Microservices and serverless in python projects

Jul 27, 20187 likes1,381 views

The document discusses microservices and serverless architectures for Python projects. It covers microservices concepts and frameworks like asyncio and GraphQL. Serverless computing with AWS Lambda and frameworks like Zappa and Chalice are explained. Benefits of microservices include separation of concerns while serverless offers automatic scaling and lower costs. Frameworks make it easier to deploy Python code to AWS Lambda.

Microservices and Serverless in
Python projects
José Manuel Ortega
Europython 2018
@jmortegac
v 0.5

Agenda
•Microservices in python
•Introducing Serverless and Function as a Service
•Python frameworks for AWS
•AWS Lambda functions with zappa and chalice
•Deploy AWS lambda functions from aws
console
2Microservices and Serverless in Python projects

Microservices vs Serverless
3Microservices and Serverless in Python projects

Microservices
4Microservices and Serverless in Python projects

Asynchronous calls with asyncio and aiohttp
5Microservices and Serverless in Python projects

REST API Development
6Microservices and Serverless in Python projects

Performance
7Microservices and Serverless in Python projects
8% less memory
6% faster response times

Microservices with graphql
8Microservices and Serverless in Python projects

9Microservices and Serverless in Python projects

10Microservices and Serverless in Python projects

11Microservices and Serverless in Python projects

12Microservices and Serverless in Python projects

13Microservices and Serverless in Python projects
SERVER

14Microservices and Serverless in Python projects
CLIENT

Microservices benefits
•Separation of concerns
•Services are decoupled from each other
•Managing smaller projects
•More scaling and deployment options
15Microservices and Serverless in Python projects

16Microservices and Serverless in Python projects
Serverless

Serverless architecture
•FaaS - Function as a Service
•Fully managed computing
• Provisioning
• Scalability
• Monitoring
• Logging
•Deploy your code
•Pay only for actual usage
17Microservices and Serverless in Python projects

Serverless architecture
18Microservices and Serverless in Python projects

Serverless uses cases
➢ REST API
• Stateless services and microservices
• Suitable for Chat bots
➢ Events
• File processing (S3 event) & Data ingestion
• Data/Stream processing
• Incidents handling (CloudWatch event log)
• IoT
➢ Scheduled tasks
• Monitoring, load testing
• Periodical jobs
19Microservices and Serverless in Python projects

20Microservices and Serverless in Python projects

Serverless benefits
•No server management
•Automatic scaling and load balancing
•Lower infrastructure costs
•Flexibility and high availability
•Infrastructure managed by service provider
21Microservices and Serverless in Python projects

Serverless drawbacks
• The tools around the deployment automation of
serverless functions are still in development.
•There is no control over containers when the
execution environments are created or
destroyed
•Debugging, Deploying and monitoring
22Microservices and Serverless in Python projects

Cloud providers
•AWS
•Microsoft Azure
•Cloud platform
•OpenWhisk(OS)
•Kubeless(OS)
23Microservices and Serverless in Python projects

Aws lambda
24Microservices and Serverless in Python projects

25Microservices and Serverless in Python projects

Aws lambda functions
26Microservices and Serverless in Python projects

Aws lambda functions
27Microservices and Serverless in Python projects

29Microservices and Serverless in Python projects

30Microservices and Serverless in Python projects

Frameworks
31Microservices and Serverless in Python projects
python-λ

32Microservices and Serverless in Python projects

33Microservices and Serverless in Python projects

Zappa architecture
34Microservices and Serverless in Python projects

Zappa
35Microservices and Serverless in Python projects

36Microservices and Serverless in Python projects

37Microservices and Serverless in Python projects

38Microservices and Serverless in Python projects

$39Microservices and Serverless in Python projects # zappa_settings.json { "dev": { "aws_region": "us-east-1", "django_settings": "hello.settings", "profile_name": "default", "project_name": "hello", "runtime": "python3.6", "s3_bucket": "zappa-huyg6op0s" } }$

