DBMS > Ingres vs. JaguarDB vs. Oracle Berkeley DB vs. Riak TS

System Properties Comparison Ingres vs. JaguarDB vs. Oracle Berkeley DB vs. Riak TS

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Editorial information provided by DB-Engines

Name

Description

Well established RDBMS

Performant, highly scalable DBMS for AI and IoT applications

Widely used in-process key-value store

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

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

Score	4.11
Rank	#81	Overall
	#44	Relational DBMS

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

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

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

www.actian.com/databases/ingres

www.jaguardb.com

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

Technical documentation

docs.actian.com/ingres

www.jaguardb.com/support.html

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

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Actian Corporation

DataJaguar, Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Open Source, formerly Basho Technologies

Initial release

1974

originally developed at University Berkely in early 1970s

2015

1994

2015

Current release

11.2, May 2022

3.3 July 2023

18.1.40, May 2020

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

Open Source

commercial license available

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

the server part. Clients available in other languages

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

Erlang

Server operating systems

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X

Data scheme

yes

schema-free

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.

but tools for importing/exporting data from/to XML-files available

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

SQL interfaced based on SQLite is available

yes, limited

APIs and other access methods

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

JDBC
ODBC

HTTP API
Native Erlang Interface

Supported programming languages

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

Erlang

Triggers

yes

only for the SQL API

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Ingres Replicator

Multi-source replication

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

User concepts

Access control

fine grained access rights according to SQL-standard

rights management via user accounts

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