DBMS > Hawkular Metrics vs. Microsoft Azure Cosmos DB vs. Microsoft Azure SQL Database vs. RethinkDB vs. ToroDB

System Properties Comparison Hawkular Metrics vs. Microsoft Azure Cosmos DB vs. Microsoft Azure SQL Database vs. RethinkDB vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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

Database as a Service offering with high compatibility to Microsoft SQL Server

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Document store

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Website

www.hawkular.org

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/products/azure-sql/database

rethinkdb.com

github.com/torodb/server

Technical documentation

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

learn.microsoft.com/azure/cosmos-db

docs.microsoft.com/en-us/azure/azure-sql

rethinkdb.com/docs

Developer

Community supported by Red Hat

Microsoft

The Linux Foundation

since July 2017

8Kdata

Initial release

2014

2010

2009

2016

Current release

V12

2.4.1, August 2020

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2

Open Source

AGPL-V3

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

C++

Java

Server operating systems

Linux
OS X
Windows

hosted

Linux
OS X
Windows

All OS with a Java 7 VM

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

string, binary, float, bool, date, geometry

yes

string, integer, double, boolean, date, object_id

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.

yes

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

HTTP REST

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

ADO.NET
JDBC
ODBC

Supported programming languages

Go
Java
Python
Ruby

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

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

JavaScript

Transact SQL

Triggers

yes

via Hawkular Alerting

JavaScript

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Implicit feature of the cloud service

Sharding

range based

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

Implicit feature of the cloud service

yes, with always 3 replicas available

Source-replica replication

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
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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.

User concepts

Access control

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

yes

users and table-level permissions

Access rights for users and roles

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