DBMS > Amazon Redshift vs. Hazelcast vs. LeanXcale vs. RethinkDB vs. STSdb

System Properties Comparison Amazon Redshift vs. Hazelcast vs. LeanXcale vs. RethinkDB vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A widely adopted in-memory data grid

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Key-value store

Key-value store
Relational DBMS

Document store

Key-value store

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

aws.amazon.com/redshift

hazelcast.com

www.leanxcale.com

rethinkdb.com

github.com/STSSoft/STSdb4

Technical documentation

docs.aws.amazon.com/redshift

hazelcast.org/imdg/docs

rethinkdb.com/docs

Developer

Amazon (based on PostgreSQL)

Hazelcast

LeanXcale

The Linux Foundation

since July 2017

STS Soft SC

Initial release

2012

2008

2015

2009

2011

Current release

5.3.6, November 2023

2.4.1, August 2020

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Apache Version 2

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

hosted

All OS with a Java VM

Linux
OS X
Windows

Windows

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

yes

primitive types and user defined types (classes)

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

the object must implement a serialization strategy

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

through Apache Derby

APIs and other access methods

JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

.NET Client API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
Java
Scala

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

C#
Java

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Event Listeners, Executor Services

Triggers

yes

Events

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

range based

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

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

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

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

one or two-phase-commit; repeatable reads; read commited

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control

yes

users and table-level permissions

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