DBMS > Milvus vs. OpenQM vs. Sequoiadb vs. XTDB

System Properties Comparison Milvus vs. OpenQM vs. Sequoiadb vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

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

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

NewSQL database with distributed OLTP and SQL

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Vector DBMS

Multivalue DBMS

Document store
Relational DBMS

Document store

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

Score	3.01
Rank	#89	Overall
	#4	Vector DBMS

Score	0.28
Rank	#291	Overall
	#10	Multivalue DBMS

Score	0.42
Rank	#263	Overall
	#41	Document stores
	#121	Relational DBMS

Score	0.13
Rank	#330	Overall
	#45	Document stores

Website

milvus.io

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

www.sequoiadb.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

milvus.io/docs/overview.md

www.sequoiadb.com/en/index.php?m=Files&a=index

www.xtdb.com/docs

Developer

Rocket Software, originally Martin Phillips

Sequoiadb Ltd.

Juxt Ltd.

Initial release

2019

1993

2013

2019

Current release

2.4.4, May 2024

3.4-12

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPLv2, extended commercial license available

Open Source

Server: AGPL; Client: Apache V2

Open Source

MIT License

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

C++

Clojure

Server operating systems

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

with some exceptions

schema-free

Typing

predefined data types such as float or date

Vector, Numeric and String

yes

oid, date, timestamp, binary, regex

yes, extensible-data-notation format

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

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP API

proprietary protocol using JSON

HTTP REST
JDBC

Supported programming languages

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

.Net
Basic
C
Java
Objective C
PHP
Python

.Net
C++
Java
PHP
Python

Clojure
Java

Server-side scripts

Stored procedures

yes

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Document is locked during a transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

simple password-based access control

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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Recent citations in the news

AI-Powered Search Engine With Milvus Vector Database on Vultr - SitePoint
30 August 2024, SitePoint

How NVIDIA GPU Acceleration Supercharged Milvus Vector Database
26 March 2024, The New Stack

IBM watsonx.data’s integrated vector database: unify, prepare, and deliver your data for AI
9 April 2024, IBM

10 top vector database options for similarity searches
8 August 2024, TechTarget

Milvus 2.4 Unveils Game-Changing Features for Enhanced Vector Search
20 March 2024, GlobeNewswire

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