DBMS > Amazon Redshift vs. Newts vs. OrigoDB vs. XTDB

System Properties Comparison Amazon Redshift vs. Newts vs. OrigoDB vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Time Series DBMS based on Cassandra

A fully ACID in-memory object graph database

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Time Series DBMS

Document store
Object oriented DBMS

Document store

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Trend Chart

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

aws.amazon.com/redshift

opennms.github.io/newts

origodb.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.aws.amazon.com/redshift

github.com/OpenNMS/newts/wiki

origodb.com/docs

www.xtdb.com/docs

Developer

Amazon (based on PostgreSQL)

OpenNMS Group

Robert Friberg et al

Juxt Ltd.

Initial release

2012

2014

2009

under the name LiveDB

2019

Current release

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Clojure

Server operating systems

hosted

Linux
OS X
Windows

Linux
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

yes, extensible-data-notation format

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC

HTTP REST
Java API

.NET Client API
HTTP API
LINQ

HTTP REST
JDBC

Supported programming languages

All languages supporting JDBC/ODBC

Java

.Net

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

horizontal partitioning

client side managed; servers are not synchronized

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

depending on model

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

Role based authorization

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