In this article, we will see how we can create AWS Lambda Function. We will see what is AWS Lambda in details.
Creating an AWS Lambda Function involves several steps, and I’ll guide you through the process in a simple and detailed manner. Before that we will see in details what is actually a AWS Lambda Function & Why we use it in our production.
Table of Contents
What is serverless computing?
Serverless computing is a cloud computing model where cloud providers manage the infrastructure, allowing developers to focus solely on writing and deploying code without worrying about servers or underlying infrastructure management. In serverless computing, resources are provisioned dynamically by the cloud provider as needed, scaling automatically based on demand. This model abstracts away the complexity of traditional server management.
Benefits of Serverless Computing:
- Cost Efficiency: You pay only for the resources used while your code is running, measured in milliseconds. There are no charges for idle time or provisioned capacity, making it cost-effective, especially for sporadic or unpredictable workloads.
- Scalability: Serverless platforms automatically scale to accommodate varying workloads. They handle the load and resources, ensuring optimal performance without manual intervention. This scalability makes them suitable for applications with fluctuating usage patterns.
- Reduced Operational Overhead: Developers don’t need to manage servers or infrastructure, reducing operational complexity. Cloud providers handle maintenance, scaling, and security patching, allowing teams to focus more on building and improving the application logic.
- Faster Time-to-Market: Serverless enables rapid deployment of code changes, updates, and new features. Developers can iterate quickly and deploy changes without managing servers or dealing with complex deployment processes.
- Event-Driven Architecture: Serverless applications are built around events. Functions (often referred to as “serverless functions” or “lambda functions”) are triggered by various events like HTTP requests, database changes, file uploads, or scheduled events, allowing for powerful and flexible integrations.
- High Availability and Fault Tolerance: Serverless platforms are designed to be highly available and fault-tolerant by nature. They run across multiple availability zones, ensuring reliability and minimizing downtime.
- Flexibility and Versatility: Serverless computing supports various programming languages and offers integration with numerous services, allowing developers to choose the language and services that best suit their applications.
- Focus on Application Logic: With serverless, developers can concentrate on writing application logic and business functionality without getting involved in managing the underlying infrastructure.
Difference between server and serverless computing
The key differences between server and serverless computing lie in how they handle infrastructure management, scalability, and pricing models:
- Infrastructure Management: In server computing, users are responsible for provisioning, managing, and maintaining the underlying servers or virtual machines. This includes tasks like OS updates, security patches, and scaling to meet demand.
- Scalability: Scaling in server-based systems often requires manual intervention or the implementation of auto-scaling mechanisms. Users need to predict and provision resources to handle peak loads, which can result in underutilized resources during low-traffic periods.
- Pricing Model: Typically, server-based systems involve fixed costs, where users pay for allocated server capacity regardless of actual usage. This can lead to higher costs during periods of low utilization.
- Infrastructure Management: In serverless computing, the cloud provider manages the infrastructure entirely. Users upload their code (functions) and define triggers. The provider handles server provisioning, scaling, maintenance, and security patches.
- Scalability: Serverless systems automatically scale based on the number of incoming requests or events. Resources are allocated dynamically, allowing for rapid scaling and efficient resource utilization. Users don’t need to manage scalability manually.
- Pricing Model: Serverless computing operates on a pay-as-you-go model. Users are billed based on actual usage—number of function executions and the duration of each execution—without charges for idle resources. This results in cost savings for sporadic or low-traffic applications.
What is AWS Lambda?
AWS Lambda is a serverless computing service offered by Amazon Web Services (AWS) that enables users to run code without provisioning or managing servers. It allows developers to upload their code and Lambda automatically handles the infrastructure required to run that code.
Key Features of AWS Lambda:
- Event-Driven Execution: Lambda functions are triggered by various events, such as HTTP requests, changes in data storage (e.g., Amazon S3), database modifications (e.g., DynamoDB), or custom events using AWS SDKs.
- Pay-As-You-Go Model: Users are charged based on the number of requests made to their Lambda functions and the duration of code execution. Costs are incurred only when functions are triggered and running.
- Automatic Scaling: AWS Lambda automatically manages the scaling of resources based on the number of incoming requests. It dynamically allocates resources to handle varying workloads without user intervention.
- Multiple Language Support: Lambda supports various programming languages such as Node.js, Python, Java, C#, Go, and others. Developers can choose their preferred language for writing functions.
- Integration with AWS Services: Lambda seamlessly integrates with other AWS services, allowing for the creation of highly scalable and event-driven architectures. It can easily interact with services like API Gateway, S3, DynamoDB, Kinesis, and more.
- Stateless Execution: By default, Lambda functions are stateless, meaning they do not retain data between invocations. However, they can interact with other AWS services to store and retrieve persistent data.
Working of AWS Lambda:
- Function Creation: Users create functions by uploading their code or using pre-built templates. They define triggers that initiate function execution when specific events occur.
- Event Processing: When an event occurs, Lambda invokes the associated function, passing event data as input. The function executes the defined logic and can interact with other services.
- Scalability: Lambda automatically scales by spinning up instances of functions in response to incoming events. It handles the required resources to execute code efficiently.
- Cost Optimization: Users are billed based on the number of requests and the duration of function execution, ensuring cost optimization by charging only for resources utilized during execution.
Supported languages in AWS Lambda.
AWS Lambda supports multiple programming languages, allowing developers to write functions in their preferred language such as:
- Python: High-level programming language known for its simplicity and readability.
- Java: Object-oriented programming language with a strong ecosystem.
- Ruby: Dynamic, object-oriented scripting language.
