M7XBeastandCursor
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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>
128 lines
3.3 KiB
YAML
128 lines
3.3 KiB
YAML
name: "f2dq_integration_tests"
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version: "1.0"
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config-version: 2
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# Changed when running tests
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profile: "f2dq_postgres"
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target-path: "target"
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clean-targets: ["target", "dbt_modules", "dbt_packages"]
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models:
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f2dq:
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enabled: true
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sources:
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f2dq_integration_tests:
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+f2dq_monitored: true
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+f2dq_time_filter: null
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vars:
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f2dq:store_table_samples: true
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# The expected_*.csv seeds encode re_data's original bucket anchoring
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# ([run_date - 1d, run_date)) and only the rows that bucket produces. The
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# package default now anchors the bucket on the run date and also emits
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# `global__*` metrics; both change the result set, and the seed values cannot
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# be regenerated without the upstream multi-database harness. Pin the legacy
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# behaviour here so this suite keeps exercising the metric engine and the
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# `global__`-vs-windowed naming contract. The corrected behaviour is covered
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# by the contract tests in the control_plane test suite.
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f2dq:emit_global_metrics: false
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f2dq:time_window_end: '{{ run_started_at.strftime("%Y-%m-%d 00:00:00") }}'
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f2dq:time_window_start: '{{ (run_started_at - modules.datetime.timedelta(1)).strftime("%Y-%m-%d 00:00:00") }}'
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f2dq:anomaly_detector:
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name: modified_z_score
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threshold: 0.6
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f2dq:max_columns_in_query: 1
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f2dq:select:
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- tag:testtag
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- sample_table
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- sample_without_time_filter
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- sample_with_anomaly
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- f2dq_source_test_table
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f2dq:metrics_groups:
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integration_test_group:
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table:
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- row_count
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- freshness
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- my_distinct_table_rows
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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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- diff # my own custom metric
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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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- nulls_percent
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- missing_percent
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- missing_count
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f2dq:default_metrics:
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- integration_test_group
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seeds:
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+schema: seeds
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+quote_columns: false
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f2dq_integration_tests:
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monitoring:
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sample_with_anomaly:
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+f2dq_monitored: true
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+f2dq_time_filter: creation_time
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sample_without_time_filter:
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+f2dq_monitored: true
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+f2dq_time_filter: null
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sample_table:
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+f2dq_monitored: true
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+f2dq_time_filter: creation_time
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+f2dq_columns:
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- event_type
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- value1
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- value2
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- null_value
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+f2dq_metrics:
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table:
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- my_custom_table_metric # my own custom metric
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- distinct_table_rows
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column:
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event_type:
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- regex_test:
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regex: ([A-Za-z0-9]+)
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- match_regex:
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regex: ^sell
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- match_regex_percent:
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regex: ^sell
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- not_match_regex:
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regex: ^buy
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- not_match_regex_percent:
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regex: ^buy
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- distinct_values
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- duplicate_values
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- duplicate_rows
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- unique_rows
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expected_z_score:
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+column_types:
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time_window_end: "TIMESTAMP"
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expected_metrics:
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+column_types:
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time_window_start: "TIMESTAMP"
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time_window_end: "TIMESTAMP"
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