Zappa deploy
40Microservices and Serverless in Python projects
$ zappa deploy <env>
•Zips code and dependencies
•Create AWS Lambda and deploys the zip
•Creates endpoint on API Gateway and
links to AWS Lambda

Zappa deploy
41Microservices and Serverless in Python projects

Zappa
42Microservices and Serverless in Python projects

Zappa Asynchronous Task
43Microservices and Serverless in Python projects

Chalice
44Microservices and Serverless in Python projects
•Python Serverless Microframework for AWS
•Each endpoint is a separate function

Chalice
45Microservices and Serverless in Python projects

Chalice
46Microservices and Serverless in Python projects

Chalice example
47Microservices and Serverless in Python projects

$Chalice methods 48Microservices and Serverless in Python projects Resource HTTP Verb AWS Lambda /talks GET get_talks /talk POST add_new_talk /talks/{ID} PUT update_talk /talks/{ID} DELETE delete_talk$

Chalice methods
49Microservices and Serverless in Python projects

Chalice options
50Microservices and Serverless in Python projects

Chalice deploy
51Microservices and Serverless in Python projects
Updating IAM policy.
Updating lambda function...
Regen deployment package...
Sending changes to lambda.
API Gateway rest API already found.
Deploying to: dev

52Microservices and Serverless in Python projects

https://github.com/lambci/docker-lambda
53Microservices and Serverless in Python projects

http://serverlesscalc.com/
54Microservices and Serverless in Python projects

55Microservices and Serverless in Python projects

https://github.com/serverless/examples
56Microservices and Serverless in Python projects

References
57Microservices and Serverless in Python projects
•https://aws.amazon.com/blogs/compute/microservic
es-without-the-servers
•https://github.com/Miserlou/Zappa
•https://github.com/pmuens/awesome-serverless
•https://github.com/aws/chalice
•https://chalice.readthedocs.io/en/latest

58Microservices and Serverless in Python projects
Serverless architecture is the
next generation of cloud
evolution

Thank you!
José Manuel Ortega
jmortega.github.io

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Los LLM (Large Language Model) son una clase de modelos de inteligencia artificial que han revolucionado la forma en que las máquinas interpretan y generan el lenguaje humano. La seguridad y la auditoría son temas críticos cuando se trata de aplicaciones basadas en grandes modelos de lenguaje, como los modelos GPT (Generative Pre-trained Transformer) o LLM (Large Language Model). Esta charla pretende analizar la seguridad de estos modelos de lenguaje desde el punto de vista del desarrollador, analizando las principales vulnerabilidades que se pueden producir en la generación de estos modelos. Entre los principales puntos a tratar podemos destacar: * Introducción a LLM * Introducción al OWASP LLM Top 10 * Seguridad en aplicaciones que manejan modelos LLM. * Herramientas de auditoría en aplicaciones que manejan modelos LLM. * Caso de uso con la herramienta textattack para realizar ataques adversarios en Python

Beyond the hype: The reality of AI security.pdfJose Manuel Ortega Candel

Artificial Intelligence (AI) has rapidly evolved, promising transformative advancements. However, as AI systems become increasingly sophisticated and integrated into our daily lives, so do the security risks associated with them. This talk delves into the realities of AI security, beyond the hype and promises. We will explore the vulnerabilities inherent in AI systems, including data privacy breaches, adversarial attacks, and model poisoning. The presentation will also discuss the ethical implications of AI, such as bias and discrimination, and how they intersect with security concerns. By the end of this talk, attendees will gain a comprehensive understanding of the challenges and best practices for securing AI systems, enabling them to build and deploy AI solutions responsibly. Between the main points of the talk we can highlight: *Introduction to OWASP LLM Top 10 *Ethical implications of AI security *Securing AI systems: Best practices and emerging technologies *The future of AI security

