DBMS > GeoMesa vs. Heroic vs. Milvus vs. Oracle Rdb

System Properties Comparison GeoMesa vs. Heroic vs. Milvus vs. Oracle Rdb

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Editorial information provided by DB-Engines

Name

Description

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

Primary database model

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

Score	0.81
Rank	#214	Overall
	#4	Spatial DBMS

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	1.81
Rank	#144	Overall
	#5	Vector DBMS

Score	1.08
Rank	#187	Overall
	#85	Relational DBMS

Website

www.geomesa.org

github.com/spotify/heroic

milvus.io

www.oracle.com/database/technologies/related/rdb.html

Technical documentation

www.geomesa.org/documentation/stable/user/index.html

spotify.github.io/heroic

milvus.io/docs/overview.md

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

CCRi and others

Spotify

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2014

2019

1984

Current release

4.0.5, February 2024

2.3.4, January 2024

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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

Scala

Java

C++, Go

Server operating systems

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

HP Open VMS

Data scheme

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

RESTful HTTP API

Supported programming languages

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

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Eventual Consistency
Immediate Consistency

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

yes, on a single node

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.

depending on storage layer

yes

User concepts

Access control

yes

depending on the DBMS used for storage

Role based access control and fine grained access rights

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

Spatial database management systems
6 April 2021, Matthias Gelbmann

show all

Vector databases
2 June 2023, Matthias Gelbmann

show all

Recent citations in the news

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

What Is Milvus Vector Database?
6 October 2023, The New Stack

AI-Powered Search Engine With Milvus Vector Database on Vultr
31 January 2024, SitePoint

Zilliz Unveils Game-Changing Features for Vector Search
22 March 2024, Datanami

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

Zilliz Cloud boosts vector database performance
31 January 2024, InfoWorld

provided by Google News

Oracle Adds New AI-Enabling Features To MySQL HeatWave
23 March 2023, Forbes

Oracle Business Model - How Oracle Makes Money?
12 June 2023, Business Model Analyst

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18 September 2020, The Register

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4 May 2013, O'Reilly Media

provided by Google News

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