CouchDB and MongoDB Compared

The application modernization era is happening across a global variety of industries. The use of NoSQL databases such as MongoDB and CouchDB over legacy relational databases is one of the key points of this process. Although MongoDB and CouchDB share a similar concept of a document-oriented database, there are some key advantages to MongoDB over CouchDB technology as well as some key differences we should address.

This article will cover the main areas of comparison between CouchDB and MongoDB which developers need to know before planning their next application stack.

Table of Contents

• The basics of MongoDB
• The basics of CouchDB
• CouchDB vs MongoDB queries explained
• FAQs

The basics of MongoDB

MongoDB is a document database built for general-purpose usage. It stores data in an optimized JSON format called BSON (Binary JSON). This allows the MongoDB document model to benefit from the flexibility of JSON documents stored in logical groups called collections. The document model supports many different data types, including strings, dates, numbers, arrays, decimals, nested objects, geo data, and binary data.

MongoDB uses replication and data partitioning called sharding to distribute data for high availability and scalability purposes, making it a highly consistent and fault tolerant database. MongoDB supports multiple deployment methods, from running it for free on a variety of platforms and servers starting from your local machine to a fully blown deployment in the cloud of your choice using MongoDB Atlas. Additionally, MongoDB Enterprise Advanced and MongoDB Atlas offer enterprise-grade security features like authentication, authorization, and LDAP support as well as end-to-end encryption.

MongoDB’s unified query API and powerful aggregations, in conjunction with the flexibility of the document model, has made it the most popular general purpose document database on the market. MongoDB was voted among the "Most Wanted" databases in StackOverflow’s 2022 annual developer survey.

The basics of CouchDB

CouchDB is a NoSQL document-oriented database from the Apache foundation, first released in 2005, and implemented in Erlang. CouchDB documents are JSON-based and stored in databases. The database software can be installed as a single instance or a cluster of instances for durability and high availability. CouchDB’s overall design favors availability over consistency, and is considered eventually consistent in many read scenarios.

CouchDB is designed on the concept of “optimistic concurrency,” which places an added burden on the application for handling data consistency. The query system does not lock database objects on writes, meaning any conflict resolution or locking mechanisms for consistency need to be implemented by the application developer. This places an additional burden on development teams, and adds unnecessary complexity to their application code.

CouchDB uses an HTTP Rest API to perform database access and document manipulation, rather than native drivers using a unified query language like many other modern databases. It supports basic types of authentication like session cookies/users and TLS/SSL for the Rest API traffic.

CouchDB vs MongoDB queries explained

Let's explore some basic and a bit more advanced queries and CRUD operations and compare them.

Create a document

With both methods, we submit the document to be stored in “planets” (Database/Collection).

MongoDB Example

db.planets.insertOne({"name" : "Earth"});

CouchDB Example

curl -X PUT 'http://127.0.0.1:5984/planets/doc' -d '{"name": "Earth"}'

Query a document by Id

Retrieving the data in MongoDB is done with a query parameter, whereas in CouchDB, it's part of the API URL.

CouchDB has to return a revision field called “_rev”.

MongoDB Example

db.companies.findOne({"_id" : "8843faaf0b831d364278331bc3001bd8"});

{
"_id"  : "8843faaf0b831d364278331bc3001bd8",
"name" : "Example, Inc."
}

CouchDB Example

curl -X GET http://127.0.0.1:5984/demo/8843faaf0b831d364278331bc3001bd8

{"_id":"8843faaf0b831d364278331bc3001bd8",
"_rev":"1-33b9fbce46930280dab37d672bbc8bb9",
 "name":"Example, Inc."}

Update a document

To update a record in MongoDB, we need to issue an updateOne command and specify the new field values under the $set operator in the updated clause.

In CouchDB, there are two operations to be performed. We need to first query the document for the latest revision and then overwrite the whole document specifying the revision.

MongoDB Example

db.albums.updateOne({"_id" : "6e1295ed6c29495e54cc05947f18c8af"}, {$set : {title : "One by One" , "year" : 2001}});

CouchDB Example

curl -X GET http://127.0.0.1:5984/albums/6e1295ed6c29495e54cc05947f18c8af

{"_id":"6e1295ed6c29495e54cc05947f18c8af","_rev":"1-2902191555","title":"There is Nothing Left to Lose","artist":"Foo Fighters"}

curl -X PUT http://127.0.0.1:5984/albums/6e1295ed6c29495e54cc05947f18c8af \
     -d '{"_rev":"1-2902191555","title":"One by One","artist":"Foo Fighters","year":2001}'

Aggregate a group by tag

Aggregations with MongoDB are a query language API receiving a pipeline of stages.

CouchDB, on the other hand, requires definitions of various views with map-reduce stages to perform simple aggregations like this one.

MongoDB Example

db.products.aggregate([{$group : {_id : "$tag" , value : {$sum : 1}}}]);

CouchDB Example

## A view and a design document is required for map-reduce.

## Map function 
curl -X PUT http://127.0.0.1:5984/db/_design/products
-d '{"views":{"my_filter":{"map":
"function(doc) {
    if(doc.tag) {
        doc.tags.forEach(function(tag) {
            emit(tag, 1);
        });
    }
}
",
"reduce" : "function(keys, values) {
    return sum(values);
}
" }}}'

curl -X GET http://127.0.0.1:5984/db/_design/products/_view/my_filter

Conclusions

At first, CouchDB looks like a compelling solution for the web world since it's built on top of the JSON document model and HTTP rest API interface. However, it lacks in security, performance, ease of use, query language, data types support, and cloud offerings when compared to MongoDB Atlas.

The release of Atlas Device Sync closes the only gap of offline-first application development, which was a historical point in favor of CouchDB mobile sync solutions.

MongoDB Atlas offers you a fully featured data platform with all the benefits of MongoDB and much more including Charts, Atlas App Services, and Data Lake.

When it comes to general-purpose document databases, MongoDB is clearly the leader.