DBMS > Couchbase vs. JanusGraph vs. Microsoft Azure SQL Database vs. SiteWhere vs. TimesTen

System Properties Comparison Couchbase vs. JanusGraph vs. Microsoft Azure SQL Database vs. SiteWhere vs. TimesTen

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Database as a Service offering with high compatibility to Microsoft SQL Server

M2M integration platform for persisting/querying time series data

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

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

Document store
Graph DBMS
Spatial DBMS

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

Score	16.74
Rank	#33	Overall
	#5	Document stores

Score	1.85
Rank	#134	Overall
	#12	Graph DBMS

Score	72.95
Rank	#16	Overall
	#11	Relational DBMS

Score	0.07
Rank	#347	Overall
	#33	Time Series DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

www.couchbase.com

janusgraph.org

azure.microsoft.com/en-us/products/azure-sql/database

github.com/sitewhere/sitewhere

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

Technical documentation

docs.couchbase.com

docs.janusgraph.org

docs.microsoft.com/en-us/azure/azure-sql

sitewhere1.sitewhere.io/index.html

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

Developer

Couchbase, Inc.

Linux Foundation; originally developed as Titan by Aurelius

Microsoft

SiteWhere

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2011

2017

2010

1998

Current release

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

1.0.0, October 2023

V12

Release 22.1

License

Commercial or Open Source

Open Source

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

Open Source

Apache 2.0

commercial

Open Source

Common Public Attribution License Version 1.0

commercial

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

C++

Java

Server operating systems

Linux
OS X
Windows

Linux
OS X
Unix
Windows

hosted

Linux
OS X
Windows

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

schema-free

yes

predefined scheme

yes

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

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

yes

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC

HTTP REST

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Clojure
Java
Python

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

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

yes

Transact SQL

PL/SQL

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

based on HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

yes

yes, with always 3 replicas available

selectable replication factor

based on HBase

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency
Immediate Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Relationships in graphs

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

Ephemeral buckets

yes

User concepts

Access control

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

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

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