DBMS > Apache IoTDB vs. Datomic vs. IRONdb vs. Realm vs. RocksDB

System Properties Comparison Apache IoTDB vs. Datomic vs. IRONdb vs. Realm vs. RocksDB

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

Primary database model

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	7.60
Rank	#52	Overall
	#9	Document stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

iotdb.apache.org

www.datomic.com

www.circonus.com/solutions/time-series-database/

realm.io

rocksdb.org

Technical documentation

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

docs.datomic.com

docs.circonus.com/irondb/category/getting-started

realm.io/docs

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

Cognitect

Circonus LLC.

Realm, acquired by MongoDB in May 2019

Facebook, Inc.

Initial release

2018

2012

2017

2014

2013

Current release

1.1.0, April 2023

1.0.6735, June 2023

V0.10.20, January 2018

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

limited edition free

commercial

Open Source

BSD

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 and C++

C++

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java VM

Linux

Android
Backend: server-less
iOS
Windows

Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

JDBC
Native API

RESTful HTTP API

HTTP API

C++ API
Java API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Clojure
Java

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
Java

with Android only
Objective-C
React Native
Swift

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

yes, in Lua

runs within the applications so server-side scripts are unnecessary

Triggers

yes

By using transaction functions

yes

Change Listeners

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

Automatic, metric affinity per node

none

horizontal partitioning

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

configurable replication factor, datacenter aware

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

yes

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

yes

In-Memory realm

yes

User concepts

Access control

yes

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