DBMS > EsgynDB vs. GBase vs. Hawkular Metrics

System Properties Comparison EsgynDB vs. GBase vs. Hawkular Metrics

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Primary database model

Relational DBMS

Time Series DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Website

www.esgyn.cn

www.gbase.cn

www.hawkular.org

Technical documentation

www.hawkular.org/hawkular-metrics/docs/user-guide

Developer

Esgyn

General Data Technology Co., Ltd.

Community supported by Red Hat

Initial release

2015

2004

2014

Current release

GBase 8a, GBase 8s, GBase 8c

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

C, Java, Python

Java

Server operating systems

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

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

yes

Standard with numerous extensions

APIs and other access methods

ADO.NET
JDBC
ODBC

ADO.NET
C API
JDBC
ODBC

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Go
Java
Python
Ruby

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

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.

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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