Add decorator for tracking execution statistics of check methods #10809
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Adds a new decorator `dbm_tracked_method` to be used for tracking the performance of methods in a standardized way. This will significantly reduce measurement boilerplate across the DBM integrations and make the measurements less error prone. It's important to track the performance and error rates of various parts of the DBM integrations in order to better troubleshoot customer issues. We already have a bunch of internal debug metrics tracking the execution time and error rate of various operations in DBM. For example, `dd.postgres.collect_statement_samples.time` tracks the execution time of the `_collect_statement_samples` method for postgres. All of these manual measurements will be updated to use this new measurement decorator instead.
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Update all internal method invocation debug instrumentation to use new decorator added in #10809
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Addressed all of your comments. I didn't measure the overhead. statsd is very low overhead generally and in this case the metrics are being aggregated directly in the agent via the internal API so it should be even lower overhead than typical statsd sent over the network. |
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Update all internal method invocation debug instrumentation to use new decorator added in #10809
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Just please edit the title to be more descriptive |
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Update all internal method invocation debug instrumentation to use new decorator added in #10809
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* improve internal check execution instrumentation Update all internal method invocation debug instrumentation to use new decorator added in #10809 * revert typo * fix for py2 * use new base version
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) * dbm add tracked method decorator Adds a new decorator `dbm_tracked_method` to be used for tracking the performance of methods in a standardized way. This will significantly reduce measurement boilerplate across the DBM integrations and make the measurements less error prone. It's important to track the performance and error rates of various parts of the DBM integrations in order to better troubleshoot customer issues. We already have a bunch of internal debug metrics tracking the execution time and error rate of various operations in DBM. For example, `dd.postgres.collect_statement_samples.time` tracks the execution time of the `_collect_statement_samples` method for postgres. All of these manual measurements will be updated to use this new measurement decorator instead. * move to new tracking module * revert * add option to disable * standardize naming * logging fix * style * fix py2
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* improve internal check execution instrumentation Update all internal method invocation debug instrumentation to use new decorator added in #10809 * revert typo * fix for py2 * use new base version
This was referenced May 18, 2022
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What does this PR do?
Adds a new decorator
tracked_methodto be used for tracking the performance of methods in a standardized way. This will significantly reduce measurement boilerplate across the DBM integrations and make the measurements less error prone. It's important to track the performance and error rates of various parts of the DBM integrations in order to better troubleshoot customer issues.We already have a bunch of internal debug metrics tracking the execution time and error rate of various operations in DBM. For example,
dd.postgres.collect_statement_samples.timetracks the execution time of the_collect_statement_samplesmethod for postgres. All of these manual measurements will be updated to use this new measurement decorator instead.Motivation
Reduce boilerplate and make debugging performance measurements more standardized and less error prone.
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