DBMS > HugeGraph vs. KeyDB vs. Quasardb vs. SiteWhere vs. Transbase

System Properties Comparison HugeGraph vs. KeyDB vs. Quasardb vs. SiteWhere vs. Transbase

Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

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

Distributed, high-performance timeseries database

M2M integration platform for persisting/querying time series data

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

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

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

github.com/hugegraph
hugegraph.apache.org

github.com/Snapchat/KeyDB
keydb.dev

quasar.ai

github.com/sitewhere/sitewhere

www.transaction.de/en/products/transbase.html

Technical documentation

hugegraph.apache.org/docs

docs.keydb.dev

doc.quasar.ai/master

sitewhere1.sitewhere.io/index.html

www.transaction.de/en/products/transbase/features.html

Developer

Baidu

EQ Alpha Technology Ltd.

quasardb

SiteWhere

Transaction Software GmbH

Initial release

2018

2019

2009

2010

1987

Current release

0.9

3.14.1, January 2024

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD-3

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Common Public Attribution License Version 1.0

commercial

free development license

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

C and C++

Server operating systems

Linux
macOS
Unix

Linux

BSD
Linux
OS X
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

predefined scheme

yes

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

integer and binary

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

also supports composite index and range index

yes

by using the Redis Search module

yes

with tags

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

Proprietary protocol

RESP - REdis Serialization Protoco

HTTP API

HTTP REST

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Groovy
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

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

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

consistent hashing

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

Multi-source replication
Source-replica replication

Source-replica replication with selectable replication factor

selectable replication factor

based on HBase

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

Optimistic locking, atomic execution of commands blocks and scripts

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

Users, roles and permissions

simple password-based access control and ACL

Cryptographically strong user authentication and audit trail

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

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