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🔍 Performance Analysis Summary

  • Overall Impact: CRITICAL

  • Estimated Runtime Change: +500% to +1000%

  • Risk Level: BLOCKING - DO NOT MERGE

📊 Detailed Model Analysis

Model: mart_sales_performance_summary.sql (NEW)

Current Performance:

  • N/A (new model)

Predicted Performance:

  • Runtime: 10-20x slower than typical mart models

  • Memory Usage: Exponential growth with data volume

  • CPU Usage: Extreme due to multiple full table scans

Critical Issues Identified:

  1. 🔴 DUPLICATE SUBQUERIES (Lines 6-11)

    -- PROBLEM: Exact same subquery executed twice
(select count(*) from {{ ref('stg_product_performance') }} p where p.price_tier = 'Premium') as premium_product_count,
(select count(*) from {{ ref('stg_product_performance') }} p where p.price_tier = 'Premium') as premium_count_duplicate,

    Impact: 2x execution time for identical operation Fix: Use a CTE to calculate once

  2. 🔴 UNNECESSARY ORDER BY IN AGGREGATION (Line 15)

    (select avg(standard_cost) from {{ ref('stg_product_performance') }} order by product_id) as avg_cost,

    Impact: Sorting entire table for no benefit in AVG() Fix: Remove ORDER BY clause

  3. 🔴 CARTESIAN PRODUCT (Lines 31-32)

    from {{ ref('stg_salesperson') }} s,
     {{ ref('stg_territory') }} t

    Impact: Creates n_salespeople × n_territories rows Fix: Add proper JOIN condition

  4. 🔴 TRIPLE-NESTED CASE STATEMENT (Lines 19-29)

    • 3 levels of nested subqueries scanning entire table repeatedly Fix: Calculate once in CTE

  5. 🟡 REDUNDANT STRING OPERATIONS (Line 17)

    upper(lower(upper(s.salesperson_name_clean))) as name_processed

    Fix: Single UPPER() call

  6. 🟡 NON-DETERMINISTIC SORT (Line 40)

    order by upper(s.salesperson_name_clean), current_timestamp()

    Fix: Remove current_timestamp()

Model: mart_sales_perfromance_dashboard.sql (MODIFIED)

Current Performance:

  • Average runtime: ~5 seconds

  • Stable performance across runs

Predicted Performance:

  • Runtime: 50-100x slower (potential timeout)

  • Memory Usage: CRITICAL - likely OOM

  • Database Load: Extreme

Critical Issues Identified:

  1. 🔴 TRIPLE CROSS JOIN EXPLOSION (Lines 25-27)

    from territories_expanded t
cross join salespeople_expanded s
cross join products_expanded p

    Impact: If you have:

    • 100 territories × 50 salespeople × 1000 products = 5,000,000 rows

    • Original had proper aggregations, this creates raw cartesian product Fix: Revert to original JOIN logic

  2. 🔴 LOST ALL AGGREGATIONS

    • Original had GROUP BY with meaningful metrics

    • New version has no aggregations, just row explosion Impact: Lost all business logic and created data bomb

  3. 🔴 POINTLESS NESTED CTEs (Lines 3-18)

    territories_expanded as (
    select * from (
        select * from territories  -- territories doesn't exist!
    )
),

    Impact: References undefined tables, adds overhead

  4. 🟡 DUPLICATE CALCULATIONS (Lines 38-48)

    • Same CASE statement calculated twice as market_type_a and market_type_b Fix: Calculate once

  5. 🟡 FAKE CORRELATED SUBQUERY (Lines 52-54)

    (select count(*) from products p2 where p2.product_id = p.product_id) as fake_count

    Impact: Always returns 1, executes per row Fix: Remove entirely

🚀 Optimization Opportunities

Immediate Fixes Required:

  1. Replace CROSS JOINs with proper relationships:

-- Instead of CROSS JOIN, use:
FROM {{ ref('stg_salesperson') }} s
INNER JOIN {{ ref('stg_territory') }} t ON s.territory_key = t.territory_key
LEFT JOIN product_metrics pm ON 1=1  -- If product metrics are global

  1. Use CTEs for repeated calculations:

WITH product_stats AS (
    SELECT 
        COUNT(CASE WHEN price_tier = 'Premium' THEN 1 END) as premium_count,
        AVG(standard_cost) as avg_cost,
        AVG(CASE WHEN price_tier = 'Premium' THEN standard_cost END) as premium_avg,
        AVG(CASE WHEN price_tier = 'Budget' THEN standard_cost END) as budget_avg
    FROM {{ ref('stg_product_performance') }}
)

  1. Consider incremental materialization:

{{ config(
    materialized='incremental',
    unique_key='composite_key',
    on_schema_change='fail'
) }}

✅ Action Items (Priority Order)

  1. 🚨 IMMEDIATE: Block PR merge - will crash production

  2. 🔴 Fix CROSS JOINs: Add proper JOIN conditions between tables

  3. 🔴 Restore aggregations: Revert dashboard to use GROUP BY logic

  4. 🔴 Remove duplicate subqueries: Use CTEs for repeated calculations

  5. 🟡 Clean up redundant operations: Remove unnecessary string manipulations

  6. 🟡 Add performance hints: Consider partitioning if tables are large

📈 Historical Context

Based on metrics:

  • Current production runtime: ~3-5 seconds per DAG run

  • These changes would increase to: 30-300+ seconds

  • Risk of timeout at default 30-second threshold

  • Previous similar cartesian product issue caused significant outage

Final Verdict

REQUEST CHANGES - The proposed changes introduce multiple critical performance issues that would severely degrade pipeline performance and potentially cause production failures. The CROSS JOIN patterns alone could increase runtime by 1000% and cause memory exhaustion. These changes must be refactored before merging.

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