# GSD-Lite Work Log


## ⚠️ This file is EPHEMERAL

Content deleted after phase promotion. Extract important outcomes before promoting phase.

---

## Current Understanding

<current_state>
Phase: PHASE-001 (JIRA Ticket Analysis)
Task: TASK-001 (Understand Grain of Models) - Paused
Session 1 progress: Determined the grain of the `int_shopify_margins` and `fct_transactions` models and proposed a plan for the next steps.
What's happening: Session is being checkpointed at user's request.
</current_state>

<vision>
The user is working on a Jira ticket (`tmp/PR_review/feat_replace_admin_costs/issue.md`). The goal is to make GM2 cost calculations more accurate by replacing the hardcoded `admin_cost` in the `int_shopify_margins` model with actual costs sourced from the `fct_transactions` model.
</vision>

<decisions>
- `int_shopify_margins` grain is **line item level**, determined by observing the `unnest(order_line_items)` call in its raw SQL.
- `fct_transactions` grain is **transaction level**, confirmed by the final `qualify row_number() over(partition by transaction_id ...)` statement in the model's raw SQL, which programmatically ensures uniqueness per transaction.
- **DECISION-003**: The best way to explore the relationship between the two models is to query the production tables directly for sample data, rather than building a new exploratory dbt model. This is faster and provides ground-truth data.
- **DECISION-004**: The data exploration must account for the two different source systems (Chargebee and Shopify) to avoid sampling bias and incorrect assumptions. We will sample transactions from each system independently.
</decisions>

<blockers>
Session is paused by the user. Work can resume when the user is ready.
</blockers>

<next_action>
Execute the revised, source-aware data exploration plan:
1. Find a sample multi-line transaction from the Shopify source (`stg_shopify_transactions`) using the `_dbt_source_relation` column.
2. Find a sample multi-line transaction from the Chargebee source (`stg_chargebee_transactions`) using the `_dbt_source_relation` column.
3. Use these two `transaction_id`s to query both `int_shopify_margins` and `fct_transactions` to get representative samples.
4. Analyze the results to inform the cost allocation strategy.
</next_action>

---

## Session Log (Chronological)

**[2026-01-27 10:00]** - [VISION] User initiated the session with a specific request.
- Details: "let's start our gsd-lite session ! I'm working on this jira ticket: tmp/PR_review/feat_replace_admin_costs/issue.md we are at task #1 in that plan. Under `tmp/PR_review/feat_replace_admin_costs/manifest_slim.json` is the manifest source code for `4+int_shopify_margins+4 4+fct_transactions+4 4+intr_orders+4"` (it's really big do use jq instead of read file  to explore it). Can you act as a coach help me understand (1) what is the grain of shopify margins model and fct_transctions model, respectively in terms of is this line item level or just order / invoice level (which includes multiple line items) and (2) how should we plan to explore the gap/ impact between the 2 model's grain if we want to pull in transaction cost from fct_transactions into shopify margin model, at CTE `add_admin_cost` , instead of currently hardcoding them by region ?"
- Files: `tmp/PR_review/feat_replace_admin_costs/issue.md`, `tmp/PR_review/feat_replace_admin_costs/manifest_slim.json`
- Impact: Set the direction for the session's analysis.

**[2026-01-27 10:05]** - [EXEC] Read the Jira ticket to understand the scope.
- Details: Reviewed the `issue.md` file to understand the business context and technical requirements.
- Files: `tmp/PR_review/feat_replace_admin_costs/issue.md`

**[2026-01-27 10:10]** - [EXEC] Explored manifest structure to find models.
- Details: Used `jq` to identify the top-level keys in the manifest (`macros`, `nodes`, `sources`).
- Command: `jq 'keys' tmp/PR_review/feat_replace_admin_costs/manifest_slim.json`

**[2026-01-27 10:12]** - [EXEC] Located the specific dbt model nodes.
- Details: Used `jq` to filter the nodes and find the full names for the target models.
- Command: `jq '.nodes | keys[] | select(contains("int_shopify_margins") or contains("fct_transactions"))' tmp/PR_review/feat_replace_admin_costs/manifest_slim.json`

**[2026-01-27 10:15]** - [EXEC] Analyzed `int_shopify_margins` model grain.
- Details: Extracted the raw SQL from the manifest. The presence of `unnest(order_line_items)` confirmed the grain is at the line item level.
- Command: `jq '.nodes."model.estrid_dw.int_shopify_margins".raw_code' tmp/PR_review/feat_replace_admin_costs/manifest_slim.json`

