DBMS > Amazon Redshift vs. Blueflood vs. Ehcache vs. LeanXcale

System Properties Comparison Amazon Redshift vs. Blueflood vs. Ehcache vs. LeanXcale

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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 widely adopted Java cache with tiered storage options

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Primary database model

Relational DBMS

Time Series DBMS

Key-value store

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	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	4.64
Rank	#68	Overall
	#8	Key-value stores

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Website

aws.amazon.com/redshift

blueflood.io

www.ehcache.org

www.leanxcale.com

Technical documentation

docs.aws.amazon.com/redshift

github.com/rax-maas/blueflood/wiki

www.ehcache.org/documentation

Developer

Amazon (based on PostgreSQL)

Rackspace

Terracotta Inc, owned by Software AG

LeanXcale

Initial release

2012

2013

2009

2015

Current release

3.10.0, March 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2; commercial licenses available

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

Server operating systems

hosted

Linux
OS X

All OS with a Java VM

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.

Secondary indexes

restricted

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

through Apache Derby

APIs and other access methods

JDBC
ODBC

HTTP REST

JCache

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Supported programming languages

All languages supporting JDBC/ODBC

Java

C
Java
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

by using Terracotta Server

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

yes

by using Terracotta Server

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Tunable Consistency (Strong, Eventual, Weak)

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

yes

supports JTA and can work as an XA resource

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

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