DBMS > Google Cloud Firestore vs. Hazelcast vs. Microsoft Access vs. MySQL vs. Oracle Coherence

System Properties Comparison Google Cloud Firestore vs. Hazelcast vs. Microsoft Access vs. MySQL vs. Oracle Coherence

Editorial information provided by DB-Engines

Name

Description

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

A widely adopted in-memory data grid

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Widely used open source RDBMS

Oracles in-memory data grid solution

Primary database model

Document store

Key-value store

Relational DBMS

Key/Value like access via memcached API

Key-value store

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	7.36
Rank	#53	Overall
	#9	Document stores

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

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Website

firebase.google.com/products/firestore

hazelcast.com

www.microsoft.com/en-us/microsoft-365/access

www.mysql.com

www.oracle.com/java/coherence

Technical documentation

firebase.google.com/docs/firestore

hazelcast.org/imdg/docs

developer.microsoft.com/en-us/access

dev.mysql.com/doc

docs.oracle.com/en/middleware/standalone/coherence

Developer

Google

Hazelcast

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Oracle

Initial release

2017

2008

1992

1995

2007

Current release

5.3.6, November 2023

1902 (16.0.11328.20222), March 2019

8.4.0, April 2024

14.1, August 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Bundled with Microsoft Office

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

C and C++

Java

Server operating systems

hosted

All OS with a Java VM

Windows

Not a real database server, but making use of DLLs

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Data scheme

schema-free

yes

schema-free

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

SQL

Support of SQL

SQL-like query language

yes

but not compliant to any SQL standard

yes

with proprietary extensions

APIs and other access methods

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

ADO.NET
JDBC
ODBC
Proprietary native API

JCache
JPA
RESTful HTTP API

Supported programming languages

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

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

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

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

.Net
C++
Java

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

Event Listeners, Executor Services

yes

since Access 2010 using the ACE-engine

yes

proprietary syntax

Triggers

yes, with Cloud Functions

yes

Events

yes

since Access 2010 using the ACE-engine

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Replicated Map

none

Multi-source replication
Source-replica replication

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Eventual Consistency
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

yes

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

ACID

but no files for transaction logging

ACID

not for MyISAM storage engine

configurable

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

optionally

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

Role-based access control

a simple user-level security was built in till version Access 2003

Users with fine-grained authorization concept

no user groups or roles

authentification to access the cache via certificates or http basic authentication

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