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We reach situations in which application teams set their alerts at the top of the hour and when we (the Splunk team) catch it, it might be too late.
Is there a way to produce a report which lists the run times and detect excessive usage times?
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Yeah, you can use the internal index for this. You should explicitly add savedsearch_name for this
index=_internal savedsearch_name=*
| timechart max(run_time) AS run_time by savedsearch_name
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Yeah, you can use the internal index for this. You should explicitly add savedsearch_name for this
index=_internal savedsearch_name=*
| timechart max(run_time) AS run_time by savedsearch_name
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Thank you @skoelpin.
I changed the max
to sum
and we can see -
We can see that at each quarter of the hour we have peak usage.
Can we find out from _internal
how many searches were skipped?
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Yes, you sure can!
index=_internal sourcetype=scheduled status=skipped NOT "_ACCELERATE*"
| timechart count by savedsearch_name
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Just ran -
index=_internal sourcetype=scheduler status=skipped NOT "_ACCELERATE*"
| timechart count
It shows -
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The totals for an hour are -
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Yeah, you have a problem with skips at 4am. You should trend this over time by using timewrap
to see if there's a pattern. Most likely, other searches are competing for resources and they run long and cause skips. You can fix this by changing search priroty away from 0 to auto.
You can split by savedsearch_name
or get a total over a span of time by adding span=1h
. We use this search to alert us and cut a ticket when we start skipping. Skips are unacceptable for us
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Much appreciated @skoelpin.
