M7XBeastandCursor
ff096b51b5
Clean package repo for Gitea installs via packages.yml git URL. Co-authored-by: Cursor <cursoragent@cursor.com>
56 lines
2.7 KiB
SQL
56 lines
2.7 KiB
SQL
{{
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config(
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materialized='view'
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)
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}}
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select
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z.id,
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z.table_name,
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z.column_name,
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z.metric,
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z.z_score_value,
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z.modified_z_score_value,
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m.anomaly_detector,
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z.last_value,
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z.last_avg,
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z.last_median,
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z.last_stddev,
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z.last_median_absolute_deviation,
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z.last_mean_absolute_deviation,
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z.last_iqr,
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z.last_first_quartile - (cast( {{ json_extract('m.anomaly_detector', 'whisker_boundary_multiplier') }} as {{numeric_type()}} ) * z.last_iqr) lower_bound,
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z.last_third_quartile + (cast( {{ json_extract('m.anomaly_detector', 'whisker_boundary_multiplier') }} as {{numeric_type()}} ) * z.last_iqr) upper_bound,
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z.last_first_quartile,
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z.last_third_quartile,
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z.time_window_end,
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z.interval_length_sec,
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z.computed_on,
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{{ f2dq.generate_anomaly_message('z.column_name', 'z.metric', 'z.last_value', 'z.last_avg') }} as message,
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{{ f2dq.generate_metric_value_text('z.metric', 'z.last_value') }} as last_value_text
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from
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{{ ref('f2dq_z_score')}} z
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left join {{ ref('f2dq_selected') }} m
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on {{ split_and_return_nth_value('table_name', '.', 1) }} = m.database
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and {{ split_and_return_nth_value('table_name', '.', 2) }} = m.schema
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and {{ split_and_return_nth_value('table_name', '.', 3) }} = m.name
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where
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case when (lower(coalesce({{ json_extract('m.anomaly_detector', 'direction') }}, 'both')) = 'up' and z.last_value > z.last_avg)
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or (lower(coalesce({{ json_extract('m.anomaly_detector', 'direction') }}, 'both')) = 'down' and z.last_value < z.last_avg)
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or (lower(coalesce({{ json_extract('m.anomaly_detector', 'direction') }}, 'both')) != 'up' and lower(coalesce({{ json_extract('m.anomaly_detector', 'direction') }}, 'both')) != 'down')
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then
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case
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when {{ json_extract('m.anomaly_detector', 'name') }} = 'z_score'
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then abs(z_score_value) > cast({{ json_extract('m.anomaly_detector', 'threshold') }} as {{ numeric_type() }})
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when {{ json_extract('m.anomaly_detector', 'name') }} = 'modified_z_score'
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then abs(modified_z_score_value) > cast( {{ json_extract('m.anomaly_detector', 'threshold') }} as {{numeric_type()}} )
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when {{ json_extract('m.anomaly_detector', 'name') }} = 'boxplot'
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then (
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z.last_value < z.last_first_quartile - (cast( {{ json_extract('m.anomaly_detector', 'whisker_boundary_multiplier') }} as {{numeric_type()}} ) * z.last_iqr)
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or
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z.last_value > z.last_third_quartile + (cast( {{ json_extract('m.anomaly_detector', 'whisker_boundary_multiplier') }} as {{numeric_type()}} ) * z.last_iqr)
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)
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else false
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end
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else false
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end
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