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[AMLII-2019] Max samples per context for Histogram, Distribution and Timing metrics (Experimental Feature) #863
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* add buffered_metrics object type * update metric_types to include histogram, distribution, timing * Run tests on any branch
…py into add-extended-aggregation
| def should_sample(self, rate): | ||
| """Determine if a sample should be kept based on the specified rate.""" | ||
| with self.random_lock: | ||
| return self.random.random() < rate |
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🔴 Code Vulnerability
do not use random (...read more)
Make sure to use values that are actually random. The random module in Python should generally not be used and replaced with the secrets module, as noted in the official Python documentation.
Learn More
…are just buffered
ddrthall
reviewed
Jan 24, 2025
ddrthall
reviewed
Jan 24, 2025
ddrthall
reviewed
Jan 24, 2025
ddrthall
previously approved these changes
Jan 24, 2025
ddrthall
approved these changes
Jan 24, 2025
skarimo
approved these changes
Jan 27, 2025
gh123man
approved these changes
Jan 27, 2025
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Requirements for Contributing to this repository
What does this PR do?
This experimental feature allows the user to limit the number of samples per context for histogram, distribution, and timing metrics.
This can be enabled with the statsd_max_samples_per_context flag. When enabled up to n samples will be kept in per context for Histogram, Distribution and Timing metrics when Aggregation is enabled. The default value is 0 which means no limit.
This is already implemented for the go client. Go Client Docs
Description of the Change
Verification Process
For local testing, follow steps here to set up local testing for the python client
Replace
testapp/main.pywithAdditional Notes
Release Notes
Review checklist (to be filled by reviewers)
changelog/label attached. If applicable it should have thebackward-incompatiblelabel attached.do-not-merge/label attached.kind/andseverity/labels attached at least.