DBMS > Dgraph vs. GBase vs. HugeGraph vs. IBM Db2 Event Store vs. Lovefield

System Properties Comparison Dgraph vs. GBase vs. HugeGraph vs. IBM Db2 Event Store vs. Lovefield

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

A fast-speed and highly-scalable Graph DBMS

Distributed Event Store optimized for Internet of Things use cases

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Graph DBMS

Relational DBMS

Graph DBMS

Event Store
Time Series DBMS

Relational DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Website

dgraph.io

www.gbase.cn

github.com/hugegraph
hugegraph.apache.org

www.ibm.com/products/db2-event-store

google.github.io/lovefield

Technical documentation

dgraph.io/docs

hugegraph.apache.org/docs

www.ibm.com/docs/en/db2-event-store

github.com/google/lovefield/blob/master/docs/spec_index.md

Developer

Dgraph Labs, Inc.

General Data Technology Co., Ltd.

Baidu

IBM

Google

Initial release

2016

2004

2018

2017

2014

Current release

GBase 8a, GBase 8s, GBase 8c

0.9

2.0

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

commercial

free developer edition available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, Java, Python

Java

C and C++

JavaScript

Server operating systems

Linux
OS X
Windows

Linux

Linux
macOS
Unix

Linux

Linux, macOS, Windows for the developer addition

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Data scheme

schema-free

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

Standard with numerous extensions

yes

through the embedded Spark runtime

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

ADO.NET
C API
JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

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

Groovy
Java
Python

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

JavaScript

Server-side scripts

Stored procedures

user defined functions

asynchronous Gremlin script jobs

yes

Triggers

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

yes

horizontal partitioning (by range, list and hash) and vertical partitioning

yes

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

yes

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

Active-active shard replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes, by using IndexedDB or the cloud service Firebase Realtime Database

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

Planned for future releases

yes

Users, roles and permissions

fine grained access rights according to SQL-standard

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More resources

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