Tutorial / Cram Notes
AWS Lambda is a serverless computing platform that executes your code in response to events and automatically manages the underlying compute resources for you. With Lambda, there’s no need to provision or manage servers. You only pay for the compute time you consume, making it a cost-effective way to run code in the cloud.
Core Features of AWS Lambda
- Event-driven: Lambda functions are triggered by AWS services such as S3, DynamoDB, Kinesis, SNS, and CloudWatch, or can be invoked directly by external systems via API Gateway or SDKs.
- Languages support: Lambda supports multiple programming languages, including Java, Go, PowerShell, Node.js, C#, Python, and Ruby.
- Automatic scaling: The service automatically scales your application by running code in response to each trigger. Your code runs in parallel and processes each trigger individually, scaling precisely with the size of the workload.
- Stateless: Functions are stateless, with no affinity to the underlying infrastructure. Lambda launches as many copies of the function as needed to handle the rate of incoming events.
- Integrated with other AWS services: Lambda integrates with various AWS services for logging, monitoring, and configuration, such as CloudWatch, AWS X-Ray, and AWS Systems Manager.
Use Cases for AWS Lambda
- Web applications: Serve web pages or provide RESTful APIs in conjunction with Amazon API Gateway.
- Data processing: Perform work on files as soon as they are uploaded to Amazon S3 or process streaming data with Amazon Kinesis.
- Back-end services: Provide back-end services for applications by accessing databases such as DynamoDB or executing complex computations on-demand.
Lambda Pricing Model
With Lambda, you pay for the number of requests served and the compute time to execute your code. Pricing depends on the amount of memory allocated to your function and the time it takes to execute your code.
Best Practices for AWS Lambda
- Optimize execution time: Write your code in a stateless style and optimize it to execute quickly.
- Use concurrency controls: Control the maximum concurrency level for your Lambda functions to optimize performance and costs.
- Use AWS provided SDKs and Libraries: Lambda integrates with AWS SDKs and libraries, which simplifies the process of writing Lambda functions that interact with AWS services.
Comparing Serverless Compute Services
| Feature | AWS Lambda | AWS Fargate | AWS Batch |
|---|---|---|---|
| Compute Paradigm | Function as a Service | Container as a Service | Batch processing |
| Pricing | Based on requests and execution time | Based on compute resources per second | Based on compute resources per second |
| Scalability | Automatically scales with events | Scales with ECS or EKS | Scales with job queues and priorities |
| Integration | Integrates with many AWS services directly | Uses ECS or EKS for orchestration | Designed for batch jobs, integrates with other compute services |
| Stateless/Stateful | Stateless (functions) | Can be stateful (containers) | Can manage stateful batch jobs |
| Ideal For | Event-driven apps, microservices | Microservices, applications requiring specific runtime environments | Large-scale, high-throughput batch processing jobs |
When studying for the AWS Certified Solutions Architect – Professional certification, it’s crucial to understand the differences between these serverless compute offerings and know when to use each one based on your architectural needs.
By leveraging the power of AWS Lambda and other serverless compute offerings effectively, architects can design robust and cost-efficient systems on AWS. It is essential to consider aspects like event-source integration, state management, performance, and cost when implementing serverless architectures for professional-grade solutions.
Practice Test with Explanation
True or False: AWS Lambda automatically scales horizontally as the number of incoming requests increases.
- True
True
AWS Lambda is designed to automatically scale with the number of incoming events or requests, creating different instances as needed without any manual intervention.
With AWS Lambda, you are charged based on the number of requests and the duration of code execution.
- True
True
AWS Lambda charges are based on the number of requests for your functions and the duration, the time it takes for your code to execute.
Which AWS service is used to automatically trigger AWS Lambda functions in response to HTTP requests?
- A) Amazon SNS
- B) Amazon SQS
- C) Amazon API Gateway
- D) AWS Step Functions
C) Amazon API Gateway
Amazon API Gateway can be used to create, publish, maintain, monitor, and secure APIs at any scale, and it can also act as a trigger for AWS Lambda functions in response to HTTP requests.
What is the maximum duration for which a single AWS Lambda function invocation can run synchronously?
- A) 5 minutes
- B) 15 minutes
- C) 30 minutes
- D) 1 hour
B) 15 minutes
As of the knowledge cutoff date, the maximum execution duration per request for a Lambda function is 15 minutes for synchronous invocations.
Which AWS service can be used to orchestrate AWS Lambda functions for complex workflows?
- A) AWS Batch
- B) AWS Data Pipeline
- C) AWS Step Functions
- D) Amazon ECS
C) AWS Step Functions
AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows so you can build and update apps quickly.
True or False: AWS Lambda supports environment variable encryption with AWS KMS.
- True
True
AWS Lambda supports environment variables and allows for encryption of these variables using AWS Key Management Service (KMS).
What is the main purpose of AWS Lambda layers?
- A) To increase the maximum execution timeout
- B) To run functions in a VPC
- C) To provide shared code or libraries among multiple Lambda functions
- D) To automatically deploy Lambda functions
C) To provide shared code or libraries among multiple Lambda functions
AWS Lambda layers are a way to share common components across multiple Lambda functions, reducing the size of deployment packages and separating dependencies from your function code.
True or False: AWS Lambda can read and process streaming data directly from Amazon Kinesis and Amazon DynamoDB Streams.
- True
True
AWS Lambda can natively poll and read data from Amazon Kinesis and Amazon DynamoDB Streams, and invoke your Lambda function.
Which of the following languages is NOT natively supported by AWS Lambda?
- A) C++
- B) Ruby
- C) Go
- D) Python
A) C++
As of the knowledge cutoff date, AWS Lambda natively supports Ruby, Go, Python, and several other programming languages but does not natively support C++.
