DBMS > eXtremeDB vs. GigaSpaces vs. Google BigQuery vs. Heroic

System Properties Comparison eXtremeDB vs. GigaSpaces vs. Google BigQuery vs. Heroic

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Editorial information provided by DB-Engines

Name

Description

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

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Primary database model

Relational DBMS
Time Series DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Time Series DBMS

Secondary database models

Graph DBMS
Search engine

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

Score	0.80
Rank	#214	Overall
	#99	Relational DBMS
	#18	Time Series DBMS

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Website

www.mcobject.com

www.gigaspaces.com

cloud.google.com/bigquery

github.com/spotify/heroic

Technical documentation

www.mcobject.com/docs/extremedb.htm

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

spotify.github.io/heroic

Developer

McObject

Gigaspaces Technologies

Google

Spotify

Initial release

2001

2000

2010

2014

Current release

8.2, 2021

15.5, September 2020

License

Commercial or Open Source

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

C and C++

Java, C++, .Net

Java

Server operating systems

AIX
HP-UX
Linux
macOS
Solaris
Windows

Linux
macOS
Solaris
Windows

hosted

Data scheme

yes

schema-free

yes

schema-free

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

XML can be used for describing objects metadata

Secondary indexes

yes

via Elasticsearch

SQL

Support of SQL

yes

with the option: eXtremeSQL

SQL-99 for query and DML statements

yes

APIs and other access methods

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

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

RESTful HTTP/JSON API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Supported programming languages

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

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Triggers

yes

by defining events

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning / sharding

Sharding

none

Sharding

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

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

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

Eventual Consistency
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

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

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)

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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