- Go: Compiled language known for its efficiency and simplicity.
- .NET Core (C#): Cross-platform, open-source framework for building different types of applications.
Applications in which AWS Lambda can be used
AWS Lambda can be used in various types of applications across different industries due to its flexibility and scalability. Some common application scenarios include:
- Web Applications: Lambda can handle HTTP requests via API Gateway, making it suitable for building serverless backends for web applications. It can manage user authentication, data processing, and other backend functionalities.
- Data Processing: It’s ideal for processing data in real-time or batch jobs. For instance, handling file uploads, transforming data, or performing ETL (Extract, Transform, Load) operations.
- IoT Applications: Lambda can process data streams from IoT devices, enabling real-time analysis, data validation, and triggering actions based on sensor data.
- Event-Driven Workflows: It excels in event-driven architectures, reacting to events from various sources like database changes, file uploads, or custom events from other AWS services.
- Automated Tasks: Lambda functions can automate routine tasks such as backups, log analysis, and scheduled operations without the need for continuous server resources.
- Chatbots and Voice Assistants: For handling conversational interfaces by integrating with services like Amazon Lex for chatbots or Alexa Skills for voice-based applications.
- Image and Video Processing: It’s used for tasks like image recognition, resizing, or video transcoding, triggered by file uploads or specific events.
- Real-time Stream Processing: Analyzing and processing streaming data from services like Kinesis for real-time insights or actions.
- Microservices: Building microservices architectures where each function represents a single service, providing modularity and flexibility in application design.
Applications in which AWS Lambda cannot be used
While AWS Lambda is incredibly versatile, there are some scenarios where its use might be limited or less suitable:
- High-Performance Computing: Applications requiring continuous, high-compute resources might not be best suited for Lambda due to its event-driven, stateless nature. Long-running, resource-intensive tasks that need sustained high CPU or memory might exceed Lambda’s capabilities or cost efficiency.
- Legacy Systems Integration: In cases where applications heavily rely on legacy systems or specific infrastructure that cannot be easily abstracted into an event-driven, serverless model, integrating with Lambda might pose challenges.
- Predictable Workloads: If an application has consistent and predictable workloads where instances can be pre-provisioned to manage the load efficiently, using traditional server-based approaches might be more cost-effective than Lambda’s pay-per-execution model.
- Continuous Workloads: For tasks that require continuous, uninterrupted execution over an extended period without any interruption or scaling down, Lambda might not be the most suitable choice as it’s designed for event-triggered, short-lived executions.
- Resource-Intensive Operations: Certain tasks that demand access to specific hardware features or require prolonged, continuous access to resources might not align well with Lambda’s execution model.
- Long-Running Processes: AWS Lambda has a maximum execution duration limit. Tasks that require execution times beyond this limit might not be feasible to implement within Lambda without breaking them into smaller, manageable chunks.
AWS Lambda Pricing Considerations:
- Free Tier: AWS Lambda offers a free tier which includes a certain number of invocations and compute time per month, allowing users to experiment without incurring charges.
- Per-GB Memory Allocation: Lambda allows you to specify the amount of memory allocated to your function. Pricing is based on this memory allocation, with higher memory configurations incurring higher costs per millisecond.
- No Charges for Idle Time: There are no charges when your code is not running. You only pay for the compute time used during execution.
- Data Transfer Costs: Data transfer costs might apply if your Lambda function interacts with other AWS services or external systems.
Summary of Key Points about AWS Lambda:
- AWS Lambda Fundamentals: Lambda is a serverless compute service by AWS, enabling code execution without server management.
- Benefits: It offers automatic scaling, event-driven execution, and supports multiple programming languages.
- Event-Driven Architecture: Lambda functions are triggered by events from various AWS services or custom events.
- Cost Model: Pay-as-you-go pricing based on the number of requests and duration of code execution.
- Automatic Scaling: Lambda automatically manages resources based on incoming requests.
- Integration: Seamlessly integrates with other AWS services, reducing operational overhead.
Encouragement for Further Exploration:
AWS Lambda unlocks incredible possibilities in building scalable, cost-efficient applications. Experimenting further can:
- Deepen Understanding: Try different triggers, languages, and integrations to understand Lambda’s versatility.
- Explore Integrations: Experiment with combining Lambda with other AWS services for powerful application architectures.
- Optimize Performance: Test and optimize functions for cost and performance efficiency.
- Real-World Applications: Apply Lambda in real projects to grasp its impact on actual applications.
Before proceeding, make sure you have the following prerequisites in place:
- You’ll need an Amazon Web Services (AWS) account. If you don’t have one, you can sign up for a free AWS account.
- Basic knowledge of AWS services and concepts.
Create Your First AWS Lambda Function in 5 minutes
Step #1:Log in to your AWS console and search for Lambda. As depicted in the image below.
Step #2:Click on Create function.
Step #3:Fill the basic information
Step #4:We will use an existing role and for that we need to create a new role
Step #5:Search for IAM in search bar
Step #6:Click on Roles from the Dashboard and the click on Create role
Step #7:Select the Use Case
Step #8:Search for AWSLambdaExecute and select the option
After that click on next
Step #9: Enter a role name
Step #10:Add Tags
Then click on create role
Our role has been created successfully
Step #11:Select an existing role which we have recently created
Then click on create Lambda function
Our Lambda Function has been created successfully
Thank you for sticking to the end. In this article, we have learned how we can create AWS Lambda Function. So, This is really very quick and simple. If you like this please share and show your appreciation by giving thumbs-ups, and don’t forget to give me suggestions on how I can improve my future articles that can suit your needs.