DBMS > 4D vs. Apache Phoenix vs. Faircom EDGE vs. Google Cloud Datastore vs. KairosDB

System Properties Comparison 4D vs. Apache Phoenix vs. Faircom EDGE vs. Google Cloud Datastore vs. KairosDB

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

A scale-out RDBMS with evolutionary schema built on Apache HBase

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

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

Distributed Time Series DBMS based on Cassandra or H2

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Document store

Time Series DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.67
Rank	#233	Overall
	#20	Time Series DBMS

Website

www.4d.com

phoenix.apache.org

www.faircom.com/products/faircom-edge

cloud.google.com/datastore

github.com/kairosdb/kairosdb

Technical documentation

developer.4d.com

phoenix.apache.org

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

cloud.google.com/datastore/docs

kairosdb.github.io

Developer

4D, Inc

Apache Software Foundation

FairCom Corporation

Google

Initial release

1984

2014

1979

2008

2013

Current release

v20, April 2023

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

V3, October 2020

1.2.2, November 2018

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Restricted, free version available

commercial

Open Source

Apache 2.0

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

ANSI C, C++

Java

Server operating systems

OS X
Windows

Linux
Unix
Windows

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Linux
OS X
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

Flexible Schema (defined schema, partial schema, schema free)

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

yes

ANSI SQL queries

SQL-like query language (GQL)

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

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

Graphite protocol
HTTP REST
Telnet API

Supported programming languages

4D proprietary IDE
PHP

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

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

Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions

yes

.Net, JavaScript, C/C++

using Google App Engine

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

File partitioning

Customizable business rules for partitioning

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication
Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency
Tunable 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

Foreign keys

Referential integrity

yes

when using SQL

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

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

simple password-based access control

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