DBMS > GigaSpaces vs. Google BigQuery vs. HyperSQL

System Properties Comparison GigaSpaces vs. Google BigQuery vs. HyperSQL

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Editorial information provided by DB-Engines

Name

Description

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Large scale data warehouse service with append-only tables

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Secondary database models

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Score	0.89
Rank	#198	Overall
	#33	Document stores
	#7	Object oriented DBMS

Score	51.18
Rank	#19	Overall
	#13	Relational DBMS

Score	3.11
Rank	#86	Overall
	#47	Relational DBMS

Website

www.gigaspaces.com

cloud.google.com/bigquery

hsqldb.org

Technical documentation

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

hsqldb.org/web/hsqlDocsFrame.html

Developer

Gigaspaces Technologies

Google

Initial release

2000

2010

2001

Current release

15.5, September 2020

2.7.2, June 2023

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

based on BSD license

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

Java

Server operating systems

Linux
macOS
Solaris
Windows

hosted

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Data scheme

schema-free

yes

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.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

APIs and other access methods

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

RESTful HTTP/JSON API

HTTP API

JDBC via HTTP
JDBC
ODBC

Supported programming languages

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC
Java

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Java, SQL

Triggers

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

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

Role-based access control

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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