DBMS > HugeGraph vs. Milvus vs. Oracle vs. Oracle NoSQL

System Properties Comparison HugeGraph vs. Milvus vs. Oracle vs. Oracle NoSQL

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Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

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

Widely used RDBMS

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Graph DBMS

Vector DBMS

Relational DBMS

Document store
Key-value store
Relational DBMS

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	2.78
Rank	#103	Overall
	#4	Vector DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Website

github.com/hugegraph
hugegraph.apache.org

milvus.io

www.oracle.com/database

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

hugegraph.apache.org/docs

milvus.io/docs/overview.md

docs.oracle.com/en/database

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Baidu

Oracle

Initial release

2018

2019

1980

2011

Current release

0.9

2.4.4, May 2024

23c, September 2023

24.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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 and C++

Java

Server operating systems

Linux
macOS
Unix

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
Solaris SPARC/x86

Data scheme

yes

Schemaless in JSON and XML columns

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

yes

optional

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

also supports composite index and range index

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

RESTful HTTP API

Supported programming languages

Groovy
Java
Python

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

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

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

Multi-source replication
Source-replica replication

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

can be realized in PL/SQL

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

isolation level can be parameterized

configurable

ACID within a storage node (=shard)

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

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

off heap cache

User concepts

Access control

Users, roles and permissions

Role based access control and fine grained access rights

fine grained access rights according to SQL-standard

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
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Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
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Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
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Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
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Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
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Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
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