Seguridad de APIs en Drupal_ herramientas, mejores prácticas y estrategias pa...Jose Manuel Ortega Candel

En el mundo digital actual, las APIs son el pilar que sostiene la interconectividad entre aplicaciones y servicios. Drupal, como uno de los CMS más populares y versátiles, no es una excepción. Sin embargo, con la expansión de las APIs, también surge la necesidad de abordar los desafíos de seguridad asociados. En esta charla, exploraremos en detalle el OWASP Top 10 de Seguridad en APIs, una lista de las principales vulnerabilidades que los desarrolladores y equipos de seguridad deben tener en cuenta al diseñar, desarrollar y asegurar sus APIs. Analizaremos los principales módulos y herramientas de seguridad disponibles en Drupal para testear nuestras aplicaciones. Por último, comentaremos las mejores prácticas para mitigar los riesgos y garantizar la seguridad de las APIs. Entre los puntos a tratar podemos destacar: 1.Fundamentos de aeguridad en APIs 2.OWASP Top 10 y su importancia para la seguridad en APIs 3.Módulos y herramientas de Seguridad en Drupal 4.Mejores prácticas para seguridad en APIs

Security and auditing tools in Large Language Models (LLM).pdfJose Manuel Ortega Candel

LLM models are a subcategory of deep learning models based on neural networks and natural language processing(NLP). Security and auditing are critical issues when dealing with applications based on large language models, such as GPT (Generative Pre-trained Transformer) or LLM (Large Language Model) models. This talk aims to analyze the security of these language models from the developer’s point of view, analyzing the main vulnerabilities that can occur in the generation of these models. Among the main points to be discussed we can highlight: -Introduction to LLM -Introduction to OWASP LLM Top 10. -Auditing tools in applications that handle LLM models. -Use case with the textattack tool(https://textattack.readthedocs.io/en/master/)

Herramientas de benchmarks para evaluar el rendimiento en máquinas y aplicaci...Jose Manuel Ortega Candel

Los benchmarks son programas que permiten evaluar el rendimiento de un sistema, componente o proceso en comparación con otros sistemas similares. Son herramientas esenciales para medir y comparar el rendimiento de hardware, software y sistemas en diferentes áreas. El objetivo es dar a conocer las principales herramientas de benchmark que disponemos hoy en día para medir el rendimiento. Entre los puntos a tratar podemos destacar: -Introducción a Benchmarks: Definición y propósito de los benchmarks en la medición del rendimiento -Tipos de Benchmarks: Benchmarks sintéticos vs. Benchmarks del mundo real.Benchmarks específicos para CPU, memoria, almacenamiento, y gráficos -Selección de Benchmarks: Consideraciones al elegir benchmarks según el tipo de aplicación y los objetivos de evaluación

Asegurando tus APIs Explorando el OWASP Top 10 de Seguridad en APIs.pdfJose Manuel Ortega Candel

En el mundo actual, las APIs juegan un papel importante en la creación de aplicaciones y servicios robustos y flexibles. Sin embargo, con la expansión de las APIs, también surge la necesidad de abordar los desafíos de seguridad asociados. En esta charla, exploraremos en detalle el OWASP Top 10 de Seguridad en APIs, una lista de las principales vulnerabilidades que los desarrolladores y equipos de seguridad deben tener en cuenta al diseñar, desarrollar y asegurar sus APIs. Por último, comentaremos las mejores prácticas para mitigar los riesgos y garantizar la seguridad de tus APIs. Entre los puntos a tratar podemos destacar: 1.Introducir el concepto de seguridad en las APIs 2.OWASP Top 10 y su importancia para la seguridad en APIs 3.Actualización del OWASP Top 10 security en 2023 4.Herramientas para evaluar y mejorar la seguridad de tus APIs. 5.Estrategias y mejores prácticas para garantizar la seguridad de tus APIs.

