DBMS > Apache IoTDB vs. Cloudflare Workers KV vs. EventStoreDB vs. GeoMesa

System Properties Comparison Apache IoTDB vs. Cloudflare Workers KV vs. EventStoreDB vs. GeoMesa

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Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

A global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.

Industrial-strength, open-source database solution built from the ground up for event sourcing.

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

Primary database model

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.39
Rank	#274	Overall
	#39	Key-value stores

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Website

iotdb.apache.org

www.cloudflare.com/developer-platform/workers-kv

www.eventstore.com

www.geomesa.org

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

developers.cloudflare.com/kv/api

developers.eventstore.com

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

Developer

Apache Software Foundation

Cloudflare

Event Store Limited

CCRi and others

Initial release

2018

2012

2014

Current release

1.1.0, April 2023

21.2, February 2021

5.0.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

SQL-like query language

APIs and other access methods

JDBC
Native API

HTTP REST
Proprietary protocol

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

depending on storage layer

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

depending on storage layer

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

depending on storage layer

Foreign keys

Referential integrity

Transaction concepts

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

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.

yes

depending on storage layer

User concepts

Access control

yes

depending on the DBMS used for storage

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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