DBMS > Drizzle vs. DuckDB vs. EsgynDB vs. HEAVY.AI

System Properties Comparison Drizzle vs. DuckDB vs. EsgynDB vs. HEAVY.AI

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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.

An embeddable, in-process, column-oriented SQL OLAP RDBMS

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	4.57
Rank	#74	Overall
	#40	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Website

duckdb.org

www.esgyn.cn

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

Technical documentation

duckdb.org/docs

docs.heavy.ai

Developer

Drizzle project, originally started by Brian Aker

Esgyn

HEAVY.AI, Inc.

Initial release

2008

2018

2015

2016

Current release

7.2.4, September 2012

0.10, February 2024

5.10, January 2022

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

MIT License

commercial

Open Source

Apache Version 2; enterprise edition 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

C++

C++, Java

C++ and CUDA

Server operating systems

FreeBSD
Linux
OS X

server-less

Linux

Data scheme

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

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

JDBC

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

ADO.NET
JDBC
ODBC

JDBC
ODBC
Thrift
Vega

Supported programming languages

C
C++
Java
PHP

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

All languages supporting JDBC/ODBC/ADO.Net

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Round robin

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Multi-source replication between multi datacenters

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

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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Recent citations in the news

My First Billion (of Rows) in DuckDB | by João Pedro | May, 2024
1 May 2024, Towards Data Science

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5 March 2024, Datanami

DuckDB and AWS — How to Aggregate 100 Million Rows in 1 Minute
25 April 2024, Towards Data Science

MotherDuck Raises $52.5 Million Series B Funding as DuckDB Adoption Soars
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