DBMS > Google BigQuery vs. mSQL vs. OrigoDB vs. Spark SQL vs. Tibero

System Properties Comparison Google BigQuery vs. mSQL vs. OrigoDB vs. Spark SQL vs. Tibero

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

mSQL (Mini SQL) is a simple and lightweight RDBMS

A fully ACID in-memory object graph database

Spark SQL is a component on top of 'Spark Core' for structured data processing

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Website

cloud.google.com/bigquery

hughestech.com.au/products/msql

origodb.com

spark.apache.org/sql

us.tmaxsoft.com/products/tibero

Technical documentation

cloud.google.com/bigquery/docs

origodb.com/docs

spark.apache.org/docs/latest/sql-programming-guide.html

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Google

Hughes Technologies

Robert Friberg et al

Apache Software Foundation

TmaxSoft

Initial release

2010

1994

2009

under the name LiveDB

2014

2003

Current release

4.4, October 2021

3.5.0 ( 2.13), September 2023

6, April 2015

License

Commercial or Open Source

commercial

free licenses can be provided

Open Source

Apache 2.0

commercial

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

Scala

C and Assembler

Server operating systems

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
Windows

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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.

can be achieved using .NET

yes

Secondary indexes

yes

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

.NET Client API
HTTP API
LINQ

JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net

Java
Python
R
Scala

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Persistent Stored Procedure (PSM)

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

client side managed; servers are not synchronized

yes, utilizing Spark Core

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

none

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

none

Immediate Consistency

Foreign keys

Referential integrity

depending on model

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

yes

In-memory capabilities

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

yes

planned for next version

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Role based authorization

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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