DBMS > Geode vs. GeoMesa vs. NSDb vs. Weaviate

System Properties Comparison Geode vs. GeoMesa vs. NSDb vs. Weaviate

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Editorial information provided by DB-Engines

Name

Description

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

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

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	1.73
Rank	#143	Overall
	#5	Vector DBMS

Website

geode.apache.org

www.geomesa.org

nsdb.io

github.com/weaviate/weaviate
weaviate.io

Technical documentation

geode.apache.org/docs

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

nsdb.io/Architecture

weaviate.io/developers/weaviate

Developer

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

CCRi and others

Weaviate B.V.

Initial release

2002

2014

2017

2019

Current release

1.1, February 2017

4.0.5, February 2024

1.19, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

Apache License 2.0

Open Source

Apache Version 2.0

Open Source

commercial license available with Weaviate Enterprise

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

Java

Scala

Java, Scala

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

Linux
macOS

Data scheme

schema-free

yes

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

yes

string, int, float, geo point, date, cross reference, fuzzy references

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

all fields are automatically indexed

yes

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

SQL-like query language (OQL)

SQL-like query language

GraphQL is used as query language

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

gRPC
HTTP REST
WebSocket

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

Java
Scala

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

depending on storage layer

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

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

Eventual Consistency

depending on storage layer

Eventual Consistency

Foreign keys

Referential integrity

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

Using Apache Lucene

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

depending on storage layer

yes

User concepts

Access control

Access rights per client and object definable

yes

depending on the DBMS used for storage

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
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Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
» more

Key customers

All companies that have data.
» more

Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
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Licensing and pricing models

Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
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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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Weaviate, an ANN Database with CRUD support
2 February 2021, Etienne Dilocker, SeMI Technologies (sponsor)

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Recent citations in the news

This is how much one of the most expensive gems costs at the Tucson gem show
11 February 2024, KGUN 9 Tucson News

Apache Geode Spawns 'All Sorts of In-Memory Things'
4 January 2017, The New Stack

Reactive Event Processing with Apache Geode
5 July 2020, InfoQ.com

1. Introduction to Pivotal GemFire In-Memory Data Grid and Apache Geode - Scaling Data Services with Pivotal ...
15 November 2018, oreilly.com

Where Does Apache Geode Fit in CQRS Architectures?
18 December 2016, InfoQ.com

provided by Google News

Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector ...
24 January 2024, AWS Blog

Weaviate Partners with Snowflake to Bring Secure GenAI to Snowpark Container Services
8 February 2024, Datanami

Weaviate Partners with Snowflake to Bring Secure GenAI to Snowpark Container Services
9 February 2024, AiThority

Foley Represents Cortical Ventures in $50M Series B Round for Weaviate
17 December 2023, Foley & Lardner LLP

Getting Started with Weaviate: A Beginner's Guide to Search with Vector Databases
18 July 2023, Towards Data Science

provided by Google News

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