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Count all the things #528
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For the Python errors, we discussed this in IRC:
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Instrument the code (either w statsd calls that we can send to Datadog or log items that will land in ES/Kibana) so we can generate dashboards and possibly notifications.
This is a follow-up to #418.
List of possible metrics (area of concern: CLUSTER = on-demand clusters, SPARKJOB = scheduled Spark jobs):
cluster-normalized-instance-hours
: Normalized instance hours of clusters (time between creation and finish multiplied by cluster size)cluster-ready
: Number of on-demand clusters spun up successfully (to see trends in usage)cluster-extension
: Number of cluster lifetime extensionscluster-time-to-ready
/sparkjob-time-to-ready
: Time between cluster creation (for both scheduled Spark jobs and on-demand clusters) and its readiness to process the first step (the "bootstrapping time" from the user perspective)cluster-emr-version
/sparkjob-emr-version
: EMR version used for clustersparkjob-run-time
: the time between the cluster's readiness to process the first step and the time when the cluster is shudown (the "runtime of the notebook code" from the user perspective)sparkjob-normalized-instance-hours
: Normalized instance hours of scheduled jobsThe text was updated successfully, but these errors were encountered: