10 Serverless I

Serverless

Microservices

Microservices

• An architecture that is loosely coupled
• An architectural and organizational approach to software development
  • Software is composed of small independent services
  • Communicate over well-defined APIs
  • Owned by small, self-contained teams

Microservicce: SW System을 작은 독립적인 서비스들로 나누는 아키텍처 스타일로 여러 모듈로 구성된 작은 서비스들이 독립적으로 배포, 확장, 관리될 수 있도록 설계한다.

 

Tightly vs Loosely Coupled

• Tightly Coupled: monolithic architecture
• Loosely Coupled: microservices architecture
  • Microservices is better approach: more durable, reliable, and manageable

Downside of Monoliths

• All processes are tightly coupled and run as a single service.
• Adding or improving a monolithic application's features becomes more complex as the code base grows.
• Dependent and tightly coupled processes increase the impact of a single process failure.

Benefits of Microservices

• Use public APIs: Microservices communicate over APIs -> Hide your data and document of your APIs
• Use the right tool for the job: built for explicit functionality
• Secure your services: provide security at every step
• Be a good citizen: 각 microservice가 책임을 가지고 서로 협력해야 함
• Company transformation: shift requires transformation in the organization and communication mechanism btw teams
• Automate everything

 

Serverless

Serverless

• With serverless computing, you can build and run applications and services without thinking about servers.
  • Developers can focus on their code product not worrying about managing and operating servers or runtimes
• Serverless technologies can complement traditional application models.
  • You can build nearly any type of application or backend service

Microservices and Serverless Architecture

• Microservice: method of building your architecture
• Serverless: service that's used to build your architcture

마이크로서비스는 소프트웨어 아키텍처의 한 방식이며, 서버리스 아키텍처는 그 방법 중 하나를 실현하기 위해 사용되는 특정한 서비스나 플랫폼

 

Benefits of Serverless

• No server management
• Pay-for-value services: you never pay for over-provisioning
• Continuous scaling: most serverless services can be configured to scale automataically
• High availability

 

Event-Driven Architecture

 

Event-Driven Architecture

• Uses events to initiate actions and communication btw decoupled services.
• When event occurs, the information is published for other services to consume it.
• In event-driven architecture, events are the primary mechanism for sharing information across services

Benefits of Event-Driven Architecture

• Scale and fail independently
  • by decoupling your services, they are only aware of the event router, not each other
  • if one service has a failure, the rest will keep running
• Audit with ease
  • event router acts as a centralized location to audit
• Develop with agility
  • event router will automatically filter and push events to customers
• Cut costs
  • on-demand -> not paying for continuous polling to check for an event

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AWS Lambda

AWS Lambda

AWS Lambda

• serverless, event-driven compute service
• can run code
• Challenge: you need coding experience and expertise with:
  • Linux OS commands and concepts
  • Familiarity with AWS IAM
  • Cloud concepts and IP networking
  • Distributed computing concepts

Design Best Practices

• Separate business logic
• Modulara functions
• Treat functions as stateless
• Only include what you need

 

AWS Lambda Function

Three Ways to Build an AWS Lambda Function

1. Author from scratch
   • Hello World examples include just enough code for your choosen language to get started
2. Use a blueprint
   • bluprints are language-specific samples that include function code and settings for a runtime and trigger combination
3. Container image
   • you can select container image to deploy for your function when creating a function
   • containers are a method of OS virtualization taht allow you to run an application and its dependencies in resource-isolated processes

Function Basic Information

• Basic components of an AWS Lambda function include the function name, runtime, and permissions.

Handler Method

• Lambda function handler is the method in your function code that processes events
• Handler Method takes two objects
  • Event object: all of the data and metadata Lambda function needs to drive its logic
  • Context object: allow your function code to interact with the Lambda execution environment

 

Configuring Options

Configuring Memory and Timeout

• Lambda allocates CPU powers in proportion to the amount of memory configured
  • Memory is the amount of memory available to your Lambda function at runtime
• Lambda runs your code for a set amount of time before timing out
  • Timeout is the maximum amount of time in seconds that a Lambda function can run

Configuring Concurrency and Scaling

• Concurrency: number of requests your function can handle at the same time
  • for each concurrent request, Lambda provisions a separate instance of your execution environment
  • Reserved Concurrency: guarantees the maximum number of cconcurrent instances
  • Provisioned Concurrency: initializes a requested number of execution environments instances so that they are prepared to respond imediately to your function's invocations
• Lambda automatically handles scaling the number of execution environments

Choosing a Trigger

• You can configure other AWS services to trigger your function each time a specified event occurs

Destination

• Destination can send records of asynchronous invocations to other services
• Can configure separete destinations for failed events and successed events
• Can configure destinations on a function, a version, or an alias
• With destinations, you can address errors and successes without needing to write more code

Types of Monitoring Graphs

• Invocations: number of times that your function code run
• Duration: amount of time that your function code spends processing an event
• Errors: number of invocations that result in a error
• Throttles: number of times that a process failed because of concurrency limits
• IteratorAge: the age of the last record in the event
• Concurrent executions: number of function instances that are processing events

 

Amazon CloudWatch Lambda Insights

Amazon CloudWatch Lambda Insights

• monitoring and troubleshooting solution for serverless applications running on Lambda
• Lambda Insights collect, aggregate, summarize system-level metrics and diagonostic information such as cold starts and Lambda worker shutdowns

Lambda Insights Dashboard

• Multi-function
  • aggregates the runtime metrics for the Lambda functions in the concurrent AWS account and Region
  • identify over and under utilized Lambda functions
• Single-function
  • shows the available runtime metrics for a single Lambda function
  • troubleshoot invidiual requests