16 lines
1.9 KiB
Markdown
16 lines
1.9 KiB
Markdown
# `MlBucketSummary` [interface-MlBucketSummary]
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| Name | Type | Description |
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| - | - | - |
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| `anomaly_score` | [double](./double.md) | The maximum anomaly score, between 0-100, for any of the bucket influencers. This is an overall, rate-limited score for the job. All the anomaly records in the bucket contribute to this score. This value might be updated as new data is analyzed. |
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| `bucket_influencers` | [MlBucketInfluencer](./MlBucketInfluencer.md)[] | |
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| `bucket_span` | [DurationValue](./DurationValue.md)<[UnitSeconds](./UnitSeconds.md)> | The length of the bucket in seconds. This value matches the bucket span that is specified in the job. |
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| `event_count` | [long](./long.md) | The number of input data records processed in this bucket. |
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| `initial_anomaly_score` | [double](./double.md) | The maximum anomaly score for any of the bucket influencers. This is the initial value that was calculated at the time the bucket was processed. |
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| `is_interim` | boolean | If true, this is an interim result. In other words, the results are calculated based on partial input data. |
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| `job_id` | [Id](./Id.md) | Identifier for the anomaly detection job. |
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| `processing_time_ms` | [DurationValue](./DurationValue.md)<[UnitMillis](./UnitMillis.md)> | The amount of time, in milliseconds, that it took to analyze the bucket contents and calculate results. |
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| `result_type` | string | Internal. This value is always set to bucket. |
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| `timestamp_string` | [DateTime](./DateTime.md) | The start time of the bucket. This timestamp uniquely identifies the bucket. Events that occur exactly at the timestamp of the bucket are included in the results for the bucket. |
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| `timestamp` | [EpochTime](./EpochTime.md)<[UnitMillis](./UnitMillis.md)> | The start time of the bucket. This timestamp uniquely identifies the bucket. Events that occur exactly at the timestamp of the bucket are included in the results for the bucket. |
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