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This commit adds the ability to tune the minimum, default and maximum fetch sizes for Kafka in the OpenTelemetry configuration file.
[receiver/kafkaexporter]: allow tunable fetch sizes
Feat/kafkareceiver fetch configurable
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Oct 19, 2024
… 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 <kentquirk@gmail.com> Co-authored-by: Tyler Helmuth <12352919+TylerHelmuth@users.noreply.github.com>
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