DBMS > Newts vs. SiteWhere vs. Splice Machine vs. TimesTen

System Properties Comparison Newts vs. SiteWhere vs. Splice Machine vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

Time Series DBMS based on Cassandra

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

In-Memory RDBMS compatible to Oracle

Primary database model

Time Series DBMS

Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

opennms.github.io/newts

github.com/sitewhere/sitewhere

splicemachine.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

github.com/OpenNMS/newts/wiki

sitewhere1.sitewhere.io/index.html

splicemachine.com/how-it-works

docs.oracle.com/database/timesten-18.1

Developer

OpenNMS Group

SiteWhere

Splice Machine

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2014

2010

2014

1998

Current release

3.1, March 2021

11 Release 2 (11.2.2.8.0)

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

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

Java

Server operating systems

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

HTTP REST
Java API

HTTP REST

JDBC
Native Spark Datasource
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Java

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

Java

PL/SQL

Triggers

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

none

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

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

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

by means of logfiles and checkpoints

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

fine grained access rights according to SQL-standard

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