DBMS > Oracle Berkeley DB vs. OrigoDB vs. Quasardb vs. XTDB

System Properties Comparison Oracle Berkeley DB vs. OrigoDB vs. Quasardb vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Widely used in-process key-value store

A fully ACID in-memory object graph database

Distributed, high-performance timeseries database

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store
Object oriented DBMS

Time Series DBMS

Document store

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

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

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

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

origodb.com

quasar.ai

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

origodb.com/docs

doc.quasar.ai/master

www.xtdb.com/docs

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Robert Friberg et al

quasardb

Juxt Ltd.

Initial release

1994

2009

under the name LiveDB

2009

2019

Current release

18.1.40, May 2020

3.14.1, January 2024

1.19, September 2021

License

Commercial or Open Source

Open Source

commercial license available

Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

MIT License

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

C++

Clojure

Server operating systems

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
Windows

BSD
Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

User defined using .NET types and collections

yes

integer and binary

yes, extensible-data-notation format

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

can be achieved using .NET

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

.NET Client API
HTTP API
LINQ

HTTP API

HTTP REST
JDBC

Supported programming languages

.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

.Net

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Clojure
Java

Server-side scripts

Stored procedures

yes

Triggers

yes

only for the SQL API

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

client side managed; servers are not synchronized

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Source-replica replication with selectable replication factor

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

depending on model

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

Write ahead log

yes

by using LevelDB

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Role based authorization

Cryptographically strong user authentication and audit trail

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