Overview

MongoDB

Stacks95.2K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

CouchDB

Stacks508

Followers584

Votes139

GitHub Stars6.7K

Forks1.1K

CouchDB vs MongoDB: What are the differences?

Introduction

When choosing a NoSQL database, it's essential to understand the key differences between CouchDB and MongoDB to make an informed decision.

Data Model: One significant difference between CouchDB and MongoDB is their data model. CouchDB uses a document-based model where all data is stored as JSON documents, allowing for easy and quick querying. In contrast, MongoDB uses a collection-based model, where documents are stored in collections and then queried using a JSON-like query language.
Replication and Scaling: Another key difference is in replication and scaling capabilities. CouchDB offers master-master replication out of the box, making it easier to implement a distributed database system. MongoDB, on the other hand, offers master-slave replication by default, but it also supports sharding for horizontal scaling.
Querying Capabilities: In terms of querying, MongoDB provides a more powerful query language with support for complex queries, aggregation pipelines, and full-text search capabilities. CouchDB, while offering a simple and efficient querying mechanism, may lack some of the advanced querying features provided by MongoDB.
Consistency Model: CouchDB follows an eventual consistency model, ensuring that data is eventually consistent across all nodes in the database. MongoDB, on the other hand, offers tunable consistency levels, allowing users to choose between strong or eventual consistency based on their requirements.
Community and Ecosystem: MongoDB has a larger and more active community, which results in a vast ecosystem of tools, libraries, and resources. CouchDB, while maintaining a dedicated user base, may not have the same level of community support and resources available.
Deployment and Integration: When it comes to deployment and integration, MongoDB is often preferred for its ease of deployment and seamless integration with popular programming languages and frameworks. While CouchDB also offers good integration options, MongoDB is more widely supported in terms of deployment environments.

In Summary, understanding the key differences in data model, replication, querying capabilities, consistency model, community support, and deployment options can help in making an informed decision between CouchDB and MongoDB for your specific use case.

🔥 Trending in Databases on StackShare

pgvector Databases

Open-source vector similarity search for Postgres

Try it View Docs Alternatives

Try

189

7

Advice on MongoDB, CouchDB

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Gabriel

CEO at Naologic

Jan 2, 2020

Decidedon

CouchDB

Couchbase

Memcached

We implemented our first large scale EPR application from naologic.com using CouchDB .

Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

592k views592k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Detailed Comparison

MongoDB	CouchDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.	Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Terrific single-node database; Clustered database ; HTTP/JSON; Offline first data sync
Statistics
GitHub Stars 27.7K	GitHub Stars 6.7K
GitHub Forks 5.7K	GitHub Forks 1.1K
Stacks 95.2K	Stacks 508
Followers 82.0K	Followers 584
Votes 4.1K	Votes 139
Pros & Cons
Pros 829 Document-oriented storage 594 No sql 554 Ease of use 465 Fast 410 High performance Cons 6 Very slowly for connected models that require joins 3 Not acid compliant 2 Proprietary query language	Pros 43 JSON 30 Open source 18 Highly available 12 Partition tolerant 11 Eventual consistency

What are some alternatives to MongoDB, CouchDB?

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.