DBMS > Badger vs. Kyligence Enterprise vs. Milvus vs. SWC-DB

System Properties Comparison Badger vs. Kyligence Enterprise vs. Milvus vs. SWC-DB

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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 distributed analytics engine for big data, built on top of Apache Kylin

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

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Time Series DBMS

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

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

Score	0.46
Rank	#266	Overall
	#124	Relational DBMS

Score	2.78
Rank	#103	Overall
	#4	Vector DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

github.com/dgraph-io/badger

kyligence.io/kyligence-enterprise

milvus.io

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

Technical documentation

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

milvus.io/docs/overview.md

Developer

DGraph Labs

Kyligence, Inc.

Alex Kashirin

Initial release

2017

2016

2019

2020

Current release

2.4.4, May 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

Open Source

GPL V3

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

Java

C++, Go

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

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

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

Proprietary protocol
Thrift

Supported programming languages

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

C++

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

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

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

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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