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[chore] add a workflow to fix a PR #2

Merged
merged 1 commit into from
Nov 2, 2023
Merged

[chore] add a workflow to fix a PR #2

merged 1 commit into from
Nov 2, 2023

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atoulme
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@atoulme atoulme commented Nov 2, 2023

test

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atoulme commented Nov 2, 2023

please fix my PR

@atoulme atoulme merged this pull request into main Nov 2, 2023
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rmfitzpatrick
rmfitzpatrick reviewed Nov 2, 2023
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.github/workflows/fix-pr.yaml
- name: "Fix PR"
run: |
.github/workflows/scripts/check-collector-module-versions.sh
make generate gogci goporto gotidy crosslink genotelcontribcol gendependabot generate-gh-issue-templates
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If these are requirements for valid PRs I think a pre-commit hook running on the devs' devices would be an immediate way to enforce that without introducing greater chances of ci flake/churn/vulnerabilities.

atoulme pushed a commit that referenced this pull request Sep 27, 2024
@daidokoro @kentquirk @TylerHelmuth
[processor/transform] Add Conversion Function to OTTL for Exponential…
74b1048 
… Histo --> Histogram (open-telemetry#33824)

## Description

This PR adds a custom metric function to the transformprocessor to
convert exponential histograms to explicit histograms.

Link to tracking issue: Resolves open-telemetry#33827

**Function Name**
```
convert_exponential_histogram_to_explicit_histogram
```

**Arguments:**

- `distribution` (_upper, midpoint, uniform, random_)
- `ExplicitBoundaries: []float64`

**Usage example:**

```yaml
processors:
  transform:
    error_mode: propagate
    metric_statements:
    - context: metric
      statements:
        - convert_exponential_histogram_to_explicit_histogram("random", [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]) 
```

**Converts:**

```
Resource SchemaURL: 
ScopeMetrics #0
ScopeMetrics SchemaURL: 
InstrumentationScope  
Metric #0
Descriptor:
     -> Name: response_time
     -> Description: 
     -> Unit: 
     -> DataType: ExponentialHistogram
     -> AggregationTemporality: Delta
ExponentialHistogramDataPoints #0
Data point attributes:
     -> metric_type: Str(timing)
StartTimestamp: 1970-01-01 00:00:00 +0000 UTC
Timestamp: 2024-07-31 09:35:25.212037 +0000 UTC
Count: 44
Sum: 999.000000
Min: 40.000000
Max: 245.000000
Bucket (32.000000, 64.000000], Count: 10
Bucket (64.000000, 128.000000], Count: 22
Bucket (128.000000, 256.000000], Count: 12
        {"kind": "exporter", "data_type": "metrics", "name": "debug"}
```

**To:**

```
Resource SchemaURL: 
ScopeMetrics #0
ScopeMetrics SchemaURL: 
InstrumentationScope  
Metric #0
Descriptor:
     -> Name: response_time
     -> Description: 
     -> Unit: 
     -> DataType: Histogram
     -> AggregationTemporality: Delta
HistogramDataPoints #0
Data point attributes:
     -> metric_type: Str(timing)
StartTimestamp: 1970-01-01 00:00:00 +0000 UTC
Timestamp: 2024-07-30 21:37:07.830902 +0000 UTC
Count: 44
Sum: 999.000000
Min: 40.000000
Max: 245.000000
ExplicitBounds #0: 10.000000
ExplicitBounds #1: 20.000000
ExplicitBounds #2: 30.000000
ExplicitBounds #3: 40.000000
ExplicitBounds open-telemetry#4: 50.000000
ExplicitBounds open-telemetry#5: 60.000000
ExplicitBounds open-telemetry#6: 70.000000
ExplicitBounds open-telemetry#7: 80.000000
ExplicitBounds open-telemetry#8: 90.000000
ExplicitBounds open-telemetry#9: 100.000000
Buckets #0, Count: 0
Buckets #1, Count: 0
Buckets #2, Count: 0
Buckets #3, Count: 2
Buckets open-telemetry#4, Count: 5
Buckets open-telemetry#5, Count: 0
Buckets open-telemetry#6, Count: 3
Buckets open-telemetry#7, Count: 7
Buckets open-telemetry#8, Count: 2
Buckets open-telemetry#9, Count: 4
Buckets open-telemetry#10, Count: 21
        {"kind": "exporter", "data_type": "metrics", "name": "debug"}
```

### Testing

- Several unit tests have been created. We have also tested by ingesting
and converting exponential histograms from the `statsdreceiver` as well
as directly via the `otlpreceiver` over grpc over several hours with a
large amount of data.

- We have clients that have been running this solution in production for
a number of weeks.

### Readme description:

### convert_exponential_hist_to_explicit_hist

`convert_exponential_hist_to_explicit_hist([ExplicitBounds])`

the `convert_exponential_hist_to_explicit_hist` function converts an
ExponentialHistogram to an Explicit (_normal_) Histogram.

`ExplicitBounds` is represents the list of bucket boundaries for the new
histogram. This argument is __required__ and __cannot be empty__.

__WARNING:__

The process of converting an ExponentialHistogram to an Explicit
Histogram is not perfect and may result in a loss of precision. It is
important to define an appropriate set of bucket boundaries to minimize
this loss. For example, selecting Boundaries that are too high or too
low may result histogram buckets that are too wide or too narrow,
respectively.

---------

Co-authored-by: Kent Quirk <[email protected]>
Co-authored-by: Tyler Helmuth <[email protected]>
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