DBMS > Amazon DocumentDB vs. Apache Phoenix vs. Datomic

System Properties Comparison Amazon DocumentDB vs. Apache Phoenix vs. Datomic

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Editorial information provided by DB-Engines

Name

Description

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

A scale-out RDBMS with evolutionary schema built on Apache HBase

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

Primary database model

Document store

Relational DBMS

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

Score	1.89
Rank	#137	Overall
	#24	Document stores

Score	2.02
Rank	#130	Overall
	#63	Relational DBMS

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Website

aws.amazon.com/documentdb

phoenix.apache.org

www.datomic.com

Technical documentation

aws.amazon.com/documentdb/resources

phoenix.apache.org

docs.datomic.com

Developer

Apache Software Foundation

Cognitect

Initial release

2019

2014

2012

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

1.0.6735, June 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

limited edition free

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

Java, Clojure

Server operating systems

hosted

Linux
Unix
Windows

All OS with a Java VM

Data scheme

schema-free

yes

late-bound, schema-on-read capabilities

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

SQL

Support of SQL

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC

RESTful HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

yes

Transaction Functions

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

But extensive use of caching in the application peers

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication
Source-replica replication

none

But extensive use of caching in the application peers

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

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

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

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

Access rights for users and roles

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

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