True or False: In AWS Lambda, you have to manually manage the underlying infrastructure such as EC2 instances.
- False
False
AWS Lambda is a serverless compute service, meaning there is no need to provision or manage servers; AWS manages the server infrastructure for you.
What feature of AWS Lambda allows you to automatically filter events before processing?
- A) Dead Letter Queues
- B) Event Source Mapping
- C) Environment Variables
- D) Event filtering patterns
D) Event filtering patterns
Lambda event filtering patterns allow developers to specify filters that determine which events will trigger the function, potentially reducing the number of unnecessary invocations.
True or False: You cannot run an AWS Lambda function on a fixed, scheduled basis, similar to a cron job.
- False
False
AWS Lambda functions can be triggered on a schedule using Amazon CloudWatch Events, allowing you to run serverless functions on a regular, scheduled basis.
Interview Questions
What is AWS Lambda, and how does it differ from traditional compute services?
AWS Lambda is a serverless compute service that allows users to run code without provisioning or managing servers. Lambda handles the heavy lifting related to compute resources, such as server and operating system maintenance, capacity provisioning and automatic scaling, code monitoring, and logging. This is in contrast to traditional compute services like Amazon EC2, where users are responsible for managing the server infrastructure.
Can you explain the concept of “event-driven” in the context of AWS Lambda?
In the context of AWS Lambda, “event-driven” means that the function execution is triggered by specific events or conditions. AWS services, such as S3, DynamoDB, Kinesis, SNS, and CloudWatch, can produce events that invoke Lambda functions. Developers can also create custom backend services that are run in response to HTTP requests via API Gateway or as a result of direct invocations via SDKs or the AWS CLI.
How is the pricing model for AWS Lambda different from EC2-based instances?
The pricing model for AWS Lambda is based on the number of requests for your functions and the time your code executes, measured in 1ms increments. There’s no charge when your code is not running, in contrast to EC2 instances, which incur costs based on instance type, size, and running time, irrespective of whether the resources are in use.
What are the main benefits of using AWS Lambda for application development?
The main benefits of using AWS Lambda include reduced operational overhead, automatic scaling, built-in high availability, and a pay-for-use pricing model. This allows developers to focus on code development rather than infrastructure management, leading to faster time-to-market for applications.
How does AWS Lambda ensure scalability for applications?
AWS Lambda automatically scales the application by running code in response to each trigger. The service handles all the administration of the compute resources, including server and operating system maintenance, scaling, deployment, capacity provisioning, and monitoring, ensuring high availability.
How do you secure an AWS Lambda function?
AWS Lambda functions can be secured by setting up IAM roles with the least privileges necessary for the function to execute, enabling VPC for private resource access, leveraging Lambda environment variables for sensitive information, using AWS KMS to encrypt environment variables, and employing resource-based policies for cross-account access.
What are the runtime execution limits for an AWS Lambda function?
As of my knowledge cutoff in 2023, the runtime execution limits for a Lambda function include a maximum execution duration per request of 15 minutes, environment variables are limited to 4KB, the deployment package size is 50 MB zipped, and the request body payload size is 6 MB for direct invokes and 256 KB for events published to Lambda by Amazon S3, SNS, or Amazon EventBridge.
What is a cold start in AWS Lambda, and how can it be minimized?
A cold start occurs when an AWS Lambda function is invoked after not being used for an extended period of time, leading to latency due to the initialization of a new instance of the function. To minimize cold starts, you can use provisioned concurrency, which keeps a specified number of function instances warm and ready to respond immediately to events.
Describe the process of setting up a continuous deployment pipeline for AWS Lambda functions.
Setting up a continuous deployment pipeline for AWS Lambda functions typically involves using AWS CodePipeline to automate the steps required to release a new version of a function. It can include source code management with AWS CodeCommit, automated builds with AWS CodeBuild, and deployment with AWS CloudFormation or AWS CodeDeploy which can integrate with Lambda.
How can monitoring and logging be achieved for AWS Lambda functions?
Monitoring AWS Lambda functions can be achieved using Amazon CloudWatch which provides metrics, logs, and can trigger alarms. AWS Lambda automatically monitors functions and reports metrics to CloudWatch. AWS X-Ray can also be used for tracing and insights into the behavior of Lambda functions.
Can Lambda functions be used in a VPC, and what are the implications?
Yes, AWS Lambda functions can be configured to access resources within a VPC. This allows functions to interact with resources that are not publicly accessible, such as databases, cache instances, or internal services. The implication of running a Lambda function in a VPC is that it may lead to an increased cold start time, as Lambda will have to establish an ENI (Elastic Network Interface) within the VPC. Additionally, there should be sufficient IP addresses and network configurations to support the scale at which the functions will run.
What are the considerations for using AWS Lambda with relational databases?
When using Lambda with relational databases, it’s important to manage connections efficiently, given the stateless nature of Lambda functions and potential for numerous function instances. Implementing connection pooling or using Amazon RDS Proxy can reduce the number of open connections, thus providing more predictable database performance, improved scaling, and better resource utilization. Additionally, you need to design your application to handle database scaling and failover, handle transactions correctly, and deal with connection timeouts and other errors gracefully.
I love how AWS Lambda can scale automatically with incoming traffic. Essential for high-traffic applications!
Thanks for the blog post, really helpful for my SAP-C02 preparation.
Be careful with cold starts in AWS Lambda; they can impact performance.
Great insights on AWS Lambda! Helped me clear some of my doubts for the SAP-C02 exam.
AWS SAM can simplify the deployment process for serverless applications. Highly recommend it!
Can someone explain the pricing model for AWS Lambda? I find it a bit confusing.
Appreciate the detailed post on serverless computing.
Serverless is the future! AWS Lambda cuts down so much operational overhead.