Add is_closed column in the orders table

[popcornylu]

This is a demo for categorizing changes. For a modified model, we can classify changes into the following categories:

• Breaking change: The change affects all downstream models.
• Partial breaking change: The change affects only downstream models that use the modified columns.
• Non-breaking change: The change does not impact any downstream models.

By using this categorization, we can more accurately evaluate the impact radius, allowing us to pinpoint which models are actually affected.

[github-actions]

Recce Summary

Manifest Information

	Manifest	Catalog
Base	2024-11-05 07:49:11	2024-11-05 07:49:12
Current	2025-01-14 08:58:14	2025-01-14 08:58:15

Lineage Graph

graph LR
model.jaffle_shop.stg_orders["stg_orders

[What's Changed]
Code, Schema"]
style model.jaffle_shop.stg_orders stroke:#ffa502
model.jaffle_shop.stg_orders---->model.jaffle_shop.orders
model.jaffle_shop.stg_orders---->model.jaffle_shop.customers
model.jaffle_shop.orders["orders

[What's Changed]
Code, Schema"]
style model.jaffle_shop.orders stroke:#ffa502
model.jaffle_shop.customers["customers

[What's Changed]
"]
model.jaffle_shop.customers---->model.jaffle_shop.customer_segments
model.jaffle_shop.customers---->model.jaffle_shop.customer_order_pattern
model.jaffle_shop.customer_segments["customer_segments"]
model.jaffle_shop.customer_order_pattern["customer_order_pattern"]

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Checks Summary

Checks Run	Data Mismatch Detected
4	2

Checks of Data Mismatch Detected

Name	Type	Mismatched Nodes
Model schema of customers, orders and modified nodes	Schema Diff	stg_orders, orders
Row count of customers, orders and modified table model	Row Count Diff	orders

See PR page: https://cloud.datarecce.io/DataRecce/jaffle_shop_duckdb/pulls/42

How to check the recce result

# Launch the recce server based on the state file
recce server --review --cloud --password yummy_jaffle

# Open the recce server http://localhost:8000 by your browser

[popcornylu]

Summary

PR #42 adds an is_closed column to the orders table as a demonstration of change categorization. The changes affect the customers model with logic modifications that impact downstream models (customer_segments, customer_order_pattern) while maintaining identical schema, row counts, and data quality metrics.

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Key Changes

• Modified models: orders.sql and stg_orders.sql files updated
• Logic update: Addition of is_closed column in orders table (+2/-1 net changes in orders.sql, +2/-3 in stg_orders.sql)
• Data consistency: No schema changes detected; all row counts remain at 1,856 records across affected models
• Downstream impact: Changes propagate to customer_segments and customer_order_pattern models
• Data quality maintained: All column metrics remain identical between base and current environments

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Impact Analysis

graph LR
    stg_customers["stg_customers<br/>(view)"]:::unchanged
    stg_orders["stg_orders<br/>(view)"]:::unchanged
    stg_payments["stg_payments<br/>(view)"]:::unchanged
    customers["customers<br/>(table)"]:::modified
    customer_segments["customer_segments<br/>(table)"]:::impacted
    customer_order_pattern["customer_order_pattern<br/>(table)"]:::impacted

    stg_customers --> customers
    stg_orders --> customers
    stg_payments --> customers
    customers --> customer_segments
    customers --> customer_order_pattern

    classDef modified fill:#fff3cd,stroke:#ffc107,color:#000
    classDef impacted fill:#ffffff,stroke:#ffc107,color:#000
    classDef unchanged fill:#ffffff,stroke:#d3d3d3,color:#999

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• ✅ No schema changes: Zero columns added, removed, or type-changed across all affected models
• ⚠️ Downstream impacted: customer_segments and customer_order_pattern inherit changes from modified customers model
• ✅ Row count stable: All models maintain 1,856 records - no data volume changes
• ✅ Data quality unchanged: All statistical profiles (null%, distinct count, min/max/avg) identical between environments
• ✅ Logic-only modification: Changes are internal transformations without altering the final dataset dimensions

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☑️ Checklist

No checks configured in the PR.

Note: Consider implementing dbt tests or Recce checks to validate data quality and transformations as part of the PR review process.

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🔍 Suggested Actions

• Verify is_closed column logic: Review the logic for the new is_closed column to ensure it correctly categorizes order statuses as intended
• Validate downstream metrics: Although row counts are stable, manually verify that customer_segments and customer_order_pattern segmentation is accurate with the new column
• Add dbt tests: Implement tests for the is_closed column (null checks, valid value checks, distribution validation)
• Document change categorization: Ensure PR description clearly documents whether this is a breaking, partial breaking, or non-breaking change per the categorization framework mentioned
• Review dependent reports: Check any downstream dashboards or reports that depend on customers to ensure they display correctly with the new column