DBMS > Badger vs. Dgraph vs. LeanXcale vs. Oracle NoSQL

System Properties Comparison Badger vs. Dgraph vs. LeanXcale vs. Oracle NoSQL

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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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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

Primary database model

Key-value store

Graph DBMS

Key-value store
Relational DBMS

Document store
Key-value store
Relational 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	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

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

Website

github.com/dgraph-io/badger

dgraph.io

www.leanxcale.com

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

Developer

DGraph Labs

Dgraph Labs, Inc.

LeanXcale

Oracle

Initial release

2017

2016

2015

2011

Current release

23.3, December 2023

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)

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

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux
Solaris SPARC/x86

Data scheme

schema-free

yes

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

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

SQL

Support of SQL

yes

through Apache Derby

SQL-like DML and DDL statements

APIs and other access methods

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

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

RESTful HTTP API

Supported programming languages

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

C
Java
Scala

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

Server-side scripts

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

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

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

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

Access rights for users and roles

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