receiver/kafka: Add support #5
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tigrannajaryan
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This will be done soon in core. |
tigrannajaryan
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codeboten
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Remove notice file - keep copyright in individual file headers See discussion here: open-telemetry/community#305
kasia-kujawa
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May 18, 2023
…-mysql-integration-test SQL Query receiver: add logs to mysql integration test
TylerHelmuth
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… Histo --> Histogram (#33824) ## Description This PR adds a custom metric function to the transformprocessor to convert exponential histograms to explicit histograms. Link to tracking issue: Resolves #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 #6: 70.000000 ExplicitBounds #7: 80.000000 ExplicitBounds #8: 90.000000 ExplicitBounds #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 #6, Count: 3 Buckets #7, Count: 7 Buckets #8, Count: 2 Buckets #9, Count: 4 Buckets #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]>
celian-garcia
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Jan 13, 2025
…ilter fix: don't send empty filter to the Azure API
mx-psi
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Feb 26, 2025
<!--Ex. Fixing a bug - Describe the bug and how this fixes the issue. Ex. Adding a feature - Explain what this achieves.--> #### Description This PR adds support for configuring metric temporality (delta or cumulative) in the telemetrygen tool. This allows users to generate metrics with different temporality types, which is particularly useful for testing different metric collection scenarios. ##### Changes - Added `temporalityType` flag for metrics pipeline that accepts values: `delta` or `cumulative` <!-- Issue number (e.g. #1234) or full URL to issue, if applicable. --> #### Link to tracking issue Fixes #38073 <!--Describe what testing was performed and which tests were added.--> #### Testing Testing was performed by setting up as simple collector with `otlp `receiver and `debug` exporter ##### Sum Datapoint ``` Resource SchemaURL: https://opentelemetry.io/schemas/1.13.0 Resource attributes: -> service.name: Str(telemetrygen) ScopeMetrics #0 ScopeMetrics SchemaURL: InstrumentationScope Metric #0 Descriptor: -> Name: gen -> Description: -> Unit: -> DataType: Histogram -> AggregationTemporality: Delta ``` ##### Histogram Datapoint ``` Count: 0 Sum: 3940.000000 ExplicitBounds #0: 0.000000 ExplicitBounds #1: 5.000000 ExplicitBounds #2: 10.000000 ExplicitBounds #3: 25.000000 ExplicitBounds #4: 50.000000 ExplicitBounds #5: 75.000000 ExplicitBounds #6: 100.000000 ExplicitBounds #7: 250.000000 ExplicitBounds #8: 500.000000 ExplicitBounds #9: 750.000000 ExplicitBounds #10: 1000.000000 ExplicitBounds #11: 2500.000000 ExplicitBounds #12: 5000.000000 ExplicitBounds #13: 7500.000000 ExplicitBounds #14: 10000.000000 Buckets #0, Count: 0 Buckets #1, Count: 0 Buckets #2, Count: 1 Buckets #3, Count: 0 Buckets #4, Count: 0 Buckets #5, Count: 0 Buckets #6, Count: 3 Buckets #7, Count: 4 Buckets #8, Count: 1 Buckets #9, Count: 1 Buckets #10, Count: 0 Buckets #11, Count: 0 Buckets #12, Count: 0 Buckets #13, Count: 0 Buckets #14, Count: 0 ``` - [x] Need to add tests for the config --------- Signed-off-by: Nikos Angelopoulos <[email protected]>
bwplotka
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Apr 14, 2025
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