DBMS > Amazon Redshift vs. Graph Engine vs. GridDB vs. PouchDB vs. searchxml

System Properties Comparison Amazon Redshift vs. Graph Engine vs. GridDB vs. PouchDB vs. searchxml

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Scalable in-memory time series database optimized for IoT and Big Data

JavaScript DBMS with an API inspired by CouchDB

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Time Series DBMS

Document store

Native XML DBMS
Search engine

Secondary database models

Key-value store
Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Website

aws.amazon.com/redshift

www.graphengine.io

griddb.net

pouchdb.com

www.searchxml.net/category/products

Technical documentation

docs.aws.amazon.com/redshift

www.graphengine.io/docs/manual

docs.griddb.net

pouchdb.com/guides

www.searchxml.net/support/handouts

Developer

Amazon (based on PostgreSQL)

Microsoft

Toshiba Corporation

Apache Software Foundation

informationpartners gmbh

Initial release

2012

2010

2013

2012

2015

Current release

5.1, August 2022

7.1.1, June 2019

1.0

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

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

.NET and C

C++

JavaScript

C++

Server operating systems

hosted

.NET

Linux

server-less, requires a JavaScript environment (browser, Node.js)

Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

SQL92, SQL-like TQL (Toshiba Query Language)

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

HTTP REST

only for PouchDB Server
JavaScript API

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
F#
Visual Basic

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

JavaScript

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

user defined functions

in Python

yes

View functions in JavaScript

yes

on the application server

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

ACID at container level

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

Domain, group and role-based access control at the document level and for application services

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.