DBMS > Badger vs. EsgynDB vs. Hive vs. TerminusDB

System Properties Comparison Badger vs. EsgynDB vs. Hive vs. TerminusDB

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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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Key-value store

Relational DBMS

Graph DBMS

Secondary database models

Document store
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	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

github.com/dgraph-io/badger

www.esgyn.cn

hive.apache.org

terminusdb.com

Technical documentation

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

cwiki.apache.org/confluence/display/Hive/Home

terminusdb.github.io/terminusdb/#

Developer

DGraph Labs

Esgyn

Apache Software Foundation

initially developed by Facebook

DataChemist Ltd.

Initial release

2017

2015

2012

2018

Current release

3.1.3, April 2022

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2

Open Source

GPL V3

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

C++, Java

Java

Prolog, Rust

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

All OS with a Java VM

Linux

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

SQL-like DML and DDL statements

SQL-like query language (WOQL)

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC
Thrift

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C++
Java
PHP
Python

JavaScript
Python

Server-side scripts

Stored procedures

Java Stored Procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication between multi datacenters

selectable replication factor

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual 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 journaling

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

Access rights for users, groups and roles

Role-based access control

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More resources

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