DBMS > RethinkDB vs. Splice Machine vs. SQLite vs. STSdb vs. Teradata

System Properties Comparison RethinkDB vs. Splice Machine vs. SQLite vs. STSdb vs. Teradata

Editorial information provided by DB-Engines

Name

Description

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Widely used embeddable, in-process RDBMS

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

rethinkdb.com

splicemachine.com

www.sqlite.org

github.com/STSSoft/STSdb4

www.teradata.com

Technical documentation

rethinkdb.com/docs

splicemachine.com/how-it-works

www.sqlite.org/docs.html

docs.teradata.com

Developer

The Linux Foundation

since July 2017

Splice Machine

Dwayne Richard Hipp

STS Soft SC

Teradata

Initial release

2009

2014

2000

2011

1984

Current release

2.4.1, August 2020

3.1, March 2021

3.46.0 (23 May 2024), May 2024

4.0.8, September 2015

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

AGPL 3.0, commercial license available

Open Source

Public Domain

Open Source

GPLv2, commercial license available

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++

Java

Server operating systems

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

server-less

Windows

hosted
Linux

Data scheme

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

yes

not rigid because of 'dynamic typing' concept.

yes

primitive types and user defined types (classes)

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

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

SQL-92 is not fully supported

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
Native Spark Datasource
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

.NET Client API

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C#
Java

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

Java

yes

UDFs, stored procedures, table functions in parallel

Triggers

Client-side triggers through changefeeds

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

range based

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes, multi-version concurrency control (MVCC)

yes

via file-system locks

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

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.