DBMS > GBase vs. Microsoft Azure Table Storage vs. RRDtool vs. Transwarp KunDB

System Properties Comparison GBase vs. Microsoft Azure Table Storage vs. RRDtool vs. Transwarp KunDB

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Editorial information provided by DB-Engines

Name

Description

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

A Wide Column Store for rapid development using massive semi-structured datasets

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

OLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle

Primary database model

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

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Score	0.10
Rank	#344	Overall
	#151	Relational DBMS

Website

www.gbase.cn

azure.microsoft.com/en-us/services/storage/tables

oss.oetiker.ch/rrdtool

www.transwarp.cn/en/product/kundb

Technical documentation

oss.oetiker.ch/rrdtool/doc

Developer

General Data Technology Co., Ltd.

Microsoft

Tobias Oetiker

Transwarp

Initial release

2004

2012

1999

Current release

GBase 8a, GBase 8s, GBase 8c

1.8.0, 2022

License

Commercial or Open Source

commercial

Open Source

GPL V2 and FLOSS

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, Java, Python

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

Linux

hosted

HP-UX
Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

Standard with numerous extensions

yes

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

RESTful HTTP API

in-process shared library
Pipes

Supported programming languages

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

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Implicit feature of the cloud service

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

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

yes

Access rights based on private key authentication or shared access signatures

yes

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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