DBMS > Amazon Redshift vs. EsgynDB vs. H2GIS vs. Splice Machine vs. SWC-DB

System Properties Comparison Amazon Redshift vs. EsgynDB vs. H2GIS vs. Splice Machine vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Spatial extension of H2

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

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Relational DBMS

Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

aws.amazon.com/redshift

www.esgyn.cn

www.h2gis.org

splicemachine.com

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

Technical documentation

docs.aws.amazon.com/redshift

www.h2gis.org/docs/home

splicemachine.com/how-it-works

Developer

Amazon (based on PostgreSQL)

Esgyn

CNRS

Splice Machine

Alex Kashirin

Initial release

2012

2015

2013

2014

2020

Current release

3.1, March 2021

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

LGPL 3.0

Open Source

AGPL 3.0, commercial license available

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java

Java

C++

Server operating systems

hosted

Linux

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Proprietary protocol
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC/ADO.Net

Java

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

C++

Server-side scripts

Stored procedures

user defined functions

in Python

Java Stored Procedures

yes

based on H2

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication between multi datacenters

yes

based on H2

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

yes

based on H2

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

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