PyGoat Analizando la seguridad en aplicaciones Django.pdfJose Manuel Ortega Candel

La seguridad en aplicaciones web es un aspecto fundamental para garantizar la protección de los datos y la confidencialidad de los usuarios. Si nuestro objetivo es aprender como Django gestiona la seguridad, PyGoat es una aplicación desarrollada con Django vulnerable de forma intencionada que puede ser utilizada para aprender a asegurar nuestras aplicaciones Django. En esta charla, analizamos como Django gestiona la seguridad utilizando la aplicación vulnerable Pygoat, identificando los problemas de seguridad subyacentes. Aprenderemos sobre vulnerabilidades de seguridad comunes como las que aparecen en el OWASP Top 10 en aplicaciones Django y cómo solucionarlas para que podamos mantener nuestras aplicaciones a salvo de atacantes. Entre los puntos a tratar podemos destacar: Introducción a la seguridad en aplicaciones Django Pygoat como ejemplo de aplicación vulnerable Vulnerabilidades OWASP top 10 y mitigación

Ciberseguridad en Blockchain y Smart Contracts: Explorando los Desafíos y Sol...Jose Manuel Ortega Candel

En la actualidad, la tecnología blockchain y los smart contracts están revolucionando la forma en que interactuamos con la información y realizamos transacciones. Sin embargo, esta innovación no está exenta de desafíos en cuanto a la ciberseguridad se refiere. En esta charla, exploraremos los desafíos desde el punto de vista de la ciberseguridad en blockchain y smart contracts, así como las soluciones y enfoques para mitigar los riesgos asociados. A medida que continuamos adoptando estas tecnologías disruptivas, es fundamental comprender y abordar adecuadamente los aspectos de seguridad para minimizar los posibles riesgos. Entre los puntos a tratar podemos destacar: 1. Fundamentos de Blockchain y Smart Contracts 2. Desafíos de Seguridad en Blockchain 3. Seguridad en Smart Contracts 4. Auditorías y Pruebas de Seguridad en smart contracts

Evolution of security strategies in K8s environments- All day devops Jose Manuel Ortega Candel

In the latest versions of K8s there has been an evolution regarding the definition of security strategies at the level of access policies to the cluster by users and developers. The security contexts (securityContext) allow you to define the configurations at the level of access control and privileges for a pod or container in a simple way using keywords in the configuration files. To facilitate the implementation of these security strategies throughout the cluster, new strategies have emerged such as the Pod Security Policy (PSP) where the cluster administrator is in charge of defining these policies at the cluster level with the aim that developers can follow these policies. Other interesting projects include Open Policy Agent (OPA) as the main cloud-native authorization policy agent for creating policies and managing user permissions for access to applications. The objective of this talk is to present the evolution that has occurred in security strategies and how we could use them together, as well as analyze their behavior in accessing resources. Among the points to be discussed we can highlight: -Introduction to security strategies in K8s environments -Pod Security Admission(PSA) vs Open Policy Agent (OPA) -Combination of different security strategies together -Access to resources in privileged and non-privileged mode

Evolution of security strategies in K8s environments.pdfJose Manuel Ortega Candel

In the latest versions of K8s there has been an evolution regarding the definition of security strategies at the level of access policies to the cluster by users and developers. The security contexts (securityContext) allow you to define the configurations at the level of access control and privileges for a pod or container in a simple way using keywords in the configuration files. To facilitate the implementation of these security strategies throughout the cluster, new strategies have emerged such as the Pod Security Policy (PSP) where the cluster administrator is in charge of defining these policies at the cluster level with the aim that developers can follow these policies. Other interesting projects include Open Policy Agent (OPA) as the main cloud-native authorization policy agent for creating policies and managing user permissions for access to applications. The objective of this talk is to present the evolution that has occurred in security strategies and how we could use them together, as well as analyze their behavior in accessing resources. Among the points to be discussed we can highlight: *Introduction to security strategies in K8s environments *Pod Security Admission(PSA) vs Open Policy Agent (OPA) *Combination of different security strategies together *Access to resources in privileged and non-privileged mode

