DBMS > Badger vs. EsgynDB vs. gStore

System Properties Comparison Badger vs. EsgynDB vs. gStore

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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.

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A native Graph DBMS to store and maintain very large RDF datasets.

Primary database model

Key-value store

Relational DBMS

Graph DBMS
RDF store

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.04
Rank	#359	Overall
	#37	Graph DBMS
	#18	RDF stores

Website

github.com/dgraph-io/badger

www.esgyn.cn

en.gstore.cn

Technical documentation

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

en.gstore.cn/#/enDocs

Developer

DGraph Labs

Esgyn

Initial release

2017

2015

2016

Current release

1.2, November 2023

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

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Implementation language

C++, Java

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

HTTP API
SPARQL 1.1

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C++
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication between multi datacenters

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

fine grained access rights according to SQL-standard

Users, roles and permissions, Role-Based Access Control (RBAC) supported

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