DBMS > eXtremeDB vs. Google Cloud Bigtable vs. Infobright vs. Oracle Berkeley DB

System Properties Comparison eXtremeDB vs. Google Cloud Bigtable vs. Infobright vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

Natively in-memory DBMS with options for persistency, high-availability and clustering

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Widely used in-process key-value store

Primary database model

Relational DBMS
Time Series DBMS

Key-value store
Wide column store

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

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

Score	0.79
Rank	#212	Overall
	#100	Relational DBMS
	#18	Time Series DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	0.88
Rank	#198	Overall
	#92	Relational DBMS

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Website

www.mcobject.com

cloud.google.com/bigtable

ignitetech.com/softwarelibrary/infobrightdb

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

Technical documentation

www.mcobject.com/docs/extremedb.htm

cloud.google.com/bigtable/docs

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

Developer

McObject

Google

Ignite Technologies Inc.; formerly InfoBright Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2001

2015

2005

1994

Current release

8.2, 2021

18.1.40, May 2020

License

Commercial or Open Source

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

commercial license available

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 and C++

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

Server operating systems

AIX
HP-UX
Linux
macOS
Solaris
Windows

hosted

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

yes

schema-free

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.

support of XML interfaces available

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

yes

with the option: eXtremeSQL

yes

SQL interfaced based on SQLite is available

APIs and other access methods

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
C
C#
C++
Java
Lua
Python
Scala

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

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.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

Server-side scripts

Stored procedures

yes

Triggers

yes

by defining events

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning / sharding

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Internal replication in Colossus, and regional replication between two clusters in different zones

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

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