DBMS > Badger vs. Bangdb vs. Milvus vs. OpenQM

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

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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.

Converged and high performance database for device data, events, time series, document and graph

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

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

Primary database model

Key-value store

Document store
Graph DBMS
Time Series DBMS

Vector DBMS

Multivalue DBMS

Secondary database models

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	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	2.78
Rank	#103	Overall
	#4	Vector DBMS

Score	0.34
Rank	#284	Overall
	#10	Multivalue DBMS

Website

github.com/dgraph-io/badger

bangdb.com

milvus.io

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

Technical documentation

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

docs.bangdb.com

milvus.io/docs/overview.md

Developer

DGraph Labs

Sachin Sinha, BangDB

Rocket Software, originally Martin Phillips

Initial release

2017

2012

2019

1993

Current release

BangDB 2.0, October 2021

2.4.4, May 2024

3.4-12

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD 3

Open Source

Apache Version 2.0

Open Source

GPLv2, extended 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

Implementation language

C, C++

C++, Go

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Data scheme

schema-free

yes

with some exceptions

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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.

yes

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

APIs and other access methods

Proprietary protocol
RESTful HTTP API

RESTful HTTP API

Supported programming languages

C
C#
C++
Java
Python

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

.Net
Basic
C
Java
Objective C
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor, Knob for CAP (enterprise version only)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Tunable consistency, set CAP knob accordingly

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Role based access control and fine grained access rights

Access rights can be defined down to the item level

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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