✔ PASS Metric Inflation / Chasm Trap task_01_fanout Scaled: 1M+ Records Ordered: No

E-Commerce Financial Aggregation without Join Fan-Out

Candidate Latency (1M Rows)
88.52 ms
(14.6x slower than golden)
Golden Latency (1M Rows)
6.05 ms
DuckDB in-memory engine
Underlying Data Volume
1,003,000 rows
23.0 MiB across 4 tables
Evaluated Model
gemini-3.8-flash-low
Gen Latency: 8.6s
✔ Correctness Verified on 1M+ Records
Candidate SQL output matches ground truth golden query across all multiset rows and numeric tolerances (tol=1e-3). Zero data skew or anti-patterns detected on full dataset scale.

1. Analytical Significance & What It Means

Scenario & Frontier Trap
Business Scenario & Problem

Demonstrates the Chasm Trap where joining multiple 1-to-many child tables (discounts and shipments) to an orders table causes row multiplication before aggregation, inflating financial sums.

⚡ The Frontier Trap (Why LLMs Fail)

Joining multiple 1:N child tables (order_discounts has 2 rows, order_shipments has 2 rows) directly to orders before GROUP BY creates an intermediate Cartesian fan-out of 2 * 2 = 4 rows per order. Aggregating over these inflated rows multiplies base_amount by 4x ($100.00 -> $400.00), total discounts by 2x ($15.00 -> $30.00), and shipping by 2x ($10.00 -> $20.00). Proper solution requires pre-aggregating child tables to 1 row per order in CTEs or correlated subqueries before joining.

Prompt / User Request Given to Model
Calculate total gross sales (sum of base_amount), total discount amount, and total shipping fees for each merchant. Ensure multiple discount line items and multiple shipment records for the same order do NOT multiply or artificially inflate the totals.

Required output columns:
- merchant_id (INT)
- merchant_name (VARCHAR)
- total_gross_sales (DECIMAL)
- total_discount_amount (DECIMAL)
- total_shipping_fees (DECIMAL)

Row order does not matter.

2. Underlying Tables, Row Counts & Data Volume

4 Tables (1,003,000 rows • 23.0 MiB)

Production-scale fixtures loaded into the isolated DuckDB sandbox with at least 1,000,000 records per table:

📊 merchants 1,000 rows 23.4 KiB Data Volume
merchant_id (INTEGER) merchant_name (VARCHAR)
Data Preview (showing sample rows from 1,000 total rows):
# merchant_id merchant_name
1 1 Acme Electronics
2 1 Acme Electronics
3 2 Acme Electronics
4 3 Acme Electronics
5 4 Acme Electronics
6 5 Acme Electronics
7 6 Acme Electronics
8 7 Acme Electronics
9 8 Acme Electronics
10 9 Acme Electronics
11 10 Acme Electronics
12 11 Acme Electronics
13 12 Acme Electronics
14 13 Acme Electronics
15 14 Acme Electronics
📊 order_discounts 1,000 rows 23.4 KiB Data Volume
discount_id (INTEGER) order_id (INTEGER) amount (DECIMAL(10,2))
Data Preview (showing sample rows from 1,000 total rows):
# discount_id order_id amount
1 1 101 10
2 2 101 5
3 2 101 10.38
4 3 102 8.25
5 4 103 11.93
6 5 104 6.49
7 6 105 8.83
8 7 106 15.72
9 8 107 19.38
10 9 108 6.95
11 10 109 5.08
12 11 110 6.79
13 12 111 5.23
14 13 112 9.65
15 14 113 7
📊 order_shipments 1,000 rows 23.4 KiB Data Volume
shipment_id (INTEGER) order_id (INTEGER) shipping_fee (DECIMAL(10,2))
Data Preview (showing sample rows from 1,000 total rows):
# shipment_id order_id shipping_fee
1 1 101 4
2 2 101 6
3 2 101 5.78
4 3 102 4.7
5 4 103 5.82
6 5 104 5.72
7 6 105 5.41
8 7 106 3.97
9 8 107 5.28
10 9 108 6.59
11 10 109 4.01
12 11 110 6.39
13 12 111 6.13
14 13 112 3.91
15 14 113 5.28
📊 orders 1,000,000 rows 22.9 MiB Data Volume
order_id (INTEGER) merchant_id (INTEGER) base_amount (DECIMAL(10,2))
Data Preview (showing sample rows from 1,000,000 total rows):
# order_id merchant_id base_amount
1 101 1 100
2 101 1 90.94
3 102 2 91.54
4 103 3 115.57
5 104 4 119.81
6 105 5 90.5
7 106 6 102.73
8 107 7 109.63
9 108 8 102.33
10 109 9 97.54
11 110 10 110.28
12 111 11 105.29
13 112 12 90.88
14 113 13 97.27
15 114 14 108.95

