DBMS > Amazon Aurora vs. Couchbase vs. Hawkular Metrics vs. KairosDB

System Properties Comparison Amazon Aurora vs. Couchbase vs. Hawkular Metrics vs. KairosDB

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Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

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

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

Distributed Time Series DBMS based on Cassandra or H2

Primary database model

Relational DBMS

Document store

Time Series DBMS

Secondary database models

Document store

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

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

Score	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Website

aws.amazon.com/rds/aurora

www.couchbase.com

www.hawkular.org

github.com/kairosdb/kairosdb

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

docs.couchbase.com

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

kairosdb.github.io

Developer

Amazon

Couchbase, Inc.

Community supported by Red Hat

Initial release

2015

2011

2014

2013

Current release

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

1.2.2, November 2018

License

Commercial or Open Source

commercial

Open Source

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

Open Source

Apache 2.0

Open Source

Apache 2.0

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

C, C++, Go and Erlang

Java

Server operating systems

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

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

APIs and other access methods

ADO.NET
JDBC
ODBC

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

HTTP REST

Graphite protocol
HTTP REST
Telnet API

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Go
Java
Python
Ruby

Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

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

Triggers

yes

via the TAP protocol

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Automatic Sharding

Sharding

based on Cassandra

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

including cross data center replication
Source-replica replication

selectable replication factor

based on Cassandra

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

Ephemeral buckets

User concepts

Access control

fine grained access rights according to SQL-standard

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

simple password-based access control

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