DBMS > Amazon Redshift vs. Badger vs. Datomic vs. RethinkDB vs. Riak KV

System Properties Comparison Amazon Redshift vs. Badger vs. Datomic vs. RethinkDB vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

Distributed, fault tolerant key-value store

Primary database model

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

aws.amazon.com/redshift

github.com/dgraph-io/badger

www.datomic.com

rethinkdb.com

Technical documentation

docs.aws.amazon.com/redshift

godoc.org/github.com/dgraph-io/badger

docs.datomic.com

rethinkdb.com/docs

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Amazon (based on PostgreSQL)

DGraph Labs

Cognitect

The Linux Foundation

since July 2017

OpenSource, formerly Basho Technologies

Initial release

2012

2017

2012

2009

Current release

1.0.6735, June 2023

2.4.1, August 2020

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

limited edition free

Open Source

Apache Version 2

Open Source

Apache version 2, commercial enterprise edition

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, Clojure

C++

Erlang

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Linux
OS X
Windows

Linux
OS X

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

restricted

SQL

Support of SQL

yes

does not fully support an SQL-standard

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

Erlang

Triggers

By using transaction functions

Client-side triggers through changefeeds

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

range based

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

links between data sets can be stored

Transaction concepts

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

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

User concepts

Access control

fine grained access rights according to SQL-standard

yes

users and table-level permissions

yes, using Riak Security

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.