DBMS > Amazon Redshift vs. Graph Engine vs. PouchDB vs. searchxml vs. Spark SQL

System Properties Comparison Amazon Redshift vs. Graph Engine vs. PouchDB vs. searchxml vs. Spark SQL

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

JavaScript DBMS with an API inspired by CouchDB

DBMS for structured and unstructured content wrapped with an application server

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Document store

Native XML DBMS
Search engine

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	2.34
Rank	#112	Overall
	#21	Document stores

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

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/redshift

www.graphengine.io

pouchdb.com

www.searchxml.net/category/products

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/redshift

www.graphengine.io/docs/manual

pouchdb.com/guides

www.searchxml.net/support/handouts

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Amazon (based on PostgreSQL)

Microsoft

Apache Software Foundation

informationpartners gmbh

Apache Software Foundation

Initial release

2012

2010

2012

2015

2014

Current release

7.1.1, June 2019

1.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

commercial

Open Source

Apache 2.0

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

JavaScript

C++

Scala

Server operating systems

hosted

.NET

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

Windows

Linux
OS X
Windows

Data scheme

yes

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

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

RESTful HTTP API
WebDAV
XQuery
XSLT

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
F#
Visual Basic

JavaScript

C++

most other programming languages supported via APIs

Java
Python
R
Scala

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

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

sychronisation to multiple collections

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

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

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

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