DBMS > Amazon DynamoDB vs. EJDB vs. Heroic vs. MonetDB vs. mSQL

System Properties Comparison Amazon DynamoDB vs. EJDB vs. Heroic vs. MonetDB vs. mSQL

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A relational database management system that stores data in columns

mSQL (Mini SQL) is a simple and lightweight RDBMS

Primary database model

Document store
Key-value store

Document store

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Website

aws.amazon.com/dynamodb

github.com/Softmotions/ejdb

github.com/spotify/heroic

www.monetdb.org

hughestech.com.au/products/msql

Technical documentation

docs.aws.amazon.com/dynamodb

github.com/Softmotions/ejdb/blob/master/README.md

spotify.github.io/heroic

www.monetdb.org/Documentation

Developer

Amazon

Softmotions

Spotify

MonetDB BV

Hughes Technologies

Initial release

2012

2014

2004

1994

Current release

Dec2023 (11.49), December 2023

4.4, October 2021

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

GPLv2

Open Source

Apache 2.0

Open Source

Mozilla Public License 2.0

commercial

free licenses can be provided

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

hosted

server-less

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

RESTful HTTP API

in-process shared library

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

yes, in SQL, C, R

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Sharding via remote tables

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

none

Source-replica replication available in experimental status

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency
Immediate Consistency

none

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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.

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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