DBMS > gStore vs. RRDtool vs. SAP Adaptive Server vs. Transwarp ArgoDB

System Properties Comparison gStore vs. RRDtool vs. SAP Adaptive Server vs. Transwarp ArgoDB

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Editorial information provided by DB-Engines

Name

Description

A native Graph DBMS to store and maintain very large RDF 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.

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Distributed Analytical Database to replace Hadoop+MPP hybrid architecture

Primary database model

Graph DBMS
RDF store

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

Search engine

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

Score	0.03
Rank	#367	Overall
	#37	Graph DBMS
	#18	RDF stores

Score	1.70
Rank	#136	Overall
	#11	Time Series DBMS

Score	32.67
Rank	#25	Overall
	#17	Relational DBMS

Score	0.04
Rank	#361	Overall
	#153	Relational DBMS

Website

en.gstore.cn

oss.oetiker.ch/rrdtool

www.sap.com/products/technology-platform/sybase-ase.html

www.transwarp.cn/en/product/argodb

Technical documentation

en.gstore.cn/#/enDocs

oss.oetiker.ch/rrdtool/doc

help.sap.com/docs/SAP_ASE

docs.transwarp.cn/#/documents-support/docs?category=ARGODB

Developer

Tobias Oetiker

SAP, Sybase

Transwarp

Initial release

2016

1999

1987

Current release

1.2, November 2023

1.8.0, 2022

16.0

License

Commercial or Open Source

Open Source

BSD

Open Source

GPL V2 and FLOSS

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++

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

C and C++

Server operating systems

Linux

HP-UX
Linux

AIX
HP-UX
Linux
Solaris
Unix
Windows

Data scheme

schema-free and OWL/RDFS-schema support

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.

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

HTTP API
SPARQL 1.1

in-process shared library
Pipes

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

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

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

C
C++
Cobol
Java
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

yes

Java and Transact-SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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

Users, roles and permissions, Role-Based Access Control (RBAC) supported

fine grained access rights according to SQL-standard

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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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