DBMS > Amazon Aurora vs. BaseX vs. Couchbase vs. Hive

System Properties Comparison Amazon Aurora vs. BaseX vs. Couchbase vs. Hive

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Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Primary database model

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	7.57
Rank	#51	Overall
	#32	Relational DBMS

Score	1.84
Rank	#135	Overall
	#4	Native XML DBMS

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Website

aws.amazon.com/rds/aurora

basex.org

www.couchbase.com

hive.apache.org

Technical documentation

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

docs.basex.org

docs.couchbase.com

cwiki.apache.org/confluence/display/Hive/Home

Developer

Amazon

BaseX GmbH

Couchbase, Inc.

Apache Software Foundation

initially developed by Facebook

Initial release

2015

2007

2011

2012

Current release

11.0, June 2024

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

3.1.3, April 2022

License

Commercial or Open Source

commercial

Open Source

BSD license

Open Source

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

Open Source

Apache Version 2

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

C, C++, Go and Erlang

Java

Server operating systems

hosted

Linux
OS X
Windows

All OS with a Java VM

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

XQuery supports 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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

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

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

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

JDBC
ODBC
Thrift

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

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

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

yes

user defined functions and integration of map-reduce

Triggers

yes

via events

yes

via the TAP protocol

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Automatic Sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication

including cross data center replication
Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

multiple readers, single writer

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

Users with fine-grained authorization concept on 4 levels

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

Access rights for users, groups and roles

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