DBMS > Heroic vs. MonetDB

System Properties Comparison Heroic vs. MonetDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

Primary database model

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	0.02
Rank	#365	Overall
	#37	Time Series DBMS

Score	1.70
Rank	#135	Overall
	#62	Relational DBMS

Website

github.com/spotify/heroic

www.monetdb.org

Technical documentation

spotify.github.io/heroic

www.monetdb.org/Documentation

Developer

Spotify

MonetDB BV

Initial release

2014

2004

Current release

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Mozilla Public License 2.0

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

Server operating systems

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

APIs and other access methods

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

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

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

Server-side scripts

Stored procedures

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

How Code Wizards load tested Heroic Labs’ Nakama to two million concurrent players with AWS
1 October 2024, AWS Blog

provided by Google News

ServiceNow ordered a year's worth of hardware to avoid supply chain hassles
25 May 2022, The Register

provided by Google News

Share this page