DBMS > Amazon DynamoDB vs. atoti vs. CouchDB vs. FoundationDB vs. Google Cloud Bigtable

System Properties Comparison Amazon DynamoDB vs. atoti vs. CouchDB vs. FoundationDB vs. Google Cloud Bigtable

Editorial information provided by DB-Engines

Name

Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Ordered key-value store. Core features are complimented by layers.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Primary database model

Document store
Key-value store

Object oriented DBMS

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Key-value store
Wide column store

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	70.06
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.45
Rank	#253	Overall
	#13	Object oriented DBMS

Score	7.46
Rank	#51	Overall
	#7	Document stores

Score	0.97
Rank	#191	Overall
	#32	Document stores
	#28	Key-value stores
	#87	Relational DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Website

aws.amazon.com/dynamodb

atoti.io

couchdb.apache.org

github.com/apple/foundationdb

cloud.google.com/bigtable

Technical documentation

docs.aws.amazon.com/dynamodb

docs.atoti.io

docs.couchdb.org/en/stable

apple.github.io/foundationdb

cloud.google.com/bigtable/docs

Developer

Amazon

ActiveViam

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

FoundationDB

Google

Initial release

2012

2005

2013

2015

Current release

3.3.3, December 2023

6.2.28, November 2020

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free versions available

Open Source

Apache version 2

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Erlang

C++

Server operating systems

hosted

Android
BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

hosted

Data scheme

schema-free

some layers support schemas

schema-free

Typing

predefined data types such as float or date

yes

some layers support typing

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

via views

SQL

Support of SQL

Multidimensional Expressions (MDX)

supported in specific SQL layer only

APIs and other access methods

RESTful HTTP API

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

C#
C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Python

View functions in JavaScript

in SQL-layer only

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

improved architecture with release 2.0

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

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

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Linearizable consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Foreign keys

Referential integrity

in SQL-layer only

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

atomic operations within a single document possible

ACID

Atomic single-row operations

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

strategy: optimistic locking

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

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights for users can be defined per database

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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