**[2026-01-27 10:20]** - [EXEC] Analyzed `fct_transactions` model grain.
- Details: Extracted the raw SQL from the manifest. The final `qualify` clause partitioning by `transaction_id` confirmed the grain is at the transaction level.
- Command: `jq '.nodes."model.estrid_dw.fct_transactions".raw_code' tmp/PR_review/feat_replace_admin_costs/manifest_slim.json`

**[2026-01-27 10:25]** - [PLAN] Proposed a plan for next steps.
- Details: Outlined a 5-step plan to bridge the grain gap between the models, focusing on joining, allocating costs, implementing the change, and validating the impact.
- Impact: Provided a clear path forward for when the session resumes.

**[2026-01-27 10:30]** - [CHECKPOINT] User requested to pause the session.
- Details: Preparing artifacts for a clean handoff to a future agent.
- Impact: Session state will be preserved.

**[2026-01-27 10:35]** - [CONCEPT] Clarified the importance of "grain" and the "fan-out trap".
- Details: Used a shopping receipt analogy to explain why joining a transaction-level model (`fct_transactions`) to a line-item-level model (`int_shopify_margins`) is dangerous.
- Impact of Current State: A naive join would multiply the transaction costs by the number of line items in an order, leading to massively inflated and incorrect cost calculations. This understanding is critical for the project's success and must be addressed in the PR.
**[2026-01-27 10:40]** - [CONFIRMATION] Solidified the grain of `int_shopify_margins`.
- Details: User confirmed their understanding. We've formally documented that the grain of `int_shopify_margins` is **line-item level**, and this is established in the `unnest_order_line_items` CTE by the `unnest(order_line_items)` function, which operates on data from the upstream `int_ecom_orders` model.
- Impact: This serves as the foundational fact for all subsequent transformation logic.
**[2026-01-27 10:45]** - [DISCOVERY] Confirmed and documented the grain of the `fct_transactions` model.
- Details: Analyzed the raw SQL for `fct_transactions` from the manifest.
- Evidence: The grain is definitively proven to be **transaction level** by the final `qualify` clause, which ensures only one row per `transaction_id` is output from the model.
  ```sql
  qualify row_number() over(partition by transaction_id order by _time_extracted desc) = 1
  ```
- Impact: This discovery confirms the grain mismatch and solidifies the need for a cost allocation strategy before joining the two models. It also refines the reasoning for decision DECISION-002.
**[2026-01-27 10:50]** - [PLAN-PIVOT] User proposed a more efficient data exploration strategy.
- Details: Instead of building an exploratory model, the user suggested querying the production tables directly to get ground-truth sample data. This is a faster and more direct approach.
- New Plan:
  1. Find `transaction_id`s with multiple line items from `estrid-dwh-prod.PROD_INTERMEDIATE.int_shopify_margins`.
  2. Sample data for those IDs from both `int_shopify_margins` and `estrid-dwh-prod.DM_SAL.fct_transactions`.
  3. Analyze the results to inform the cost allocation strategy.
- Impact: This pivot accelerates our discovery process and grounds our decision-making in real production data.
**[2026-01-27 10:55]** - [DISCOVERY & REFINEMENT] Uncovered dual-source system risk and refined the sampling plan.
- Details: User prompted an investigation into anomalies from different e-commerce sources (Chargebee vs. Shopify).
- Evidence: Analysis of the `int_ecom_transactions` model revealed it uses `dbt_utils.union_relations` to combine `stg_chargebee_transactions` and `stg_shopify_transactions`. This confirms the presence of two distinct data sources.
  ```sql
  dbt_utils.union_relations(
      relations=[
          ref('stg_chargebee_transactions'),
          ref('stg_shopify_transactions')
      ]
  )
  ```
- Reasoning: This dual-source reality introduces risks (e.g., inconsistent IDs, logic, data completeness) that make a naive sampling query unsafe. The plan must be refined to sample from each source independently to get representative data. The user identified the `_dbt_source_relation` column as the key to differentiating them.

**[2026-01-27 11:00]** - [DISCOVERY] Attempted to query for sample data and failed, leading to a deeper investigation.
- Details: The initial BigQuery query to find a multi-line Shopify order from `int_ecom_orders` failed with the error `Unrecognized name: transaction_id`.
- Impact: This failure proved that making assumptions about column names is incorrect. It forced a pivot to a more rigorous, evidence-based approach using the dbt manifest as the source of truth.