DBMS > Newts vs. Riak TS vs. SiteWhere vs. Splice Machine vs. Warp 10

System Properties Comparison Newts vs. Riak TS vs. SiteWhere vs. Splice Machine vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Time Series DBMS based on Cassandra

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

M2M integration platform for persisting/querying time series data

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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Time Series DBMS

Relational DBMS

Time Series DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

opennms.github.io/newts

github.com/sitewhere/sitewhere

splicemachine.com

www.warp10.io

Technical documentation

github.com/OpenNMS/newts/wiki

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

sitewhere1.sitewhere.io/index.html

splicemachine.com/how-it-works

www.warp10.io/content/02_Getting_started

Developer

OpenNMS Group

Open Source, formerly Basho Technologies

SiteWhere

Splice Machine

SenX

Initial release

2014

2015

2010

2014

2015

Current release

3.0.0, September 2022

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Common Public Attribution License Version 1.0

Open Source

AGPL 3.0, commercial license available

Open Source

Apache License 2.0

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

Java

Erlang

Java

Server operating systems

Linux
OS X
Windows

Linux
OS X

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

predefined scheme

yes

schema-free

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 REST
Java API

HTTP API
Native Erlang Interface

HTTP REST

JDBC
Native Spark Datasource
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

Java

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

Server-side scripts

Stored procedures

Erlang

yes

Java

yes

WarpScript

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

based on HBase

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

selectable replication factor

based on HBase

Multi-source replication
Source-replica replication

selectable replication factor

based on HBase

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Immediate Consistency

based on HBase

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

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Users with fine-grained authorization concept

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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