DBMS > Amazon Redshift vs. Blueflood vs. GBase vs. HugeGraph

System Properties Comparison Amazon Redshift vs. Blueflood vs. GBase vs. HugeGraph

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Scalable TimeSeries DBMS based on Cassandra

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

A fast-speed and highly-scalable Graph DBMS

Primary database model

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Website

aws.amazon.com/redshift

blueflood.io

www.gbase.cn

github.com/hugegraph
hugegraph.apache.org

Technical documentation

docs.aws.amazon.com/redshift

github.com/rax-maas/blueflood/wiki

hugegraph.apache.org/docs

Developer

Amazon (based on PostgreSQL)

Rackspace

General Data Technology Co., Ltd.

Baidu

Initial release

2012

2013

2004

2018

Current release

GBase 8a, GBase 8s, GBase 8c

0.9

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, Java, Python

Java

Server operating systems

hosted

Linux
OS X

Linux

Linux
macOS
Unix

Data scheme

yes

predefined 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.

yes

Secondary indexes

restricted

yes

also supports composite index and range index

SQL

Support of SQL

yes

does not fully support an SQL-standard

Standard with numerous extensions

APIs and other access methods

JDBC
ODBC

HTTP REST

ADO.NET
C API
JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

All languages supporting JDBC/ODBC

Groovy
Java
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

asynchronous Gremlin script jobs

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

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

yes

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

yes

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

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

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

edges in graph

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

fine grained access rights according to SQL-standard

yes

Users, roles and permissions

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