DBMS > Datomic vs. Firebase Realtime Database vs. Hazelcast vs. Hypertable vs. MySQL

System Properties Comparison Datomic vs. Firebase Realtime Database vs. Hazelcast vs. Hypertable vs. MySQL

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

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

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

A widely adopted in-memory data grid

An open source BigTable implementation based on distributed file systems such as Hadoop

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

www.datomic.com

firebase.google.com/products/realtime-database

hazelcast.com

www.mysql.com

Technical documentation

docs.datomic.com

firebase.google.com/docs/database

hazelcast.org/imdg/docs

dev.mysql.com/doc

Developer

Cognitect

Google

acquired by Google 2014

Hazelcast

Hypertable Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2012

2008

2009

1995

Current release

1.0.7075, December 2023

5.3.6, November 2023

0.9.8.11, March 2016

8.4.0, April 2024

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

GNU version 3. Commercial license available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java, Clojure

Java

C++

C and C++

Server operating systems

All OS with a Java VM

hosted

All OS with a Java VM

Linux
OS X
Windows

an inofficial Windows port is available

FreeBSD
Linux
OS X
Solaris
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.

yes

the object must implement a serialization strategy

yes

Secondary indexes

yes

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP API

Android
iOS
JavaScript API
RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

C++ API
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Clojure
Java

Java
JavaScript
Objective-C

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C++
Java
Perl
PHP
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

Transaction Functions

limited functionality with using 'rules'

yes

Event Listeners, Executor Services

yes

proprietary syntax

Triggers

By using transaction functions

Callbacks are triggered when data changes

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

Replicated Map

selectable replication factor on file system level

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

yes

one or two-phase-commit; repeatable reads; read commited

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

yes, based on authentication and database rules

Role-based access control

Users with fine-grained authorization concept

no user groups or roles

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