Implementing Observability for Kubernetes.pdfJose Manuel Ortega Candel

No production system is complete without a way to monitor it. In software, we define observability as the ability to understand how our system is performing. This talk dives into capabilities and tools that are recommended for implementing observability when running K8s in production as the main platform today for deploying and maintaining containers with cloud-native solutions. We start by introducing the concept of observability in the context of distributed systems such as K8s and the difference with monitoring. We continue by reviewing the observability stack in K8s and the main functionalities. Finally, we will review the tools K8s provides for monitoring and logging, and get metrics from applications and infrastructure. Between the points to be discussed we can highlight: -Introducing the concept of observability -Observability stack in K8s -Tools and apps for implementing Kubernetes observability -Integrating Prometheus with OpenMetrics

Computación distribuida usando PythonJose Manuel Ortega Candel

La computación distribuída es un nuevo modelo de computación que surgió con el objetivo de resolver problemas de computación masiva donde diferentes máquinas trabajan en paralelo formando un clúster de computación. En los últimos años han surgido diferentes frameworks como Apache Hadoop, Apache Spark y Apache Flink que permiten resolver este tipo de problemas donde tenemos datos masivos desde diferentes fuentes de datos. Dentro del ecosistema de Python podemos destacar las librerías de Pyspark y Dask de código abierto que permiten la ejecución de tareas de forma paralela y distribuida en Python. Entre los puntos a tratar podemos destacar: Introducción a la computación distribuida Comparando tecnologías de computación distribuida Frameworks y módulos en Python para computación distribuida Casos de uso en proyectos Big Data

Seguridad en arquitecturas serverless y entornos cloudJose Manuel Ortega Candel

En los últimos años, las arquitecturas cloud han evolucionado a un modelo serverless que trae como principales ventajas la posibilidad de ejecutar código sin aprovisionar ni administrar servidores. Este tipo de arquitecturas permite ejecutar el código en una infraestructura con alta disponibilidad y escalado automático, así como capacidades de monitorización de forma automática. Sin embargo, estos tipos de arquitecturas introducen un conjunto completamente nuevo de implicaciones de seguridad que deben tenerse en cuenta al crear sus aplicaciones. El OWASP Serverless Top 10 es una excelente referencia para conocer los posibles riesgos de seguridad y las consecuencias de implementar una arquitectura serverless, así como también cómo mitigarlos. En esta charla se analizará el estado actual de la seguridad en arquitecturas serverless, los principales riesgos y cómo podríamos mitigarlos de una forma sencilla. Entre los puntos a tratar podemos destacar: -Introducción a las arquitecturas serverless -Seguridad en arquitecturas serverless y OWASP Serverless Top 10 -Pentesting sobre aplicaciones serverless -Mejoras prácticas de seguridad al trabajar en entornos cloud

Construyendo arquitecturas zero trust sobre entornos cloud Jose Manuel Ortega Candel

La adopción de arquitecturas basadas en microservicios ha crecido de manera exponencial en los últimos años. Cuando se trata de obtener la máxima seguridad utilizamos lo que se denomina arquitecturas de “confianza cero” (zero trust architecture). Las arquitecturas de este tipo establecen mecanismos de autenticación y autorización entre nuestros propios microservicios, aumentando de esta manera la seguridad en entornos altamente regulados. El objetivo de esta charla es dar a conocer los principios básicos para construir aplicaciones utilizando arquitecturas zero trust y algunas herramientas para realizar auditorías de seguridad en entornos cloud. Entre los puntos a tratar podemos destacar: Introducción a DevSecOps y modelado de amenazas Modelo de confianza cero(zero trust) en la nube Mejoras prácticas a nivel de permisos y estrategias de seguridad al trabajar en entornos cloud Herramientas de análisis orientadas al pentesting en entornos cloud

