DBMS > Drizzle vs. EsgynDB vs. Heroic vs. Kinetica vs. SQLite

System Properties Comparison Drizzle vs. EsgynDB vs. Heroic vs. Kinetica vs. SQLite

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.

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

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

Fully vectorized database across both GPUs and CPUs

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

www.esgyn.cn

github.com/spotify/heroic

www.kinetica.com

www.sqlite.org

Technical documentation

spotify.github.io/heroic

docs.kinetica.com

www.sqlite.org/docs.html

Developer

Drizzle project, originally started by Brian Aker

Esgyn

Spotify

Kinetica

Dwayne Richard Hipp

Initial release

2008

2015

2014

2012

2000

Current release

7.2.4, September 2012

7.1, August 2021

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache 2.0

commercial

Open Source

Public Domain

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

Java

C, C++

Server operating systems

FreeBSD
Linux
OS X

Linux

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

with proprietary extensions

yes

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC

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

JDBC
ODBC
RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C++
Java
PHP

All languages supporting JDBC/ODBC/ADO.Net

C++
Java
JavaScript (Node.js)
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

Triggers

hooks for callbacks inside the server can be used.

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication between multi datacenters

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

via file-system locks

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

GPU vRAM or System RAM

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

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