DBMS > Drizzle vs. HEAVY.AI vs. Heroic vs. mSQL vs. Prometheus

System Properties Comparison Drizzle vs. HEAVY.AI vs. Heroic vs. mSQL vs. Prometheus

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

Open-source Time Series DBMS and monitoring system

Primary database model

Secondary database models

Spatial DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

github.com/spotify/heroic

hughestech.com.au/products/msql

prometheus.io

Technical documentation

docs.heavy.ai

spotify.github.io/heroic

prometheus.io/docs

Developer

Drizzle project, originally started by Brian Aker

HEAVY.AI, Inc.

Spotify

Hughes Technologies

Initial release

2008

2016

2014

1994

2015

Current release

7.2.4, September 2012

5.10, January 2022

4.4, October 2021

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

commercial

free licenses can be provided

Open Source

Apache 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

C++

C++ and CUDA

Java

Server operating systems

FreeBSD
Linux
OS X

Linux

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

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

A subset of ANSI SQL is implemented

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

APIs and other access methods

JDBC

JDBC
ODBC
Thrift
Vega

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

JDBC
ODBC

RESTful HTTP/JSON API

Supported programming languages

C
C++
Java
PHP

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

yes

none

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

none

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.

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

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