Once you’ve integrated with AWS CloudWatch, you have access to metrics from AWS Athena, which is utilizing conventional SQL, data may be easily analyzed directly in Amazon Simple Storage Service (Amazon S3) using an interactive query service. You can aim Athena at your data stored in Amazon S3 and start using conventional SQL to conduct ad-hoc queries and obtain results in seconds with a few clicks in the AWS Management Console.
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To verify metrics are reporting, search for the metrics in the Metric details section of the Organization settings page.
The following table shows the Athena metrics ingested by Cloud Observability.
|The time it took the query to execute.
|The volume of data that each DML query required Athena to scan. This comprises the quantity of data scanned prior to the cancellation period for queries that were canceled (either by the users or automatically, if they exceed the limit). For DDL queries, this metric is not reported.
|The length of time it took Athena to organize the query processing flow. This takes into account the time required to retrieve table partitions from the data source. Keep in mind that query planning time is a subset of engine execution time because the query engine handles query planning.
|The length of time that the query was in the query queue while waiting for resources. It should be noted that the query may be added back to the queue automatically if momentary faults happen.
|The number of milliseconds that Athena required to process the query's outcomes after the query engine had completed its work.
|The amount of time Athena needed to execute a DDL or DML query. The following execution times are included in total execution time: query queue time, query planning time, engine execution time, and service processing time.
Updated Dec 9, 2022