DBMS > Dragonfly vs. Hazelcast vs. Microsoft Azure Cosmos DB vs. Stardog

System Properties Comparison Dragonfly vs. Hazelcast vs. Microsoft Azure Cosmos DB vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

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 widely adopted in-memory data grid

Globally distributed, horizontally scalable, multi-model database service

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS
RDF store

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	0.44
Rank	#255	Overall
	#38	Key-value stores

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.93
Rank	#121	Overall
	#10	Graph DBMS
	#6	RDF stores

Website

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

hazelcast.com

azure.microsoft.com/services/cosmos-db

www.stardog.com

Technical documentation

www.dragonflydb.io/docs

hazelcast.org/imdg/docs

learn.microsoft.com/azure/cosmos-db

docs.stardog.com

Developer

DragonflyDB team and community contributors

Hazelcast

Microsoft

Stardog-Union

Initial release

2023

2008

2014

2010

Current release

1.0, March 2023

5.3.6, November 2023

7.3.0, May 2020

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

Apache Version 2; commercial licenses available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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++

Java

Server operating systems

Linux

All OS with a Java VM

hosted

Linux
macOS
Windows

Data scheme

scheme-free

schema-free

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

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

yes

JSON types

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

the object must implement a serialization strategy

Import/export of XML data possible

Secondary indexes

yes

All properties auto-indexed by default

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

JCache
JPA
Memcached protocol
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

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++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

Lua

yes

Event Listeners, Executor Services

JavaScript

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

publish/subscribe channels provide some trigger functionality

yes

Events

JavaScript

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Replicated Map

yes

Implicit feature of the cloud service

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

Atomic execution of command blocks and scripts

one or two-phase-commit; repeatable reads; read commited

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

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

Password-based authentication

Role-based access control

Access rights can be defined down to the item level

Access rights for users and roles

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