DBMS > GeoMesa vs. JaguarDB vs. Oracle Coherence vs. Qdrant

System Properties Comparison GeoMesa vs. JaguarDB vs. Oracle Coherence vs. Qdrant

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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.

Performant, highly scalable DBMS for AI and IoT applications

Oracles in-memory data grid solution

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

Primary database model

Spatial DBMS

Key-value store
Vector DBMS

Key-value store

Vector DBMS

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

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#14	Vector DBMS

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Score	1.28
Rank	#167	Overall
	#7	Vector DBMS

Website

www.geomesa.org

www.jaguardb.com

www.oracle.com/java/coherence

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

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

www.jaguardb.com/support.html

docs.oracle.com/en/middleware/standalone/coherence

qdrant.tech/documentation

Developer

CCRi and others

DataJaguar, Inc.

Oracle

Qdrant

Initial release

2014

2015

2007

2021

Current release

5.0.0, May 2024

3.3 July 2023

14.1, August 2023

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

GPL V3.0

commercial

Open Source

Apache Version 2.0

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.

Implementation language

Scala

C++

the server part. Clients available in other languages

Java

Rust

Server operating systems

Linux

All OS with a Java VM

Docker
Linux
macOS
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

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

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

JDBC
ODBC

JCache
JPA
RESTful HTTP API

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

.Net
C++
Java

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

Server-side scripts

Stored procedures

Triggers

yes

Live Events

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

Multi-source replication

yes, with selectable consistency level

Collection-level replication

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

Eventual Consistency
Immediate Consistency

Eventual Consistency, tunable consistency

Foreign keys

Referential integrity

Transaction concepts

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

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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

rights management via user accounts

authentification to access the cache via certificates or http basic authentication

Key-based authentication

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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