DBMS > KeyDB vs. Milvus vs. SWC-DB vs. Valentina Server

System Properties Comparison KeyDB vs. Milvus vs. SWC-DB vs. Valentina Server

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Editorial information provided by DB-Engines

Name

Description

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

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

A high performance, scalable Wide Column DBMS

Object-relational database and reports server

Primary database model

Secondary database models

Time Series DBMS

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

Score	0.70
Rank	#229	Overall
	#32	Key-value stores

Score	2.78
Rank	#103	Overall
	#4	Vector DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

github.com/Snapchat/KeyDB
keydb.dev

milvus.io

github.com/kashirin-alex/swc-db
www.swcdb.org

www.valentina-db.net

Technical documentation

docs.keydb.dev

milvus.io/docs/overview.md

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

EQ Alpha Technology Ltd.

Alex Kashirin

Paradigma Software

Initial release

2019

2020

1999

Current release

2.4.4, May 2024

0.5, April 2021

5.7.5

License

Commercial or Open Source

Open Source

BSD-3

Open Source

Apache Version 2.0

Open Source

GPL V3

commercial

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

Implementation language

C++

C++, Go

C++

Server operating systems

Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

Vector, Numeric and String

yes

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

yes

by using the Redis Search module

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protoco

RESTful HTTP API

Proprietary protocol
Thrift

ODBC

Supported programming languages

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

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

C++

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong eventual consistency with CRDTs

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Optimistic locking, atomic execution of commands blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

simple password-based access control and ACL

Role based access control and fine grained access rights

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
» more

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
» more

Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
» more

Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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