DBMS > Apache IoTDB vs. Datomic vs. EsgynDB vs. EventStoreDB

System Properties Comparison Apache IoTDB vs. Datomic vs. EsgynDB vs. EventStoreDB

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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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Primary database model

Time Series DBMS

Relational DBMS

Event Store

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Website

Technical documentation

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

docs.datomic.com

developers.eventstore.com

Developer

Apache Software Foundation

Cognitect

Esgyn

Event Store Limited

Initial release

2018

2012

2015

2012

Current release

1.1.0, April 2023

1.0.6735, June 2023

21.2, February 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

limited edition free

commercial

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, Clojure

C++, Java

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java VM

Linux

Linux
Windows

Data scheme

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

yes

APIs and other access methods

JDBC
Native API

RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Clojure
Java

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

Transaction Functions

Java Stored Procedures

Triggers

yes

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

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

none

But extensive use of caching in the application peers

Sharding

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

none

But extensive use of caching in the application peers

Multi-source replication between multi datacenters

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

User concepts

Access control

yes

fine grained access rights according to SQL-standard

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