Cloud computing enables enterprises to build and deploy a variety of applications and services without investing in servers. While developing and deploying applications in the cloud, the enterprises can access servers just like other computing services over the internet. As a cloud computing execution model, serverless computing enables developers to build and deploy software application smoothly without managing and maintaining servers.
The model requires the cloud service provider to manage the code execution fully. An enterprise has the option to choose from serverless computing services provided by various cloud vendors. Amazon Web Services (AWS) enables enterprises to build and deploy software applications without managing and maintaining servers through AWS Lambda. As a serverless compute service, AWS Lambda enables developers to run code without thinking about servers.
The developers can run the code on AWS Lambda as Lambda function. The Lambda function will include the code, configuration settings, and resource requirements. The developers even have the option to associate the Lambda function with various AWS resources. However, they need to pay only for the compute time required to run the code. AWS Lambda further provides a number of features to facilitate serverless code execution.
Event Driven Execution
AWS Lambda is designed as an event-driven execution environment. It requires programmers to run the code as Lambda functions. Also, it executes the Lambda functions automatically each time the event occurs. It further allocates the required resources dynamically at the time of code execution. As the Lambda functions are stateless, they can launch multiple copies of the code simultaneously according to the number of incoming events. The developers simply have to focus on the quality of the code instead of the code execution process.
Avail Varying AWS Resources
While uploading code to AWS Lambda, the developers can be associated with function with a variety of AWS resources – Amazon DynamoDB table, Amazon SNS Notification, Amazon Kinesis stream and Amazon S3 bucket. They can even accelerate code execution by adding custom logic to specific AWS resources. Each time the resources change, AWS Lambda will execute the function and allocate the computing resources automatically.
Customize Back-end Services
The developers can use Lambda APIs to create custom back-end services for the application which can be triggered on-demand. Likewise, they can use Amazon API Gateway to create custom API endpoints. The developers can even consider getting the custom events processed through Lambda to boost the performance and user experience of their applications by overcoming client platform variations and preventing battery drain.
Built-in Fault Tolerance
AWS Lambda runs the code across multiple AWS locations through Lambda Edge. Hence, the code execution is not impacted by the failure of individual machines or data center facilities. AWS Lambda further comes with built-in fault tolerance to keep the code execution process consistent by providing adequate resources and predictable service to the functions. The feature helps developers to improve the software’s user experience by eliminating chances of downtime.
Automatic Resource Scaling
As mentioned earlier, AWS Lambda is developed as an event-driven execution model. It executes code based within milliseconds of an event like incoming requests. It even scales the resources required by the function automatically. The automatic scaling boosts the performance of the application significantly by handling more incoming requests and creating many instances of the code. The computing service further scales the resources for multiple instances any deployment or configuration delays.
Like other serverless computing services, AWS Lambda enables developers to focus extensively on the code quality by managing the infrastructure. It further automates the entire infrastructure management process to accelerate code execution. The automated administration process does not require developers to perform routine tasks by updating the operating system and adding new servers. AWS Lambda performs the maintenance and administration tasks efficiently while allowing users to monitor the process consistently.
Integrated Security Model
While uploading code to AWS, the developers can associate it with various AWS services. They can even access the AWS services securely through AWS SDK. Likewise, they can integrate the services with AWS Identity and Access (IAS) to run the code securely. AWS Lambda further allows developers to run code more securely by dividing users into multiple security groups and creating network access control lists.
Personalized Content Delivery
AWS Lambda allows developers to run their code with Lambda Edge. Lambda Edge runs the code globally across various AWS locations. Hence, it runs the code closer to each end user, while reducing latency. At the same time, Lambda Edge gets the code triggered through Amazon CloudFront events like the location of the client and content requests. The Amazon CloudFront events accelerate server response and facilitate the delivery of personalized content.
Bring Your Own Code
Pay per Use
While uploading code to AWS Lambda, the developers have the option to choose memory, timeout time, and AWS services. Also, they can decide the amount of memory to be allocated to each function. Based on the amount of memory, Lambda will allocate proportional network bandwidth, CPU power, and disk I/O to each function. The user is still required to pay according to the number of incoming requests and the compute time required to run the code. Amazon helps enterprises to save money by billing the computing services in increments of 100 milliseconds.
On the whole, AWS Lambda enables enterprises to deploy software applications without managing and maintaining servers. In addition to simplifying deployment, cloud computing execution model allows developers to focus only on improving code quality and accelerating software delivery. An enterprise even can even save money by paying for the server resources according to a number of requests and compute time.