3. Graphical Query Execution Plan & Cost Bottlenecks

Physical Operator Tree (DuckDB 2.0 Profiler)

Visual flowchart of the physical execution pipeline. Operators with high relative costs (≥ 20% of query runtime or heavy latency) are visually highlighted:

Pipeline Latency Distribution (Total Plan Time: 359.79 ms) Nodes consuming ≥ 20% of query runtime are highlighted with glowing borders
📐 PROJECTION 0.0%
Latency: 0.10 ms
Rows In/Out: 1,000
Data Vol: 0.06 MB
📁 Projections: ['merchant_id', 'me...
⚡ HASH_JOIN 2.4%
Latency: 8.47 ms
Rows In/Out: 1,000
Data Vol: 0.06 MB
📐 PROJECTION 3.0%
Latency: 10.91 ms
Rows In/Out: 999,999
Data Vol: 49.59 MB
📁 Projections: ['merchant_id', 'to...
📊 HASH_GROUP_BY 🔥 86.0% Cost
Latency: 309.58 ms
Rows In/Out: 999,999
Data Vol: 49.84 MB
⚠️ Heavy Execution Bottleneck (86.0% of query runtime)
📐 PROJECTION 0.1%
Latency: 0.19 ms
Rows In/Out: 1,000,000
Data Vol: 42.20 MB
📁 Projections: ['merchant_id', 'ba...
⚡ HASH_JOIN 3.1%
Latency: 11.25 ms
Rows In/Out: 1,000,000
Data Vol: 42.20 MB
⚡ HASH_JOIN 4.0%
Latency: 14.43 ms
Rows In/Out: 1,000,000
Data Vol: 30.64 MB
🔍 TABLE_SCAN 1.3%
Latency: 4.76 ms
Rows In/Out: 1,000,000
Data Vol: 15.26 MB
📁 memory.main.orders
📐 PROJECTION 0.0%
Latency: 0.00 ms
Rows In/Out: 998
Data Vol: 0.02 MB
📁 Projections: ['CAST(#0 AS INTEGE...
📊 PERFECT_HASH_GROUP_BY 0.0%
Latency: 0.02 ms
Rows In/Out: 998
Data Vol: 0.02 MB
📐 PROJECTION 0.0%
Latency: 0.00 ms
Rows In/Out: 1,000
Data Vol: 10,000 B
📁 Projections: ['order_id', 'amoun...
📐 PROJECTION 0.0%
Latency: 0.00 ms
Rows In/Out: 1,000
Data Vol: 10,000 B
📁 Projections: ['CAST(#0 AS SMALLI...
🔍 TABLE_SCAN 0.0%
Latency: 0.02 ms
Rows In/Out: 1,000
Data Vol: 0.01 MB
📁 memory.main.order_discounts
📐 PROJECTION 0.0%
Latency: 0.00 ms
Rows In/Out: 998
Data Vol: 0.02 MB
📁 Projections: ['CAST(#0 AS INTEGE...
📊 PERFECT_HASH_GROUP_BY 0.0%
Latency: 0.02 ms
Rows In/Out: 998
Data Vol: 0.02 MB
📐 PROJECTION 0.0%
Latency: 0.00 ms
Rows In/Out: 1,000
Data Vol: 10,000 B
📁 Projections: ['order_id', 'shipp...
📐 PROJECTION 0.0%
Latency: 0.01 ms
Rows In/Out: 1,000
Data Vol: 10,000 B
📁 Projections: ['CAST(#0 AS SMALLI...
🔍 TABLE_SCAN 0.0%
Latency: 0.01 ms
Rows In/Out: 1,000
Data Vol: 0.01 MB
📁 memory.main.order_shipments
🔍 TABLE_SCAN 0.0%
Latency: 0.02 ms
Rows In/Out: 1,000
Data Vol: 0.02 MB
📁 memory.main.merchants
Operator Cost % Latency Processed Rows Data Volume Details / Table Status
HASH_GROUP_BY 86.0% 309.58 ms 999,999 49.84 MB - 🔥 BOTTLENECK
HASH_JOIN 4.0% 14.43 ms 1,000,000 30.64 MB - Normal
HASH_JOIN 3.1% 11.25 ms 1,000,000 42.20 MB - Normal
PROJECTION 3.0% 10.91 ms 999,999 49.59 MB Projections: ['merchant_id', 'total_gross_sales' Normal
HASH_JOIN 2.4% 8.47 ms 1,000 0.06 MB - Normal
TABLE_SCAN 1.3% 4.76 ms 1,000,000 15.26 MB memory.main.orders Normal
PROJECTION 0.1% 0.19 ms 1,000,000 42.20 MB Projections: ['merchant_id', 'base_amount', 'dis Normal
PROJECTION 0.0% 0.10 ms 1,000 0.06 MB Projections: ['merchant_id', 'merchant_name', 't Normal
TABLE_SCAN 0.0% 0.02 ms 1,000 0.02 MB memory.main.merchants Normal
PERFECT_HASH_GROUP_BY 0.0% 0.02 ms 998 0.02 MB - Normal
PERFECT_HASH_GROUP_BY 0.0% 0.02 ms 998 0.02 MB - Normal
TABLE_SCAN 0.0% 0.02 ms 1,000 0.01 MB memory.main.order_discounts Normal
TABLE_SCAN 0.0% 0.01 ms 1,000 0.01 MB memory.main.order_shipments Normal
PROJECTION 0.0% 0.01 ms 1,000 0.01 MB Projections: ['CAST(#0 AS SMALLINT)', '#1'] Normal
PROJECTION 0.0% 0.00 ms 998 0.02 MB Projections: ['CAST(#0 AS INTEGER)', '#1'] Normal
PROJECTION 0.0% 0.00 ms 998 0.02 MB Projections: ['CAST(#0 AS INTEGER)', '#1'] Normal
PROJECTION 0.0% 0.00 ms 1,000 0.01 MB Projections: ['CAST(#0 AS SMALLINT)', '#1'] Normal
PROJECTION 0.0% 0.00 ms 1,000 0.01 MB Projections: ['order_id', 'amount'] Normal
PROJECTION 0.0% 0.00 ms 1,000 0.01 MB Projections: ['order_id', 'shipping_fee'] Normal

