README.md

---
license: apache-2.0
language:
- en
tags:
- data-pipelines
- root-cause-analysis
- evaluation
- healthcare
- etl
- enterprise
pretty_name: Toph Eval — Pipeline Failure Diagnosis Benchmark
size_categories:
- n<1K
---

# Toph Eval · Pipeline Failure Diagnosis Benchmark

**Version:** 0.1  
**Maintainer:** Virgo Machine Labs  
**License:** Apache 2.0  
**Taxonomy:** [github.com/vaishsagar-cfo/toph-eval](https://github.com/vaishsagar-cfo/toph-eval)

---

## Overview

Toph Eval is a structured evaluation benchmark for automated root cause analysis (RCA) of data pipeline failures in enterprise health technology environments.

Standard LLM benchmarks address single-step tasks (question answering, multiple choice, text completion). Pipeline RCA is a multi-step reasoning task: a system must read logs across multiple heterogeneous tools simultaneously, construct a causal graph of pipeline execution, and distinguish the origin of a failure from the component at which the error surfaces. No existing general-purpose benchmark addresses this task category.

This dataset provides evaluation scenarios for systems performing automated pipeline RCA. Each scenario includes realistic simulated logs from a 4-tool enterprise ETL pipeline and a structured ground truth answer key scored across four evaluation dimensions.

---

## Pipeline Architecture

All scenarios are built on the Optum pharmacy claims nightly extract pipeline:

| Phase | Tool | Description |
|---|---|---|
| Orchestrate | Jenkins | Triggers pipeline, monitors stage execution |
| Extract | Azure Data Factory | Pulls from source systems, lands raw files in ADLS Gen2 |
| Transform | Databricks | Normalizes clinical codes, applies business rules |
| Load | Azure Synapse Analytics | Writes clean records to data warehouse |

Log formats reflect real production tool output: Jenkins produces plain text logs; ADF, Databricks, and Synapse produce structured JSON (as exported to Azure Monitor).

---

## Dataset Structure

```
data/
  {FAILURE_CODE}_{pipeline}_{scenario_number}/
    success_run/
      jenkins.log       # Plain text orchestration log (baseline)
      adf.json          # Azure Data Factory structured log
      databricks.json   # Databricks job event log
      synapse.json      # Azure Synapse Analytics pipeline log
    failure_run/
      jenkins.log
      adf.json
      databricks.json
      synapse.json
    ground_truth.json   # Answer key in toph-eval scoring format
```

Each scenario includes a **success run** (normal operation baseline) and a **failure run** (the scenario under evaluation). The success run enables duration-based anomaly detection and establishes expected row counts.

---

## Scenarios (v0.1)

| Scenario ID | Failure Code | Category | Description |
|---|---|---|---|
| DEPEND_002_optum_pharmacy_001 | `DEPEND_002` | Upstream Dependency | Silent zero-row extract — all statuses Succeeded |
| PERM_001_optum_pharmacy_001 | `PERM_001` | Access & Permissions | INSERT permission denied on Synapse target table |
| AUTH_003_optum_pharmacy_001 | `AUTH_003` | Authentication | Expired client secret in Key Vault — Databricks auth failure |
| SCHEMA_002_optum_pharmacy_001 | `SCHEMA_002` | Schema & Data Contract | Column removed from upstream source schema |
| VOLUME_001_optum_pharmacy_001 | `VOLUME_001` | Data Volume & Quality | Zero rows from direct extract, HTTP 200, no error raised |
| CONN_006_optum_pharmacy_001 | `CONN_006` | Connectivity | EHR vendor API rate limit hit mid-extract |
| RESOURCE_001_optum_pharmacy_001 | `RESOURCE_001` | Resource Exhaustion | Spark executor OOM on month-end batch volume |
| ORCH_001_optum_pharmacy_001 | `ORCH_001` | Orchestration | DST timezone shift triggers duplicate pipeline run |
| FILE_003_optum_pharmacy_001 | `FILE_003` | File & Format | Upstream payer changed EDI file naming convention |
| HEALTH_001_optum_pharmacy_001 | `HEALTH_001` | Healthcare-Specific | ICD-10 FY2025 codes reference table not updated before October 1 |

---

## Evaluation Dimensions

Each scenario's `ground_truth.json` scores across four dimensions, assessed independently:

| Dimension | Definition | Weight |
|---|---|---|
| **System identification** | Correctly identifies the infrastructure component where failure manifested | Standard |
| **Error classification** | Correctly assigns the failure type code from the toph-eval taxonomy | Standard |
| **Causal chain accuracy** | Correctly traces failure to its origin, not just where error surfaced | **Highest** |
| **Fix correctness** | Recommended remediation correctly addresses the identified root cause | Standard |

Causal chain accuracy is weighted highest. It is the dimension that distinguishes root cause analysis from surface-level error detection, and the dimension on which general-purpose language models without domain-specific tooling perform worst.

---

## Ground Truth Format

```json
{
  "scenario_id": "DEPEND_002_optum_pharmacy_001",
  "taxonomy_version": "0.1",
  "failure_code": "DEPEND_002",
  "failure_category": "Upstream Dependency Failure",
  "description": "Upstream job completed with success status but wrote zero rows",
  "evaluation_dimensions": {
    "system_identification": { "correct_answer": "..." },
    "error_classification": { "correct_answer": "DEPEND_002" },
    "causal_chain_accuracy": {
      "correct_answer": ["step 1", "step 2", "..."],
      "failure_originates_at": "...",
      "error_surfaces_at": "..."
    },
    "fix_correctness": {
      "correct_answer": "...",
      "preventive_fix": "..."
    }
  },
  "diagnostic_signals": [...],
  "common_misdiagnoses": [...]
}
```

---

## Why Healthcare-Specific Scenarios Matter

The `HEALTH_001` scenario (ICD-10 code set update) represents a failure mode with no analog in general-purpose pipeline monitoring literature. Healthcare pipelines operate under annual CMS code set update cycles (ICD-10 effective October 1, CPT effective January 1, NDC updated quarterly). A pipeline that ran correctly on September 30 will fail on October 1 if reference tables are not updated before the effective date. General-purpose LLMs without domain-specific tooling consistently misclassify this as a schema failure or data quality issue rather than a healthcare code set versioning failure.

---

## Taxonomy

The full taxonomy of 63 failure types across 10 categories is maintained at:  
[github.com/vaishsagar-cfo/toph-eval/blob/main/TAXONOMY.md](https://github.com/vaishsagar-cfo/toph-eval/blob/main/TAXONOMY.md)

This dataset covers one scenario per category. Contributions adding additional scenarios are welcomed via the GitHub repository.

---

## Citation

```
Sagar, V. (2026). Pipeline Failure Taxonomy and Evaluation Benchmark
for Enterprise Health Technology Data Pipelines (Version 0.1).
Virgo Machine Labs.
https://github.com/vaishsagar-cfo/toph-eval
```

---

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