Which solution will provide this information?
Create a CloudWatch Logs metric filter on the log group. Use a filter pattern that matches the username. Publish a CloudWatch metric that sums the number of logins over the past 7 days.
Create a CloudWatch Logs subscription on the log group. Use a filter pattern that matches the username. Publish a CloudWatch metric that sums the number of logins over the past 7 days.
Create a CloudWatch Logs Insights query that uses an aggregation function to count the number of logins for the username over the past 7 days. Run the query against the log group.
Create a CloudWatch dashboard. Add a number widget that has a filter pattern that counts the number of logins for the username over the past 7 days directly from the log group.
Explanations:
Creating a metric filter to match the username will allow for real-time metric creation, but it will not provide historical data directly for the past 7 days without an additional query mechanism. This method only counts logins from the time the filter is created.
A subscription filter is primarily used for streaming logs to other services (like Lambda or Kinesis) for real-time processing. It does not inherently provide a historical count of logins over the past 7 days.
CloudWatch Logs Insights allows for running queries against the log data, including using aggregation functions to count logins for a specific username over the past 7 days. This is the most efficient and effective way to analyze historical log data.
A CloudWatch dashboard can display metrics but cannot directly filter and count logs from the log group without predefined metrics. It does not provide a direct method to count logins over the past 7 days without prior metric aggregation.
From my point of view, the answer is:
Create a CloudWatch Logs Insights query that uses an aggregation function to count the number of logins for the username over the past 7 days. Run the query against the log group.