4. Queries Executed (Candidate vs Golden Ground Truth)

Beautified & Syntax Highlighted
Candidate SQL Executed by gemini-3.8-flash-low (Runtime on 1M Rows: 88.52 ms)
WITH order_level_discounts AS
  (SELECT order_id,
          sum(amount) AS discount_amount
   FROM order_discounts
   GROUP BY order_id),
     order_level_shipments AS
  (SELECT order_id,
          sum(shipping_fee) AS shipping_fee
   FROM order_shipments
   GROUP BY order_id),
     merchant_order_metrics AS
  (SELECT o.merchant_id,
          sum(o.base_amount) AS total_gross_sales,
          coalesce(sum(d.discount_amount), 0.00) AS total_discount_amount,
          coalesce(sum(s.shipping_fee), 0.00) AS total_shipping_fees
   FROM orders o
   LEFT JOIN order_level_discounts d ON o.order_id = d.order_id
   LEFT JOIN order_level_shipments s ON o.order_id = s.order_id
   GROUP BY o.merchant_id)
SELECT m.merchant_id,
       m.merchant_name,
       coalesce(mom.total_gross_sales, 0.00) AS total_gross_sales,
       coalesce(mom.total_discount_amount, 0.00) AS total_discount_amount,
       coalesce(mom.total_shipping_fees, 0.00) AS total_shipping_fees
FROM merchants m
LEFT JOIN merchant_order_metrics mom ON m.merchant_id = mom.merchant_id;
Golden Reference Ground Truth SQL (Runtime on 1M Rows: 6.05 ms)
WITH agg_discounts AS
  (SELECT order_id,
          sum(amount) AS total_discount
   FROM order_discounts
   GROUP BY order_id),
     agg_shipments AS
  (SELECT order_id,
          sum(shipping_fee) AS total_shipping
   FROM order_shipments
   GROUP BY order_id)
SELECT m.merchant_id,
       m.merchant_name,
       sum(o.base_amount) AS total_gross_sales,
       coalesce(sum(d.total_discount), 0.00) AS total_discount_amount,
       coalesce(sum(s.total_shipping), 0.00) AS total_shipping_fees
FROM merchants m
JOIN orders o ON m.merchant_id = o.merchant_id
LEFT JOIN agg_discounts d ON o.order_id = d.order_id
LEFT JOIN agg_shipments s ON o.order_id = s.order_id
GROUP BY m.merchant_id,
         m.merchant_name;
DuckDB Fixture Setup DDL & Data Population
CREATE TABLE merchants (merchant_id INT, merchant_name VARCHAR);


