DBMS > Apache Phoenix vs. Microsoft Azure Cosmos DB vs. MonetDB vs. Oracle Coherence vs. Warp 10

System Properties Comparison Apache Phoenix vs. Microsoft Azure Cosmos DB vs. MonetDB vs. Oracle Coherence vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

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

Globally distributed, horizontally scalable, multi-model database service

A relational database management system that stores data in columns

Oracles in-memory data grid solution

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Key-value store

Time Series DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

phoenix.apache.org

azure.microsoft.com/services/cosmos-db

www.monetdb.org

www.oracle.com/java/coherence

www.warp10.io

Technical documentation

phoenix.apache.org

learn.microsoft.com/azure/cosmos-db

www.monetdb.org/Documentation

docs.oracle.com/en/middleware/standalone/coherence

www.warp10.io/content/02_Getting_started

Developer

Apache Software Foundation

Microsoft

MonetDB BV

Oracle

SenX

Initial release

2014

2004

2007

2015

Current release

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

Dec2023 (11.49), December 2023

14.1, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Mozilla Public License 2.0

commercial

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux
Unix
Windows

hosted

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

yes

SQL 2003 with some extensions

APIs and other access methods

JDBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JCache
JPA
RESTful HTTP API

HTTP API
Jupyter
WebSocket

Supported programming languages

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

.Net
C++
Java

Server-side scripts

Stored procedures

user defined functions

JavaScript

yes, in SQL, C, R

yes

WarpScript

Triggers

JavaScript

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding via remote tables

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

none

Source-replica replication available in experimental status

yes, with selectable consistency level

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

authentification to access the cache via certificates or http basic authentication

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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