Function execution model
The function execution model involves Lambda provisioning and managing the resources required to run your functions. Whenever an event occurs, AWS Lambda automatically scales your function instances based on the number of concurrent requests. Each instance of your function, known as a container, is instantiated with specific settings such as memory, compute power, runtime, and trigger configuration. Lambda is designed to horizontally scale without direct user interaction or intervention, allowing you to focus on the code and logic.
Guide to AWS Lambda Function Execution Model
The Function Execution Model of AWS Lambda is a significant part of understanding how Lambda functions operate.
What is Function Execution Model?
In AWSLambda, when a function is invoked, AWS runs the function by creating a container that will perform the task. The Function Execution Model concerns this process of invoking and executing functions.
Why is it important?
Grasping the Function Execution Model aids in understanding how the scaling process works, how AWS reuses resources, and how cold/hot start instances influence latency. It plays a crucial role to optimize the Lambda function.
How does it work?
When a function is invoked for the first time, a new container is created to handle the request. This is called a 'Cold Start'. Consecutive requests are handled by the same container in what is known as 'Warm Start'. Understanding these starts is a part of Function Execution Model.
Exam Tips: Answering Questions on Function Execution Model
In an exam setting:
1. Understand the difference between Cold and Warm starts.
2. Know how Lambda functions scale and how AWS manages function concurrency.
3. Remember the impact of execution context reuse on global variables and connection management.
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