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Lambda

AWS Lambda is a serverless computing service that allows you to run code without the need to provision or manage servers. It enables you to create functions that respond to events, such as file uploads in S3, changes in Kinesis streams, or API requests from Amazon API Gateway. Lambda is scalable, reliable, and can be used for a variety of use cases, from real-time data processing to creating microservices for web applications.

Features
  • Serverless: AWS Lambda is a serverless service, which means that you only pay for the time your code is executed and do not need to worry about provisioning or managing servers.
  • Integration with other AWS services: AWS Lambda can easily be integrated with other AWS services such as S3, DynamoDB, Kinesis, and API Gateway.
  • Scalability: AWS Lambda is highly scalable and can be used to handle workloads of any size.
  • Customizable runtime: AWS Lambda supports multiple runtimes, including Node.js, Python, Java, Go, C#, and Ruby.
  • High availability: AWS Lambda is designed for high availability, with multiple availability zones and automatic failover resources.

Terms and Concepts
  • Functions: An AWS Lambda function is a unit of code that is executed in response to events.
  • Events: An event is an action that occurs in an AWS service, such as file upload in S3 or an API request from Amazon API Gateway, that can trigger the execution of a Lambda function.
  • Runtime: The runtime is the environment in which the code of the Lambda function is executed.
  • Layers: Layers allow you to include libraries, frameworks, and other dependency files in your Lambda function, while keeping the separation of your business logic code.
  • Execution policy: The execution policy controls the permissions that a Lambda function has to access other AWS resources.
  • Alias: An alias is a pointer to a specific version of a Lambda function.

Best practices for using AWS Lambda

Some best practices for using AWS Lambda include:

  • Design Lambda functions to be small and perform specific tasks
  • Limit the execution time of functions to avoid unnecessary execution or failure due to time limits
  • Use environment variables to store sensitive information, such as API keys and passwords
  • Manage and monitor the logging of function for troubleshooting and debugging
  • Use versioning and access control options to track and manage changes to Lambda functions
  • Configure access control policies to limit access to Lambda functions and the resources they use
  • Use monitoring resources, such as CloudWatch Metrics and CloudWatch Logs, to monitor and analyze the performance and efficiency of Lambda functions
  • Test and validate Lambda functions before deploying them to production

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