DBMS > Citus vs. EsgynDB vs. TerminusDB vs. Trafodion vs. VoltDB

System Properties Comparison Citus vs. EsgynDB vs. TerminusDB vs. Trafodion vs. VoltDB

Editorial information provided by DB-Engines

Name

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

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Transactional SQL-on-Hadoop DBMS

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store

Document store
RDF store

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

Technical documentation

docs.citusdata.com

terminusdb.github.io/terminusdb/#

trafodion.apache.org/documentation.html

docs.voltdb.com

Developer

Esgyn

DataChemist Ltd.

Apache Software Foundation, originally developed by HP

VoltDB Inc.

Initial release

2010

2015

2018

2014

2010

Current release

8.1, December 2018

11.0.0, January 2023

2.3.0, February 2019

11.3, April 2022

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

commercial

Open Source

GPL V3

Open Source

Apache 2.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

C++, Java

Prolog, Rust

C++, Java

Java, C++

Server operating systems

Linux

Linux
OS X

for development

Data 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

specific XML type available, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

yes

SQL-like query language (WOQL)

yes

only a subset of SQL 99

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ADO.NET
JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

ADO.NET
JDBC
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

All languages supporting JDBC/ODBC/ADO.Net

JavaScript
Python

All languages supporting JDBC/ODBC/ADO.Net

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Java Stored Procedures

yes

Java Stored Procedures

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Graph Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication between multi datacenters

Journaling Streams

yes, via HBase

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

in-memory journaling

yes

Snapshots and command logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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