DBMS > FeatureBase vs. Milvus vs. RavenDB
System Properties Comparison FeatureBase vs. Milvus vs. RavenDB
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|Editorial information provided by DB-Engines
|FeatureBase Xexclude from comparison
|Milvus Xexclude from comparison
|RavenDB Xexclude from comparison
|Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.
|A DBMS designed for efficient storage of vector data and vector similarity searches
|Open Source Operational and Transactional Enterprise NoSQL Document Database
|Primary database model
|Secondary database models
Time Series DBMS
|Molecula and Pilosa Open Source Contributors
|2022, May 2022
|2.3.4, January 2024
|5.4, July 2022
|License Commercial or Open Source
|Open Source Apache Version 2.0
|Open Source AGPL version 3, commercial license available
|Cloud-based only Only available as a cloud service
|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
|Server operating systems
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.
|SQL Support of SQL
|SQL-like query language (RQL)
|APIs and other access methods
|RESTful HTTP API
|.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API
|Supported programming languages
|Server-side scripts Stored procedures
|Partitioning methods Methods for storing different data on different nodes
|Replication methods Methods for redundantly storing data on multiple nodes
|MapReduce Offers an API for user-defined Map/Reduce methods
|Consistency concepts Methods to ensure consistency in a distributed system
|Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.
|Foreign keys Referential integrity
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data
|ACID, Cluster-wide transaction available
|Concurrency Support for concurrent manipulation of data
|Durability Support for making data persistent
|yes, using Linux fsync
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.
|User concepts Access control
|Role based access control and fine grained access rights
|Authorization levels configured per client per database
|More information provided by the system vendor
|Milvus is an open-source and cloud-native vector database built for production-ready...
|Highly available, versatile, and robust with millisecond latency. Supports batch...
|Typical application scenarios
|RAG: retrieval augmented generation Video media : video understanding, video deduplication....
|Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
|As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
|Licensing and pricing models
|Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
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