feat: detect changes to settled historical metrics - #1050
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Included review availability: Your plan provides up to 4 included reviews per hour; 3 remain after this review. 📝 WalkthroughWalkthroughAdds a ChangesMetric stability validation
Estimated code review effort: 3 (Moderate) | ~25 minutes Merge Risk: 🟡 Moderate · up to A fractional explicit observation window may result in no eligible buckets being evaluated, allowing metric stability checks to pass without detecting changes. This should be resolved before merge. Sequence Diagram(s)sequenceDiagram
participant dbt_test
participant metric_stability_query
participant metrics_tables
dbt_test->>metric_stability_query: Generate stability query
metric_stability_query->>metrics_tables: Read historical metric measurements
metric_stability_query->>metrics_tables: Filter settled buckets
metric_stability_query->>metric_stability_query: Compare previous and initial values
metric_stability_query-->>dbt_test: Return threshold violations
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✅ Passed checks (4 passed)
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✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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Inline comments:
In `@macros/edr/data_monitoring/monitors_query/metric_stability_query.sql`:
- Around line 107-115: Update the window functions for previous_value,
initial_value, and row_number in the metric stability query to partition by id,
dimension, and dimension_value so each dimension series is evaluated
independently. Add a regression test covering at least two dimension values and
verify each combination receives its own history, baseline, and recency result.
In `@macros/edr/tests/test_metric_stability.sql`:
- Around line 221-226: Update both min_bucket_age handling sites in
macros/edr/tests/test_metric_stability.sql lines 221-226 and
macros/edr/data_monitoring/monitors_query/metric_stability_query.sql lines
38-41, using calendar-aware logic for month periods instead of passing months to
datetime.timedelta, or explicitly reject unsupported periods with a compiler
error; preserve existing handling for supported periods and add monthly
coverage.
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🧹 Nitpick comments (1)
integration_tests/tests/test_metric_stability.py (1)
143-147: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick winUse
itertools.pairwisefor the successive-pair loop.Ruff flags this
zip()call for both B905 and RUF007.itertools.pairwiseremoves both warnings and keeps the intent explicit.♻️ Proposed refactor
+ steps = [ + later - earlier + for earlier, later in pairwise(measurements) + if later != earlier + ] - steps = [ - later - earlier - for earlier, later in zip(measurements, measurements[1:]) - if later != earlier - ]Add the import:
from itertools import pairwise🤖 Prompt for AI Agents
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Inline comments:
In `@macros/edr/data_monitoring/monitors_query/metric_stability_query.sql`:
- Around line 78-97: Update the change_since validation in test_metric_stability
to reject an empty list before metric-stability SQL generation, while preserving
validation of each provided element. Ensure the validation fails clearly when no
baseline is supplied so the query never renders an empty where predicate.
In `@macros/edr/tests/test_metric_stability.sql`:
- Around line 235-245: Update the test metric stability setup before calling
column_monitoring_query so column_metrics is built from
column_obj_and_monitors["monitors"] for the current column, rather than reusing
the full metrics list. Preserve the existing filtered monitor selection and pass
that column-specific collection to column_monitoring_query.
---
Nitpick comments:
In `@integration_tests/tests/test_metric_stability.py`:
- Around line 143-147: Update the successive-pair comprehension assigning steps
to use itertools.pairwise(measurements) instead of zip(measurements,
measurements[1:]); add the corresponding pairwise import and preserve the
existing unequal-value filtering and subtraction.
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Inline comments:
In `@macros/edr/tests/test_metric_stability.sql`:
- Line 412: Update the validation around metric_stability_query to compare
resolved_days_back after applying the same integer conversion used by the query,
or reject fractional days_back values before execution. Ensure fractional inputs
such as 0.5 cannot produce a zero-day window that bypasses settled-bucket
validation.
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| {%- if days_back is none %} {%- set resolved_days_back = derived %} | ||
| {%- else %} | ||
| {%- set resolved_days_back = days_back %} | ||
| {%- if resolved_days_back <= age_days %} |
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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win
Validate the effective days_back value.
Line 412 compares the raw value, but metric_stability_query converts days_back with | int. For min_bucket_age: {count: 1, period: hour} and days_back: 0.5, validation succeeds and the query uses a zero-day window. The bucket window is empty, so the test passes without checking a settled bucket.