Tips and tricks for data science projects with Python Jose Manuel Ortega Candel

Python has become the most widely used language for machine learning and data science projects due to its simplicity and versatility. Furthermore, developers get to put all their effort into solving an Machine Learning or data science problem instead of focusing on the technical aspects of the language. For this purpose, Python provides access to great libraries and frameworks for AI and machine learning (ML), flexibility and platform independence In this talk I will try to get a selection of libraries and frameworks that can help us introduce in the Machine Learning world and answer the question that all people is doing, What makes Python the best programming language for machine learning?

Sharing secret keys in Docker containers and K8sJose Manuel Ortega Candel

In this talk I will show how to save secret keys in Docker containers and K8s in production and best practices for saving and securing distribution of secrets. With Docker and k8s secrets we can manage information related to keys that are needed at runtime but cannot be exposed in the Docker image or source code repository. These could be the main talking points: 1.Challenges of security and secret keys in containers 2.Best practices for saving and securing distribution of secrets in Docker Containers 3.Managing secrets in Kubernetes using volumes and sealed-secrets 4.Other tools for distributing secrets in containers like Hashicorp Vault and KeyWhiz

Implementing cert-manager in K8sJose Manuel Ortega Candel

One of the best practices from a security point of view is to introduce the management of the certificates that we are going to use to support protocols such as SSL / TLS. In this talk we will explain cert-manager and his implementation in K8s as a native Kubernetes certificate management controller that allows us to manage connection certificates and secure communications through SSL/TLS protocols. Later I will explain the main functionalities and advantages that cert-manager provides, for example it allows us to validate that the certificates we are using in different environments are correct. Finally, some use cases are studied in which to use cert-manager and the integration with other services such as Let's Encrypt or HashiCorp Vault.

Python para equipos de ciberseguridad(pycones)Jose Manuel Ortega Candel

Python se ha convertido en el lenguaje más usado para desarrollar herramientas dentro del ámbito de la seguridad. Esta charla se centrará en las diferentes formas en que un analista puede aprovechar el lenguaje de programación Python tanto desde el punto de vista defensivo como ofensivo. Desde el punto de vista defensivo Python es una de las mejores opciones como herramienta de pentesting por la gran cantidad de módulos que nos pueden ayudar a desarrollar nuestras propias herramientas con el objetivo de realizar un análisis de nuestro objetivo. Desde el punto de vista ofensivo podemos utilizar Python para recolección de información de nuestro objetivo de forma pasiva y activa. El objetivo final es obtener el máximo conocimiento posible en el contexto que estamos auditando. Entre los principales puntos a tratar podemos destacar: 1.Introducción a Python para proyectos de ciberseguridad(5 min) 2.Herramientas de pentesting(10 min) 3.Herramientas Python desde el punto de vista defensivo(10 min) 4.Herramientas Python desde el punto de vista ofensivo(10 min)

Python para equipos de ciberseguridad Jose Manuel Ortega Candel

Shodan Tips and tricks. Automatiza y maximiza las búsquedas shodanJose Manuel Ortega Candel

Shodan es una de las plataformas de hacking más utilizadas en todo el mundo que nos brinda un completo motor de búsqueda avanzado desde el que podemos encontrar cualquier dispositivo conectado a la red junto con sus servicios activos, puertos abiertos y posibles vulnerabilidades. En esta charla mostraremos las principales herramientas que podemos utilizar para maximizar nuestras búsquedas en Shodan, así como desarrollar nuestros propios scripts con python para automatizar las búsquedas. Entre los puntos a tratar podemos destacar: -Filtros y búsquedas personalizadas en Shodan -Detectando vulnerabilidades con Shodan -Buscar bases de datos abiertas en Shodan -Shodan desde lineas de comandos con ShodanCLI

Seguridad y auditorías en Modelos grandes del lenguaje (LLM).pdfJose Manuel Ortega Candel

