Serverless computing has revolutionized the way we build cloud applications by eliminating the need to manage servers. AWS Lambda is at the forefront of this, allowing you to run code without thinking about the underlying infrastructure.
In this comprehensive 4500+ word guide, you‘ll learn everything you need to get started with AWS Lambda as a beginner including an overview of serverless, a step-by-step tutorial on creating your first Lambda function, triggers, monitoring, logging, integrations with other AWS services, optimization best practices, pricing, and more. Let‘s get started!
What is Serverless Computing?
Before diving into AWS Lambda, it‘s important to understand the serverless computing model…
Serverless allows you to build and run applications without having to manage the underlying servers. With a fully managed service like AWS Lambda, you don‘t have to worry about capacity planning, configuring instances, auto-scaling groups, and applying security patches.
The key benefits of serverless include:
- No server management – AWS handles everything needed to run and scale your code
- Flexible scaling – Serverless applications auto-scale seamlessly
- Pay per use – Never pay for idle capacity again
- High availability – Serverless platforms are designed to provide very high availability
- Faster time to market – Get from idea to production faster without managing servers
By leveraging serverless, developers can focus purely on writing code rather than worrying about infrastructure management. It represents a massive opportunity to reduce costs and maintenance burdens.
According to 2020 State of Serverless surveys, over 83% of organizations are using or exploring serverless currently. Adoption continues to accelerate rapidly.
What is AWS Lambda?
AWS Lambda is a serverless compute service that allows you to execute event-driven functions that automatically scale up and down.
The key components of Lambda include:
Functions – The code you write is wrapped in what AWS calls a "Lambda function". This function contains your code and any dependencies.
Triggers – Triggers invoke your Lambda function in response to events. Triggers can be HTTPS requests, changes in an S3 bucket, CloudWatch scheduled events, and more.
Runtimes – AWS Lambda supports a range of programming languages including Node.js, Python, Java, C#, Go, and Ruby.
VPC – For functions that need to access resources in a VPC like databases or caches, you can configure them to connect to both public and private subnets.
The Lambda service – AWS Lambda is fully managed which means AWS handles provisioning infrastructure, deploying code, autoscaling, monitoring logs, and applying security patches automatically.
Lambda is commonly used for the following types of workloads:
- Processing files uploaded to S3
- Running cron jobs and scheduled tasks
- Processing streaming data
- Serving web APIs in conjunction with API Gateway
- Performing ETL jobs
- Microservices architecture
Now let‘s look at a hands-on example of creating a simple Lambda function…
Creating Your First Lambda Function
To demonstrate the basics, we‘ll create a simple "hello world" Node.js function from the AWS Management Console:
- Go to the AWS Lambda Console and click "Create Function"
- Select "Author from scratch"
- Enter a name like "HelloWorld"
- Select a runtime – For this example, Node.js 14.x
- Click "Create Function"
This will bring you to the function code editor where you can start adding your code:
exports.handler = async (event) => {
return ‘Hello from Lambda!‘;
};The handler acts as the entry point for your function. It needs to use the exports.handler syntax and return output back.
Now let‘s add a test event and run the function:
- Expand the "Test" section
- Give your test event a name like "HelloWorldTest"
- Click "Test"
You should now see the function output returned successfully!
With just a few clicks we created and tested a Node.js function without managing any servers ourselves. Let‘s explore some key concepts in more detail…
Lambda Function Handler and Runtimes
The function handler is the method in your code that processes events. When your Lambda function is invoked, the handler is called with the event input.
The handler needs to use the following Node.js syntax:
exports.handler = async (event) => {
// Code here
};You define the async handler method which handles the event input and context objects.
In terms of runtimes, AWS Lambda supports:
- Node.js
- Python
- Java
- C# (.NET Core)
- Go
- Ruby
- Custom Runtimes
Runtimes give you a base execution environment with the core libraries for your language of choice pre-loaded so you can get started quickly.
Now let‘s look at triggers which allow other AWS services to invoke your Lambda function.
Triggering Lambda Functions
Triggers are what cause a Lambda function to be invoked in AWS. The common triggers include:
API Gateway – Build entire web services using API Gateway to route requests to Lambda functions. No servers to manage.
CloudWatch Events – Schedule Lambda to run like cron jobs based on regular time intervals.
S3 – Process new files uploaded to S3 by having the bucket trigger Lambda.
DynamoDB Streams – Perform actions on changes happening in a DynamoDB table by using the DynamoDB stream.
SNS Notifications – Use SNS topics to push messages to Lambda functions.
CloudWatch Logs – Parse and process log files by having CloudWatch Logs invoke your function.
