DBMS > Milvus vs. ReductStore

System Properties Comparison Milvus vs. ReductStore

Please select another system to include it in the comparison.

Our visitors often compare Milvus and ReductStore with Typesense, Apache Solr and Oracle.

Editorial information provided by DB-Engines

Name

Description

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

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Vector DBMS

Time Series DBMS

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

Score	2.77
Rank	#91	Overall
	#7	Vector DBMS

Score	0.07
Rank	#354	Overall
	#37	Time Series DBMS

Website

milvus.io

github.com/reductstore
www.reduct.store

Technical documentation

milvus.io/docs/overview.md

www.reduct.store/docs

Developer

ReductStore LLC

Initial release

2019

2023

Current release

2.4.4, May 2024

1.9, March 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Business Source License 1.1

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.

Implementation language

C++, Go

C++, Rust

Server operating systems

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Docker
Linux
macOS
Windows

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

HTTP API

Supported programming languages

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

C++
JavaScript (Node.js)
Python
Rust

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.

yes

User concepts

Access control

Role based access control and fine grained access rights

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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

More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

show all

Recent citations in the news

Milvus 2.5 Creates the Best of Both Worlds With Hybrid Vector-Keyword Search
17 December 2024, GlobeNewswire

Hands-On Vector Similarity Search with Milvus
20 October 2024, packtpub.com

AI-Powered Search Engine With Milvus Vector Database on Vultr
7 November 2024, SitePoint

Dance between dense and sparse embeddings: Enabling Hybrid Search in LangChain-Milvus
19 November 2024, Towards Data Science

Open source vector database vendor targets enterprise AI costs with cloud update
19 November 2024, VentureBeat

provided by Google News

Share this page