DBMS > Badger vs. Milvus vs. OpenQM vs. Vitess

System Properties Comparison Badger vs. Milvus vs. OpenQM vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

QpenQM is a high-performance, self-tuning, multi-value DBMS

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	2.78
Rank	#103	Overall
	#4	Vector DBMS

Score	0.34
Rank	#284	Overall
	#10	Multivalue DBMS

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

github.com/dgraph-io/badger

milvus.io

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

vitess.io

Technical documentation

godoc.org/github.com/dgraph-io/badger

milvus.io/docs/overview.md

vitess.io/docs

Developer

DGraph Labs

Rocket Software, originally Martin Phillips

The Linux Foundation, PlanetScale

Initial release

2017

2019

1993

2013

Current release

2.4.4, May 2024

3.4-12

15.0.2, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

GPLv2, extended commercial license available

Open Source

Apache Version 2.0, commercial licenses 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++, Go

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Docker
Linux
macOS

Data scheme

schema-free

yes

with some exceptions

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

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

.Net
Basic
C
Java
Objective C
PHP
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

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

none

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Access rights can be defined down to the item level

Users with fine-grained authorization concept

no user groups or roles

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

show all

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provided by Google News

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