DBMS > Dragonfly vs. GigaSpaces vs. GridGain vs. Snowflake

System Properties Comparison Dragonfly vs. GigaSpaces vs. GridGain vs. Snowflake

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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

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

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

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Key-value store

Document store
Object oriented DBMS

Values are user defined objects

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Graph DBMS
Search engine

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

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

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

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

www.gigaspaces.com

www.gridgain.com

www.snowflake.com

Technical documentation

www.dragonflydb.io/docs

docs.gigaspaces.com/latest/landing.html

www.gridgain.com/docs/index.html

docs.snowflake.net/manuals/index.html

Developer

DragonflyDB team and community contributors

Gigaspaces Technologies

GridGain Systems, Inc.

Snowflake Computing Inc.

Initial release

2023

2000

2007

2014

Current release

1.0, March 2023

15.5, September 2020

GridGain 8.5.1

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

Apache Version 2; Commercial licenses available

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

C++

Java, C++, .Net

Server operating systems

Linux

Linux
macOS
Solaris
Windows

Linux
OS X
Solaris
Windows

hosted

Data scheme

scheme-free

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

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

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

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

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

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

CLI Client
JDBC
ODBC

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++
Java
Python
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Lua

yes

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

user defined functions

Triggers

publish/subscribe channels provide some trigger functionality

yes, event driven architecture

yes (cache interceptors and events)

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes (replicated cache)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

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

Security Hooks for custom implementations

Users with fine-grained authorization concept, user roles and pluggable authentication

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