DBMS > 4D vs. AntDB vs. Apache Phoenix vs. KairosDB vs. Spark SQL

System Properties Comparison 4D vs. AntDB vs. Apache Phoenix vs. KairosDB vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

A scalable, multi-tenant, MPP-architectured RDBMS for OLTP and OLAP operations, highly compatible with Oracle

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

Distributed Time Series DBMS based on Cassandra or H2

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	0.23
Rank	#317	Overall
	#141	Relational DBMS

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	0.67
Rank	#233	Overall
	#20	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.4d.com

www.asiainfo.com/en_us/product_aisware_antdb_detail.html

phoenix.apache.org

github.com/kairosdb/kairosdb

spark.apache.org/sql

Technical documentation

developer.4d.com

phoenix.apache.org

kairosdb.github.io

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

4D, Inc

AsiaInfo Technologies Limited

Apache Software Foundation

Initial release

1984

2014

2013

2014

Current release

v20, April 2023

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

1.2.2, November 2018

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Scala

Server operating systems

OS X
Windows

Linux
Unix
Windows

Linux
OS X
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

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

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

SQL2016 compliant

yes

SQL-like DML and DDL statements

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
MySQL protocol compliant
ODBC

JDBC

Graphite protocol
HTTP REST
Telnet API

JDBC
ODBC

Supported programming languages

4D proprietary IDE
PHP

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

Java
JavaScript

Node.js
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

User concepts

Access control

Users and groups

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

simple password-based access control

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