DBMS > Drizzle vs. HEAVY.AI vs. mSQL vs. PouchDB vs. Snowflake

System Properties Comparison Drizzle vs. HEAVY.AI vs. mSQL vs. PouchDB vs. Snowflake

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

JavaScript DBMS with an API inspired by CouchDB

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

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

hughestech.com.au/products/msql

pouchdb.com

www.snowflake.com

Technical documentation

docs.heavy.ai

pouchdb.com/guides

docs.snowflake.net/manuals/index.html

Developer

Drizzle project, originally started by Brian Aker

HEAVY.AI, Inc.

Hughes Technologies

Apache Software Foundation

Snowflake Computing Inc.

Initial release

2008

2016

1994

2012

2014

Current release

7.2.4, September 2012

5.10, January 2022

4.4, October 2021

7.1.1, June 2019

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache Version 2; enterprise edition available

commercial

free licenses can be provided

Open Source

commercial

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

C++

C++ and CUDA

JavaScript

Server operating systems

FreeBSD
Linux
OS X

Linux

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

server-less, requires a JavaScript environment (browser, Node.js)

hosted

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

via views

SQL

Support of SQL

yes

with proprietary extensions

yes

A subset of ANSI SQL is implemented

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

yes

APIs and other access methods

JDBC

JDBC
ODBC
Thrift
Vega

JDBC
ODBC

HTTP REST

only for PouchDB Server
JavaScript API

CLI Client
JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

JavaScript

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

Triggers

hooks for callbacks inside the server can be used.

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding

with a proxy-based framework, named couchdb-lounge

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

none

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

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

by using IndexedDB, WebSQL or LevelDB as backend

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

Users with fine-grained authorization concept, user roles and pluggable authentication

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.