AWS Serverless Lambda Functions 2 hr. Hands On Crash Course

In this lesson, the concept of microservices is introduced and compared to traditional monolithic application architecture. Microservices involve breaking down an application into smaller, specialized components, each with its own defined function and the ability to communicate through well-defined APIs. The benefits of microservices include increased resilience, flexibility in resource allocation, and ease of scaling, allowing developers to replace parts of the application independently, resulting in faster development and scalability. The lesson also highlights the advantages of implementing microservices in a cloud computing environment, with examples from AWS, Google Cloud, and other cloud providers.

In this lesson, the concept of containers and their advantages over virtual machines is explained. Containers are lightweight, self-contained environments that package an application along with its dependencies, libraries, and configuration files. They allow applications to run reliably across different computing environments without being affected by variations in operating systems or infrastructure, making them an ideal choice for microservices and software deployment. The lesson also covers the components of containers, including source code, dependencies, the container engine, and the container image, which can be stored in a registry for easy deployment.

In this lesson, the basics of creating serverless applications, primarily focusing on AWS Lambda, are introduced. Serverless computing, exemplified by Lambda, is a paradigm where developers can run code without the need to provision or manage servers. Unlike traditional virtual machines like EC2, Lambda functions are short-lived, cost-effective, and run only when invoked, making them a more efficient and scalable choice for many application scenarios. The lesson emphasizes the elimination of server management in serverless computing, allowing developers to focus on writing code rather than managing infrastructure.

In this lesson, you'll learn how AWS Lambda functions are invoked and understand the event-driven nature of Lambda. AWS Lambda functions are executed in response to specific events, and you only pay for the compute time consumed during execution. Various AWS services, such as API Gateway, Kinesis, DynamoDB, S3, SNS, and SQS, can trigger Lambda functions based on specific events or changes, making Lambda a versatile and event-driven serverless compute service.

In this lesson, you will learn about the scalability and performance of AWS Lambda functions using a simple analogy of a funnel. The lesson discusses how Lambda scales with concurrent invocations and how increasing memory allocation can improve function execution performance. It also introduces the concept of concurrency and explains unreserved concurrency, which is the default behavior in AWS Lambda, where no specific guarantees are made about the number of concurrent invocations.

This lesson introduces the concept of reserved concurrency and provisioned concurrency for AWS Lambda functions. Reserved concurrency allows you to allocate a specific number of concurrent invocations to a particular Lambda function, ensuring it won't be throttled, but potentially at the expense of other functions. Provisioned concurrency, on the other hand, aims to improve the performance of Lambda functions by reducing cold start times, and it will be discussed in more detail in the following lesson.

In this lesson, you'll delve into the concept of Lambda provisioned concurrency, a method to reduce latency and enhance the performance of Lambda functions. You'll learn how provisioned concurrency differs from reserved concurrency, how it pre-initializes execution environments to eliminate cold starts, and the associated costs. Additionally, the lesson explores strategies to optimize costs through auto-scaling and scheduling of provisioned concurrency units, providing a comprehensive overview of this vital Lambda feature.

In this demo, you'll learn how to effectively monitor and analyze Lambda activity through logs. The demonstration covers accessing Lambda function metrics, checking invocation counts, and understanding error rates, while also delving into CloudWatch Logs and how to set up log retention settings. Furthermore, the tutorial introduces the use of CloudWatch Logs Insights to streamline log analysis, demonstrating built-in queries that assist in identifying issues and optimizing Lambda function performance.

In this lesson, you'll explore the differences between traditional and serverless architectures in AWS. The traditional architecture involves managing EC2 instances, server maintenance, and database configuration, which can be complex and costly. On the other hand, the serverless architecture in AWS eliminates the need for managing servers, allowing you to focus on writing code and efficiently handling tasks, making it a more cost-effective and scalable solution.

This lesson explains the difference between synchronous and asynchronous Lambda invocation using a restaurant analogy, where synchronous is like a waiter waiting for a meal to be prepared (resulting in downtime), and asynchronous is like waiters leaving orders for cooks and moving on (more efficient). The lesson further delves into AWS Lambda, illustrating how a main lambda function invoking other functions synchronously can lead to inefficiencies and higher costs due to the main function waiting for others to complete. Conversely, asynchronous invocation, using a queue for events, allows the main function to finish quickly, only incurring costs for the time taken by each function, which is more efficient but requires careful error handling by developers.

Lambda functions in AWS can be deployed within a Virtual Private Cloud (VPC) to access resources like EC2 and RDS with private IP addresses. Using Elastic Network Interfaces (ENIs) and potentially a NAT Gateway, Lambda can communicate within the VPC and access the internet. VPC endpoints enable Lambda to securely access AWS services like S3 over AWS's backbone network, avoiding public internet. For Lambda to interact with other AWS services, it must have an IAM role with the necessary permissions.

This demo guides on creating a Lambda function to snapshot EC2 instances, starting with setting up the necessary IAM permissions. It covers creating the function, providing code, deploying it, and scheduling regular execution using EventBridge, demonstrating Lambda's integration with AWS services like EC2 and IAM for resource management and automated tasks.

This lesson introduces the concept of Lambda function versions, which allow developers to manage, test, and safely roll back changes to their code and configuration. By using versions and strategies like blue-green deployments, you can direct traffic to specific versions for testing while keeping production stable, enabling safer updates and simplified change management.

In this lesson, we explore Lambda aliases, which act as named pointers to specific function versions, enabling safe and flexible traffic routing during updates. You'll learn how aliases can support blue-green deployments and traffic weighting, allowing gradual rollouts of new Lambda versions while maintaining stability in production environments.

In this demonstration, you'll learn how to create and manage Lambda versions and aliases in the AWS Console to control code deployments and ensure application stability. The demo shows how to publish new versions, create aliases like prod, and update or route traffic using version pointers and weighted traffic distribution for flexible rollout strategies.