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Add metric for command processor latency #1529
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86b65a9
Add one IT for binary vector support
Hazel-Datastax 36b3c84
Add test
Hazel-Datastax 430c1b2
Merge branch 'main' into hazel/command_processor_latency
Hazel-Datastax 2d9056f
Merge branch 'main' into hazel/command_processor_latency
Hazel-Datastax ce13c3c
reduce the number of bucket
Hazel-Datastax 5b7b158
remove the hard coded value
Hazel-Datastax 88273a8
Merge branch 'main' into hazel/command_processor_latency
Hazel-Datastax 65ce427
fix
Hazel-Datastax 8d75ff1
Merge branch 'main' into hazel/command_processor_latency
tatu-at-datastax 7244c62
Update the min
Hazel-Datastax 5ff5366
Merge branch 'main' into hazel/command_processor_latency
tatu-at-datastax 40a53e1
Merge branch 'main' into hazel/command_processor_latency
Hazel-Datastax 0ccdc73
Merge branch 'main' into hazel/command_processor_latency
Hazel-Datastax 232fe91
Merge branch 'main' into hazel/command_processor_latency
tatu-at-datastax cfb7e04
Merge branch 'main' into hazel/command_processor_latency
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To address the potential high cardinality issue in Grafana, I adjusted the bucket range by increasing the minimum value (from 0.001 sec to 0.1 sec) and decreasing the maximum value (from 30 sec to 15 sec). This change reduces the number of buckets from 69 to 36.
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While this makes sense wrt cardinality, 0.1 secs is 100 msec which is quite coarse. This might be ok when trying to find slowest queries, but we need to make sure it is useful for us in general. Could see what using 0.05 (for example) would give for number of buckets.
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Changed to 0.05 and there are 41 buckets. The metrics will be like:
The numbers are from my local laptop so it's not accurate, will deploy this to dev to see if 0.05 makes sense and if Grafana could work