DBMS > Cubrid vs. Riak TS vs. SAP Adaptive Server vs. TimescaleDB vs. Transbase

System Properties Comparison Cubrid vs. Riak TS vs. SAP Adaptive Server vs. TimescaleDB vs. Transbase

Editorial information provided by DB-Engines

Name

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

Relational DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	36.31
Rank	#25	Overall
	#17	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

cubrid.com (korean)
cubrid.org (english)

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

www.timescale.com

www.transaction.de/en/products/transbase.html

Technical documentation

cubrid.org/manuals

www.tiot.jp/riak-docs/riak/ts/latest

help.sap.com/docs/SAP_ASE

docs.timescale.com

www.transaction.de/en/products/transbase/features.html

Developer

CUBRID Corporation, CUBRID Foundation

Open Source, formerly Basho Technologies

SAP, Sybase

Timescale

Transaction Software GmbH

Initial release

2008

2015

1987

2017

1987

Current release

11.0, January 2021

3.0.0, September 2022

16.0

2.15.0, May 2024

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial

Open Source

Apache 2.0

commercial

free development license

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, C++, Java

Erlang

C and C++

Server operating systems

Linux
Windows

Linux
OS X

AIX
HP-UX
Linux
Solaris
Unix
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

restricted

yes

SQL

Support of SQL

yes

yes, limited

yes

full PostgreSQL SQL syntax

yes

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

HTTP API
Native Erlang Interface

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C++
Cobol
Java
Java
Perl
PHP
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

Java Stored Procedures

Erlang

Java and Transact-SQL

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

yes

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Multi-source replication
Source-replica replication

Source-replica replication with hot standby and reads on replicas

Source-replica replication

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

Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

yes

Transaction concepts

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

ACID

yes

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

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