DBMS > AnzoGraph DB vs. Milvus vs. OpenQM vs. SpaceTime

System Properties Comparison AnzoGraph DB vs. Milvus vs. OpenQM vs. SpaceTime

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Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

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

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

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Secondary database models

Relational DBMS

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

Score	0.22
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	3.01
Rank	#89	Overall
	#4	Vector DBMS

Score	0.28
Rank	#291	Overall
	#10	Multivalue DBMS

Score	0.00
Rank	#385	Overall
	#7	Spatial DBMS

Website

cambridgesemantics.com/anzograph

milvus.io

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

www.mireo.com/spacetime

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

milvus.io/docs/overview.md

Developer

Cambridge Semantics

Rocket Software, originally Martin Phillips

Mireo

Initial release

2018

2019

1993

2020

Current release

2.3, January 2021

2.4.4, May 2024

3.4-12

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2.0

Open Source

GPLv2, extended commercial license available

commercial

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++

Server operating systems

Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

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

SPARQL and SPARQL* as primary query language. Cypher preview.

A subset of ANSI SQL is implemented

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

RESTful HTTP API

Supported programming languages

C++
Java
Python

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

.Net
Basic
C
Java
Objective C
PHP
Python

C#
C++
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

yes

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes

Real-time block device replication (DRBD)

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

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

Access rights for users and roles

Role based access control and fine grained access rights

Access rights can be defined down to the item level

yes

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

AnzoGraph: A graph database for deep analytics
15 April 2019, InfoWorld

Cambridge Semantics Fits AnzoGraph DB with More Speed, Free Access
23 January 2020, Solutions Review

AnzoGraph: A W3C Standards-Based Graph Database | by Jo Stichbury
8 February 2019, Towards Data Science

Cambridge Semantics Unveils AnzoGraph DB with Geospatial Analytics
19 June 2020, Solutions Review

AWS Launches New Analytics Engine That Combines the Power Of Vector Search And Graph Data
1 December 2023, EnterpriseAI

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10 top vector database options for similarity searches
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20 March 2024, GlobeNewswire

provided by Google News

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