AWS Lambda allows for __________, such as database connections or SDK clients, to be reused across multiple invocations of a function.
- Cold starts
- Environment variables
- Event triggers
- Execution contexts
Execution contexts in AWS Lambda can be reused across multiple invocations, allowing for efficient reuse of resources such as database connections or SDK clients.
How do Lambda Layers impact the deployment time and performance of AWS Lambda functions?
- They always increase deployment time and degrade performance
- They can decrease deployment time and improve performance
- They have no impact on deployment time and performance
- They only impact deployment time
Lambda Layers can decrease deployment time by reducing the size of deployment packages and improve performance by enabling code reuse across multiple functions.
Lambda Layers can be shared across multiple __________ to promote code reuse and maintainability.
- Containers
- Databases
- Endpoints
- Functions
Lambda Layers can be shared across multiple functions to promote code reuse and maintainability, reducing duplication and ensuring consistency across applications.
Lambda Layers can be managed and versioned using __________ for better control and tracking.
- AWS CLI
- AWS Management Console
- AWS Marketplace
- AWS SDK
Lambda Layers can be managed and versioned using the AWS Management Console for better control and tracking.
Using Lambda Layers can help in reducing __________ for Lambda function deployment.
- Complexity
- Cost
- Latency
- Redundancy
Using Lambda Layers can help in reducing redundancy for Lambda function deployment.
__________ allows you to define and manage Lambda Layers within the AWS Management Console.
- AWS CLI
- AWS CloudFormation
- AWS Management Console
- AWS SDK
The AWS Management Console allows you to define and manage Lambda Layers within the AWS Management Console.
What are some common examples of resource reuse in AWS Lambda functions?
- Database connections and API clients
- Reusing environment variables
- Reusing temporary files
- Sharing Lambda layers
Common examples of resource reuse in AWS Lambda include reusing database connections and API clients to avoid the overhead of reinitializing these resources on each function invocation.
In what scenarios would you prioritize resource reuse over other optimization techniques in AWS Lambda?
- High latency tolerance
- High-frequency invocations
- Low memory usage
- Rarely invoked functions
In scenarios with high-frequency invocations, resource reuse helps minimize initialization time, enhancing overall performance and efficiency.
How can you ensure thread safety when implementing resource reuse in AWS Lambda functions?
- Deploy multiple versions
- Implement global variables
- Use stateless functions
- Utilize local storage
Using stateless functions ensures that there are no shared resources between invocations, which helps maintain thread safety.
What strategies can you employ to monitor and optimize resource reuse in AWS Lambda?
- Enable VPC integration
- Implement custom logging
- Increase timeout settings
- Use larger memory sizes
Implementing custom logging helps track resource utilization and can provide insights into how resources are being reused, aiding in optimization efforts.