Beyond the hype: The reality of AI security.pdfJose Manuel Ortega Candel

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Security and auditing tools in Large Language Models (LLM).pdfJose Manuel Ortega Candel

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Asegurando tus APIs Explorando el OWASP Top 10 de Seguridad en APIs.pdfJose Manuel Ortega Candel

PyGoat Analizando la seguridad en aplicaciones Django.pdfJose Manuel Ortega Candel

Ciberseguridad en Blockchain y Smart Contracts: Explorando los Desafíos y Sol...Jose Manuel Ortega Candel

Evolution of security strategies in K8s environments- All day devops Jose Manuel Ortega Candel

Evolution of security strategies in K8s environments.pdfJose Manuel Ortega Candel

Implementing Observability for Kubernetes.pdfJose Manuel Ortega Candel

Computación distribuida usando PythonJose Manuel Ortega Candel

Seguridad en arquitecturas serverless y entornos cloudJose Manuel Ortega Candel

Construyendo arquitecturas zero trust sobre entornos cloud Jose Manuel Ortega Candel

Tips and tricks for data science projects with Python Jose Manuel Ortega Candel

Sharing secret keys in Docker containers and K8sJose Manuel Ortega Candel

Implementing cert-manager in K8sJose Manuel Ortega Candel

Python para equipos de ciberseguridad(pycones)Jose Manuel Ortega Candel

Python para equipos de ciberseguridad Jose Manuel Ortega Candel

Shodan Tips and tricks. Automatiza y maximiza las búsquedas shodanJose Manuel Ortega Candel

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Microservices and serverless in python projects

1. Microservices and Serverless in Python projects José Manuel Ortega Europython 2018 @jmortegac v 0.5

2. Agenda •Microservices in python •Introducing Serverless and Function as a Service •Python frameworks for AWS •AWS Lambda functions with zappa and chalice •Deploy AWS lambda functions from aws console 2Microservices and Serverless in Python projects

3. Microservices vs Serverless 3Microservices and Serverless in Python projects

4. Microservices 4Microservices and Serverless in Python projects

5. Asynchronous calls with asyncio and aiohttp 5Microservices and Serverless in Python projects

6. REST API Development 6Microservices and Serverless in Python projects

7. Performance 7Microservices and Serverless in Python projects 8% less memory 6% faster response times

8. Microservices with graphql 8Microservices and Serverless in Python projects

9. 9Microservices and Serverless in Python projects

10. 10Microservices and Serverless in Python projects

11. 11Microservices and Serverless in Python projects

12. 12Microservices and Serverless in Python projects

13. 13Microservices and Serverless in Python projects SERVER

14. 14Microservices and Serverless in Python projects CLIENT

15. Microservices benefits •Separation of concerns •Services are decoupled from each other •Managing smaller projects •More scaling and deployment options 15Microservices and Serverless in Python projects

16. 16Microservices and Serverless in Python projects Serverless

17. Serverless architecture •FaaS - Function as a Service •Fully managed computing • Provisioning • Scalability • Monitoring • Logging •Deploy your code •Pay only for actual usage 17Microservices and Serverless in Python projects

18. Serverless architecture 18Microservices and Serverless in Python projects

19. Serverless uses cases ➢ REST API • Stateless services and microservices • Suitable for Chat bots ➢ Events • File processing (S3 event) & Data ingestion • Data/Stream processing • Incidents handling (CloudWatch event log) • IoT ➢ Scheduled tasks • Monitoring, load testing • Periodical jobs 19Microservices and Serverless in Python projects

20. 20Microservices and Serverless in Python projects

21. Serverless benefits •No server management •Automatic scaling and load balancing •Lower infrastructure costs •Flexibility and high availability •Infrastructure managed by service provider 21Microservices and Serverless in Python projects

