DBMS > HBase vs. Microsoft Azure Cosmos DB vs. RavenDB vs. RRDtool

System Properties Comparison HBase vs. Microsoft Azure Cosmos DB vs. RavenDB vs. RRDtool

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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

Open Source Operational and Transactional Enterprise NoSQL Document Database

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Primary database model

Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Time Series DBMS

Secondary database models

Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

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

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Website

hbase.apache.org

azure.microsoft.com/services/cosmos-db

ravendb.net

oss.oetiker.ch/rrdtool

Technical documentation

hbase.apache.org/book.html

learn.microsoft.com/azure/cosmos-db

ravendb.net/docs

oss.oetiker.ch/rrdtool/doc

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

Hibernating Rhinos

Tobias Oetiker

Initial release

2008

2014

2010

1999

Current release

2.3.4, January 2021

5.4, July 2022

1.8.0, 2022

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

AGPL version 3, commercial license available

Open Source

GPL V2 and FLOSS

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

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

Linux
Unix
Windows

using Cygwin

hosted

Linux
macOS
Raspberry Pi
Windows

HP-UX
Linux

Data scheme

schema-free, schema definition possible

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

JSON types

Numeric data only

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.

Exporting into and restoring from XML files possible

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (RQL)

APIs and other access methods

Java API
RESTful HTTP API
Thrift

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

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

in-process shared library
Pipes

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#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Server-side scripts

Stored procedures

yes

Coprocessors in Java

JavaScript

yes

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

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

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

none

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

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

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

Access rights can be defined down to the item level

Authorization levels configured per client per database

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