DBMS > KeyDB vs. Milvus vs. RavenDB vs. Sadas Engine

System Properties Comparison KeyDB vs. Milvus vs. RavenDB vs. Sadas Engine

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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

Open Source Operational and Transactional Enterprise NoSQL Document Database

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
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
	#3	Vector DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

github.com/Snapchat/KeyDB
keydb.dev

milvus.io

ravendb.net

www.sadasengine.com

Technical documentation

docs.keydb.dev

milvus.io/docs/overview.md

ravendb.net/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

EQ Alpha Technology Ltd.

Hibernating Rhinos

SADAS s.r.l.

Initial release

2019

2010

2006

Current release

2.3.4, January 2024

5.4, July 2022

8.0

License

Commercial or Open Source

Open Source

BSD-3

Open Source

Apache Version 2.0

Open Source

AGPL version 3, commercial license available

commercial

free trial version 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

Implementation language

C++

C++, Go

C++

Server operating systems

Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
macOS
Raspberry Pi
Windows

AIX
Linux
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 (RQL)

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protoco

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

JDBC
ODBC
Proprietary protocol

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

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.

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

ACID, Cluster-wide transaction available

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

managed by 'Learn by Usage'

User concepts

Access control

simple password-based access control and ACL

Role based access control and fine grained access rights

Authorization levels configured per client per database

Access rights for users, groups and roles 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

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2 June 2023, Matthias Gelbmann

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