DBMS > EJDB vs. Heroic vs. mSQL vs. Sadas Engine

System Properties Comparison EJDB vs. Heroic vs. mSQL vs. Sadas Engine

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Editorial information provided by DB-Engines

Name

Description

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Document store

Time Series DBMS

Relational DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

github.com/Softmotions/ejdb

github.com/spotify/heroic

hughestech.com.au/products/msql

www.sadasengine.com

Technical documentation

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

spotify.github.io/heroic

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Softmotions

Spotify

Hughes Technologies

SADAS s.r.l.

Initial release

2012

2014

1994

2006

Current release

4.4, October 2021

8.0

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache 2.0

commercial

free licenses can be provided

commercial

free trial version available

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

C++

Server operating systems

server-less

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

AIX
Linux
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

A subset of ANSI SQL is implemented

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

yes

APIs and other access methods

in-process shared library

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

JDBC
ODBC

JDBC
ODBC
Proprietary protocol

Supported programming languages

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

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

none

Immediate Consistency

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

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.

yes

managed by 'Learn by Usage'

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

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