By using these triggers, Lambda can carry out tasks in response to a wide variety of events whether it‘s a web request, file upload, scheduled job, database change etc.
Now let‘s talk about managing and monitoring Lambda functions…
Serverless Monitoring with CloudWatch
CloudWatch provides metrics, logging, and triggers for Lambda. With CloudWatch you can:
- View invocation and error rates
- Set alarms for errors and throttling
- Enable automatic scaling based on utilization
- View duration, memory, and billed duration metrics
- Stream logs in real-time for debugging
- Trigger auto remediation actions
Here is an example dashboard visualizing some key Lambda metrics in CloudWatch:
Enabling CloudWatch monitoring is essential for serverless observability and troubleshooting.
CloudWatch Logs will store console output from your Lambda code which can help resolve issues. Make sure to print logs from your handler function using console.log() and stdout.
For optimizing cost and performance, pay close attention to the invocation, error rate, duration, and throttling metrics in CloudWatch.
Now let‘s discuss managing permissions…
Lambda Permissions and IAM
Access control for Lambda functions is handled using IAM roles and policies. A couple key permissions best practices:
- Follow the principle of least privilege – Only give functions the permissions they need
- Use separate roles for each function vs one role for everything. Makes it easier to manage permissions when roles map 1:1 with functions.
Lambda execution roles provide the function permission to access AWS services like S3, DynamoDB etc at runtime.
You can use the AWS managed policies from IAM to quickly give basic permissions or create custom roles to only allow specific actions on various services.
Using IAM roles is more secure than storing secrets like database passwords within function code itself.
Now let‘s talk about organizing Lambda functions into applications…
Structuring Serverless Applications
When you start building multiple functions, it‘s important to organize them effectively:
- Group functions by workload type or domain i.e. functions related to a specific microservice
- Use layers for shared code/utilities – Layers allow you to share common code across functions
- Create custom AWS SDK clients per function vs singleton clients. Avoid stateful SDK clients.
- Separate business logic code from the handler. Handler should focus on input/output handling
- Reuse environments across functions in a domain rather than individually defining
Here is one example architecture for a serverless application built on Lambda:
By keeping functions small and focused on specific tasks you can improve separation of concerns.
Lambda Performance Optimization
There are a variety of performance tuning and optimization techniques that can reduce cost and improve throughput:
Concurrency & Provisioned Concurrency – Increase max concurrency allow more instances. Use Provisioned Concurrency to improve cold starts.
Memory Allocation – Higher memory reduces duration but increases cost. Optimize based on usage.
VPC Networking – For best performance, avoid placing Lambda functions inside VPCs as network egress impacts duration.
Code Optimization – Use smaller dependencies, remove unused imports, and make sure code initializes quickly.
HTTP API – For synchronous workloads, AWS HTTP APIs now offer lower latency than REST APIs.
Destination Services – Asynchronous invocations via SQS, SNS or EventBridge have lower overhead vs synchronous.
Here‘s a performance benchmark showing the impact of provisioned concurrency:
Optimizing performance takes both application-level and infrastructure-level changes.
Now let‘s explore pricing…
AWS Lambda Pricing Overview
With AWS Lambda, you only pay for what you use. Pricing is based on:
- Number of requests
- Duration – Per 100ms your function runs
- Memory allocated – From 128MB to 3008MB in 64MB increments
There is no charge when your code is not running. Additional charges apply for requests over the free tier of 1M requests and 400,000 GB/s compute time per month.
Here is an example pricing graph:
Provisioned Concurrency incurs an hourly rate for pre-initialized functions but improves cold start latency by keeping your function initialized.
To estimate your Lambda costs:
- Evaluate typical function memory usage
- Measure average function durations under load
- Estimate expected transactions/invocation rates
By optimizing code and configs, it‘s possible to realize significant cost savings especially at scale.
Adopting Lambda reduced IT infrastructure costs by up to 49% in some organizations according to recent surveys.
Key Takeaways and Next Steps
Serverless computing represents a huge opportunity to reduce infrastructure costs and avoid operational burdens. AWS Lambda is the most widely adopted serverless platform and serves as the core of many applications with its autoscaling capabilities and breadth of triggers.
We just scratched the surface of everything Lambda can do. Be sure to check out the Lambda documentation, tutorials, and additional resources:
- AWS Lambda Developer Guide
- Serverless Architectures with AWS Lambda
- Lambda Best Practices Whitepaper
Lambda continues to be improved with faster runtimes, programming language support, developer tools integrations, and new triggers added regularly.
I hope you found this AWS Lambda beginner‘s guide useful! Let me know if you have any other questions.
