DBMS > Drizzle vs. FatDB vs. Heroic vs. Kinetica vs. Pinecone

System Properties Comparison Drizzle vs. FatDB vs. Heroic vs. Kinetica vs. Pinecone

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.

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

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

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

Fully vectorized database across both GPUs and CPUs

A managed, cloud-native vector database

Primary database model

Relational DBMS

Document store
Key-value store

Time Series DBMS

Relational DBMS

Vector DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	3.02
Rank	#87	Overall
	#3	Vector DBMS

Website

github.com/spotify/heroic

www.kinetica.com

www.pinecone.io

Technical documentation

spotify.github.io/heroic

docs.kinetica.com

docs.pinecone.io/docs/overview

Developer

Drizzle project, originally started by Brian Aker

FatCloud

Spotify

Kinetica

Pinecone Systems, Inc

Initial release

2008

2012

2014

2012

2019

Current release

7.2.4, September 2012

7.1, August 2021

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache 2.0

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++

Java

C, C++

Server operating systems

FreeBSD
Linux
OS X

Windows

Linux

hosted

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

String, Number, Boolean

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

Via inetgration in SQL Server

SQL-like DML and DDL statements

APIs and other access methods

JDBC

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

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

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

Supported programming languages

C
C++
Java
PHP

C++
Java
JavaScript (Node.js)
Python

Python

Server-side scripts

Stored procedures

yes

via applications

user defined functions

Triggers

hooks for callbacks inside the server can be used.

yes

via applications

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Can implement custom security layer via applications

Access rights for users and roles on table level

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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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Vector databases
2 June 2023, Matthias Gelbmann

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Recent citations in the news

Kinetica Elevates RAG with Fast Access to Real-Time Data
26 March 2024, Datanami

Kinetica Delivers Real-Time Vector Similarity Search
21 March 2024, insideBIGDATA

Kinetica ramps up RAG for generative AI, empowering enterprises with real-time operational data
18 March 2024, SiliconANGLE News

Kinetica: AI is a ‘killer app’ for data analytics
2 May 2023, Blocks & Files

Kinetica Taps Dell for Hardware
12 June 2018, Finovate

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Pinecone serverless goes multicloud as vector database market heats up
27 August 2024, VentureBeat

Using the Pinecone vector database in .NET
12 September 2024, InfoWorld

Pinecone launches its serverless vector database out of preview
21 May 2024, TechCrunch

Pinecone launches serverless vector database on Azure, GCP
27 August 2024, TechTarget

Pinecone Makes Accurate, Fast, Scalable Generative AI Accessible to Organizations Large and Small with Launch of its Serverless Vector Database
21 May 2024, PR Newswire

provided by Google News

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