DBMS > 4D vs. GridDB vs. Oracle vs. Vertica vs. WakandaDB

System Properties Comparison 4D vs. GridDB vs. Oracle vs. Vertica vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

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

Widely used RDBMS

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Column oriented

Secondary database models

Key-value store
Relational DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

Spatial DBMS
Time Series DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

www.4d.com

griddb.net

www.oracle.com/database

www.vertica.com

wakanda.github.io

Technical documentation

developer.4d.com

docs.griddb.net

docs.oracle.com/en/database

vertica.com/documentation

wakanda.github.io/doc

Developer

4D, Inc

Toshiba Corporation

Oracle

OpenText

previously Micro Focus and Hewlett Packard

Wakanda SAS

Initial release

1984

2013

1980

2005

2012

Current release

v20, April 2023

5.1, August 2022

23c, September 2023

12.0.3, January 2023

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

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

commercial

restricted free version is available

commercial

Limited community edition free

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

C and C++

C++

C++, JavaScript

Server operating systems

OS X
Windows

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux

Linux
OS X
Windows

Data scheme

yes

Schemaless in JSON and XML columns

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

yes

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.

yes

Secondary indexes

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

close to SQL 92

SQL92, SQL-like TQL (Toshiba Query Language)

yes

with proprietary extensions

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

RESTful HTTP API

Supported programming languages

4D proprietary IDE
PHP

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

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

JavaScript

Server-side scripts

Stored procedures

yes

PL/SQL

also stored procedures in Java possible

yes, PostgreSQL PL/pgSQL, with minor differences

yes

Triggers

yes

yes, called Custom Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Sharding, horizontal partitioning

horizontal partitioning, hierarchical partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

Multi-source replication
Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

can be realized in PL/SQL

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Users and groups

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

yes

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
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Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

Cost-based models and subscription-based models are both available. One license is...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.