DBMS > EJDB vs. Heroic vs. ITTIA vs. mSQL vs. OpenTSDB

System Properties Comparison EJDB vs. Heroic vs. ITTIA vs. mSQL vs. OpenTSDB

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

Edge Time Series DBMS with Real-Time Stream Processing

mSQL (Mini SQL) is a simple and lightweight RDBMS

Scalable Time Series DBMS based on HBase

Primary database model

Secondary database models

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	0.39
Rank	#273	Overall
	#24	Time Series DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	1.68
Rank	#146	Overall
	#12	Time Series DBMS

Website

github.com/Softmotions/ejdb

github.com/spotify/heroic

www.ittia.com

hughestech.com.au/products/msql

opentsdb.net

Technical documentation

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

spotify.github.io/heroic

opentsdb.net/docs/build/html/index.html

Developer

Softmotions

Spotify

ITTIA L.L.C.

Hughes Technologies

currently maintained by Yahoo and other contributors

Initial release

2012

2014

2007

1994

2011

Current release

8.7

4.4, October 2021

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache 2.0

commercial

free licenses can be provided

Open Source

LGPL

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 and C++

Java

Server operating systems

server-less

Linux
Windows

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

Linux
Windows

Data scheme

schema-free

Fixed schema

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

Float/Blob/Integer/String/Unicode/Date/Time/Timestamp/Interval

yes

numeric data for metrics, strings for tags

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

A subset of ANSI SQL is implemented

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

APIs and other access methods

in-process shared library

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

JDBC
ODBC
RESTful HTTP API
WebSocket

JDBC
ODBC

HTTP API
Telnet API

Supported programming languages

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

C
C++
JavaScript (Node.js)
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

Erlang
Go
Java
Python
R
Ruby

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

none

selectable replication factor

based on HBase

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

based on HBase

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

yes

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

Database file passwords

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

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