DBMS > Badger vs. GridGain vs. OrigoDB vs. RocksDB

System Properties Comparison Badger vs. GridGain vs. OrigoDB vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

A fully ACID in-memory object graph database

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

Primary database model

Key-value store

Key-value store
Relational DBMS

Document store
Object oriented DBMS

Key-value store

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

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

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

github.com/dgraph-io/badger

www.gridgain.com

origodb.com

rocksdb.org

Technical documentation

godoc.org/github.com/dgraph-io/badger

www.gridgain.com/docs/index.html

origodb.com/docs

github.com/facebook/rocksdb/wiki

Developer

DGraph Labs

GridGain Systems, Inc.

Robert Friberg et al

Facebook, Inc.

Initial release

2017

2007

2009

under the name LiveDB

2013

Current release

GridGain 8.5.1

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

BSD

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++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux
Windows

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

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

APIs and other access methods

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

.NET Client API
HTTP API
LINQ

C++ API
Java API

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

.Net

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

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

yes

Triggers

yes (cache interceptors and events)

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

client side managed; servers are not synchronized

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes (replicated cache)

Source-replica replication

yes

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

none

Immediate Consistency

Foreign keys

Referential integrity

depending on model

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

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

Security Hooks for custom implementations

Role based authorization

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