DBMS > Graph Engine vs. HugeGraph vs. TigerGraph vs. TimesTen

System Properties Comparison Graph Engine vs. HugeGraph vs. TigerGraph vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

A fast-speed and highly-scalable Graph DBMS

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

In-Memory RDBMS compatible to Oracle

Primary database model

Graph DBMS
Key-value store

Graph DBMS

Relational DBMS

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

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

www.graphengine.io

github.com/hugegraph
hugegraph.apache.org

www.tigergraph.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

www.graphengine.io/docs/manual

hugegraph.apache.org/docs

docs.tigergraph.com

docs.oracle.com/database/timesten-18.1

Developer

Microsoft

Baidu

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2010

2018

2017

1998

Current release

0.9

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache Version 2.0

commercial

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

.NET and C

Java

C++

Server operating systems

.NET

Linux
macOS
Unix

Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

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.

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like query language (GSQL)

yes

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C#
C++
F#
Visual Basic

Groovy
Java
Python

C++
Java

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

yes

PL/SQL

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes

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

none

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

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

edges in graph

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

by means of logfiles and checkpoints

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

Role-based access control

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

Trinity
30 October 2010, Microsoft

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13 February 2017, InfoWorld

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27 July 2016, GeekWire

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