DBMS > HugeGraph vs. IBM Db2 vs. RocksDB vs. Splice Machine vs. Stardog

System Properties Comparison HugeGraph vs. IBM Db2 vs. RocksDB vs. Splice Machine vs. Stardog

Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

Common in IBM host environments, 2 different versions for host and Windows/Linux

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Graph DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Key-value store

Relational DBMS

Graph DBMS
RDF store

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

github.com/hugegraph
hugegraph.apache.org

www.ibm.com/products/db2

rocksdb.org

splicemachine.com

www.stardog.com

Technical documentation

hugegraph.apache.org/docs

www.ibm.com/docs/en/db2

github.com/facebook/rocksdb/wiki

splicemachine.com/how-it-works

docs.stardog.com

Developer

Baidu

IBM

Facebook, Inc.

Splice Machine

Stardog-Union

Initial release

2018

1983

host version

2013

2014

2010

Current release

0.9

12.1, October 2016

8.11.4, April 2024

3.1, March 2021

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free version is available

Open Source

BSD

Open Source

AGPL 3.0, commercial license available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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 and C++

C++

Java

Server operating systems

Linux
macOS
Unix

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux

Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

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.

Import/export of XML data possible

Secondary indexes

yes

also supports composite index and range index

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

C++ API
Java API

JDBC
Native Spark Datasource
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Groovy
Java
Python

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

C
C++
Go
Java
Perl
Python
Ruby

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

Java

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding

only with Windows/Unix/Linux Version

horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

with separate tools (MQ, InfoSphere)

yes

Multi-source replication
Source-replica replication

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

edges in graph

yes

relationships in graphs

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

Users, roles and permissions

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

Access rights for users and roles

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