22. Serverless drawbacks • The tools around the deployment automation of serverless functions are still in development. •There is no control over containers when the execution environments are created or destroyed •Debugging, Deploying and monitoring 22Microservices and Serverless in Python projects

23. Cloud providers •AWS •Microsoft Azure •Cloud platform •OpenWhisk(OS) •Kubeless(OS) 23Microservices and Serverless in Python projects

24. Aws lambda 24Microservices and Serverless in Python projects

25. 25Microservices and Serverless in Python projects

26. Aws lambda functions 26Microservices and Serverless in Python projects

27. Aws lambda functions 27Microservices and Serverless in Python projects

28. Create lambda function with awscli 28Microservices and Serverless in Python projects $ aws lambda create-function --region eu-west-1 --function-name MyHandler --zip-file fileb://handler.zip --role arn:aws:iam::XXX:role/MyLambdaRole --vpc-config SubnetIds=XXX,SecurityGroupIds=XXX --handler handler.handler --runtime python3.6 --profile personal --timeout 10 --memory-size 512

29. 29Microservices and Serverless in Python projects

30. 30Microservices and Serverless in Python projects

31. Frameworks 31Microservices and Serverless in Python projects python-λ

32. 32Microservices and Serverless in Python projects

33. 33Microservices and Serverless in Python projects

34. Zappa architecture 34Microservices and Serverless in Python projects

35. Zappa 35Microservices and Serverless in Python projects

36. 36Microservices and Serverless in Python projects

37. 37Microservices and Serverless in Python projects

38. 38Microservices and Serverless in Python projects

39. 39Microservices and Serverless in Python projects # zappa_settings.json { "dev": { "aws_region": "us-east-1", "django_settings": "hello.settings", "profile_name": "default", "project_name": "hello", "runtime": "python3.6", "s3_bucket": "zappa-huyg6op0s" } }

40. Zappa deploy 40Microservices and Serverless in Python projects $ zappa deploy <env> •Zips code and dependencies •Create AWS Lambda and deploys the zip •Creates endpoint on API Gateway and links to AWS Lambda

41. Zappa deploy 41Microservices and Serverless in Python projects

42. Zappa 42Microservices and Serverless in Python projects

43. Zappa Asynchronous Task 43Microservices and Serverless in Python projects

44. Chalice 44Microservices and Serverless in Python projects •Python Serverless Microframework for AWS •Each endpoint is a separate function

45. Chalice 45Microservices and Serverless in Python projects

46. Chalice 46Microservices and Serverless in Python projects

47. Chalice example 47Microservices and Serverless in Python projects

48. Chalice methods 48Microservices and Serverless in Python projects Resource HTTP Verb AWS Lambda /talks GET get_talks /talk POST add_new_talk /talks/{ID} PUT update_talk /talks/{ID} DELETE delete_talk

49. Chalice methods 49Microservices and Serverless in Python projects

50. Chalice options 50Microservices and Serverless in Python projects

51. Chalice deploy 51Microservices and Serverless in Python projects Updating IAM policy. Updating lambda function... Regen deployment package... Sending changes to lambda. API Gateway rest API already found. Deploying to: dev

52. 52Microservices and Serverless in Python projects

53. https://github.com/lambci/docker-lambda 53Microservices and Serverless in Python projects

54. http://serverlesscalc.com/ 54Microservices and Serverless in Python projects

55. 55Microservices and Serverless in Python projects

56. https://github.com/serverless/examples 56Microservices and Serverless in Python projects

57. References 57Microservices and Serverless in Python projects •https://aws.amazon.com/blogs/compute/microservic es-without-the-servers •https://github.com/Miserlou/Zappa •https://github.com/pmuens/awesome-serverless •https://github.com/aws/chalice •https://chalice.readthedocs.io/en/latest

58. 58Microservices and Serverless in Python projects Serverless architecture is the next generation of cloud evolution

59. Thank you! José Manuel Ortega jmortega.github.io

Microservices and serverless in python projects

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