DBMS > Badger vs. Dgraph vs. EsgynDB vs. Oracle NoSQL vs. Riak TS

System Properties Comparison Badger vs. Dgraph vs. EsgynDB vs. Oracle NoSQL vs. Riak TS

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

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Key-value store

Graph DBMS

Relational DBMS

Document store
Key-value store
Relational DBMS

Time Series DBMS

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

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

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Website

github.com/dgraph-io/badger

dgraph.io

www.esgyn.cn

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

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

dgraph.io/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

www.tiot.jp/riak-docs/riak/ts/latest

Developer

DGraph Labs

Dgraph Labs, Inc.

Esgyn

Oracle

Open Source, formerly Basho Technologies

Initial release

2017

2016

2015

2011

2015

Current release

24.1, May 2024

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

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

Erlang

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux

Linux
Solaris SPARC/x86

Linux
OS X

Data scheme

schema-free

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

Typing

predefined data types such as float or date

yes

optional

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

restricted

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes, limited

APIs and other access methods

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

ADO.NET
JDBC
ODBC

RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

C
C#
Go
Java
JavaScript (Node.js)
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Java Stored Procedures

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

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

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

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

off heap cache

User concepts

Access control

Planned for future releases

fine grained access rights according to SQL-standard

Access rights for users and roles

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