DBMS > Amazon DocumentDB vs. Amazon Redshift vs. Badger vs. HEAVY.AI

System Properties Comparison Amazon DocumentDB vs. Amazon Redshift vs. Badger vs. HEAVY.AI

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Large scale data warehouse service for use with business intelligence tools

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Primary database model

Secondary database models

Spatial DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Website

aws.amazon.com/documentdb

aws.amazon.com/redshift

github.com/dgraph-io/badger

github.com/heavyai/heavydb
www.heavy.ai

Technical documentation

aws.amazon.com/documentdb/resources

docs.aws.amazon.com/redshift

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

docs.heavy.ai

Developer

Amazon (based on PostgreSQL)

DGraph Labs

HEAVY.AI, Inc.

Initial release

2019

2012

2017

2016

Current release

5.10, January 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition 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

C++ and CUDA

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

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

yes

restricted

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
ODBC

JDBC
ODBC
Thrift
Vega

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Round robin

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

yes

none

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

informational only, not enforced by the system

Transaction concepts

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

Atomic single-document operations

ACID

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

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

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.