DBMS > Amazon Redshift vs. GigaSpaces vs. PouchDB vs. RRDtool

System Properties Comparison Amazon Redshift vs. GigaSpaces vs. PouchDB vs. RRDtool

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

JavaScript DBMS with an API inspired by CouchDB

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Document store

Time Series DBMS

Secondary database models

Graph DBMS
Search engine

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Score	1.90
Rank	#132	Overall
	#11	Time Series DBMS

Website

aws.amazon.com/redshift

www.gigaspaces.com

pouchdb.com

oss.oetiker.ch/rrdtool

Technical documentation

docs.aws.amazon.com/redshift

docs.gigaspaces.com/latest/landing.html

pouchdb.com/guides

oss.oetiker.ch/rrdtool/doc

Developer

Amazon (based on PostgreSQL)

Gigaspaces Technologies

Apache Software Foundation

Tobias Oetiker

Initial release

2012

2000

2012

1999

Current release

15.5, September 2020

7.1.1, June 2019

1.8.0, 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

Open Source

GPL V2 and FLOSS

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, C++, .Net

JavaScript

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

hosted

Linux
macOS
Solaris
Windows

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

HP-UX
Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

XML can be used for describing objects metadata

Exporting into and restoring from XML files possible

Secondary indexes

restricted

yes

via views

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-99 for query and DML statements

APIs and other access methods

JDBC
ODBC

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

HTTP REST

only for PouchDB Server
JavaScript API

in-process shared library
Pipes

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C++
Java
Python
Scala

JavaScript

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes

View functions in JavaScript

Triggers

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Eventual Consistency

none

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

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

Durability

Support for making data persistent

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

Role-based access control

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.