DBMS > Cloudflare Workers KV vs. FileMaker vs. Geode vs. Google Cloud Firestore vs. Microsoft SQL Server

System Properties Comparison Cloudflare Workers KV vs. FileMaker vs. Geode vs. Google Cloud Firestore vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

A global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

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.

Microsofts flagship relational DBMS

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.39
Rank	#274	Overall
	#39	Key-value stores

Score	47.91
Rank	#20	Overall
	#14	Relational DBMS

Score	1.86
Rank	#134	Overall
	#24	Key-value stores

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

www.cloudflare.com/developer-platform/workers-kv

www.claris.com/filemaker

geode.apache.org

firebase.google.com/products/firestore

www.microsoft.com/en-us/sql-server

Technical documentation

developers.cloudflare.com/kv/api

www.claris.com/resources/documentation

geode.apache.org/docs

firebase.google.com/docs/firestore

learn.microsoft.com/en-US/sql/sql-server

Developer

Cloudflare

Claris

a subsidiary of Apple

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Google

Microsoft

Initial release

2018

1983

2002

2017

1989

Current release

19.4.1, November 2021

1.1, February 2017

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Server operating systems

hosted

iOS

client part only
Linux
OS X
Windows

All OS with a Java VM

the JDK (8 or later) is also required

hosted

Linux
Windows

Data scheme

schema-free

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

A http query request can return the data in XML format

yes

Secondary indexes

yes

SQL

Support of SQL

yes

via plugins

SQL-like query language (OQL)

yes

APIs and other access methods

HTTP REST
Proprietary protocol

Filemaker WebDirect
JDBC
ODBC

Java Client API
Memcached protocol
RESTful HTTP API

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

PHP

.Net
All JVM based languages
C++
Groovy
Java
Scala

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

user defined functions

yes, Firebase Rules & Cloud Functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Cache Event Listeners

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication, warm standby

since Version 14

Multi-source replication

yes, but depending on the SQL-Server Edition

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes, on a single node

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

simple rights management via user accounts and connection to external directory services

Access rights per client and object definable

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

fine grained access rights according to SQL-standard

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