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

System Properties Comparison 4D vs. Apache Phoenix vs. Elasticsearch 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

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

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

Secondary database models

Document store
Spatial DBMS
Vector 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	132.83
Rank	#7	Overall
	#1	Search engines

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.elastic.co/elasticsearch

cloud.google.com/datastore

github.com/kairosdb/kairosdb

Technical documentation

developer.4d.com

phoenix.apache.org

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

cloud.google.com/datastore/docs

kairosdb.github.io

Developer

4D, Inc

Apache Software Foundation

Elastic

Google

Initial release

1984

2014

2010

2008

2013

Current release

v20, April 2023

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

8.6, January 2023

1.2.2, November 2018

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Elastic License

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

Server operating systems

OS X
Windows

Linux
Unix
Windows

All OS with a Java VM

hosted

Linux
OS X
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

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

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

close to SQL 92

yes

SQL-like query language

SQL-like query language (GQL)

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC

Java API
RESTful HTTP/JSON 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

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

.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

using Google App Engine

Triggers

yes

by using the 'percolation' feature

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication
Source-replica replication

yes

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

ES-Hadoop Connector

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

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

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

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

Memcached and Redis integration

User concepts

Access control

Users and groups

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

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

simple password-based access control

More information provided by the system vendor

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