DBMS > Kinetica vs. RethinkDB vs. Sadas Engine vs. Sphinx vs. Transbase

System Properties Comparison Kinetica vs. RethinkDB vs. Sadas Engine vs. Sphinx vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Open source search engine for searching in data from different sources, e.g. relational databases

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

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	2.58
Rank	#105	Overall
	#20	Document stores

Score	0.00
Rank	#385	Overall
	#159	Relational DBMS

Score	5.97
Rank	#56	Overall
	#5	Search engines

Score	0.05
Rank	#355	Overall
	#150	Relational DBMS

Website

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

Technical documentation

docs.kinetica.com

rethinkdb.com/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

sphinxsearch.com/docs

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

Developer

Kinetica

The Linux Foundation

since July 2017

SADAS s.r.l.

Sphinx Technologies Inc.

Transaction Software GmbH

Initial release

2012

2009

2006

2001

1987

Current release

7.1, August 2021

2.4.1, August 2020

8.0

3.5.1, February 2023

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

free trial version available

Open Source

GPL version 2, commercial licence available

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++

C++

C and C++

Server operating systems

Linux

Linux
OS X
Windows

AIX
Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like query language (SphinxQL)

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
Proprietary protocol

Proprietary protocol

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

.Net
C
C#
C++
Groovy
Java
PHP
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

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

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Client-side triggers through changefeeds

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

range based

horizontal partitioning

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

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 or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-document operations

yes

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

GPU vRAM or System RAM

yes

managed by 'Learn by Usage'

User concepts

Access control

Access rights for users and roles on table level

yes

users and table-level permissions

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

More information provided by the system vendor

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More resources

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