DBMS > Drizzle vs. Google BigQuery vs. HEAVY.AI vs. Pinecone

System Properties Comparison Drizzle vs. Google BigQuery vs. HEAVY.AI vs. Pinecone

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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.

Large scale data warehouse service with append-only tables

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

A managed, cloud-native vector database

Primary database model

Relational DBMS

Vector DBMS

Secondary database models

Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	3.23
Rank	#92	Overall
	#3	Vector DBMS

Website

cloud.google.com/bigquery

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

www.pinecone.io

Technical documentation

cloud.google.com/bigquery/docs

docs.heavy.ai

docs.pinecone.io/docs/overview

Developer

Drizzle project, originally started by Brian Aker

Google

HEAVY.AI, Inc.

Pinecone Systems, Inc

Initial release

2008

2010

2016

2019

Current release

7.2.4, September 2012

5.10, January 2022

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C++ and CUDA

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux

hosted

Data scheme

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

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

JDBC

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

RESTful HTTP API

Supported programming languages

C
C++
Java
PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Since BigQuery is designed for querying data

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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