DBMS > Apache Druid vs. Couchbase vs. HyperSQL vs. Oracle NoSQL vs. SpaceTime

System Properties Comparison Apache Druid vs. Couchbase vs. HyperSQL vs. Oracle NoSQL vs. SpaceTime

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 integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Relational DBMS
Time Series DBMS

Document store

Relational DBMS

Document store
Key-value store
Relational DBMS

Spatial DBMS

Secondary database models

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

Relational DBMS

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

Score	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	16.74
Rank	#33	Overall
	#5	Document stores

Score	3.14
Rank	#85	Overall
	#46	Relational DBMS

Score	3.07
Rank	#86	Overall
	#15	Document stores
	#11	Key-value stores
	#47	Relational DBMS

Score	0.00
Rank	#385	Overall
	#7	Spatial DBMS

Website

druid.apache.org

www.couchbase.com

hsqldb.org

www.oracle.com/database/nosql/technologies/nosql

www.mireo.com/spacetime

Technical documentation

druid.apache.org/docs/latest/design

docs.couchbase.com

hsqldb.org/web/hsqlDocsFrame.html

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Apache Software Foundation and contributors

Couchbase, Inc.

Oracle

Mireo

Initial release

2012

2011

2001

2011

2020

Current release

30.0.0, June 2024

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

2.7.2, June 2023

24.1, May 2024

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

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

Open Source

based on BSD license

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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

Java

C++

Server operating systems

Linux
OS X
Unix

Linux
OS X
Windows

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
Solaris SPARC/x86

Linux

Data scheme

yes

schema-less columns are supported

schema-free

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

yes

optional

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

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

yes

SQL-like DML and DDL statements

A subset of ANSI SQL is implemented

APIs and other access methods

JDBC
RESTful HTTP/JSON API

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

HTTP API

JDBC via HTTP
JDBC
ODBC

RESTful HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC
Java

C
C#
Go
Java
JavaScript (Node.js)
Python

C#
C++
Python

Server-side scripts

Stored procedures

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

Java, SQL

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Automatic Sharding

none

Sharding

Fixed-grid hypercubes

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

none

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Real-time block device replication (DRBD)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

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

yes

off heap cache

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. Role-base access control.

fine grained access rights according to SQL-standard

Access rights for users and roles

yes

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