DBMS > Riak TS vs. RocksDB vs. SiteWhere vs. Teradata vs. Vitess

System Properties Comparison Riak TS vs. RocksDB vs. SiteWhere vs. Teradata vs. Vitess

Editorial information provided by DB-Engines

Name

Description

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

M2M integration platform for persisting/querying time series data

A hybrid cloud data analytics software platform (Teradata Vantage)

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

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

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

rocksdb.org

github.com/sitewhere/sitewhere

www.teradata.com

vitess.io

Technical documentation

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

github.com/facebook/rocksdb/wiki

sitewhere1.sitewhere.io/index.html

docs.teradata.com

vitess.io/docs

Developer

Open Source, formerly Basho Technologies

Facebook, Inc.

SiteWhere

Teradata

The Linux Foundation, PlanetScale

Initial release

2015

2013

2010

1984

2013

Current release

3.0.0, September 2022

8.11.4, April 2024

Teradata Vantage 1.0 MU2, January 2019

15.0.2, December 2022

License

Commercial or Open Source

Open Source

BSD

Open Source

Common Public Attribution License Version 1.0

commercial

Open Source

Apache Version 2.0, commercial licenses available

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

Erlang

C++

Java

Server operating systems

Linux
OS X

Linux

Linux
OS X
Windows

hosted
Linux

Docker
Linux
macOS

Data scheme

schema-free

predefined scheme

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.

yes

Secondary indexes

restricted

yes

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

yes

SQL

Support of SQL

yes, limited

yes

SQL 2016 + extensions

yes

with proprietary extensions

APIs and other access methods

HTTP API
Native Erlang Interface

C++ API
Java API

HTTP REST

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

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

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++
Go
Java
Perl
Python
Ruby

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Erlang

yes

UDFs, stored procedures, table functions in parallel

yes

proprietary syntax

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

based on HBase

Sharding

Hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

selectable replication factor

based on HBase

Multi-source 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

Eventual Consistency

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

links between datasets can be stored

yes

not for MyISAM storage engine

Transaction concepts

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

yes

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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 with fine-grained authorization concept

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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