DBMS > Dgraph vs. Dragonfly vs. FatDB vs. Oracle NoSQL

System Properties Comparison Dgraph vs. Dragonfly vs. FatDB vs. Oracle NoSQL

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Distributed and scalable native Graph DBMS

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

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Graph DBMS

Key-value store

Document store
Key-value store

Document store
Key-value store
Relational DBMS

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

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Website

dgraph.io

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

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

dgraph.io/docs

www.dragonflydb.io/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Dgraph Labs, Inc.

DragonflyDB team and community contributors

FatCloud

Oracle

Initial release

2016

2023

2012

2011

Current release

1.0, March 2023

23.3, December 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSL 1.1

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Java

Server operating systems

Linux
OS X
Windows

Linux

Windows

Linux
Solaris SPARC/x86

Data scheme

schema-free

scheme-free

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

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

yes

optional

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

Via inetgration in SQL Server

SQL-like DML and DDL statements

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

Proprietary protocol

RESP - REdis Serialization Protocol

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

RESTful HTTP API

Supported programming languages

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

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

Server-side scripts

Stored procedures

Lua

yes

via applications

Triggers

publish/subscribe channels provide some trigger functionality

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Source-replica replication

selectable replication factor

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Atomic execution of command blocks and scripts

configurable

ACID within a storage node (=shard)

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

off heap cache

User concepts

Access control

Planned for future releases

Password-based authentication

Can implement custom security layer via applications

Access rights for users and roles

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