DBMS > GigaSpaces vs. Google BigQuery vs. Hazelcast vs. Linter vs. Trafodion

System Properties Comparison GigaSpaces vs. Google BigQuery vs. Hazelcast vs. Linter vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

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

A widely adopted in-memory data grid

RDBMS for high security requirements

Transactional SQL-on-Hadoop DBMS

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Graph DBMS
Search engine

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Website

www.gigaspaces.com

cloud.google.com/bigquery

hazelcast.com

linter.ru

trafodion.apache.org

Technical documentation

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

trafodion.apache.org/documentation.html

Developer

Gigaspaces Technologies

Google

Hazelcast

relex.ru

Apache Software Foundation, originally developed by HP

Initial release

2000

2010

2008

1990

2014

Current release

15.5, September 2020

5.3.6, November 2023

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Apache 2.0

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

Java, C++, .Net

Java

C and C++

C++, Java

Server operating systems

Linux
macOS
Solaris
Windows

hosted

All OS with a Java VM

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux

Data scheme

schema-free

yes

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

yes

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

SQL-like query language

yes

APIs and other access methods

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

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

yes

proprietary syntax with the possibility to convert from PL/SQL

Java Stored Procedures

Triggers

yes, event driven architecture

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

Replicated Map

Source-replica replication

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

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

one or two-phase-commit; repeatable reads; read commited

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)

Role-based access control

fine grained access rights according to SQL-standard

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