DBMS > Milvus

Milvus Systemeigenschaften

Bitte wählen Sie ein weiteres System aus, um es mit Milvus zu vergleichen.

Unsere Besucher vergleichen Milvus oft mit Weaviate, Elasticsearch und PostgreSQL.

Redaktionelle Informationen bereitgestellt von DB-Engines

Name

Kurzbeschreibung

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

Primäres Datenbankmodell

Suchmaschine

DB-Engines Ranking misst die Popularität von Datenbankmanagement- systemen
Trend Chart

Punkte	0,46
Rang	#250	Overall
	#17	Suchmaschinen

Website

milvus.io

Technische Dokumentation

milvus.io/docs/overview.md

Erscheinungsjahr

2019

Aktuelle Version

2.0, Jänner 2022

Lizenz

Commercial or Open Source

Open Source

Apache Version 2.0

Ausschließlich ein Cloud-Service

Nur als Cloud-Service verfügbar

nein

DBaaS Angebote (gesponserte Links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementierungssprache

C++, Go

Server Betriebssysteme

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Typisierung

vordefinierte Datentypen, z.B. float oder date

Numeric and String

XML Unterstützung

Verarbeitung von Daten in XML Format, beispielsweise Speicherung von XML-Strukturen und/oder Unterstützung von XPath, XQuery, XSLT

nein

Sekundärindizes

nein

SQL

Support of SQL

nein

APIs und andere Zugriffskonzepte

RESTful HTTP API

Unterstützte Programmiersprachen

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

Server-seitige Scripts

Stored Procedures

nein

Triggers

nein

Partitionierungsmechanismen

Methoden zum Speichern von unterschiedlichen Daten auf unterschiedlichen Knoten

Sharding

MapReduce

Bietet ein API für Map/Reduce Operationen

nein

Konsistenzkonzept

Methoden zur Sicherstellung der Konsistenz in einem verteilten System

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Fremdschlüssel

referenzielle Integrität

nein

Transaktionskonzept

Unterstützung zur Sicherstellung der Datenintegrität bei nicht-atomaren Datenmanipulationen

nein

Concurrency

Unterstützung von gleichzeitig ausgeführten Datenmanipulationen

Durability

Dauerhafte Speicherung der Daten

In-Memory Unterstützung

Gibt es Möglichkeiten einige oder alle Strukturen nur im Hauptspeicher zu halten

nein

Berechtigungskonzept

Zugriffskontrolle

nein

Weitere Informationen bereitgestellt vom Systemhersteller

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).

Zugehörige Produkte und Dienstleistungen

Wir laden Vertreter von Anbietern von zugehörigen Produkten ein uns zu kontaktieren, um hier Informationen über ihre Angebote zu präsentieren.

Weitere Ressourcen

Erwähnungen in aktuellen Nachrichten

Zilliz Pioneers Vector Database R&D, Shares New Findings at VLDB 2022
12. September 2022, Business Wire

Frank Liu, Director of Operations at Zilliz - Interview Series
9. September 2022, Unite.AI

Zilliz raises $60M for open source Milvus vector database
24. August 2022, TechTarget

Zilliz Announces Key Contributions to Milvus 2.1, the Leading Open-Source Vector Database for Structured and Unstructured Data
3. August 2022, Business Wire

Pinecone Announces New Features to Lower the Barrier of Entry for Vector Search
17. August 2022, insideBIGDATA

bereitgestellt von Google News

Jobangebote

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