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[pkg/stanza/operator/transformer/recombine] Add max_unmatched_batch_size to recombine operator #5

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[pkg/stanza/operator/transformer/recombine] Add max_unmatched_batch_size to recombine operator #5

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@kasia-kujawa
[pkg/stanza/operator/transformer/recombine] Add max_unmatched_batch_s…
a84cd58 
…ize to recombine operator
@kasia-kujawa
[pkg/stanza/operator/transformer/recombine] reduce complexity for max…
5abae2d 
…_unmatched_batch_size tests

to isolate the root cause of potential problems
overwrite_with config option is tested in other test cases
@kasia-kujawa kasia-kujawa marked this pull request as ready for review April 4, 2024 08:53
@kasia-kujawa
[pkg/stanza/operator/transformer/recombine] remove force_flush_period…
b7da983 
… in tests with is_last_entry

where last entry matches regular expression to avoid of time condition in flushing
kasia-kujawa pushed a commit that referenced this pull request Oct 13, 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 #4: 50.000000
ExplicitBounds #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 #4, Count: 5
Buckets #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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