AWS CloudWatch __________ allows you to create custom metrics for monitoring specific aspects of AWS Lambda functions.
- Alarms
- Custom namespaces
- Dashboards
- Log groups
AWS CloudWatch custom namespaces allow you to create custom metrics for monitoring specific aspects of AWS Lambda functions, providing detailed insights into performance and behavior.
AWS Lambda function __________ can help identify performance bottlenecks and improve efficiency.
- Monitoring
- Packaging
- Scaling
- Versioning
Monitoring AWS Lambda functions can help identify performance bottlenecks and improve efficiency by providing insights into resource utilization and execution metrics.
__________ is a service that provides centralized logging for AWS Lambda functions.
- AWS Lambda Logs
- AWS X-Ray
- Amazon CloudWatch Logs
- Amazon S3
Amazon CloudWatch Logs is a service that provides centralized logging for AWS Lambda functions.
__________ are used to trigger alarms based on predefined thresholds for AWS Lambda metrics.
- CloudFormation
- CloudWatch Alarms
- IAM Roles
- S3 Buckets
CloudWatch Alarms are used to trigger alarms based on predefined thresholds for AWS Lambda metrics.
What are some strategies for optimizing monitoring costs in AWS Lambda?
- Enabling detailed monitoring
- Filtering logs by severity
- Increasing log retention periods
- Sending all logs to monitoring services
By filtering logs based on severity levels, you can reduce the volume of data sent to monitoring services like AWS CloudWatch, thus lowering costs associated with log ingestion and storage.
What role does AWS X-Ray play in monitoring serverless applications?
- Configuring security
- Generating logs
- Managing infrastructure
- Tracing and analyzing requests
AWS X-Ray allows you to trace and analyze requests as they travel through your serverless applications, providing insights into performance bottlenecks and dependencies.
How can you use AWS CloudWatch Logs Insights for monitoring AWS Lambda functions?
- Analyzing logs with advanced queries
- Configuring triggers
- Setting up alarms
- Viewing real-time metrics
AWS CloudWatch Logs Insights allows you to analyze logs generated by AWS Lambda functions using advanced queries, helping you to identify trends, troubleshoot issues, and gain insights into function behavior.
What are some recommended practices for logging in AWS Lambda?
- Use CloudTrail for logging, Include sensitive information in logs, Log only errors for simplicity, Use a single log group for all functions
- Use CloudWatch Logs, Include relevant context in logs, Implement log rotation, Set appropriate log levels
- Use S3 buckets for logging, Disable logging for performance optimization, Store logs locally in the Lambda function, Use generic log messages without context
- Use custom logging solutions, Share log groups across AWS accounts, Log all events regardless of importance, Keep logs indefinitely
Recommended practices for logging in AWS Lambda include using CloudWatch Logs, including relevant context in logs, implementing log rotation, and setting appropriate log levels.
How can you set up alarms for monitoring Lambda function metrics?
- Installing third-party software
- Manual scripting
- Using AWS CloudWatch
- Using Lambda itself
Using AWS CloudWatch allows you to set up alarms based on Lambda function metrics, triggering notifications or automated actions when certain thresholds are met.
What are some key metrics to monitor in AWS Lambda functions?
- Invocation count, Error count, Duration, Concurrency
- Latency, Throughput, Disk I/O, Queue depth
- Response time, Uptime, Bandwidth, Cache hit ratio
- Storage usage, Network traffic, CPU utilization, Memory usage
Key metrics to monitor in AWS Lambda functions include Invocation count, Error count, Duration, and Concurrency.
What is one way to monitor AWS Lambda function performance?
- Configure VPC
- Enable detailed CloudWatch metrics
- Use IAM roles
- Utilize S3 buckets
Enabling detailed CloudWatch metrics allows for comprehensive monitoring of AWS Lambda function performance, providing critical data on various metrics.
Why is monitoring important in a serverless architecture?
- To identify performance bottlenecks
- To manage security policies
- To provision resources
- To reduce costs
Monitoring is essential in a serverless architecture to identify performance bottlenecks and ensure applications run smoothly.