DBMS > Datastax Enterprise vs. GBase vs. Graph Engine

System Properties Comparison Datastax Enterprise vs. GBase vs. Graph Engine

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Editorial information provided by DB-Engines

Name

Description

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

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

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

Primary database model

Wide column store

Relational DBMS

Graph DBMS
Key-value store

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

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

Score	3.94
Rank	#67	Overall
	#4	Wide column stores

Score	1.49
Rank	#142	Overall
	#64	Relational DBMS

Score	0.52
Rank	#243	Overall
	#21	Graph DBMS
	#34	Key-value stores

Website

www.datastax.com/products/datastax-enterprise

www.gbase.cn

www.graphengine.io

Technical documentation

docs.datastax.com

www.graphengine.io/docs/manual

Developer

DataStax

General Data Technology Co., Ltd.

Microsoft

Initial release

2011

2004

2010

Current release

6.8, April 2020

GBase 8a, GBase 8s, GBase 8c

License

Commercial or Open Source

commercial

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, Java, Python

.NET and C

Server operating systems

Linux
OS X

Linux

.NET

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

SQL-like DML and DDL statements (CQL); Spark SQL

Standard with numerous extensions

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

ADO.NET
C API
JDBC
ODBC

RESTful HTTP API

Supported programming languages

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

C#
C++
F#
Visual Basic

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

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

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware, advanced replication for edge computing

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users can be defined per object

yes

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