DBMS > Badger vs. EJDB vs. Oracle NoSQL vs. SwayDB

System Properties Comparison Badger vs. EJDB vs. Oracle NoSQL vs. SwayDB

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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.

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Key-value store

Document store

Document store
Key-value store
Relational DBMS

Key-value store

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

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	0.30
Rank	#294	Overall
	#44	Document stores

Score	2.96
Rank	#103	Overall
	#18	Document stores
	#17	Key-value stores
	#52	Relational DBMS

Score	0.04
Rank	#373	Overall
	#57	Key-value stores

Website

github.com/dgraph-io/badger

github.com/Softmotions/ejdb

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

swaydb.simer.au

Technical documentation

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

github.com/Softmotions/ejdb/blob/master/README.md

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

Developer

DGraph Labs

Softmotions

Oracle

Simer Plaha

Initial release

2017

2012

2011

2018

Current release

23.3, December 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPLv2

Open Source

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

Open Source

GNU Affero GPL V3.0

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

Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

server-less

Linux
Solaris SPARC/x86

Data scheme

schema-free

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

string, integer, double, bool, date, object_id

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

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

in-process shared library

RESTful HTTP API

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

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

Java
Kotlin
Scala

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

configurable

ACID within a storage node (=shard)

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

yes

User concepts

Access control

Access rights for users and roles

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