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Documentation Index

Fetch the complete documentation index at: https://webscraping.titannet.io/docs/llms.txt

Use this file to discover all available pages before exploring further.

The difference is not only when work runs. It shapes how you think about task ownership, execution history, and how downstream systems consume datasets over time.

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One-off runs

One-off execution is best for:
  • Ad hoc extraction or discovery
  • Testing a new schema or modular plan
  • Running a job on demand
  • Collecting a one-time snapshot
The usual pattern is:
  1. Create a task
  2. Trigger execution manually
  3. Monitor the execution
  4. Download results

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Scheduled runs

Scheduled execution is best for:
  • Ongoing monitoring
  • Recurring collection
  • Change detection
  • Dataset accumulation over time
In a scheduled workflow, the task remains stable while the platform creates new executions according to the configured schedule.

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Why the distinction matters

Both paths use the same task and execution objects, but they serve different operating models:
  • One-off tasks emphasize immediate output.
  • Scheduled tasks emphasize repeatability and a history of runs.
That distinction matters for downstream consumers: one-off work is often consumed as a single export, while scheduled work is often consumed as a time series of executions or datasets.

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Triggering execution

Manual task runs typically use: 
curl -X POST "$TITAN_API_URL/api/v1/tasks/$TASK_ID/run" \
  -H "Authorization: Bearer $TITAN_TOKEN"
You can also trigger work through execution endpoints, depending on your integration style (see the API Reference).

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Scheduling guidance

When using schedules:
  • Keep the output schema stable when consumers depend on a fixed shape.
  • Keep the task identity stable so history stays grouped.
  • Treat executions as the units of runtime history.
That yields a clearer long-term dataset model and simpler monitoring.

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Next steps