DBMS > Amazon DocumentDB vs. Apache Phoenix vs. Hawkular Metrics vs. Heroic

System Properties Comparison Amazon DocumentDB vs. Apache Phoenix vs. Hawkular Metrics vs. Heroic

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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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

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

Primary database model

Document store

Relational DBMS

Time Series DBMS

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	1.90
Rank	#125	Overall
	#59	Relational DBMS

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Website

aws.amazon.com/documentdb

phoenix.apache.org

www.hawkular.org

github.com/spotify/heroic

Technical documentation

aws.amazon.com/documentdb/resources

phoenix.apache.org

www.hawkular.org/hawkular-metrics/docs/user-guide

spotify.github.io/heroic

Developer

Apache Software Foundation

Community supported by Red Hat

Spotify

Initial release

2019

2014

Current release

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

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache 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

Server operating systems

hosted

Linux
Unix
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

late-bound, schema-on-read capabilities

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

via Elasticsearch

SQL

Support of SQL

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC

HTTP REST

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

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

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

Go
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

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

selectable replication factor

based on Cassandra

yes

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

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

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

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More resources

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