DBMS > GeoMesa vs. Microsoft Azure AI Search vs. Pinecone vs. RocksDB

System Properties Comparison GeoMesa vs. Microsoft Azure AI Search vs. Pinecone vs. RocksDB

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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.

Search-as-a-service for web and mobile app development

A managed, cloud-native vector database

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Secondary database models

Vector DBMS

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

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	3.23
Rank	#92	Overall
	#2	Vector DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

www.geomesa.org

azure.microsoft.com/en-us/services/search

www.pinecone.io

rocksdb.org

Technical documentation

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

learn.microsoft.com/en-us/azure/search

docs.pinecone.io/docs/overview

github.com/facebook/rocksdb/wiki

Developer

CCRi and others

Microsoft

Pinecone Systems, Inc

Facebook, Inc.

Initial release

2014

2015

2019

2013

Current release

5.0.0, May 2024

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

C++

Server operating systems

hosted

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

String, Number, 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

SQL

Support of SQL

APIs and other access methods

RESTful HTTP API

C++ API
Java API

Supported programming languages

C#
Java
JavaScript
Python

Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Sharding

Implicit feature of the cloud service

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

yes

Implicit feature of the cloud service

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

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

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

yes

using Azure authentication

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