DBMS > LevelDB vs. Riak TS vs. SAP Adaptive Server vs. Transbase

System Properties Comparison LevelDB vs. Riak TS vs. SAP Adaptive Server vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

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 resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Key-value store

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	2.75
Rank	#107	Overall
	#19	Key-value stores

Score	0.25
Rank	#308	Overall
	#26	Time Series DBMS

Score	37.12
Rank	#25	Overall
	#17	Relational DBMS

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Website

github.com/google/leveldb

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

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

Technical documentation

github.com/google/leveldb/blob/main/doc/index.md

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

help.sap.com/docs/SAP_ASE

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

Developer

Google

Open Source, formerly Basho Technologies

SAP, Sybase

Transaction Software GmbH

Initial release

2011

2015

1987

Current release

1.23, February 2021

3.0.0, September 2022

16.0

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

BSD

Open Source

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++

Erlang

C and C++

Server operating systems

Illumos
Linux
OS X
Windows

Linux
OS X

AIX
HP-UX
Linux
Solaris
Unix
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes, limited

yes

APIs and other access methods

HTTP API
Native Erlang Interface

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

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

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

Server-side scripts

Stored procedures

Erlang

Java and Transact-SQL

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

Multi-source replication
Source-replica replication

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

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

with automatic compression on writes

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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