DBMS > Amazon DocumentDB vs. Couchbase vs. etcd vs. RavenDB

System Properties Comparison Amazon DocumentDB vs. Couchbase vs. etcd vs. RavenDB

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

A distributed reliable key-value store

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Document store

Key-value store

Document store

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	2.92
Rank	#101	Overall
	#18	Document stores

Website

aws.amazon.com/documentdb

www.couchbase.com

etcd.io
github.com/etcd-io/etcd

ravendb.net

Technical documentation

aws.amazon.com/documentdb/resources

docs.couchbase.com

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

ravendb.net/docs

Developer

Couchbase, Inc.

Hibernating Rhinos

Initial release

2019

2011

2010

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

3.4, August 2019

5.4, July 2022

License

Commercial or Open Source

commercial

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

Apache Version 2.0

Open Source

AGPL version 3, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++, Go and Erlang

Server operating systems

hosted

Linux
OS X
Windows

FreeBSD
Linux
Windows

experimental

Linux
macOS
Raspberry Pi
Windows

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

SQL-like query language (RQL)

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

gRPC
JSON over HTTP

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes

Triggers

yes

via the TAP protocol

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic Sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Multi-source replication

including cross data center replication
Source-replica replication

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Atomic single-document operations

ACID

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Ephemeral buckets

User concepts

Access control

Access rights for users and roles

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Authorization levels configured per client per database

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