DBMS > Dragonfly vs. Ignite vs. Microsoft Azure Cosmos DB vs. Realm

System Properties Comparison Dragonfly vs. Ignite vs. Microsoft Azure Cosmos DB vs. Realm

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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

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Globally distributed, horizontally scalable, multi-model database service

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Primary database model

Key-value store

Key-value store
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Secondary database models

Spatial DBMS

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

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

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	7.41
Rank	#52	Overall
	#8	Document stores

Website

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

ignite.apache.org

azure.microsoft.com/services/cosmos-db

realm.io

Technical documentation

www.dragonflydb.io/docs

apacheignite.readme.io/docs

learn.microsoft.com/azure/cosmos-db

realm.io/docs

Developer

DragonflyDB team and community contributors

Apache Software Foundation

Microsoft

Realm, acquired by MongoDB in May 2019

Initial release

2023

2015

2014

Current release

1.0, March 2023

Apache Ignite 2.6

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

Apache 2.0

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++

C++, Java, .Net

Server operating systems

Linux

Linux
OS X
Solaris
Windows

hosted

Android
Backend: server-less
iOS
Windows

Data scheme

scheme-free

yes

schema-free

yes

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

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

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

SQL-like query language

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

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

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

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

C#
C++
Java
PHP
Python
Ruby
Scala

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

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

Lua

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

JavaScript

runs within the applications so server-side scripts are unnecessary

Triggers

publish/subscribe channels provide some trigger functionality

yes (cache interceptors and events)

JavaScript

yes

Change Listeners

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 cache)

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

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

In-Memory realm

User concepts

Access control

Password-based authentication

Security Hooks for custom implementations

Access rights can be defined down to the item level

yes

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