DBMS > Amazon DocumentDB vs. Apache IoTDB vs. EsgynDB vs. Hazelcast vs. MongoDB

System Properties Comparison Amazon DocumentDB vs. Apache IoTDB vs. EsgynDB vs. Hazelcast vs. MongoDB

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A widely adopted in-memory data grid

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

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS
Search engine

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

Time Series Collections introduced in Release 5.0
Vector DBMS

currently available in the MongoDB Atlas cloud service only

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	421.65
Rank	#5	Overall
	#1	Document stores

Website

aws.amazon.com/documentdb

Technical documentation

aws.amazon.com/documentdb/resources

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

hazelcast.org/imdg/docs

www.mongodb.com/docs/manual

Developer

Apache Software Foundation

Esgyn

Hazelcast

MongoDB, Inc

Initial release

2019

2018

2015

2008

2009

Current release

1.1.0, April 2023

5.3.6, November 2023

6.0.7, June 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2; commercial licenses available

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

yes

MongoDB available as DBaaS (MongoDB Atlas)

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++, Java

Java

C++

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

Linux

All OS with a Java VM

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

Read-only SQL queries via the MongoDB Atlas SQL Interface

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
Native API

ADO.NET
JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
C#
C++
Go
Java
Python
Scala

All languages supporting JDBC/ODBC/ADO.Net

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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
Kotlin
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

yes

Java Stored Procedures

yes

Event Listeners, Executor Services

JavaScript

Triggers

yes

Events

yes

in MongoDB Atlas only

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

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-availability zones for high availability, asynchronous replication for up to 15 read replicas

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication between multi datacenters

yes

Replicated Map

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

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

can be individually decided for each read operation
Immediate Consistency

default behaviour

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Atomic single-document operations

ACID

one or two-phase-commit; repeatable reads; read commited

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 rights for users and roles

yes

fine grained access rights according to SQL-standard

Role-based access control

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

MongoDB provides an integrated suite of cloud database and data services to accelerate...
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Competitive advantages

Built around the flexible document data model and unified API, MongoDB is a developer...
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Typical application scenarios

AI-enriched intelligent apps (Continental, Telefonica, Iron Mountain) Internet of...
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Key customers

ADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...
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Market metrics

Hundreds of millions downloads, over 150,000+ Atlas clusters provisioned every month...
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Licensing and pricing models

MongoDB database server: Server-Side Public License (SSPL) . Commercial licenses...
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