✔ PASS Graph & Hierarchy task_081_Q81_incident_root_cause_dependency_chain Scaled: 1M+ Records Ordered: Yes

Causal Microservice Incident Root-Cause Dependency Chain

Candidate Latency (1M Rows)

0.76 ms

(1.1x faster than golden)

Golden Latency (1M Rows)

0.87 ms

DuckDB in-memory engine

Underlying Data Volume

7 rows

312 B across 2 tables

Evaluated Model

gemini-3.8-flash-low

Gen Latency: 10.8s

✔ 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

When a user checkout fails, it could be caused by payments, auth, database, or network. Tracing dependencies upwards from the affected user service identifies the underlying root-cause service failure.

⚡ The Frontier Trap (Why LLMs Fail)

Diagnosing downstream symptoms rather than the root upstream failed dependency.

Prompt / User Request Given to Model

When a user checkout fails, it could be caused by payments, auth, database, or network. Tracing dependencies upwards from the affected user service identifies the underlying root-cause service failure.

Execute the required analytical SQL calculation in DuckDB.
Expected output columns: depth, service, upstream_service, upstream_status, upstream_error_rate.
Results should be ordered as specified.

2. Underlying Tables, Row Counts & Data Volume

2 Tables (7 rows • 312 B)

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

📊 service_alerts 4 rows 192 B Data Volume

service (VARCHAR) status (VARCHAR) error_rate (DOUBLE)

Data Preview (showing sample rows from 4 total rows):

#	service	status	error_rate
1	checkout-api	DEGRADED	0.15
2	payment-gateway	DEGRADED	0.2
3	fraud-evaluator	DEGRADED	0.85
4	redis-cache	CRITICAL_DOWN	1

📊 service_mesh 3 rows 120 B Data Volume

service (VARCHAR) depends_on (VARCHAR)

Data Preview (showing sample rows from 3 total rows):

#	service	depends_on
1	checkout-api	payment-gateway
2	payment-gateway	fraud-evaluator
3	fraud-evaluator	redis-cache

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

✂️ TOP_N 3.6%

Latency: 0.03 ms

Rows In/Out: 1

Data Vol: 8,252 B

📐 PROJECTION 0.1%

Latency: 0.00 ms

Rows In/Out: 3

Data Vol: 0.01 MB

📁 Projections: ['depth', 'service'...

⚡ HASH_JOIN 17.4%

Latency: 0.13 ms

Rows In/Out: 3

Data Vol: 0.01 MB

🔄 RECURSIVE_CTE 56.9%

Latency: 0.43 ms

Rows In/Out: 3

Data Vol: 108 B

📐 PROJECTION 0.5%

Latency: 0.00 ms

Rows In/Out: 1

Data Vol: 36 B

📁 Projections: ['service', 'depend...

🔍 TABLE_SCAN 3.3%

Latency: 0.03 ms

Rows In/Out: 1

Data Vol: 32 B

📁 memory.main.service_mesh

📐 PROJECTION 0.8%

Latency: 0.01 ms

Rows In/Out: 2

Data Vol: 0.01 MB

📁 Projections: ['service', 'depend...

⚡ HASH_JOIN 13.0%

Latency: 0.10 ms

Rows In/Out: 2

Data Vol: 0.01 MB

🔍 RECURSIVE_CTE_SCAN 0.5%

Latency: 0.00 ms

Rows In/Out: 3

Data Vol: 108 B

🔍 TABLE_SCAN 1.2%

Latency: 0.01 ms

Rows In/Out: 3

Data Vol: 96 B

📁 memory.main.service_mesh

🔍 TABLE_SCAN 2.7%

Latency: 0.02 ms

Rows In/Out: 4

Data Vol: 160 B

📁 memory.main.service_alerts

Operator	Cost %	Latency	Processed Rows	Data Volume	Details / Table	Status
RECURSIVE_CTE	56.9%	0.43 ms	3	0.00 MB	-	Normal
HASH_JOIN	17.4%	0.13 ms	3	0.01 MB	-	Normal
HASH_JOIN	13.0%	0.10 ms	2	0.01 MB	-	Normal
TOP_N	3.6%	0.03 ms	1	0.01 MB	-	Normal
TABLE_SCAN	3.3%	0.03 ms	1	0.00 MB	memory.main.service_mesh	Normal
TABLE_SCAN	2.7%	0.02 ms	4	0.00 MB	memory.main.service_alerts	Normal
TABLE_SCAN	1.2%	0.01 ms	3	0.00 MB	memory.main.service_mesh	Normal
PROJECTION	0.8%	0.01 ms	2	0.01 MB	Projections: ['service', 'depends_on', '(depth +	Normal
PROJECTION	0.5%	0.00 ms	1	0.00 MB	Projections: ['service', 'depends_on', 'depth']	Normal
RECURSIVE_CTE_SCAN	0.5%	0.00 ms	3	0.00 MB	-	Normal
PROJECTION	0.1%	0.00 ms	3	0.01 MB	Projections: ['depth', 'service', 'upstream_serv	Normal

4. Queries Executed (Candidate vs Golden Ground Truth)

Beautified & Syntax Highlighted

WITH RECURSIVE upstream_chain AS
  (SELECT service,
          depends_on,
          1 AS depth
   FROM service_mesh
   WHERE service = 'checkout-api'
   UNION ALL SELECT m.service,
                    m.depends_on,
                    c.depth + 1
   FROM upstream_chain c
   JOIN service_mesh m ON c.depends_on = m.service)
SELECT c.depth,
       c.service,
       c.depends_on AS upstream_service,
       a.status AS upstream_status,
       a.error_rate AS upstream_error_rate
FROM upstream_chain c
JOIN service_alerts a ON c.depends_on = a.service
ORDER BY c.depth DESC
LIMIT 1;

-- Trace dependencies backwards from checkout-api to find root critical failure
 WITH RECURSIVE upstream_chain AS
  (SELECT service,
          depends_on,
          1 AS depth
   FROM service_mesh
   WHERE service = 'checkout-api'
   UNION ALL SELECT m.service,
                    m.depends_on,
                    c.depth + 1
   FROM upstream_chain c
   JOIN service_mesh m ON c.depends_on = m.service)
SELECT c.depth,
       c.service,
       c.depends_on AS upstream_service,
       a.status AS upstream_status,
       a.error_rate AS upstream_error_rate
FROM upstream_chain c
JOIN service_alerts a ON c.depends_on = a.service
ORDER BY c.depth DESC
LIMIT 1;

CREATE TABLE service_mesh (service VARCHAR, depends_on VARCHAR);


CREATE TABLE service_alerts (service VARCHAR, status VARCHAR, error_rate DOUBLE);


INSERT INTO service_mesh
VALUES ('checkout-api', 'payment-gateway'),
       ('payment-gateway', 'fraud-evaluator'),
       ('fraud-evaluator', 'redis-cache');


INSERT INTO service_alerts
VALUES ('checkout-api', 'DEGRADED', 0.15),
       ('payment-gateway', 'DEGRADED', 0.20),
       ('fraud-evaluator', 'DEGRADED', 0.85),
       ('redis-cache', 'CRITICAL_DOWN', 1.00);

5. Query Results & Expected Ground Truth

Tabular Comparison

Actual Candidate Output (1 rows × 5 cols)

#	depth	service	upstream_service	upstream_status	upstream_error_rate
1	3	fraud-evaluator	redis-cache	CRITICAL_DOWN	1

Expected Golden Output (1 rows × 5 cols)

#	depth	service	upstream_service	upstream_status	upstream_error_rate
1	3	fraud-evaluator	redis-cache	CRITICAL_DOWN	1

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