Running independent jobs in parallel

Context — sales and inventory can load at the same time

The task is currently manual and its assumptions are not recorded. Hydra turns it into files that can be reviewed and rerun.

Where things stand

• The current result is fragile. Its assumptions are split between tools, clicks and memory.
• Reruns are uncertain. The write mode or orchestration rule is not visible beside the data.
• Evidence is missing. A colleague cannot compare a declared rule with a concrete before and after state.

The question

How do you make depends_on explicit in workflow.yaml and verify the resulting execution states?

• Keep jobs independently runnable
• Give every workflow step a unique name
• Validate the graph before execution
• Read every step status in the summary

The solution in one line

A small workflow.yaml, one validation command and one run with observable step states.

Steps

1. prepare the jobs and boundary

1. Name the work the workflow will coordinate.
2. Compare the file with the explanation in the workbench.
3. Record the shown check before moving to the next step.

workflow.yaml
jobs/
  sales/
  inventory/
  publish/

2. declare depends_on

1. Write the trigger, steps and policy.
2. Compare the file with the explanation in the workbench.
3. Record the shown check before moving to the next step.

workflow:
  version: "1.0"
  name: depends_on_workshop
  description: "Running independent jobs in parallel"
  trigger:
    type: manual
  steps:
    - name: sales
      type: job
      job: ./jobs/sales
    - name: inventory
      type: job
      job: ./jobs/inventory
    - name: publish
      type: job
      job: ./jobs/publish
      depends_on: [sales, inventory]

3. validate the graph

1. Catch missing jobs, duplicate names and invalid dependencies.
2. Compare the file with the explanation in the workbench.
3. Record the shown check before moving to the next step.

$ hdrctl workflow validate workflow.yaml

✅ Workflow valid: depends_on_workshop
Steps: 3

4. run the workflow

1. Execute and follow step states.
2. Compare the file with the explanation in the workbench.
3. Record the shown check before moving to the next step.

$ hdrctl workflow run workflow.yaml

sales      succeeded ┐
inventory  succeeded ├─ parallel
publish    succeeded ┘ after both

5. read the orchestration result

1. Distinguish success, skip, retry and continuation.
2. Compare the file with the explanation in the workbench.
3. Record the shown check before moving to the next step.

sales      succeeded ┐
inventory  succeeded ├─ parallel
publish    succeeded ┘ after both

Expected result

• The workflow validates before execution.
• sales succeeded ┐
• The run summary explains every terminal state.

Reading the results

Read the summary as a graph, not a flat log. Dependencies form a DAG. Steps with no unmet dependency are eligible to run together.

What the counters do—and do not—prove

They prove how many records entered and left this run. They do not replace checking the target schema, the business meaning of values or the reason a workflow step was skipped.

The rule to carry forward

Dependencies form a DAG. Steps with no unmet dependency are eligible to run together.

Did we answer the question?

Before and after

• Before — sales and inventory can load at the same time requires a person to remember the order, options and checks.
• After — one reviewed manifest and one command produce the same observable result.
• What is really gained — The durable gain is the contract: a colleague can read the configuration, reproduce the run and challenge the assumptions.

Close by

• sources.yaml
• transformations.yaml
• destinations.yaml
• workflow.yaml
• Tutorial — your first job
• All Guide workshops