DBMS > Badger vs. Dgraph vs. EsgynDB vs. EventStoreDB

System Properties Comparison Badger vs. Dgraph vs. EsgynDB vs. EventStoreDB

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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.

Distributed and scalable native Graph DBMS

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

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Primary database model

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

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Website

github.com/dgraph-io/badger

dgraph.io

www.esgyn.cn

www.eventstore.com

Technical documentation

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

dgraph.io/docs

developers.eventstore.com

Developer

DGraph Labs

Dgraph Labs, Inc.

Esgyn

Event Store Limited

Initial release

2017

2016

2015

2012

Current release

21.2, February 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

Open Source

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

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux

Linux
Windows

Data scheme

schema-free

yes

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

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Synchronous replication via Raft

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

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

Planned for future releases

fine grained access rights according to SQL-standard

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More resources

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