Support training for latency based anomalies specifically for perf-anomaly#409
Support training for latency based anomalies specifically for perf-anomaly#409shashank10456 wants to merge 4 commits intomainfrom
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Signed-off-by: shashank10456 <shashank10456@gmail.com>
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## main #409 +/- ##
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- Coverage 93.07% 92.17% -0.90%
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Files 97 97
Lines 4492 4781 +289
Branches 387 430 +43
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+ Hits 4181 4407 +226
- Misses 231 276 +45
- Partials 80 98 +18 ☔ View full report in Codecov by Sentry. |
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Please replace "Explain what this PR does." with the real description & purpose of this PR. |
shashank10456
changed the title
| if not self.post_aggregations: | ||
| self.post_aggregations = { | ||
| "p90": _post_agg.QuantilesDoublesSketchToQuantile( | ||
| output_name="agg_out", field=postaggregator.Field("agg_out"), fraction=0.90 |
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As a library feature, percentile is better to be configurable.
| if not self.aggregations: | ||
| self.aggregations = {"count": doublesum("count")} | ||
| self.aggregations = { | ||
| "agg_out": _agg.quantiles_doubles_sketch("valuesDoublesSketch", "agg0", 64) |
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What does this value 64 imply? Does it need to be configurable for different latency anomaly use cases?
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| ARG PYTHON_VERSION=3.11 | ||
| FROM python:${PYTHON_VERSION}-slim-bookworm AS builder | ||
| FROM python:${PYTHON_VERSION}-bookworm AS builder |
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The -slim-bookworm here replaced with -bookworm. However, the same -slim-bookworm at line 31 remains unchanged. Why is the difference?
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Explain what this PR does.
This PR supports Anomaly Detection on fields that use valuesDoubleSketches. We add aggregations and postaggregations which run natively on druid. These sketches are converted to values using these postaggregations and are run on druid.
This would enable us to use anomaly detection for inputs using sketches(https://datasketches.apache.org/). For example, latency based anomaly.
Also, I have made few changes to DockerFile and added a patch for Numalogic 0.9.1 to avoid CVE issues. This is important for the perf-anomaly team to avoid moving to Numaflow 1.2.1 and updating all the UDFs and UDSinks. This would help them save lot of time by just upgrading the ML vertices.