DBMS > 4D vs. GigaSpaces vs. Google Cloud Spanner vs. Lovefield vs. TimesTen

System Properties Comparison 4D vs. GigaSpaces vs. Google Cloud Spanner vs. Lovefield vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

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

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Embeddable relational database for web apps written in pure JavaScript

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Secondary database models

Graph DBMS
Search engine

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

Score	1.92
Rank	#119	Overall
	#58	Relational DBMS

Score	1.10
Rank	#173	Overall
	#29	Document stores
	#6	Object oriented DBMS

Score	2.83
Rank	#91	Overall
	#50	Relational DBMS

Score	0.16
Rank	#322	Overall
	#143	Relational DBMS

Score	1.17
Rank	#167	Overall
	#76	Relational DBMS

Website

www.4d.com

www.gigaspaces.com

cloud.google.com/spanner

google.github.io/lovefield

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

developer.4d.com

docs.gigaspaces.com/latest/landing.html

cloud.google.com/spanner/docs

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

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

4D, Inc

Gigaspaces Technologies

Google

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

1984

2000

2017

2014

1998

Current release

v20, April 2023

15.5, September 2020

2.1.12, February 2017

Release 22.1

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

commercial

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

Java, C++, .Net

JavaScript

Server operating systems

OS X
Windows

Linux
macOS
Solaris
Windows

hosted

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

tested with Chrome, Firefox, IE, Safari

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

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.

yes

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-99 for query and DML statements

yes

Query statements complying to ANSI 2011

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

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

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

4D proprietary IDE
PHP

.Net
C++
Java
Python
Scala

Go
Java
JavaScript (Node.js)
Python

JavaScript

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

yes

PL/SQL

Triggers

yes

yes, event driven architecture

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Multi-source replication with 3 replicas for regional instances.

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

using Google Cloud Dataflow

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 depending on configuration

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

ACID

Strict serializable isolation

ACID

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

by means of logfiles and checkpoints

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

Users and groups

Role-based access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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2 March 2016, Paul Andlinger

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