DBMS > Amazon Redshift vs. FatDB vs. InfinityDB vs. Percona Server for MongoDB vs. Spark SQL

System Properties Comparison Amazon Redshift vs. FatDB vs. InfinityDB vs. Percona Server for MongoDB vs. Spark SQL

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Large scale data warehouse service for use with business intelligence tools

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

A Java embedded Key-Value Store which extends the Java Map interface

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

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

Primary database model

Relational DBMS

Document store
Key-value store

Key-value store

Document store

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	0.52
Rank	#254	Overall
	#39	Document stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/redshift

boilerbay.com

www.percona.com/mongodb/software/percona-server-for-mongodb

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/redshift

boilerbay.com/infinitydb/manual

docs.percona.com/percona-distribution-for-mongodb

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

Developer

Amazon (based on PostgreSQL)

FatCloud

Boiler Bay Inc.

Percona

Apache Software Foundation

Initial release

2012

2002

2015

2014

Current release

4.0

3.4.10-2.10, November 2017

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

GPL Version 2

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

Java

C++

Scala

Server operating systems

hosted

Windows

All OS with a Java VM

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

Via inetgration in SQL Server

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Java

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

via applications

JavaScript

Triggers

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

none

Source-replica replication

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

Immediate Consistency

READ-COMMITTED or SERIALIZED

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

Concurrency

Support for concurrent manipulation of data

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

via In-Memory Engine

User concepts

Access control

fine grained access rights according to SQL-standard

Can implement custom security layer via applications

Access rights for users and roles

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