DBMS > eXtremeDB vs. Google BigQuery vs. Oracle Rdb vs. Sphinx vs. Stardog

System Properties Comparison eXtremeDB vs. Google BigQuery vs. Oracle Rdb vs. Sphinx vs. Stardog

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

Open source search engine for searching in data from different sources, e.g. relational databases

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Search engine

Graph DBMS
RDF store

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

Score	0.74
Rank	#223	Overall
	#103	Relational DBMS
	#18	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

www.mcobject.com

cloud.google.com/bigquery

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

sphinxsearch.com

www.stardog.com

Technical documentation

www.mcobject.com/docs/extremedb.htm

cloud.google.com/bigquery/docs

www.oracle.com/database/technologies/related/rdb-doc.html

sphinxsearch.com/docs

docs.stardog.com

Developer

McObject

Google

Oracle, originally developed by Digital Equipment Corporation (DEC)

Sphinx Technologies Inc.

Stardog-Union

Initial release

2001

2010

1984

2001

2010

Current release

8.2, 2021

7.4.1.1, 2021

3.5.1, February 2023

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

GPL version 2, commercial licence available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

Server operating systems

AIX
HP-UX
Linux
macOS
Solaris
Windows

hosted

HP Open VMS

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
macOS
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free and OWL/RDFS-schema support

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

Import/export of XML data possible

Secondary indexes

yes

full-text index on all search fields

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

with the option: eXtremeSQL

yes

SQL-like query language (SphinxQL)

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

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

RESTful HTTP/JSON API

Proprietary protocol

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

by defining events

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning / sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

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

none

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

eXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
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Competitive advantages

eXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
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Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
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Key customers

Schneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
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Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
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Licensing and pricing models

For server use cases, there is a simple per-server license irrespective of the number...
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