DBMS > Hawkular Metrics vs. Microsoft Azure Cosmos DB vs. Realm vs. Riak KV

System Properties Comparison Hawkular Metrics vs. Microsoft Azure Cosmos DB vs. Realm vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

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

Globally distributed, horizontally scalable, multi-model database service

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Distributed, fault tolerant key-value store

Primary database model

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Spatial DBMS

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

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

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

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

www.hawkular.org

azure.microsoft.com/services/cosmos-db

realm.io

Technical documentation

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

learn.microsoft.com/azure/cosmos-db

realm.io/docs

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Community supported by Red Hat

Microsoft

Realm, acquired by MongoDB in May 2019

OpenSource, formerly Basho Technologies

Initial release

2014

2009

Current release

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache version 2, commercial enterprise edition

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

Erlang

Server operating systems

Linux
OS X
Windows

hosted

Android
Backend: server-less
iOS
Windows

Linux
OS X

Data scheme

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

restricted

SQL

Support of SQL

SQL-like query language

APIs and other access methods

HTTP REST

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

HTTP API
Native Erlang Interface

Supported programming languages

Go
Java
Python
Ruby

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

.Net
Java

with Android only
Objective-C
React Native
Swift

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

JavaScript

runs within the applications so server-side scripts are unnecessary

Erlang

Triggers

yes

via Hawkular Alerting

JavaScript

yes

Change Listeners

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Implicit feature of the cloud service

none

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

Implicit feature of the cloud service

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

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

In-Memory realm

User concepts

Access control

Access rights can be defined down to the item level

yes

yes, using Riak Security

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