CouchDB vs. MongoDB:
What You Need to Know

NoSQL databases have shaken up the world of big data, presenting an appealing alternative to traditional relational databases. CouchDB and MongoDB are two popular choices for this type of database. Deciding between them can be difficult, so we created a detailed comparison between MongoDB vs. CouchDB to help you make that decision for your organization. 

1. CouchDB vs. MongoDB: Technical Details

CouchDB and MongoDB are both document-oriented NoSQL databases, but they diverge significantly in their implementations. 

CouchDB uses the semi-structured JSON format for storing data. Queries to a CouchDB database are made via a RESTful HTTP API, using HTTP or JavaScript. MongoDB uses BSON, a JSON variant that stores data in a binary format. MongoDB uses its own query language that is distinct from SQL, although they have some similarities. 

In other areas, CouchDB and MongoDB have several features in common. The document is the primary unit of data in both of these databases. They’re also both schemaless, which means that you can store documents in the database without first defining a schema or structure for that document. This functionality gives you greater flexibility over the data stored in CouchDB and MongoDB. 

2. MongoDB vs CouchDB: 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 of the data.
• Availability: All clients can 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 favors availability and partition tolerance, while MongoDB prefers consistency and partition tolerance. 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 but at the cost of availability. 

3. CouchDB vs. MongoDB: Scalability and Performance

MongoDB generally has the edge over CouchDB when you’re working with huge datasets, high-performance requirements, including faster read speeds. This database is also better suited for deployments where you’re uncertain of your resource consumption, or you anticipate rapid growth in the future. 

CouchDB offers both master-master and master-slave replication, while MongoDB only covers master-slave configurations. In master-master, also known as multi-master, replication, any node in the cluster can act as the master and accept read and write requests. This functionality enables automatic failover. 

4. CouchDB vs. MongoDB: Popularity

Evaluating MongoDB vs Couch DB shouldn’t come down to a popularity contest, but choosing the most popular database provides significant secondary benefits. For example, when you use technologies with larger communities, you’re more able to find support and hire people experienced with these solutions. 

According to DB-Engines’ ranking chart, a popular resource for tracking the popularity of database technology, MongoDB is fifth on the most popular database management systems list. It’s also the first NoSQL database in the rankings. CouchDB is 39th and comes in as the fourth most popular document-oriented NoSQL database. 

5. MongoDB vs CouchDB: Pricing

CouchDB and MongoDB are open-source projects and are completely free of charge to use. However, when using these databases in production for business-critical workloads, you need to consider the total cost of deployment. It’s common to pay for a managed database service featuring the technology of your choice, as you gain access to cloud-based infrastructure, high-quality support, streamlined maintenance, and other useful features. 

CouchDB

CouchDB is available as a fully managed service on Amazon Web Services (AWS) and Google Cloud Platform (GCP). As of this writing, Google projects the cost of deploying CouchDB on GCP at $34.72 per month. This estimate is based on a 30 day, 24 hours per day usage in the Central US region, a VM instance with 2 vCPUs and 8 GB of memory, and 10GB of a standard persistent disk.

Several CouchDB managed services are available on the AWS Marketplace, with prices starting at $0.019 per hour for databases hosted on t3a.small in US East (North Virginia). 

IBM Cloudant is a fully managed, closed-source CouchDB fork built by IBM. They optimized the database technology to handle heavy workloads for web and mobile applications. Three plan tiers are available: free, standard, and transaction engine. The free tier includes 1 GB of storage and has a fixed provision throughput capacity of 20 reads per second, 10 writes per second, and 5 global queries per second. The standard tier offers 100 reads per second, 50 writes per second, 5 global queries per second, and 20 GB of storage with a starting monthly price of $75. The transaction engine has 50 reads and 50 writes per second, 25 GB of storage, and a starting price of $22. 

MongoDB

MongoDB Atlas is the managed cloud database service for MongoDB. The shared tier is free at its lowest capacity, starting at 512 MB of storage and goes up to 5 GB. It also has shared RAM. The dedicated tier starts at $57 per month and offers 10 GB to 4 TB of storage, 2 GB to 768 GB of RAM, elastic scalability, auto-scaling, and point-in-time data recovery. The multi-region tier starts at $95 per month and offers cross-region replication, multi-cloud clusters, and geo-partitioned data storage. 

Simplifying Your Database Migration with Xplenty

The four most important differences between CouchDB and MongoDB are: 

1. CouchDB accepts queries via a RESTful HTTP API, while MongoDB uses its own query language. 
2. CouchDB prioritizes availability, while MongoDB prioritizes consistency. 
3. MongoDB has a much larger user base than CouchDB, making it easier to find support and hire employees for this database solution. 
4. Both databases are free and open-source projects but likely require a paid fully managed service to deploy your production workloads. 

No matter which database you chose, you’ll need a plan to migrate large volumes of data to your new deployment smoothly. Xplenty offers an industry-leading data integration platform with a user-friendly, drag-and-drop visual interface. Xplenty comes with a built-in MongoDB connector and 100 other pre-built integrations to make it easy to prepare and process your enterprise data.

Ready to find out how Xplenty can enhance your ETL process? Contact us to get started with a 14-day demo.