✖ VALUE_MISMATCH Graph & Hierarchy task_086_Q86_multiparent_allocation_cost_rollup Scaled: 1M+ Records Ordered: Yes

Multi-Parent Shared Infrastructure Cost Allocation Rollup

Candidate Latency (1M Rows)

0.21 ms

(1.1x slower than golden)

Golden Latency (1M Rows)

0.20 ms

DuckDB in-memory engine

Underlying Data Volume

3 rows

132 B across 2 tables

Evaluated Model

gemini-3.8-flash-low

Gen Latency: 4.1s

⚠️ Failure Analysis (VALUE_MISMATCH)

Reason: Data discrepancy: DataFrame.iloc[:, 1] (column name="child_node") are different

Diverged Column: child_node at row index 0
Expected: PRODUCT_SEARCH | Actual: PRODUCT_CHECKOUT

1. Analytical Significance & What It Means

Scenario & Frontier Trap

Business Scenario & Problem

Cloud infrastructure (e.g. shared Kubernetes cluster) is co-owned by multiple products with fractional split percentages. Costs must cascade down to consumer business units.

⚡ The Frontier Trap (Why LLMs Fail)

Failing to multiply split percentages down the DAG results in allocated costs exceeding 100% of actual spend.

Prompt / User Request Given to Model

Cloud infrastructure (e.g. shared Kubernetes cluster) is co-owned by multiple products with fractional split percentages. Costs must cascade down to consumer business units.

Execute the required analytical SQL calculation in DuckDB.
Expected output columns: parent_node, child_node, allocation_pct, raw_parent_cost, allocated_cost.
Results should be ordered as specified.

2. Underlying Tables, Row Counts & Data Volume

2 Tables (3 rows • 132 B)

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

📊 cost_allocations 2 rows 96 B Data Volume

parent_node (VARCHAR) child_node (VARCHAR) allocation_pct (DOUBLE)

Data Preview (showing sample rows from 2 total rows):

#	parent_node	child_node	allocation_pct
1	K8S_SHARED	PRODUCT_SEARCH	0.6
2	K8S_SHARED	PRODUCT_CHECKOUT	0.4

📊 cost_nodes 1 rows 36 B Data Volume

node_id (VARCHAR) monthly_cost (DECIMAL(10,2))

Data Preview (showing sample rows from 1 total rows):

#	node_id	monthly_cost
1	K8S_SHARED	10,000

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: 0.21 ms) Nodes consuming ≥ 20% of query runtime are highlighted with glowing borders

📐 PROJECTION 0.6%

Latency: 0.00 ms

Rows In/Out: 2

Data Vol: 4,208 B

📁 Projections: ['__internal_decomp...

⏳ ORDER_BY 44.5%

Latency: 0.09 ms

Rows In/Out: 2

Data Vol: 4,208 B

📐 PROJECTION 2.8%

Latency: 0.01 ms

Rows In/Out: 2

Data Vol: 4,208 B

📁 Projections: ['__internal_compre...

📐 PROJECTION 4.3%

Latency: 0.01 ms

Rows In/Out: 2

Data Vol: 4,208 B

📁 Projections: ['parent_node', 'ch...

⚡ HASH_JOIN 38.9%

Latency: 0.08 ms

Rows In/Out: 2

Data Vol: 4,192 B

🔍 TABLE_SCAN 4.7%

Latency: 0.01 ms

Rows In/Out: 2

Data Vol: 80 B

📁 memory.main.cost_allocations

🔍 TABLE_SCAN 4.2%

Latency: 0.01 ms

Rows In/Out: 1

Data Vol: 24 B

📁 memory.main.cost_nodes

Operator	Cost %	Latency	Processed Rows	Details / Table	Status
ORDER_BY	44.5%	0.09 ms	2	-	Normal
HASH_JOIN	38.9%	0.08 ms	2	-	Normal
TABLE_SCAN	4.7%	0.01 ms	2	memory.main.cost_allocations	Normal
PROJECTION	4.3%	0.01 ms	2	Projections: ['parent_node', 'child_node', 'allo	Normal
TABLE_SCAN	4.2%	0.01 ms	1	memory.main.cost_nodes	Normal
PROJECTION	2.8%	0.01 ms	2	Projections: ['__internal_compress_string_uhugei	Normal
PROJECTION	0.6%	0.00 ms	2	Projections: ['__internal_decompress_string(#0)'	Normal

4. Queries Executed (Candidate vs Golden Ground Truth)

Beautified & Syntax Highlighted

SELECT ca.parent_node,
       ca.child_node,
       ca.allocation_pct,
       cn.monthly_cost AS raw_parent_cost,
       round(cn.monthly_cost * cast(ca.allocation_pct AS decimal(10, 4)), 2) AS allocated_cost
FROM cost_allocations AS ca
JOIN cost_nodes AS cn ON ca.parent_node = cn.node_id
ORDER BY ca.parent_node,
         ca.child_node;

-- Allocate parent cost to child business units

SELECT a.parent_node,
       a.child_node,
       a.allocation_pct,
       n.monthly_cost AS raw_parent_cost,
       round(n.monthly_cost * a.allocation_pct, 2) AS allocated_cost
FROM cost_allocations a
JOIN cost_nodes n ON a.parent_node = n.node_id
ORDER BY allocated_cost DESC;

CREATE TABLE cost_nodes (node_id VARCHAR, monthly_cost decimal(10, 2));


CREATE TABLE cost_allocations (parent_node VARCHAR, child_node VARCHAR, allocation_pct DOUBLE);


INSERT INTO cost_nodes
VALUES ('K8S_SHARED', 10000.00);


INSERT INTO cost_allocations
VALUES ('K8S_SHARED', 'PRODUCT_SEARCH', 0.60),
       ('K8S_SHARED', 'PRODUCT_CHECKOUT', 0.40);

5. Query Results & Expected Ground Truth

Tabular Comparison

Actual Candidate Output (2 rows × 5 cols)

#	parent_node	child_node	allocation_pct	raw_parent_cost	allocated_cost
1	K8S_SHARED	PRODUCT_CHECKOUT	0.4	10,000	4,000
2	K8S_SHARED	PRODUCT_SEARCH	0.6	10,000	6,000

Expected Golden Output (2 rows × 5 cols)

#	parent_node	child_node	allocation_pct	raw_parent_cost	allocated_cost
1	K8S_SHARED	PRODUCT_SEARCH	0.6	10,000	6,000
2	K8S_SHARED	PRODUCT_CHECKOUT	0.4	10,000	4,000

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