DBMS > Amazon Redshift vs. Blueflood vs. CouchDB vs. GridGain vs. SAP SQL Anywhere

System Properties Comparison Amazon Redshift vs. Blueflood vs. CouchDB vs. GridGain vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Scalable TimeSeries DBMS based on Cassandra

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

GridGain is an in-memory computing platform, built on Apache Ignite

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Relational DBMS

Time Series DBMS

Document store

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

aws.amazon.com/redshift

blueflood.io

couchdb.apache.org

www.gridgain.com

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

docs.aws.amazon.com/redshift

github.com/rax-maas/blueflood/wiki

docs.couchdb.org/en/stable

www.gridgain.com/docs/index.html

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Amazon (based on PostgreSQL)

Rackspace

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

GridGain Systems, Inc.

SAP

formerly Sybase

Initial release

2012

2013

2005

2007

1992

Current release

3.3.3, December 2023

GridGain 8.5.1

17, July 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache version 2

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

Java

Erlang

Java, C++, .Net

Server operating systems

hosted

Linux
OS X

Android
BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

predefined scheme

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

Secondary indexes

restricted

yes

via views

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

JDBC
ODBC

HTTP REST

RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

View functions in JavaScript

yes (compute grid and cache interceptors can be used instead)

yes, in C/C++, Java, .Net or Perl

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

improved architecture with release 2.0

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

yes (replicated cache)

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

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

atomic operations within a single document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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

Access rights for users can be defined per database

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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