Validate the integer value used by the query, or reject fractional days_back values.
Proposed fix
- {%- if resolved_days_back <= age_days %}
+ {%- if (resolved_days_back | int) <= age_days %}📝 Committable suggestion
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Suggested change
| {%- if resolved_days_back <= age_days %} | |
| {%- if (resolved_days_back | int) <= age_days %} |
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In `@macros/edr/tests/test_metric_stability.sql` at line 412, Update the
validation around metric_stability_query to compare resolved_days_back after
applying the same integer conversion used by the query, or reject fractional
days_back values before execution. Ensure fractional inputs such as 0.5 cannot
produce a zero-day window that bypasses settled-bucket validation.
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Regular anomaly detection compares one bucket against neighbouring buckets, which cannot see a value being rewritten for a period that was already measured. A restatement spanning many historical buckets moves the training baseline along with the data, so the score barely changes, and normal period-to-period variation is usually far wider than the change being looked for. Tests stay green while the numbers underneath them change. metric_stability compares a bucket against its own earlier measurements instead. The version history it needs is already collected: metric ids hash the table, column, metric name and bucket_end while excluding updated_at and metric_value, and rows are appended by the on-run-end hook, so re-measuring a bucket leaves the earlier measurements in place. It is a threshold test rather than an anomaly test by design. For settled data the expected change is zero, so the series has no variance to learn from: with the value excluded from its own training set the stddev is zero and the score is forced to zero, and with it included the score reduces to n/sqrt(n+1), independent of magnitude. A relative threshold also transfers across metrics, where an absolute one has to be retuned per metric. backfill_days is derived from min_bucket_age, because a bucket can only be compared while it is still being re-measured. The default of 2 would freeze every older bucket before it became eligible, and an explicit value too small to produce a comparison now raises rather than passing silently. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Three cases, all driven through the shared harness so they run on every supported adapter: - a restatement of a settled bucket is caught, after two runs establish that the bucket had been measured and was stable - a change inside min_bucket_age is ignored, since recent data is expected to keep moving as late records arrive - max_change_percent tolerates a change below the threshold and still fails one above it, which is what makes a single relative threshold usable across metrics with very different magnitudes Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Two compile errors on adapters Postgres does not exercise: - clickhouse__first_value called first_valueInFrame, which does not exist. The premise was wrong too: ClickHouse needs lagInFrame because it has no lag at all, not because of framing, and its first_value does respect an ordered frame. The override and its dispatch macro are removed. - A CASE returning a boolean is invalid T-SQL, which has no first-class boolean value, so the parser failed on the "!". Conditions now keep booleans in boolean position. Two correctness bugs: - With more than one column, collect_column_metrics created a table per column and left the cache pointing at the last one, so every other column was compared against the previous run rather than this one and a restatement surfaced a run late. Columns now share one temp table, built the way all_columns_anomalies does it. - The read had no lower bound on bucket_end, so a bucket that stopped being re-measured kept satisfying the predicate on every subsequent run: one restatement failed the test permanently, with no way to clear it. Every run also scanned the table's whole metric history. days_back now bounds the read, making the eligible set a band and giving partition pruning. min_bucket_age becomes required, since defaulting it meant the out-of-the-box configuration compared buckets still inside the backfill window at zero tolerance, which is the noise the design exists to avoid. It is also shape validated, as is metrics, so a bad value gives a compiler error rather than a raw traceback or a query rendered against None. The window guard now checks the parameter that actually governs. backfill_days only widens the measurement window on the incremental branch of get_metric_buckets_min_and_max; a plain table model takes the regular branch, where days_back alone decides. Guarding backfill_days there bought nothing while reporting everything as fine. detection_delay is dropped rather than left half-wired, since it shifted the read cutoff but not the measurement window and min_bucket_age already covers the same ground. Tests now isolate the two baselines, so swapping them can no longer pass, and assert the compared values rather than only the pass/fail status. Multi-column coverage is added, which is how the per-column bug got through. