What is the MOST efficient approach to accomplish this?
Write an AWS Lambda function that will query the logs every minute and contain the logic of which team to notify on which patterns and issues.
Set up different metric filters for each team based on patterns and alerts. Each alarm will notify the appropriate notification list.
Redesign the aggregation of logs so that each team’s relevant parts are sent to a separate log group, then subscribe each team to its respective log group.
Create an AWS Auto Scaling group of Amazon EC2 instances that will scale based on the amount of ingested log entries. This group will pull log streams, look for patterns, and send notifications to relevant teams.
Explanations:
Writing a Lambda function to query logs every minute is inefficient and not scalable for real-time log processing. It adds unnecessary complexity and delays in alerting.
Setting up metric filters for each team based on patterns is an efficient approach. Each alarm can trigger notifications to specific teams, making it easy to manage alerts.
Redesigning the log aggregation into separate log groups could increase complexity. It is unnecessary when metric filters can be used to isolate relevant patterns for each team.
Using an Auto Scaling group of EC2 instances to process logs is an over-complicated and inefficient solution. Metric filters in CloudWatch are a simpler and more cost-effective way to handle alerts.
I scheme that the answer is:
Set up different metric filters for each team based on patterns and alerts. Each alarm will notify the appropriate notification list.
I surmise that the answer is:
Set up different metric filters for each team based on patterns and alerts. Each alarm will notify the appropriate notification list.