DBMS > Splice Machine vs. SwayDB vs. SWC-DB vs. TimesTen

System Properties Comparison Splice Machine vs. SwayDB vs. SWC-DB vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

A high performance, scalable Wide Column DBMS

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Secondary database models

Time Series DBMS

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

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	0.00
Rank	#384	Overall
	#60	Key-value stores

Score	0.01
Rank	#378	Overall
	#13	Wide column stores

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

splicemachine.com

swaydb.simer.au

github.com/kashirin-alex/swc-db
www.swcdb.org

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

Technical documentation

splicemachine.com/how-it-works

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Splice Machine

Simer Plaha

Alex Kashirin

Oracle

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

Initial release

2014

2018

2020

1998

Current release

3.1, March 2021

0.5, April 2021

Release 22.1

License

Commercial or Open Source

Open Source

AGPL 3.0, commercial license available

Open Source

GNU Affero GPL V3.0

Open Source

GPL V3

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

Scala

C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

yes

schema-free

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

SQL-like query language

yes

APIs and other access methods

JDBC
Native Spark Datasource
ODBC

Proprietary protocol
Thrift

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

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

Java
Kotlin
Scala

C++

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

yes

Java

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

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

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

Atomic execution of operations

ACID

Concurrency

Support for concurrent manipulation of data

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

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

fine grained access rights according to SQL-standard

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More resources

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