Azure Cosmos DB vs CouchDB

Overview

CouchDB

Stacks529

Followers584

Votes139

GitHub Stars6.7K

Forks1.1K

Azure Cosmos DB

Stacks594

Followers1.1K

Votes130

Azure Cosmos DB vs CouchDB: What are the differences?

Introduction

Azure Cosmos DB and CouchDB are both NoSQL databases that are designed to handle big data and provide scalable, flexible, and highly available solutions for modern applications. While they share some similarities, there are important differences between Azure Cosmos DB and CouchDB that make each platform unique. In this article, we will explore the key differences between these two databases.

Database Model: Azure Cosmos DB is a multimodal database that supports multiple data models, including key-value, document, graph, and columnar. It offers versatility in data modeling and allows developers to choose the best data model for their application needs. On the other hand, CouchDB primarily focuses on the document-oriented database model where data is stored as JSON documents.
Scalability: Azure Cosmos DB offers built-in global distribution and automatic scaling capabilities, allowing users to transparently scale their applications to accommodate high traffic and workload demands. It replicates data across multiple regions globally, ensuring low latency and high availability. In contrast, CouchDB offers distributed scalability through a multi-master replication model, but it does not have built-in global distribution capabilities.
Consistency Models: Azure Cosmos DB provides five well-defined consistency models to allow developers to fine-tune the trade-off between consistency and performance. These models range from strong consistency to eventual consistency. This flexibility enables developers to choose the appropriate consistency level for their application requirements. In comparison, CouchDB offers a single consistency model, which is called eventual consistency.
Query Languages: Azure Cosmos DB supports multiple query languages, including SQL, MongoDB's query language, Gremlin, and JavaScript-based user-defined functions. This allows developers to leverage their existing skills and query data in a familiar language. Conversely, CouchDB uses a JavaScript-based query language called MapReduce for querying and aggregating data.
Integrated Operations: Azure Cosmos DB supports integrated operations for data migration, backup, and restore, allowing users to easily manage their data lifecycle. It provides built-in tools for data import/export, along with seamless integration with Azure services and development tools. In contrast, CouchDB may require additional third-party tools or custom scripts for similar operations.
Availability Guarantees: Azure Cosmos DB provides comprehensive Service Level Agreements (SLAs) for various key metrics, such as uptime, throughput, latency, and availability. Customers can choose the level of guarantees they require based on their application's needs. In comparison, CouchDB does not offer predefined SLAs and availability guarantees, as it depends on the deployment and configuration of the database.

In Summary, Azure Cosmos DB offers a multimodal database model, built-in global distribution, multiple consistency models, support for various query languages, integrated operations, and comprehensive availability guarantees. On the other hand, CouchDB primarily focuses on the document-oriented database model, offers distributed scalability through multi-master replication, provides eventual consistency, uses MapReduce for querying, and has more flexible availability requirements based on deployment and configuration.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on CouchDB, Azure Cosmos DB

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

Detailed Comparison

CouchDB	Azure Cosmos DB
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.	Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.
Terrific single-node database; Clustered database ; HTTP/JSON; Offline first data sync	Fully managed with 99.99% Availability SLA;Elastically and highly scalable (both throughput and storage);Predictable low latency: <10ms @ P99 reads and <15ms @ P99 fully-indexed writes;Globally distributed with multi-region replication;Rich SQL queries over schema-agnostic automatic indexing;JavaScript language integrated multi-record ACID transactions with snapshot isolation;Well-defined tunable consistency models: Strong, Bounded Staleness, Session, and Eventual
Statistics
GitHub Stars 6.7K	GitHub Stars -
GitHub Forks 1.1K	GitHub Forks -
Stacks 529	Stacks 594
Followers 584	Followers 1.1K
Votes 139	Votes 130
Pros & Cons
Pros 43 JSON 30 Open source 18 Highly available 12 Partition tolerant 11 Eventual consistency	Pros 28 Best-of-breed NoSQL features 22 High scalability 15 Globally distributed 14 Automatic indexing over flexible json data model 10 Tunable consistency Cons 18 Pricing 4 Poor No SQL query support
Integrations
No integrations available	Azure Machine Learning MongoDB Hadoop Java Azure Functions Azure Container Service Azure Storage Azure Websites Apache Spark Python

What are some alternatives to CouchDB, Azure Cosmos DB?

MongoDB

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.

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.

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

Related Comparisons

Azure Cosmos DB vs CouchDB: What are the differences?

Introduction

Database Model: Azure Cosmos DB is a multimodal database that supports multiple data models, including key-value, document, graph, and columnar. It offers versatility in data modeling and allows developers to choose the best data model for their application needs. On the other hand, CouchDB primarily focuses on the document-oriented database model where data is stored as JSON documents.
Scalability: Azure Cosmos DB offers built-in global distribution and automatic scaling capabilities, allowing users to transparently scale their applications to accommodate high traffic and workload demands. It replicates data across multiple regions globally, ensuring low latency and high availability. In contrast, CouchDB offers distributed scalability through a multi-master replication model, but it does not have built-in global distribution capabilities.
Consistency Models: Azure Cosmos DB provides five well-defined consistency models to allow developers to fine-tune the trade-off between consistency and performance. These models range from strong consistency to eventual consistency. This flexibility enables developers to choose the appropriate consistency level for their application requirements. In comparison, CouchDB offers a single consistency model, which is called eventual consistency.
Query Languages: Azure Cosmos DB supports multiple query languages, including SQL, MongoDB's query language, Gremlin, and JavaScript-based user-defined functions. This allows developers to leverage their existing skills and query data in a familiar language. Conversely, CouchDB uses a JavaScript-based query language called MapReduce for querying and aggregating data.
Integrated Operations: Azure Cosmos DB supports integrated operations for data migration, backup, and restore, allowing users to easily manage their data lifecycle. It provides built-in tools for data import/export, along with seamless integration with Azure services and development tools. In contrast, CouchDB may require additional third-party tools or custom scripts for similar operations.
Availability Guarantees: Azure Cosmos DB provides comprehensive Service Level Agreements (SLAs) for various key metrics, such as uptime, throughput, latency, and availability. Customers can choose the level of guarantees they require based on their application's needs. In comparison, CouchDB does not offer predefined SLAs and availability guarantees, as it depends on the deployment and configuration of the database.

Azure Cosmos DB vs CouchDB

Overview

Azure Cosmos DB vs CouchDB: What are the differences?