> ## Documentation Index
> Fetch the complete documentation index at: https://docs.elementary-data.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Anomaly Tests Troubleshooting

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<Accordion title="Verify the data collection for your anomaly test">
  First, check if your test uses a timestamp column:

  ```yaml theme={null}
  # In your YAML configuration
  data_tests:
    - elementary.volume_anomalies:
        arguments:
          timestamp_column: created_at# If this is configured, you have a timestamp-based test
  ```

  <Accordion title="If you have a timestamp-based test (recommended)">
    * Metrics are calculated by grouping data into time buckets (default: 'day')
    * Detection period (default: 2 days) determines how many buckets are being tested
    * Training period data (default: 14 days) comes from historical buckets, allowing immediate anomaly detection with sufficient history

    Verify data collection:

    ```sql theme={null}
    -- Check if metrics are being collected in time buckets
    SELECT
    bucket_end,
    metric_value,
    COUNT(*) as metrics_per_bucket
    FROM your_schema.data_monitoring_metrics
    WHERE full_table_name = 'your_table'
    GROUP BY bucket_end, metric_value
    ORDER BY bucket_end DESC;

    ```

    * Each bucket should represent one time bucket (e.g., daily metrics)
    * Gaps in `metric_timestamp` might indicate data collection issues
    * Training uses historical buckets for anomaly detection
    * The format for full\_table\_name is DATABASE.SCHEMA.TABLE\_NAME

    **Common collection issues:**

    * Missing or null values in timestamp column
    * Timestamp column not in expected format
    * No data in specified training period
  </Accordion>

  <Accordion title="If you don't have a timestamp configured">
    * Training period data builds up over multiple test runs, using the test run time as its timestamp column. This requires time to collect enough points; for a 14 day training period, the test would need 14 different runs on different days to have a full training set.
    * Metrics are calculated for the entire table in each test run
    * Detection period (default: 2 days) determines how many buckets are being tested

    Check metric collection across test runs:

    ```sql theme={null}
    -- Check metrics from different test runs
    SELECT
    updated_at,
    metric_value
    FROM your_schema.data_monitoring_metrics
    WHERE full_table_name = 'your_table'
    ORDER BY updated_at DESC;
    ```

    * Should see one metric per test run and per dimension
    * Training requires multiple test runs over time
    * Each new test run creates the training point for a time bucket. A second test run within the same bucket will override the first one.
    * The format for full\_table\_name is DATABASE.SCHEMA.TABLE\_NAME

    **Common collection issues:**

    * Test hasn't run enough times
    * Previous test runs failed
    * Metrics not being saved between runs
  </Accordion>
</Accordion>

<Accordion title="Verify anomaly calculations">
  Anomaly detection is influenced by:

  * Detection period (default: 2 days) - the time window being tested
  * Sensitivity (default: 3.0) - how many standard deviations from normal before flagging
  * Training data from previous periods/runs
  * `metrics_anomaly_score` calculates the anomaly based on the data in `data_monitoring metrics`.

  Check calculations in `metrics_anomaly_score`:

  ```sql theme={null}
  -- Check how anomalies are being calculated
  SELECT
      metric_name,
      latest_metric_value,
      training_avg,
      training_stddev,
      anomaly_score,
      is_anomaly
  FROM your_schema.metrics_anomaly_score
  WHERE full_table_name = 'your_table'
  ORDER BY bucket_end DESC;
  ```

  * `anomaly_score`: The standardized score that measures how many standard deviations a data point is from the mean
  * `is_anomaly`: A boolean field that indicates whether the anomaly score exceeds the configured threshold
</Accordion>

<Accordion title="'Not enough data to calculate anomaly' error">
  This occurs when there are fewer than 7 training data points. To resolve:

  ### For timestamp-based tests:

  * Check if your timestamp column has enough historical data
  * Verify time buckets are being created correctly in `data_monitoring_metrics`
  * Look for gaps in your data that might affect bucket creation

  ### For non-timestamp tests:

  * Run your tests multiple times to build up training data.
  * Check `data_monitoring_metrics` to verify the data collection. The test will need data for at least 7 time buckets (e.g 7 days) to calculate the anomaly.
</Accordion>

<Accordion title="Missing data in data_monitoring_metrics">
  If your test isn't appearing in `data_monitoring_metrics`:

  Verify test configuration:

  ```yaml theme={null}
  data_tests:
    - elementary.volume_anomalies:
        arguments:
          timestamp_column: created_at# Check if specified correctly
  ```

  ### Common causes:

  * Incorrect timestamp column name
  * Timestamp column contains null values or is not of type timestamp or date
  * For non-timestamp tests: Test hasn't run successfully
  * Incorrect test syntax
</Accordion>

<Accordion title="Training period changed, but results are the same">
  If you change it after executing elementary tests, you will need to run a full refresh to the metrics collected. This will make the next tests collect data for the new **`training_period`** timeframe. The steps are:

  1. Change var **`training_period`** in your **`dbt_project.yml`**.
  2. Full refresh of the model ‘data\_monitoring\_metrics’ by running **`dbt run --select data_monitoring_metrics --full-refresh`**.
  3. Running the elementary tests again.

  If you want the Elementary UI to show data for a longer period of time, use the days-back option of the CLI: **`edr report --days-back 45`**
</Accordion>