DBMS > Milvus

Milvus System Properties

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Editorial information provided by DB-Engines

Name

Description

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

Primary database model

Vector DBMS

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

Score	1.31
Rank	#205	Overall
	#4	Vector DBMS

Website

milvus.io

Technical documentation

milvus.io/docs/overview.md

Initial release

2019

Current release

2.2, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

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Database as a Service

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Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C++, Go

Server operating systems

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Typing

predefined data types such as float or date

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

SQL

Support of SQL

APIs and other access methods

RESTful HTTP API

Supported programming languages

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

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Foreign keys

Referential integrity

Transaction concepts

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

User concepts

Access control

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready AI. It was created to store,

index, and manage billion, or even trillion-scale embedding vectors generated by deep neural networks and other machine learning (ML) models.

As a database specifically designed to handle queries over vectors, it is capable of indexing vectors on a trillion 

scale. Unlike existing relational databases which mainly deal with structured data following a pre-defined 

pattern, Milvus is designed from the bottom-up to handle embedding vectors converted from unstructured data 

like emails, papers, IoT sensor data, Facebook photos, protein structures, and much more.

Competitive advantages

Highly available, versatile, and robust with millisecond latency.
Supports batch and stream data processing in real time.
Supports flexible scaling of each component by using Kubernetes to manage its execution engine and separating read, write and other services with microservices.
Supports Time Travel with distributed logging.
Supports heterogenous computing, which includes support for different types of hardware (CPU, GPU, FPGA, MLU) and instruction sets.
Supported by Rich APIs (Python, Node.js, Go, etc.) and tools (Milvus CLI, Attu, etc.) to facilitate DevOps.
Supports high performance ANN algorithms like FAISS，HNSW，ANNOY, etc.

Typical application scenarios

Video media: video understanding, video deduplication.
E-commerce and mobile applications: image understanding, reverse image search.
Finance/Telecommunications/Retail: AI-aided customer support, QA chatbots.
Internet: personalized recommender systems, personalized search.
Autonomous vehicles: automated data labeling and annotation, object detection.
Biopharmaceutical: virtual compound screening, compound retrosynthetic analysis, protein property prediction, and DNA testing.
Cybersecurity: malware detection and cyberattack alert.
Quantitative trading: data analysis and prediction.
Metaverse: environmental perception and interaction in virtual world.

See more in Milvus Bootcamp.

Key customers

Milvus is trusted by over 1,000 enterprises, including Baidu, eBay, IKEA, LINE, Shopee, Shutterstock, SmartNews,

Tencent, Tokopedia, TrendMicro, Walmart, and much more.

See Milvus Adopters for more Milvus users.

Market metrics

As of February 2023,

14k+ GitHub stars
2M+ downloads and installations
1000+ enterprise adopters

Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019.

Fully-managed vector database cloud service available now. Sign up for a free trial of Zilliz Cloud.

Related products and services

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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Recent citations in the news

What Is a Vector Database, and How Do They Boost AI? - MUO
25 May 2023, MakeUseOf

The Rise Of Vector Databases
19 May 2023, Forbes

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22 May 2023, The New Stack

LLM integration takes Cloudera data lakehouse from Big Data to Big AI
6 June 2023, VentureBeat

How to create a private ChatGPT that interacts with your local documents
1 June 2023, TechTalks

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