DBMS > Couchbase vs. InfluxDB vs. Oracle Berkeley DB vs. RDF4J

System Properties Comparison Couchbase vs. InfluxDB vs. Oracle Berkeley DB vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

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

DBMS for storing time series, events and metrics

Widely used in-process key-value store

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Document store

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

RDF store

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

Spatial DBMS

with GEO package

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	24.39
Rank	#28	Overall
	#1	Time Series DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

www.couchbase.com

www.influxdata.com/products/influxdb-overview

www.oracle.com/database/technologies/related/berkeleydb.html

rdf4j.org

Technical documentation

docs.couchbase.com

docs.influxdata.com/influxdb

docs.oracle.com/cd/E17076_05/html/index.html

rdf4j.org/documentation

Developer

Couchbase, Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2011

2013

1994

2004

Current release

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

2.7.6, April 2024

18.1.40, May 2020

License

Commercial or Open Source

Open Source

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

Open Source

MIT-License; commercial enterprise version available

Open Source

commercial license available

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

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

C, Java, C++ (depending on the Berkeley DB edition)

Java

Server operating systems

Linux
OS X
Windows

Linux
OS X

through Homebrew

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like query language

yes

SQL interfaced based on SQLite is available

APIs and other access methods

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

HTTP API
JSON over UDP

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Java
PHP
Python

Server-side scripts

Stored procedures

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

yes

Triggers

yes

via the TAP protocol

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

in enterprise version only

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

selectable replication factor

in enterprise version only

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Ephemeral buckets

yes

Depending on used storage engine

yes

User concepts

Access control

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

simple rights management via user accounts

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Key customers

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Market metrics

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Licensing and pricing models

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