DBMS > 4D vs. Apache IoTDB vs. Blazegraph vs. GridGain vs. TigerGraph

System Properties Comparison 4D vs. Apache IoTDB vs. Blazegraph vs. GridGain vs. TigerGraph

Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

Application development environment with integrated database management system

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

GridGain is an in-memory computing platform, built on Apache Ignite

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

Primary database model

Relational DBMS

Time Series DBMS

Graph DBMS
RDF store

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Graph DBMS

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

Score	2.37
Rank	#110	Overall
	#54	Relational DBMS

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	0.74
Rank	#217	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	1.44
Rank	#152	Overall
	#14	Graph DBMS

Website

Technical documentation

developer.4d.com

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

wiki.blazegraph.com

www.gridgain.com/docs/index.html

docs.tigergraph.com

Developer

4D, Inc

Apache Software Foundation

Blazegraph

GridGain Systems, Inc.

Initial release

1984

2018

2006

2007

2017

Current release

v20, April 2023

1.1.0, April 2023

2.1.5, March 2019

GridGain 8.5.1

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

extended commercial license available

commercial, open source

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

Java

Java, C++, .Net, Python, REST, SQL

C++

Server operating systems

OS X
Windows

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

Linux
OS X
Solaris
Windows
z/OS

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal types

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

SPARQL is used as query language

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language (GSQL)

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
Native API

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

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

Supported programming languages

4D proprietary IDE
PHP

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

C++
Java

Server-side scripts

Stored procedures

yes

yes (compute grid and cache interceptors can be used instead)

yes

Triggers

yes

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

yes (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

yes

Relationships in graphs

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

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 and groups

yes

Security and Authentication via Web Application Container (Tomcat, Jetty)

Role-based access control
Security Hooks for custom implementations

Role-based access control

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