DBMS > Amazon DynamoDB vs. Dragonfly vs. FoundationDB vs. Google Cloud Bigtable vs. GridGain

System Properties Comparison Amazon DynamoDB vs. Dragonfly vs. FoundationDB vs. Google Cloud Bigtable vs. GridGain

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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

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.

GridGain is an in-memory computing platform, built on Apache Ignite

Primary database model

Document store
Key-value store

Key-value store

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Key-value store
Wide column store

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

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	0.44
Rank	#255	Overall
	#38	Key-value 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

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Website

aws.amazon.com/dynamodb

github.com/dragonflydb/dragonfly
www.dragonflydb.io

github.com/apple/foundationdb

cloud.google.com/bigtable

www.gridgain.com

Technical documentation

docs.aws.amazon.com/dynamodb

www.dragonflydb.io/docs

apple.github.io/foundationdb

cloud.google.com/bigtable/docs

www.gridgain.com/docs/index.html

Developer

Amazon

DragonflyDB team and community contributors

FoundationDB

Google

GridGain Systems, Inc.

Initial release

2012

2023

2013

2015

2007

Current release

1.0, March 2023

6.2.28, November 2020

GridGain 8.5.1

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

BSL 1.1

Open Source

Apache 2.0

commercial

commercial, open source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, C++, .Net, Python, REST, SQL

Server operating systems

hosted

Linux

Linux
OS X
Windows

hosted

Linux
OS X
Solaris
Windows
z/OS

Data scheme

schema-free

scheme-free

schema-free

some layers support schemas

schema-free

yes

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

some layers support typing

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.

yes

Secondary indexes

yes

SQL

Support of SQL

supported in specific SQL layer only

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protocol

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

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

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Lua

in SQL-layer only

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

by integration with AWS Lambda

publish/subscribe channels provide some trigger functionality

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

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

yes (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

yes (compute grid and hadoop accelerator)

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)

Immediate Consistency

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 execution of command blocks and scripts

ACID

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

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)

Password-based authentication

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

Role-based access control
Security Hooks for custom implementations

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