DBMS > EventStoreDB vs. Faircom DB vs. Google Cloud Firestore vs. Oracle Coherence vs. SwayDB

System Properties Comparison EventStoreDB vs. Faircom DB vs. Google Cloud Firestore vs. Oracle Coherence vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

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

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

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.

Oracles in-memory data grid solution

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Event Store

Key-value store
Relational DBMS

Document store

Key-value store

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

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

www.eventstore.com

www.faircom.com/products/faircom-db

firebase.google.com/products/firestore

www.oracle.com/java/coherence

swaydb.simer.au

Technical documentation

developers.eventstore.com

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

firebase.google.com/docs/firestore

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

Developer

Event Store Limited

FairCom Corporation

Google

Oracle

Simer Plaha

Initial release

2012

1979

2017

2007

2018

Current release

21.2, February 2021

V12, November 2020

14.1, August 2023

License

Commercial or Open Source

Open Source

commercial

Restricted, free version available

commercial

Open Source

GNU Affero GPL V3.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

ANSI C, C++

Java

Scala

Server operating systems

Linux
Windows

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

hosted

All OS with a Java VM

Data scheme

schema free, schema optional, schema required, partial schema,

schema-free

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

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

yes, ANSI SQL with proprietary extensions

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

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

JCache
JPA
RESTful HTTP API

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

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

.Net
C++
Java

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes, Firebase Rules & Cloud Functions

Triggers

yes

yes, with Cloud Functions

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Multi-source replication

yes, with selectable consistency level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

yes

configurable

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

yes

optionally

yes

In-memory capabilities

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

yes

User concepts

Access control

Fine grained access rights according to SQL-standard with additional protections for files

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

authentification to access the cache via certificates or http basic authentication

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More resources

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