DBMS > 4D vs. Google Cloud Datastore vs. Splice Machine vs. TimesTen vs. WakandaDB

System Properties Comparison 4D vs. Google Cloud Datastore vs. Splice Machine vs. TimesTen vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

In-Memory RDBMS compatible to Oracle

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

Primary database model

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

www.4d.com

cloud.google.com/datastore

splicemachine.com

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

wakanda.github.io

Technical documentation

developer.4d.com

cloud.google.com/datastore/docs

splicemachine.com/how-it-works

docs.oracle.com/database/timesten-18.1

wakanda.github.io/doc

Developer

4D, Inc

Google

Splice Machine

Oracle, TimesTen Performance Software, HP

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

Wakanda SAS

Initial release

1984

2008

2014

1998

2012

Current release

v20, April 2023

3.1, March 2021

11 Release 2 (11.2.2.8.0)

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, commercial license available

commercial

Open Source

AGPLv3, extended commercial license available

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++, JavaScript

Server operating systems

OS X
Windows

hosted

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language (GQL)

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
Native Spark Datasource
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

RESTful HTTP API

Supported programming languages

4D proprietary IDE
PHP

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C
C++
Java
PL/SQL

JavaScript

Server-side scripts

Stored procedures

yes

using Google App Engine

yes

Java

PL/SQL

yes

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication using Paxos

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

Users and groups

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

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

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Related products and services

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More resources

DB-Engines blog posts

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

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