DBMS > DataFS vs. GridGain vs. RocksDB vs. SQream DB

System Properties Comparison DataFS vs. GridGain vs. RocksDB vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Object oriented DBMS

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Graph DBMS

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

Technical documentation

dev.mobiland.com/Overview.xsp

www.gridgain.com/docs/index.html

github.com/facebook/rocksdb/wiki

docs.sqream.com

Developer

Mobiland AG

GridGain Systems, Inc.

Facebook, Inc.

SQream Technologies

Initial release

2018

2007

2013

2017

Current release

1.1.263, October 2022

GridGain 8.5.1

8.11.4, April 2024

2022.1.6, December 2022

License

Commercial or Open Source

commercial

Open Source

BSD

commercial

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

Java, C++, .Net

C++

C++, CUDA, Haskell, Java, Scala

Server operating systems

Windows

Linux
OS X
Solaris
Windows

Linux

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

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

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

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

C++ API
Java API

.Net
JDBC
ODBC

Supported programming languages

.Net
C
C#
C++
VB.Net

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Go
Java
Perl
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

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

user defined functions in Python

Triggers

no, except callback-events from server when changes happened

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

horizontal partitioning

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

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

Windows-Profile

Security Hooks for custom implementations

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