CREATE TABLE orders (order_id INT, merchant_id INT, base_amount decimal(10, 2));


CREATE TABLE order_discounts (discount_id INT, order_id INT, amount decimal(10, 2));


CREATE TABLE order_shipments (shipment_id INT, order_id INT, shipping_fee decimal(10, 2));


INSERT INTO merchants
VALUES (1, 'Acme Electronics');


INSERT INTO orders
VALUES (101, 1, 100.00);

-- 2 discounts applied to Order 101 ($10 and $5 = $15 total)

INSERT INTO order_discounts
VALUES (1, 101, 10.00),
       (2, 101, 5.00);

-- 2 split shipments for Order 101 ($4 and $6 = $10 total)

INSERT INTO order_shipments
VALUES (1, 101, 4.00),
       (2, 101, 6.00);

5. Query Results & Expected Ground Truth

Tabular Comparison
Actual Candidate Output (1,000 rows × 5 cols)
# merchant_id merchant_name total_gross_sales total_discount_amount total_shipping_fees
1 31 Acme Electronics 92.69 4.83 3.71
2 32 Acme Electronics 119.22 9.37 6.3
3 97 Acme Electronics 115.51 10.39 2.13
4 163 Acme Electronics 98.63 9.34 4.57
5 179 Acme Electronics 113.99 7.01 5.52
6 204 Acme Electronics 93.09 10.3 6.45
7 338 Acme Electronics 111.76 5 2.15
8 351 Acme Electronics 121.5 11.84 4.45
9 355 Acme Electronics 94.53 6.81 4.81
10 364 Acme Electronics 139.5 3.62 6.48
11 403 Acme Electronics 118.61 9.09 5.16
12 440 Acme Electronics 95.5 8.88 3.25
13 479 Acme Electronics 109.52 8.13 3.33
14 480 Acme Electronics 83.99 10.21 3.89
15 522 Acme Electronics 110.12 7.71 2.53
16 617 Acme Electronics 111.08 12.58 1
17 620 Acme Electronics 95.24 6.17 4.46
18 636 Acme Electronics 111.45 3.75 4.42
19 681 Acme Electronics 69.56 9.35 4.03
20 733 Acme Electronics 99.75 10.81 7.01
21 738 Acme Electronics 117.04 3.89 2.91
22 789 Acme Electronics 110.6 7.56 3.66
23 918 Acme Electronics 99.24 6.97 1.67
24 933 Acme Electronics 116.51 9.18 5.77
25 939 Acme Electronics 104.92 5.71 4.61
Showing first 25 of 1,000 total rows.
Expected Golden Output (999 rows × 5 cols)
# merchant_id merchant_name total_gross_sales total_discount_amount total_shipping_fees
1 2 Acme Electronics 91.54 8.25 4.7
2 5 Acme Electronics 90.5 8.83 5.41
3 9 Acme Electronics 97.54 5.08 4.01
4 10 Acme Electronics 110.28 6.79 6.39
5 19 Acme Electronics 90.41 8.24 4.4
6 20 Acme Electronics 111.25 9.58 3.42
7 22 Acme Electronics 87.27 6 4.06
8 24 Acme Electronics 107.25 14.07 6.7
9 27 Acme Electronics 96.76 14.68 4
10 42 Acme Electronics 117.51 7.85 1.4
11 44 Acme Electronics 97.93 8.3 5.22
12 49 Acme Electronics 88.7 7.37 5.28
13 53 Acme Electronics 87.56 4.77 4.09
14 79 Acme Electronics 110.47 7.95 2.86
15 84 Acme Electronics 84.88 8.09 4.64
16 88 Acme Electronics 83.44 10.31 7.32
17 89 Acme Electronics 110.14 8.73 6.36
18 98 Acme Electronics 122.46 7.25 4.66
19 106 Acme Electronics 91.53 2.66 4.61
20 117 Acme Electronics 104.09 9.72 5.57
21 125 Acme Electronics 104.44 7.99 3.13
22 137 Acme Electronics 74.95 5.93 6.2
23 140 Acme Electronics 109.34 2.21 6.6
24 141 Acme Electronics 100.44 12.53 4.92
25 145 Acme Electronics 117.68 9.2 3.45
Showing first 25 of 999 total rows.
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