DBMS > Apache Phoenix vs. InterSystems IRIS vs. MongoDB

System Properties Comparison Apache Phoenix vs. InterSystems IRIS vs. MongoDB

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Editorial information provided by DB-Engines

Name

Description

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

A containerised multi-model DBMS, interoperability and analytics data platform with wide capabilities for vertical and horizontal scalability

One of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructure

Primary database model

Relational DBMS

Document store
Key-value store
Object oriented DBMS
Relational DBMS

Document store

Secondary database models

Spatial DBMS
Search engine

integrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS

Time Series Collections introduced in Release 5.0

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

Score	3.09
Rank	#103	Overall
	#50	Relational DBMS

Score	3.06
Rank	#105	Overall
	#21	Document stores
	#20	Key-value stores
	#1	Object oriented DBMS
	#52	Relational DBMS

Score	458.78
Rank	#5	Overall
	#1	Document stores

Website

phoenix.apache.org

www.intersystems.com/products/intersystems-iris

www.mongodb.com

Technical documentation

phoenix.apache.org

docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls

docs.mongodb.com/manual

Developer

Apache Software Foundation

InterSystems

MongoDB, Inc

Initial release

2014

2018

2009

Current release

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

2021.1, June 2021

6.0.1, August 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

MongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.

Cloud-based only

Only available as a cloud service

MongoDB available as DBaaS (MongoDB Atlas)

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Implementation language

Java

C++

Server operating systems

Linux
Unix
Windows

AIX
Linux
macOS
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

depending on used data model

schema-free

Although schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.

Typing

predefined data types such as float or date

yes

string, integer, double, decimal, boolean, date, object_id, geospatial

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

Read-only SQL queries via the MongoDB Connector for BI

APIs and other access methods

JDBC

JDBC
ODBC
RESTful HTTP API

proprietary protocol using JSON

Supported programming languages

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

.Net
C++
Java
JavaScript (Node.js)
Python

Actionscript

unofficial driver
C
C#
C++
Clojure

unofficial driver
ColdFusion

unofficial driver
D

unofficial driver
Dart

unofficial driver
Delphi

unofficial driver
Erlang
Go
Groovy

unofficial driver
Haskell
Java
JavaScript
Lisp

unofficial driver
Lua

unofficial driver
MatLab

unofficial driver
Perl
PHP
PowerShell

unofficial driver
Prolog

unofficial driver
Python
R

unofficial driver
Ruby
Rust
Scala
Smalltalk

unofficial driver
Swift

Server-side scripts

Stored procedures

user defined functions

yes

JavaScript

Triggers

yes

in MongoDB Atlas only

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

Multi-Source deployments with MongoDB Atlas Global Clusters
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

yes

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

ACID

Multi-document ACID Transactions with snapshot isolation

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional, enabled by default

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

In-memory storage engine introduced with MongoDB version 3.2

User concepts

Access control

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

yes

Access rights for users and roles

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