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Cross-test contamination: the history read filtered by table, metric name and metric_properties, but not by column. metric_properties does not carry the column, so two metric_stability tests on the same model would each load the other's history, and a change in a column this test never configured could surface as its failure. The read is now scoped to the monitored columns. Invalid metrics per column: the per-column loop resolved the monitors that apply to a column's data type and used them for bucket selection, but still handed the unfiltered list to column_monitoring_query. Monitoring a mixed set across numeric and string columns would generate sum(<string column>) and fail on the warehouse. Each column now gets only its applicable monitors. Sub-day min_bucket_age: the age was ceiled to whole days before being compared against days_back, so an age of one hour was treated as a day and days_back of 1 was rejected, even though it covers 23 settled hourly buckets. The comparison now uses a fraction of a day, ceiling only when deriving the default, and the error reports the units the user wrote. Argument shapes: yaml allows a single value as a scalar, and iterating a string in jinja walks it character by character, so `change_since: last_check` failed with "Unsupported change_since value 'l'". Scalars are normalised to lists, columns are deduplicated, and an empty change_since now raises instead of rendering a WHERE with no predicate. timestamp_column is commonly set once in a model's elementary config rather than repeated per test. It is now resolved through get_test_argument before the column type is validated, instead of failing with "Column 'None' is not a timestamp type". Failing rows also carry the relative change, which is what the threshold is applied to, so a failure is interpretable without recomputing it by hand. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Two silent failures, both reproduced on DuckDB. The metrics table was created empty and filled with INSERT statements. On adapters where dbt rolls back the test's transaction those rows are gone before the on-run-end flush, so data_monitoring_metrics never receives any history and the comparison has nothing to compare. Four of the six tests failed this way on DuckDB, Vertica and Redshift. Each column now gets its own table created directly from its select, and they are unioned at read time. The observation window was derived purely in days and never looked at time_bucket, and the bucket grid was anchored on a value that moves by a day between runs. For any period longer than a day that gave every measurement a fresh surrogate id, so no bucket was ever measured twice. The window now accounts for the bucket length, the grid anchor is snapped to the bucket period, and a time_bucket count above 1 raises instead of passing forever. Also: dedupe columns case-insensitively so a duplicate spelling cannot make 'last_check' compare a run against itself; type-check the numeric arguments before comparing them; skip the backfill_days validation when force_metrics_backfill makes it irrelevant; share one change-percent expression between the predicate and the reported columns; drop an unused local and an unread macro parameter. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
A bucket's first measurements are taken while late records are still arriving, which is the period min_bucket_age exists to exclude. They were eligible as the 'first_check' baseline, so every comparison carried the settling as a permanent offset and the slow drift 'first_check' exists to find sat underneath it. Measurements are now bounded by the same age as the buckets. The current run's own measurement always qualifies, since a bucket is only eligible once bucket_end + min_bucket_age has passed. Repeating a float aggregate can differ in the last bits when the scan is partitioned differently between runs, and a strict comparison against the default max_change_percent of 0 reported that as a failure on data nobody touched. A noise floor well above float error and well below any real movement now sits under the threshold, leaving the zero-crossing rule alone. Also documents two things that are not being changed: 'first_check' needs several measurements per bucket to differ from 'last_check', so min_bucket_age should be a multiple of the run interval; and a bucket that loses all of its rows produces no measurement rather than a zero, so total deletion is not reported while partial deletion still is. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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- Cast the settled-measurement lower bound to a timestamp: on BigQuery edr_timeadd returns a DATE for week/month/quarter/year parts, so comparing it against the TIMESTAMP updated_at column failed. - ClickHouse has no plain UPDATE; the settling test now issues an ALTER TABLE ... UPDATE mutation (synchronously) on that target. Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
haritamar
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Sep 6, 2026
| {%- if metrics is string %} {% set metrics = [metrics] %} {%- endif %} | ||
| {%- if change_since is string %} | ||
| {% set change_since = [change_since] %} | ||
| {%- endif %} |
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What is all this commented out code? is it relevant?
