DBMS > Google BigQuery vs. Oracle Rdb vs. Sphinx vs. UniData,UniVerse vs. VoltDB

System Properties Comparison Google BigQuery vs. Oracle Rdb vs. Sphinx vs. UniData,UniVerse vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

MultiValue database and application server with SQL mapping layer and meta database capabilities

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

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

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	3.16
Rank	#97	Overall
	#2	Multivalue DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

cloud.google.com/bigquery

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

sphinxsearch.com

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-unidata

www.voltdb.com

Technical documentation

cloud.google.com/bigquery/docs

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

sphinxsearch.com/docs

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

docs.voltdb.com

Developer

Google

Oracle, originally developed by Digital Equipment Corporation (DEC)

Sphinx Technologies Inc.

Rocket Software

VoltDB Inc.

Initial release

2010

1984

2001

1985

2010

Current release

7.4.1.1, 2021

3.5.1, February 2023

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

GPL version 2, commercial licence available

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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++

Java, C++

Server operating systems

hosted

HP Open VMS

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X

for development

Data scheme

yes

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

yes

schema-free

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.

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

yes

only a subset of SQL 99

APIs and other access methods

RESTful HTTP/JSON API

Proprietary protocol

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

.Net
Basic

U2 Basic
C
Java

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Java

Triggers

yes

U2 Basic

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

Since BigQuery is designed for querying data

yes, on a single node

ACID

configurable

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

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

yes

Snapshots and command logging

In-memory capabilities

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights according to SQL-standard and operating system based

Users and roles with access to stored procedures

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