DBMS > Couchbase vs. DataFS vs. Hawkular Metrics vs. HBase vs. OpenTSDB

System Properties Comparison Couchbase vs. DataFS vs. Hawkular Metrics vs. HBase vs. OpenTSDB

Editorial information provided by DB-Engines

Name

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

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

Scalable Time Series DBMS based on HBase

Primary database model

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Graph DBMS

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

Website

Technical documentation

docs.couchbase.com

dev.mobiland.com/Overview.xsp

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

hbase.apache.org/book.html

opentsdb.net/docs/build/html/index.html

Developer

Couchbase, Inc.

Mobiland AG

Community supported by Red Hat

Apache Software Foundation

Apache top-level project, originally developed by Powerset

currently maintained by Yahoo and other contributors

Initial release

2011

2018

2014

2008

2011

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

1.1.263, October 2022

2.3.4, January 2021

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

Open Source

Apache 2.0

Open Source

Apache version 2

Open Source

LGPL

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++, Go and Erlang

Java

Server operating systems

Linux
OS X
Windows

Windows

Linux
OS X
Windows

Linux
Unix
Windows

using Cygwin

Linux
Windows

Data scheme

schema-free

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

numeric data for metrics, strings for tags

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

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

.NET Client API
Proprietary client DLL
WinRT client

HTTP REST

Java API
RESTful HTTP API
Thrift

HTTP API
Telnet API

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

.Net
C
C#
C++
VB.Net

Go
Java
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

Erlang
Go
Java
Python
R
Ruby

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes

Coprocessors in Java

Triggers

yes

via the TAP protocol

no, except callback-events from server when changes happened

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Proprietary Sharding system

Sharding

based on Cassandra

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency or Eventual 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

Single row ACID (across millions of columns)

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

Ephemeral buckets

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Windows-Profile

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

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