othersyntheticqadagworkflowschedulingtopological-sortcsvpythonexpected-results

Synthetic DAG scheduling QA: 120 workflows, 2,400 jobs, expected starts/finishes

Artificial workflow QA snapshot v1.0; no real-world time coverage · Updated Nov 10, 2026

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Sample structure: Not assessed

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Seller: Alan

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Source, license and coverage

Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.

License: Internal testing/demos only. No redistribution, resale, sublicensing or model training. No warranty, updates or support SLA. Full terms in README.txt.
Source / creator: Alan - original synthetic fixture prepared with AI assistance
Collection method: Original deterministic Python generator with AI assistance; fixed seed20261110; no source dataset or observed business records.
Coverage / snapshot: Artificial workflow QA snapshot v1.0; no real-world time coverage
Data last updated: Not documented
Update schedule: Fixed v1.0 snapshot. No future updates or support promised.

Artificial topology/durations; one root and20jobs per workflow; unlimited workers. No resource contention, failures or retries. Labels encode topological order. QA fixture only, no statistical fidelity or real-system benchmark claim.

Sample structure score: Not assessed

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About this data

Test a DAG parser or earliest-start scheduler against supplied expected results. AI-assisted artificial QA fixture, not real workflow telemetry or a calibrated benchmark. ZIP: 120 isolated workflows x 20 jobs; jobs.csv (2,400 rows), dependencies.csv (4,363 finish-to-start edges), expected_schedule.csv (2,400 start/finish pairs), schema, deterministic Python standard-library generator, independent validator and 16 corruption/integrity tests. Clean extraction passes tests. A separate recursive calculation matches all 2,400 expected schedules; rebuilding reproduces identical CSV bytes. Suggested checks: import all three CSVs, shuffle rows to test topological discovery, calculate earliest starts and finishes, then compare by workflow/job ID. The supplied labels encode generation order, so unshuffled labels are not a test of discovering an unknown ordering. Model: positive integer durations 1-1,800 fictional seconds, one root per workflow, unlimited workers and no cross-workflow edges. No resource limits, queues, retries, failures, priorities or calendars. Arbitrary topology/durations, not representative business data or an ML benchmark. No source customer records. Internal lawful testing/demos only; no redistribution, resale, sublicensing or model training. No warranty, updates, support SLA or production compatibility. Full terms in README.txt. Automatic public samples remain disabled; the full files are paid.

Retrieve with your agent or Python

Create an account and configure DATABAZAAR_API_KEY. This example retrieves free or already purchased data; it never makes a purchase. For a multi-file dataset, choose a file index from the manifest.

Download the Python example

python3 retrieve-dataset.py bb8d7d0d-e4c9-4a1c-8051-066cb712eeef --output dataset.bin

Full supplier documentation

AI-assisted artificial QA fixture for testing DAG ingestion, topological sorting and earliest-start scheduling. Not real workflow telemetry or a calibrated benchmark. Includes 120 isolated workflows with 20 jobs each: jobs.csv (2,400 rows), dependencies.csv (4,363 edges), expected_schedule.csv (2,400 expected starts and finishes), schema, deterministic Python standard-library generator, independent validator and 16 integrity tests. Tests pass from a clean ZIP extraction. A second recursive calculation matches all 2,400 expected schedules; deterministic rebuilding produces identical CSV bytes. Model: integer durations 1-1,800 seconds, one root per workflow, unlimited parallel workers, finish-to-start dependencies. No queues, retries, failures, calendars, priorities, resource limits or cross-workflow edges. Offsets are fictional seconds, not real timestamps. No personal records. Topology and durations are arbitrary, not representative of real systems. Job labels encode generation order; shuffle rows when testing discovery of topological order. Use internally for lawful testing and demos. No redistribution, resale or sublicensing of files or generated records. No warranty, future updates, support SLA or production-system compatibility. No claims of suitability for ML training or real-world performance evaluation. Full package is paid; automatic public samples are disabled.

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For AI Agents

Via MCP Server

# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
  "mcpServers": {
    "databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
  }
}

# 2. Your agent can then call:
search_datasets({ query: "Synthetic DAG scheduling QA: 1" })
// Found: bb8d7d0d-e4c9-4a1c-8051-066cb712eeef
buy_now({ dataset_id: "bb8d7d0d-e4c9-4a1c-8051-066cb712eeef" })  // uses your saved card

Via REST API

# Authenticate with your API key:
curl https://api.databazaar.io/datasets/bb8d7d0d-e4c9-4a1c-8051-066cb712eeef \
  -H "Authorization: Bearer YOUR_API_KEY"

# Purchase:
curl -X POST https://api.databazaar.io/datasets/bb8d7d0d-e4c9-4a1c-8051-066cb712eeef/buy \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"dataset_id": "bb8d7d0d-e4c9-4a1c-8051-066cb712eeef"}'