DBMS > JanusGraph vs. KeyDB vs. NSDb vs. Oracle NoSQL

System Properties Comparison JanusGraph vs. KeyDB vs. NSDb vs. Oracle NoSQL

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Editorial information provided by DB-Engines

Name

Description

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

Graph DBMS

Key-value store

Time Series DBMS

Document store
Key-value store
Relational DBMS

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

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Website

janusgraph.org

github.com/Snapchat/KeyDB
keydb.dev

nsdb.io

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

Technical documentation

docs.janusgraph.org

docs.keydb.dev

nsdb.io/Architecture

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

Developer

Linux Foundation; originally developed as Titan by Aurelius

EQ Alpha Technology Ltd.

Oracle

Initial release

2017

2019

2017

2011

Current release

0.6.3, February 2023

23.3, December 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD-3

Open Source

Apache Version 2.0

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

C++

Java, Scala

Java

Server operating systems

Linux
OS X
Unix
Windows

Linux

Linux
macOS

Linux
Solaris SPARC/x86

Data scheme

yes

schema-free

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

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

yes: int, bigint, decimal, string

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

by using the Redis Search module

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Proprietary protocol

RESP - REdis Serialization Protoco

gRPC
HTTP REST
WebSocket

RESTful HTTP API

Supported programming languages

Clojure
Java
Python

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Java
Scala

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

Server-side scripts

Stored procedures

yes

Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

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

yes

via Faunus, a graph analytics engine

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Eventual Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Optimistic locking, atomic execution of commands blocks and scripts

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

Using Apache Lucene

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

User authentification and security via Rexster Graph Server

simple password-based access control and ACL

Access rights for users and roles

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