DBMS > Apache Druid vs. Couchbase vs. OushuDB

System Properties Comparison Apache Druid vs. Couchbase vs. OushuDB

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

A distributed document store with a powerful search engine and in-built operational and analytical capabilities

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Primary database model

Relational DBMS
Time Series DBMS

Document store

Relational DBMS

Secondary database models

Key-value store

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

using the Geocouch extension

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

Score	3.26
Rank	#112	Overall
	#57	Relational DBMS
	#8	Time Series DBMS

Score	25.34
Rank	#31	Overall
	#5	Document stores

Score	0.12
Rank	#387	Overall
	#159	Relational DBMS

Website

druid.apache.org

www.couchbase.com

www.oushu.com/product/oushuDB

Technical documentation

druid.apache.org/docs/latest/design

docs.couchbase.com

www.oushu.com/documentation

Developer

Apache Software Foundation and contributors

Couchbase, Inc.

Oushu

Initial release

2012

2011

Current release

26.0.0, May 2023

Server: 7.0, July 2021; Mobile: 3, February 2022; Couchbase Capella, October 2021

4.0.1, August 2020

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache version 2; Commercial licenses also available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++, Go and Erlang

Server operating systems

Linux
OS X
Unix

Linux
OS X
Windows

Linux

Data scheme

yes

schema-less columns are supported

schema-free

yes

Typing

predefined data types such as float or date

yes

String, Boolean, Number

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

SQL

Support of SQL

SQL for querying

Declarative query language (N1QL) that extends ANSI SQL to JSON. First commercial implementation of SQL++.

Full-featured ANSI SQL support

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Native language bindings for CRUD, Query, Search and Analytics APIs

JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Scala

C
C++

Server-side scripts

Stored procedures

Functions and timers in JavaScript

yes

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication

including cross data center replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Foreign keys

Referential integrity

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

User and Administrator separation with password-based and LDAP integrated Authentication

Kerberos, SSL and role based access

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