DBMS > GeoMesa vs. Manticore Search vs. Milvus vs. STSdb

System Properties Comparison GeoMesa vs. Manticore Search vs. Milvus vs. STSdb

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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.

Multi-storage database for search, including full-text search.

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

www.geomesa.org

manticoresearch.com

milvus.io

github.com/STSSoft/STSdb4

Technical documentation

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

manual.manticoresearch.com

milvus.io/docs/overview.md

Developer

CCRi and others

Manticore Software

STS Soft SC

Initial release

2014

2017

2019

2011

Current release

4.0.5, February 2024

6.0, February 2023

2.3.4, January 2024

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

GPL version 2

Open Source

Apache Version 2.0

Open Source

GPLv2, commercial license available

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

C++

C++, Go

Server operating systems

FreeBSD
Linux
macOS
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Windows

Data scheme

yes

Fixed schema

yes

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

Vector, Numeric and String

yes

primitive types and user defined types (classes)

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 index from XML

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

APIs and other access methods

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

RESTful HTTP API

.NET Client API

Supported programming languages

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

C#
Java

Server-side scripts

Stored procedures

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

Synchronous replication based on Galera library

none

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

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

isolated transactions for atomic changes and binary logging for safe writes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

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

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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

Spatial database management systems
6 April 2021, Matthias Gelbmann

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Vector databases
2 June 2023, Matthias Gelbmann

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