dpla-run-ingest

name: dpla-run-ingest description: Execute a single-hub or manual ingest by following the correct runbook and scripts. Use when the user says run ingest for [hub], harvest [hub], remap [hub], or run the full pipeline for [hub]. Ensures harvest type is identified, correct runbook and scripts are used, and outputs are verified.

DPLA Run Ingest

Purpose

Run an ingest for a specific hub using the right runbook and scripts, then verify output. Use this for single-hub or manual runs (for multi-hub/scheduled runs, use the orchestrator instead).

When to Use

• "Run ingest for [hub]"
• "Harvest [hub]"
• "Remap [hub]"
• "Full pipeline for [hub]"
• "Start harvest for [hub]"

Environment: Scripts that source common.sh (harvest.sh, ingest.sh, remap.sh, etc.) automatically load $I3_HOME/.env when present, so JAVA_HOME, DPLA_DATA, I3_CONF, SLACK_WEBHOOK, etc. are set before the JAR is built or the pipeline runs. You do not need to run source .env separately. Full checklist: AGENTS.md § Environment and build.

Checklist

0. JAR is built automatically: When you run ./scripts/harvest.sh (or ingest.sh, remap.sh, etc.), run_entry in common.sh runs sbt assembly if the JAR is missing or if any Scala source is newer than the JAR. So "harvest indiana" will use current code without a separate build step. (You can still run sbt assembly first to avoid a build delay on the first harvest.)
1. Identify the hub (e.g. from the user message).
2. Get harvest type from i3.conf ($I3_CONF, default ~/dpla/code/ingestion3-conf/i3.conf): <hub>.harvest.type. Values: localoai, api, file, nara.file.delta.
3. Pick the runbook: See runbooks/README.md for harvest-type to runbook mapping.
4. Run the scripts from the runbook (see scripts/SCRIPTS.md). Examples:
   • Full pipeline: ./scripts/ingest.sh <hub>
   • Harvest only: ./scripts/harvest.sh <hub>
   • Remap (mapping + enrich + jsonl): ./scripts/remap.sh <hub>
   • NARA: ./scripts/harvest/nara-ingest.sh <nara-export.zip>
5. Verify outputs: _SUCCESS in the step output dirs; _MANIFEST / _SUMMARY for counts.
6. S3 sync when the runbook says so: ./scripts/s3-sync.sh <hub>.
7. On failure: Post to #tech-alerts or email tech@dp.la with hub, stage, and error or path to escalation report.

Critical Rules

• NARA / Smithsonian: Do not run the standard ingest pipeline without their dedicated runbooks (NARA: delta merge; Smithsonian: preprocessing e.g. fix-si.sh).
• Output path: All Scala --output must be $DPLA_DATA (the data root), never $DPLA_DATA/<hub>. Scripts handle this. OutputHelper builds paths as rootPath / shortName / activity / timestamp-schema.
• Python/scripts: Use ./venv/bin/python for Python; use ./scripts/ scripts from repo root. AWS: --profile dpla.

Verification Commands

ls $DPLA_DATA/<hub>/harvest/<timestamped-dir>/_SUCCESS
ls $DPLA_DATA/<hub>/mapping/<timestamped-dir>/_SUCCESS
ls $DPLA_DATA/<hub>/jsonl/<timestamped-dir>/_SUCCESS

cat $DPLA_DATA/<hub>/harvest/<timestamped-dir>/_MANIFEST
cat $DPLA_DATA/<hub>/mapping/<timestamped-dir>/_SUMMARY

Incomplete runs (e.g. _temporary but no _SUCCESS) should be deleted before retrying.

Before/after checklist

Before running:

• If the run will use the pipeline, run sbt assembly so the fat JAR reflects the current code (or confirm no Scala changes since last build).
• Confirm hub and harvest type; open the correct runbook (or scripts/SCRIPTS.md if runbooks are not yet available).
• If using the orchestrator, ensure SLACK_WEBHOOK is set (or plan to email tech@dp.la on failure).

After a run:

• If any hub failed: post failure summary to #tech-alerts or email tech@dp.la; include stage and reference escalation report if present.
• If the run completed: completion notification is sent by the orchestrator when applicable; if you ran only scripts, consider notifying status to #tech-alerts or tech@dp.la if that's standard.

Resuming failed steps

1. Check which steps completed (look for _SUCCESS files in harvest/, mapping/, enrichment/, jsonl/).
2. Re-run only the failed step and later steps. Example: if mapping succeeded but enrichment failed, run ./scripts/enrich.sh <hub> then ./scripts/jsonl.sh <hub>.
3. For the full pipeline (IngestRemap), it must be re-run from scratch since it does mapping+enrichment+jsonl in one Spark application.

Key References