DBMS > JaguarDB vs. Newts vs. Oracle Berkeley DB vs. Oracle NoSQL vs. TimesTen

System Properties Comparison JaguarDB vs. Newts vs. Oracle Berkeley DB vs. Oracle NoSQL vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Performant, highly scalable DBMS for AI and IoT applications

Time Series DBMS based on Cassandra

Widely used in-process key-value store

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

In-Memory RDBMS compatible to Oracle

Primary database model

Key-value store
Vector DBMS

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store
Key-value store
Relational DBMS

Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

www.jaguardb.com

opennms.github.io/newts

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

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

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

Technical documentation

www.jaguardb.com/support.html

github.com/OpenNMS/newts/wiki

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

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

docs.oracle.com/database/timesten-18.1

Developer

DataJaguar, Inc.

OpenNMS Group

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Oracle

Oracle, TimesTen Performance Software, HP

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

Initial release

2015

2014

1994

2011

1998

Current release

3.3 July 2023

18.1.40, May 2020

23.3, December 2023

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

GPL V3.0

Open Source

Apache 2.0

Open Source

commercial license available

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

the server part. Clients available in other languages

Java

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

Java

Server operating systems

Linux

Linux
OS X
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
Solaris SPARC/x86

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

schema-free

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

SQL interfaced based on SQLite is available

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

HTTP REST
Java API

RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Java

.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

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

PL/SQL

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

based on Cassandra

Source-replica replication

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency or Eventual Consistency depending on configuration

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)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

off heap cache

yes

User concepts

Access control

rights management via user accounts

Access rights for users and roles

fine grained access rights according to SQL-standard

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