DBMS > OrigoDB vs. Sequoiadb vs. Splice Machine vs. Trafodion

System Properties Comparison OrigoDB vs. Sequoiadb vs. Splice Machine vs. Trafodion

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

A fully ACID in-memory object graph database

NewSQL database with distributed OLTP and SQL

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Document store
Object oriented DBMS

Document store
Relational DBMS

Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

Technical documentation

origodb.com/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

splicemachine.com/how-it-works

trafodion.apache.org/documentation.html

Developer

Robert Friberg et al

Sequoiadb Ltd.

Splice Machine

Apache Software Foundation, originally developed by HP

Initial release

2009

under the name LiveDB

2013

2014

Current release

3.1, March 2021

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Server: AGPL; Client: Apache V2

Open Source

AGPL 3.0, commercial license available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

C++

Java

C++, Java

Server operating systems

Linux
Windows

Linux

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

User defined using .NET types and collections

yes

oid, date, timestamp, binary, regex

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

.NET Client API
HTTP API
LINQ

proprietary protocol using JSON

JDBC
Native Spark Datasource
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

.Net

.Net
C++
Java
PHP
Python

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

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

JavaScript

yes

Java

Java Stored Procedures

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

client side managed; servers are not synchronized

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

depending on model

yes

Transaction concepts

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

ACID

Document is locked during a transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

Write ahead log

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

Role based authorization

simple password-based 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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