DBMS > Amazon DocumentDB vs. Amazon Redshift vs. Heroic vs. Tkrzw

System Properties Comparison Amazon DocumentDB vs. Amazon Redshift vs. Heroic vs. Tkrzw

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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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

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

Score	1.91
Rank	#131	Overall
	#24	Document stores

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

aws.amazon.com/documentdb

aws.amazon.com/redshift

github.com/spotify/heroic

dbmx.net/tkrzw

Technical documentation

aws.amazon.com/documentdb/resources

docs.aws.amazon.com/redshift

spotify.github.io/heroic

Developer

Amazon (based on PostgreSQL)

Spotify

Mikio Hirabayashi

Initial release

2019

2012

2014

2020

Current release

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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

C++

Server operating systems

hosted

Linux
macOS

Data scheme

schema-free

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.

Secondary indexes

yes

restricted

yes

via Elasticsearch

SQL

Support of SQL

yes

does not fully support an SQL-standard

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
ODBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

All languages supporting JDBC/ODBC

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

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

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

Eventual Consistency
Immediate Consistency

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

using specific database classes

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

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