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DBMS > Milvus

Milvus System Properties

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Editorial information provided by DB-Engines
DescriptionA DBMS designed for efficient storage of vector data and vector similarity searches
Primary database modelSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Rank#259  Overall
#17  Search engines
Technical documentationmilvus.io/­docs/­overview.md
Initial release2019
Current release2.0, January 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++, Go
Server operating systemsLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Typing infopredefined data types such as float or dateNumeric and String
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.no
Secondary indexesno
SQL infoSupport of SQLno
APIs and other access methodsRESTful HTTP API
Supported programming languagesC++
JavaScript (Node.js)
Server-side scripts infoStored proceduresno
Partitioning methods infoMethods for storing different data on different nodesSharding
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Foreign keys infoReferential integrityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano
Concurrency infoSupport for concurrent manipulation of datayes
Durability infoSupport for making data persistentyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.no
User concepts infoAccess controlno
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 March 2022,
  • 9.8k+ GitHub stars
  • 1.4k+ Github forks
  • 871k+ docker pulls
  • 2k+ community members on Slack alone (represents only a portion of the community)​
Licensing and pricing models
Milvus was released under the open-source Apache License 2.0 in October 2019.

Managed Cloud Service (coming soon).

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
Recent citations in the news

Top 10 Companies Working on Autonomous Mobile Robots in 2022
22 May 2022, Analytics Insight

Milvus, the AI Search Engine, Originally Developed by Zilliz Joins LF AI as New Incubation Project
10 April 2020, PR Newswire

What Is Vector Similarity Search? | Business Applications of Vector Search
11 August 2021, CIO Insight

Database technology evolves to combine machine learning and data storage
7 March 2022, VentureBeat

The Recommendation System
15 February 2022, News | Europeana Pro

provided by Google News

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