DBMS > 4D vs. Google Cloud Datastore vs. Hawkular Metrics vs. Interbase vs. STSdb

System Properties Comparison 4D vs. Google Cloud Datastore vs. Hawkular Metrics vs. Interbase vs. STSdb

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Light-weight proven RDBMS

originally from Borland

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

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	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

www.4d.com

cloud.google.com/datastore

www.hawkular.org

www.embarcadero.com/products/interbase

github.com/STSSoft/STSdb4

Technical documentation

developer.4d.com

cloud.google.com/datastore/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

docs.embarcadero.com/products/interbase

Developer

4D, Inc

Google

Community supported by Red Hat

Embarcadero

STS Soft SC

Initial release

1984

2008

2014

1984

2011

Current release

v20, April 2023

InterBase 2020, December 2019

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

GPLv2, 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

Server operating systems

OS X
Windows

hosted

Linux
OS X
Windows

Android
iOS
Linux
OS X
Windows

Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

primitive types and user defined types (classes)

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

export as XML data possible

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

HTTP REST

ADO.NET
JDBC
ODBC

.NET Client API

Supported programming languages

4D proprietary IDE
PHP

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

Go
Java
Python
Ruby

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

C#
Java

Server-side scripts

Stored procedures

yes

using Google App Engine

yes

Interbase procedure and trigger language

Triggers

yes

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

Interbase Change Views

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

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.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Multiversion concurreny control

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

Users and groups

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

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

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