DBMS > Amazon Redshift vs. BoltDB vs. CouchDB vs. Hazelcast

System Properties Comparison Amazon Redshift vs. BoltDB vs. CouchDB vs. Hazelcast

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

A widely adopted in-memory data grid

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store

JSON support with IMDG 3.12

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Trend Chart

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	9.30
Rank	#45	Overall
	#7	Document stores

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

Website

aws.amazon.com/redshift

github.com/boltdb/bolt

couchdb.apache.org

hazelcast.com

Technical documentation

docs.aws.amazon.com/redshift

docs.couchdb.org/en/stable

hazelcast.org/imdg/docs

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Hazelcast

Initial release

2012

2013

2005

2008

Current release

3.3.3, December 2023

5.3.6, November 2023

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

Apache version 2

Open Source

Apache Version 2; commercial licenses available

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

Erlang

Java

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

Android
BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Data scheme

yes

schema-free

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

the object must implement a serialization strategy

Secondary indexes

restricted

yes

via views

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

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

Server-side scripts

Stored procedures

user defined functions

in Python

View functions in JavaScript

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

improved architecture with release 2.0

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication
Source-replica replication

yes

Replicated Map

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Eventual Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

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

yes

atomic operations within a single document possible

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

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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

Access rights for users can be defined per database

Role-based access control

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