DBMS > Drizzle vs. Google BigQuery vs. HEAVY.AI vs. mSQL vs. Weaviate

System Properties Comparison Drizzle vs. Google BigQuery vs. HEAVY.AI vs. mSQL vs. Weaviate

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

An AI-native realtime vector database engine that integrates scalable machine learning models.

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	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	1.52
Rank	#153	Overall
	#6	Vector DBMS

Website

cloud.google.com/bigquery

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

hughestech.com.au/products/msql

github.com/weaviate/weaviate
weaviate.io

Technical documentation

cloud.google.com/bigquery/docs

docs.heavy.ai

weaviate.io/developers/weaviate

Developer

Drizzle project, originally started by Brian Aker

Google

HEAVY.AI, Inc.

Hughes Technologies

Weaviate B.V.

Initial release

2008

2010

2016

1994

2019

Current release

7.2.4, September 2012

5.10, January 2022

4.4, October 2021

1.19, May 2023

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

free licenses can be provided

Open Source

commercial license available with Weaviate Enterprise

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

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux

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

Data scheme

yes

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

string, int, float, geo point, date, cross reference, fuzzy references

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

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

yes

with proprietary extensions

yes

A subset of ANSI SQL is implemented

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

GraphQL is used as query language

APIs and other access methods

JDBC

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

JDBC
ODBC

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

C
C++
Java
PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

JavaScript / TypeScript
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

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Eventual 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

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
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Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
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Key customers

All companies that have data.
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Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
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Licensing and pricing models

Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
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