DBMS > Amazon Redshift vs. BigchainDB vs. Citus vs. eXtremeDB vs. TimesTen

System Properties Comparison Amazon Redshift vs. BigchainDB vs. Citus vs. eXtremeDB vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

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

Natively in-memory DBMS with options for persistency, high-availability and clustering

In-Memory RDBMS compatible to Oracle

Primary database model

Relational DBMS

Document store

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Document store

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	0.74
Rank	#223	Overall
	#103	Relational DBMS
	#18	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

aws.amazon.com/redshift

www.bigchaindb.com

www.citusdata.com

www.mcobject.com

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

Technical documentation

docs.aws.amazon.com/redshift

bigchaindb.readthedocs.io/en/latest

docs.citusdata.com

www.mcobject.com/docs/extremedb.htm

docs.oracle.com/database/timesten-18.1

Developer

Amazon (based on PostgreSQL)

McObject

Oracle, TimesTen Performance Software, HP

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

Initial release

2012

2016

2010

2001

1998

Current release

8.1, December 2018

8.2, 2021

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

AGPL v3

Open Source

AGPL, commercial license also available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Python

C and C++

Server operating systems

hosted

Linux

AIX
HP-UX
Linux
macOS
Solaris
Windows

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

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.

yes

specific XML type available, but no XML query functionality

support of XML interfaces available

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

standard, with numerous extensions

yes

with the option: eXtremeSQL

yes

APIs and other access methods

JDBC
ODBC

CLI Client
RESTful HTTP API

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

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages supporting JDBC/ODBC

Go
Haskell
Java
JavaScript
Python
Ruby

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

.Net
C
C#
C++
Java
Lua
Python
Scala

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

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

yes

PL/SQL

Triggers

yes

by defining events

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning / sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Source-replica replication

other methods possible by using 3rd party extensions

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

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

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

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Licensing and pricing models

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