll give you a detailed comparison between MongoDB vs.CouchDB so that you can make the decision that’s right for your organization.
Table of Contents
- CouchDB vs. MongoDB: Features and Benefits
- CouchDB vs. MongoDB: Pricing
- CouchDB vs. MongoDB: Reviews
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CouchDB vs. MongoDB: Features and Benefits
1. CouchDB vs. MongoDB: Technical Details
Before getting into broader features and use cases, let’s talk about the nitty-gritty technical details of both options. Although both CouchDB and MongoDB are document-oriented NoSQL databases, they diverge noticeably in their implementations:
- MongoDB uses BSON, a variant of JSON that stores data in binary format. MongoDB uses its own query language that is distinct from SQL, although with some similarities.
However, CouchDB and MongoDB also have a few things in common. The primary unit of data in a CouchDB or MongoDB database is the document. In addition, both CouchDB and MongoDB are sometimes referred to as “schemaless” databases. This means that you can store documents in the database without first defining a schema or structure for that document, giving you greater flexibility.
2. CouchDB vs. MongoDB: The CAP Theorem
The CAP theorem states that any distributed database can only have a maximum of two of three desirable qualities: consistency, availability, and partition tolerance.
- Consistency: All clients always have the same view into the data.
- Availability: All clients are able to read from and write to the database at all times.
- Partition tolerance: The database cluster can keep working even if communication between nodes breaks down.
CouchDB and MongoDB differ in their approach to the CAP theorem: CouchDB prefers availability and partition tolerance, while MongoDB prefers consistency and partition tolerance. The right choice here will depend on your database needs and use cases.
- CouchDB uses “eventual consistency”: clients can write to a single database node, and this information is guaranteed to eventually propagate to the rest of the database.
- MongoDB uses “strict consistency”: the database uses a replica set to provide redundancy and high availability, but at the cost of availability.
3. CouchDB vs. MongoDB: Scalability and Performance
MongoDB generally has the edge over CouchDB when it comes to very large datasets and high-performance requirements, including faster read speeds. MongoDB is also preferable if you aren't yet sure of your resource consumption, or if you anticipate rapid growth in the future.
On the other hand, CouchDB is able to offer both master-master and master-slave replication, while MongoDB users can only use master-slave replication. In master-master (also known as "multi-master") replication, any node in the cluster is able to act as the master and accept read and write requests, providing automatic failover.
4. CouchDB vs. MongoDB: Popularity
Of course, the question of CouchDB vs. MongoDB shouldn’t come down to a simple popularity contest, but using a popular database does have secondary benefits. For example, using technologies with a larger community makes it more likely that you’ll be able to find support and hire people experienced with the technology.
According to the widely used DB-Engines ranking, MongoDB is currently ranked fifth on the list of the most popular database management systems, as well as the most popular NoSQL database. CouchDB is much further down the list at number 35, and is the fifth most popular document-oriented NoSQL database.
CouchDB vs. MongoDB: Pricing
Both CouchDB and MongoDB are open-source projects and completely free of charge to use—at least when you’re first starting out. However, if you’re deploying these databases in production for business-critical workloads, it’s highly advisable to pay for a managed service that can offer high-quality support and maintenance.
CouchDB, for one, is available as a fully managed service on Amazon Web Services and Google Cloud Platform. As of this writing, Google projects that the costs of deploying CouchDB in the cloud are $24.75/month. This estimate is based on running a 10-gigabyte database 24 hours per day, 30 days per month, in the Central US region.
IBM Cloudant is a fully managed, closed-source fork of CouchDB built by IBM that has been optimized to deal with heavy workloads for web and mobile applications. Cloudant offers a free tier with a storage limit of 1 gigabyte, while standard plans start at $75/month. If you want to use CouchDB on-premises, you can make use of the service’s Cloudant Local option.
Meanwhile, if you’re interested in using MongoDB in the cloud, you can deploy the MongoDB Atlas database on the public cloud provider of your choice: AWS, Google Cloud Platform, or Microsoft Azure. You can also choose to go with the MongoDB Atlas internal database service, which can be deployed in the cloud, on-premises, or even on a mobile device.
The MongoDB Atlas service has the following pricing tiers:
- Shared clusters are free of charge and offer up to 512 megabytes of storage for new MongoDB users.
- Dedicated clusters start at $57/month for teams building applications that will be deployed to production.
- Dedicated multi-region clusters start at $95/month for “world-class applications” that require high availability and resilience and/or very low latency.
CouchDB vs. MongoDB: Reviews
Thus far, we’ve talked about how CouchDB and MongoDB compare in theory, including their features, technologies, and pricing. But how have CouchDB and MongoDB been received by users in the real world?
CouchDB currently has an average of 3.9 out of 5 stars on the business software review website G2. Developer Sean L. gives CouchDB a 5-star review, writing:
While most CouchDB reviews are largely positive, some users note a few issues with the platform:
- Senior software engineer Sumantra D. says that CouchDB is “very slow in querying large data sets.”
- Another reviewer notes that “the JSON model is a little more complicated to create a model for, compared to MongoDB.”
- Systems engineer Juan F. complains about a steep CouchDB learning curve, due to the lack of good documentation.
MongoDB has both more reviews and a higher rating than CouchDB on G2, with 4.4 out of 5 stars. Reviewer William K. writes:
“I love how fast I can develop solutions using MongoDB. I also love the reliability I have experienced. When I am able to use MongoDB, I know I get scalability out of the box and my application can grow as the demand increases.”
Big data architect Stephen D. praises MongoDB’s flexibility and ease of use:
“Although MongoDB is very flexible, and allows one to store data how they wish, it also allows one via best practices to achieve some very fast data ingestion when working with more complex distributed environments. Also, you can bring a novice up to speed to work with MongoDB queries relatively quickly.”
Criticisms of MongoDB generally focus on a few issues:
- Poor support for complex queries compared with relational databases in certain use cases.
- Limitations on the size of results from simple queries.
- Problems with concurrency prior to MongoDB 2.2.
- No built-in compliance with ACID transactions prior to MongoDB 4.0.
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Based on the criteria of features, pricing, and overall customer experience, we'd tend to give MongoDB the edge. But whether you end up choosing CouchDB or MongoDB, you need an ETL (extract, transform, load) solution that will efficiently transfer the information in your database for reporting and analytics.
Xplenty is an industry-leading data integration platform with a user-friendly, drag-and-drop visual interface. Even non-technical Xplenty users can rapidly build data pipelines to your cloud data warehouse or data lake. Xplenty also comes with a MongoDB connector and more than 100 other pre-built integrations that make it easy to prepare and process your enterprise data.
Ready to find out how Xplenty can enhance your ETL process? Contact us for a chat about your business needs and objectives, and get started on the Xplenty platform today.