DBMS > Altibase vs. Milvus vs. OrigoDB vs. Qdrant vs. RDF4J

System Properties Comparison Altibase vs. Milvus vs. OrigoDB vs. Qdrant vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

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

A fully ACID in-memory object graph database

A high-performance vector database with neural network or semantic-based matching

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Relational DBMS

Vector DBMS

Document store
Object oriented DBMS

Vector DBMS

RDF store

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

Score	1.00
Rank	#194	Overall
	#91	Relational DBMS

Score	1.81
Rank	#144	Overall
	#5	Vector DBMS

Score	0.03
Rank	#378	Overall
	#51	Document stores
	#18	Object oriented DBMS

Score	1.23
Rank	#171	Overall
	#6	Vector DBMS

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Website

altibase.com

milvus.io

origodb.com

github.com/qdrant/qdrant
qdrant.tech

rdf4j.org

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

milvus.io/docs/overview.md

origodb.com/docs

qdrant.tech/documentation

rdf4j.org/documentation

Developer

Altibase

Robert Friberg et al

Qdrant

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

1999

2019

2009

under the name LiveDB

2021

2004

Current release

v7.3, 2023, August 2023

2.3.4, January 2024

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

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++, Go

Rust

Java

Server operating systems

AIX
HP-UX
Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
Windows

Docker
Linux
macOS
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

User defined using .NET types and collections

Numbers, Strings, Geo, Boolean

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.

can be achieved using .NET

Secondary indexes

yes

Keywords, numberic ranges, geo, full-text

yes

SQL

Support of SQL

ANSI SQL-92

APIs and other access methods

JDBC
ODBC
Proprietary protocol

RESTful HTTP API

.NET Client API
HTTP API
LINQ

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C
C++
Java

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

.Net

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

Java
PHP
Python

Server-side scripts

Stored procedures

stored procedures and stored functions

yes

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

client side managed; servers are not synchronized

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Collection-level replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency, tunable consistency

Foreign keys

Referential integrity

yes

depending on model

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

yes

in-memory storage is supported as well

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

Role based authorization

Key-based authentication

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

We invite representatives of system vendors to contact us for updating and extending the system information,
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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