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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. CouchDB vs RavenDB

CouchDB vs RavenDB

OverviewDecisionsComparisonAlternatives

Overview

CouchDB
CouchDB
Stacks529
Followers584
Votes139
GitHub Stars6.7K
Forks1.1K
RavenDB
RavenDB
Stacks79
Followers82
Votes9
GitHub Stars3.9K
Forks850

CouchDB vs RavenDB: What are the differences?

# Introduction

Key differences between CouchDB and RavenDB are enumerated below:

1. **Data Model**: CouchDB follows a schema-less document model. RavenDB, on the other hand, allows for a schema model where documents are defined by CLR objects.
   
2. **Query Language**: CouchDB uses JavaScript-based MapReduce functions for querying data. RavenDB supports SQL-based querying, allowing for more familiar query syntax.

3. **Replication**: CouchDB utilizes a master-master replication model, where any node can accept write operations. RavenDB features a master-slave replication model, enhancing data consistency and control.

4. **Transactions**: CouchDB employs eventual consistency with MVCC (Multiversion Concurrency Control) for handling transactions. In comparison, RavenDB offers ACID transactions for guaranteed consistency and data integrity.

5. **Indexing**: CouchDB utilizes views for indexing data, leading to potential performance issues with large datasets. RavenDB includes automatic indexing capabilities, ensuring efficient query performance across varying data volumes.

6. **Deployment**: CouchDB is designed for easy deployment on a variety of platforms, while RavenDB is optimized for Windows environments, offering seamless integration with Microsoft technologies.

# Summary
In summary, CouchDB and RavenDB differ in their data model, query language, replication strategy, transaction handling, indexing mechanisms, and deployment focus.

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Advice on CouchDB, RavenDB

Gabriel
Gabriel

CEO at Naologic

Jan 2, 2020

DecidedonCouchDBCouchDBCouchbaseCouchbaseMemcachedMemcached

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
CouchDB
RavenDB
RavenDB

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.

As a document database it remains true to the core principles of these type of storage mechanisms. Somehow it managed to combine the best of relational databases with that of document databases.

Terrific single-node database; Clustered database ; HTTP/JSON; Offline first data sync
Multi-Platform; ACID Transactions
Statistics
GitHub Stars
6.7K
GitHub Stars
3.9K
GitHub Forks
1.1K
GitHub Forks
850
Stacks
529
Stacks
79
Followers
584
Followers
82
Votes
139
Votes
9
Pros & Cons
Pros
  • 43
    JSON
  • 30
    Open source
  • 18
    Highly available
  • 12
    Partition tolerant
  • 11
    Eventual consistency
Pros
  • 4
    Embedded Library
  • 3
    Easy of use
  • 2
    NoSql
Integrations
No integrations available
Python
Python
Windows
Windows
Java
Java
Ruby
Ruby
Linux
Linux

What are some alternatives to CouchDB, RavenDB?

MongoDB

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

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

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

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

SQLite

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

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

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

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

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

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.

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