DBMS > Milvus vs. OpenQM vs. Vertica

System Properties Comparison Milvus vs. OpenQM vs. Vertica

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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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Vector DBMS

Multivalue DBMS

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	2.90
Rank	#87	Overall
	#7	Vector DBMS

Score	0.25
Rank	#293	Overall
	#10	Multivalue DBMS

Score	9.85
Rank	#42	Overall
	#26	Relational DBMS

Website

milvus.io

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

www.vertica.com

Technical documentation

milvus.io/docs/overview.md

vertica.com/documentation

Developer

Rocket Software, originally Martin Phillips

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2019

1993

2005

Current release

2.4.4, May 2024

3.4-12

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPLv2, extended commercial license available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Go

C++

Server operating systems

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux

Data scheme

yes

with some exceptions

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

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

.Net
Basic
C
Java
Objective C
PHP
Python

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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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

What Is Milvus? A Distributed Vector Database
3 March 2025, Oracle

Milvus 2.5 Creates the Best of Both Worlds With Hybrid Vector-Keyword Search
17 December 2024, GlobeNewswire

AI-Powered Search Engine With Milvus Vector Database on Vultr
7 November 2024, SitePoint

Dance between dense and sparse embeddings: Enabling Hybrid Search in LangChain-Milvus
19 November 2024, Towards Data Science

Open source vector database vendor targets enterprise AI costs with cloud update
19 November 2024, VentureBeat

provided by Google News

Introducing the Future of Data Analysis: A Revolutionary Tool for Vertica Users
25 October 2024, OpenText Blogs

Leveraging Vertica Performance by Reducing CPU System Calls
23 January 2025, Taboola.com

New browser-based query editor for OpenText Core Analytics Database accelerates and simplifies querying your data
25 November 2024, OpenText Blogs

Querying a Vertica data source in Amazon Athena using the Athena Federated Query SDK
11 February 2021, Amazon Web Services (AWS)

Vertica on Kubernetes
20 June 2024, OpenText Blogs

provided by Google News

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