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>
f2dq integration tests — dbt project for running f2dq dbt-package integration tests