DBMS > HBase vs. Microsoft Azure Cosmos DB vs. Oracle Coherence vs. Quasardb

System Properties Comparison HBase vs. Microsoft Azure Cosmos DB vs. Oracle Coherence vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

Globally distributed, horizontally scalable, multi-model database service

Oracles in-memory data grid solution

Distributed, high-performance timeseries database

Primary database model

Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

hbase.apache.org

azure.microsoft.com/services/cosmos-db

www.oracle.com/java/coherence

quasar.ai

Technical documentation

hbase.apache.org/book.html

learn.microsoft.com/azure/cosmos-db

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

doc.quasar.ai/master

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

Oracle

quasardb

Initial release

2008

2014

2007

2009

Current release

2.3.4, January 2021

14.1, August 2023

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

C++

Server operating systems

Linux
Unix
Windows

using Cygwin

hosted

All OS with a Java VM

BSD
Linux
OS X
Windows

Data scheme

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

JSON types

yes

integer and binary

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

with tags

SQL

Support of SQL

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP API
Thrift

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

JCache
JPA
RESTful HTTP API

HTTP API

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

.Net
C++
Java

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

Coprocessors in Java

JavaScript

Triggers

yes

JavaScript

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

yes, with selectable consistency level

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

with Hadoop integration

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

Foreign keys

Referential integrity

Transaction concepts

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

Single row ACID (across millions of columns)

Multi-item ACID transactions with snapshot isolation within a partition

configurable

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

yes

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access rights can be defined down to the item level

authentification to access the cache via certificates or http basic authentication

Cryptographically strong user authentication and audit trail

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