DBMS > GigaSpaces vs. GridDB vs. Lovefield vs. SwayDB vs. Yanza

System Properties Comparison GigaSpaces vs. GridDB vs. Lovefield vs. SwayDB vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. 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

Scalable in-memory time series database optimized for IoT and Big Data

Embeddable relational database for web apps written in pure JavaScript

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Time Series DBMS for IoT Applications

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Secondary database models

Graph DBMS
Search engine

Key-value store
Relational DBMS

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

Score	0.91
Rank	#197	Overall
	#33	Document stores
	#7	Object oriented DBMS

Score	1.91
Rank	#123	Overall
	#10	Time Series DBMS

Score	0.22
Rank	#306	Overall
	#138	Relational DBMS

Score	0.00
Rank	#384	Overall
	#60	Key-value stores

Website

www.gigaspaces.com

griddb.net

google.github.io/lovefield

swaydb.simer.au

yanza.com

Technical documentation

docs.gigaspaces.com/latest/landing.html

docs.griddb.net

github.com/google/lovefield/blob/master/docs/spec_index.md

Developer

Gigaspaces Technologies

Toshiba Corporation

Google

Simer Plaha

Yanza

Initial release

2000

2013

2014

2018

2015

Current release

15.5, September 2020

5.1, August 2022

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

Apache 2.0

Open Source

GNU Affero GPL V3.0

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net

C++

JavaScript

Scala

Server operating systems

Linux
macOS
Solaris
Windows

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

HTTP API

Supported programming languages

.Net
C++
Java
Python
Scala

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

JavaScript

Java
Kotlin
Scala

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

Triggers

yes, event driven architecture

yes

Using read-only observers

yes

Timer and event based

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

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID at container level

ACID

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

Role-based access control

Access rights for users can be defined per database

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More resources

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