| ) %} | ||
| {{ config(tags=["elementary-tests"]) }} | ||
|
|
||
| {%- if execute and elementary.is_test_command() and elementary.is_elementary_enabled() %} |
Collaborator
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It's better to do:
{%- if not(execute and elementary.is_test_command() and elementary.is_elementary_enabled()) %}
{% do return() %}
{% endif %}
So we don't have to ident all the code below
| is append-only (rows are inserted by the on-run-end hook), and a metric `id` | ||
| hashes the table, column, metric name and bucket_end while deliberately | ||
| excluding `updated_at` and `metric_value`. So re-measuring a bucket appends a | ||
| new row, and the earlier measurements remain. |
Collaborator
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I think this comment is a bit too long, can we shorten it a bit?
| "metric_stability requires at least one baseline in `change_since`: 'last_check', 'first_check', or both." | ||
| ) | ||
| }} | ||
| {%- endif %} |
Collaborator
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Please create a _validate_metric_stability_arguments macro, and move all the argument validation logic there.
I think it will make the test more readable.
(also will be good that this macro will be palced after the test, at the bottom)
| loses the rows on adapters where dbt rolls back the test's | ||
| transaction, which leaves data_monitoring_metrics with no history | ||
| at all and makes this test a silent permanent pass. The tables are | ||
| unioned at read time instead. -#} |
Collaborator
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I think this comment should be removed, or at least simplified.
For example it mentions collect_column_metrics even though it's not even called here - why is it relevant?
| {%- set age_kwargs = {min_bucket_age.period ~ "s": min_bucket_age.count} %} | ||
| {#- Kept as a fraction of a day. Ceiling it first would turn a sub-day age | ||
| into a whole day and reject a days_back that in fact covers many | ||
| settled buckets. -#} |
| {#- Twice the age, so a bucket is observed over a stretch rather than for a | ||
| single run, which is what lets 'first_check' see drift accumulate; and | ||
| at least two whole buckets past the age, or the settled band is narrower | ||
| than one bucket and nothing is ever both settled and still measured. -#} |
Collaborator
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This doesn't explain the age_days + 1 line - is it actually needed?
| {%- set change_percent_noise_floor = 0.000000001 %} | ||
| {%- set change_threshold = "%.10f" | format( | ||
| [max_change_percent, change_percent_noise_floor] | max | ||
| ) %} |
Collaborator
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Is all this commented out code needed?
| ~ " > " | ||
| ~ change_threshold | ||
| ~ ")))" | ||
| ) %} |
Collaborator
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This condition is not readable.
Use perhaps a {% set %} ... {% endset %} block to create each condition with Jinja.
| ~ ") / abs(" | ||
| ~ baseline_column | ||
| ~ ") * 100.0" | ||
| ) %} |
Collaborator
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I think formatting with Jinja is more readable:
{%- macro metric_stability_change_percent(baseline_column) -%}
abs( {{metric_value}} - ....)
{%- endmacro -%}
(this is also true in a couple of other places)
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Detect historical restatements: if a settled day's revenue changes from 100 to 120 after a merge or full refresh,
elementary.metric_stabilityflags it by comparing that bucket with its own earlier measurements. Comparing different days with anomaly detection does not directly check this expectation.Behavior
last_check, default) or earliest retained post-settlement measurement (first_check), or both.min_bucket_age. The first eligible run establishes a baseline.max_change_percent(1 means 1%). The default is zero with a tiny relative floor for floating-point noise; movement away from a zero baseline always fails.missing_bucket, with a NULL current value rather than an invented zero.This example watches daily buckets roughly 28–90 days old. It does not protect all history. Longer windows increase rescanning and history-storage costs; incremental models and sources also use
backfill_days, which defaults todays_back.Review guide
test_metric_stability.sql: validate settings, resolve column names and scan windows, collect per-column metrics, and materialize failures for sampling.metric_stability_query.sql: combine persisted/current measurements, choose baselines, and detect changes or missing buckets. History is restricted to each column's actual scan bounds to avoid treating unscanned data as deleted.test_metric_stability.py: integration coverage for restatements, gradual drift, settling, thresholds, history persistence, weekly buckets, multiple/quoted columns, failure samples, and missing buckets/dimensions inside and outside coverage.Operational limits
last_checkautomatically advances: 100 → 120 fails, then another 120 passes.first_checkcontinues failing against 100 while that baseline remains retained and the bucket remains in coverage. There is no explicit baseline acceptance/reset operation. Stable aggregates also cannot detect offsetting row changes. See usage and baseline guidance.Validation