DBMS > Dgraph vs. GBase vs. IBM Db2 Event Store vs. SAP SQL Anywhere

System Properties Comparison Dgraph vs. GBase vs. IBM Db2 Event Store vs. SAP SQL Anywhere

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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.

Distributed Event Store optimized for Internet of Things use cases

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Graph DBMS

Relational DBMS

Event Store
Time Series DBMS

Relational DBMS

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

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Website

dgraph.io

www.gbase.cn

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

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

dgraph.io/docs

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

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Dgraph Labs, Inc.

General Data Technology Co., Ltd.

IBM

SAP

formerly Sybase

Initial release

2016

2004

2017

1992

Current release

GBase 8a, GBase 8s, GBase 8c

2.0

17, July 2015

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free developer edition available

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

C, Java, Python

C and C++

Server operating systems

Linux
OS X
Windows

Linux

Linux, macOS, Windows for the developer addition

AIX
HP-UX
Linux
OS X
Solaris
Windows

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

SQL

Support of SQL

Standard with numerous extensions

yes

through the embedded Spark runtime

yes

APIs and other access methods

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

ADO.NET
C API
JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

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

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

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

Server-side scripts

Stored procedures

user defined functions

yes

yes, in C/C++, Java, .Net or Perl

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

yes

Active-active shard replication

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

In-memory capabilities

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

yes

User concepts

Access control

Planned for future releases

yes

fine grained access rights according to SQL-standard

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