M7XBeastandCursor
109e009210
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Metrics reported row_count 0/1 against tables that plainly had 10+ rows. 1. The metric bucket was [run_date - 1d, run_date) - it ENDED at midnight at the start of the run day. A model stamping `dbt_updated_at = CURRENT_DATETIME()` writes rows during the run, so every row fell outside the bucket that run had just computed. Incremental merges that re-stamp rows each run then pushed them into the wrong bucket or out of every bucket. The bucket now covers the run's calendar day: [midnight of run date, +1d). interval_length_sec stays 86400 and the daily series is unchanged. 2. There was no way to see a table as a whole. `metrics_base_insert` now also emits `global__<metric>` computed over the whole table (`where true`) alongside the windowed series, so metrics stay meaningful for models whose time_filter holds historical/backfilled timestamps that miss the current bucket. A model with no time filter already computed globally and is not duplicated. Disable with `f2dq:emit_global_metrics: false`. 3. overview.json metric rows dropped `time_window_start`, so no consumer could tell a bucket's width or spot an empty one. It is exported now. Also: keep the guard that skips save_test_history when the f2dq_test_history relation does not exist. The integration_tests project pins the original re_data bucket anchoring and `f2dq:emit_global_metrics: false` because its expected_*.csv seeds encode the values that bucket produces and cannot be regenerated without the upstream multi-database harness. Co-authored-by: Cursor <cursoragent@cursor.com>
88 lines
2.7 KiB
YAML
88 lines
2.7 KiB
YAML
name: "f2dq"
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version: "1.0.2" # dbt_f2dq: modernized fork (dbt_utils 1.4.1); dual-read re_data_* keys
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config-version: 2
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require-dbt-version: [">=1.0.0", "<2.0.0"]
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profile: "f2dq_postgres"
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target-path: "target" # directory which will store compiled SQL files
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clean-targets: ["target", "dbt_modules", "dbt_packages"]
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on-run-end:
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# Dual-read: f2dq:save_test_history or legacy re_data:save_test_history
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- "{% if var('f2dq:save_test_history') or var('re_data:save_test_history', false) %} {{ f2dq.save_test_history(results) }} {% endif %}"
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vars:
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f2dq:max_columns_in_query: 10
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# The metric bucket is the run's calendar day: [midnight of the run date, +1d).
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#
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# re_data used [run_date - 1d, run_date) - i.e. the bucket ENDED at midnight at
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# the start of the run day. Any row stamped during the run (models that set
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# `dbt_updated_at = CURRENT_DATETIME()`) therefore fell outside the bucket the
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# run had just written it into, and incremental merges that re-stamp rows every
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# run made those rows land in the wrong bucket or never. row_count came out 0/1
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# against tables that plainly had rows. Anchoring the bucket on the run date
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# instead of the day before keeps `interval_length_sec` at 86400 and the daily
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# series intact, but includes everything written on the day being measured.
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f2dq:time_window_start: '{{ run_started_at.strftime("%Y-%m-%d 00:00:00") }}'
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f2dq:time_window_end: '{{ (run_started_at + modules.datetime.timedelta(1)).strftime("%Y-%m-%d 00:00:00") }}'
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# Also emit `global__<metric>` (whole-table) alongside the windowed series, so
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# metrics stay meaningful for models whose time_filter holds historical
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# timestamps that miss the current bucket.
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f2dq:emit_global_metrics: true
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f2dq:anomaly_detection_look_back_days: 30
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f2dq:select: null
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f2dq:anomalies_filtered: f2dq_anomalies
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f2dq:alerting_z_score: 3
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f2dq:save_test_history: false
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f2dq:show_warns_as_alerts: false
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f2dq:anomaly_detector:
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name: modified_z_score
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threshold: 3
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f2dq:store_table_samples: false
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f2dq:metrics_groups:
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table_metrics:
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table:
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- row_count
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- freshness
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column_metrics:
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column:
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numeric:
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- min
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- max
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- avg
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- stddev
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- variance
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- nulls_count
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- nulls_percent
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text:
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- min_length
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- max_length
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- avg_length
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- nulls_count
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- missing_count
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- nulls_percent
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- missing_percent
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boolean:
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- count_true
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- count_false
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- nulls_count
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- nulls_percent
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f2dq:default_metrics:
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- table_metrics
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- column_metrics
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models:
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f2dq:
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+schema: f2dq
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internal:
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+schema: f2dq_internal
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