DBMS > Drizzle vs. Google Cloud Datastore vs. Milvus vs. PouchDB

System Properties Comparison Drizzle vs. Google Cloud Datastore vs. Milvus vs. PouchDB

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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.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

A DBMS designed for efficient storage of vector data and vector similarity searches

JavaScript DBMS with an API inspired by CouchDB

Primary database model

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Website

cloud.google.com/datastore

milvus.io

pouchdb.com

Technical documentation

cloud.google.com/datastore/docs

milvus.io/docs/overview.md

pouchdb.com/guides

Developer

Drizzle project, originally started by Brian Aker

Google

Apache Software Foundation

Initial release

2008

2019

2012

Current release

7.2.4, September 2012

2.3.4, January 2024

7.1.1, June 2019

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache Version 2.0

Open Source

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C++

C++, Go

JavaScript

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

Vector, Numeric and String

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 views

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language (GQL)

APIs and other access methods

JDBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

C
C++
Java
PHP

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++
Go
Java
JavaScript (Node.js)
Python

JavaScript

Server-side scripts

Stored procedures

using Google App Engine

View functions in JavaScript

Triggers

hooks for callbacks inside the server can be used.

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication using Paxos

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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 rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
» more

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
» more

Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
» more

Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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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?
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Vector databases
2 June 2023, Matthias Gelbmann

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New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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