Nightly PIM Sync Times Out at Row 6,200
Corvus Parts migrates product content from a PIM to SuiteCommerce Advanced. The PIM exports a 20,000-row CSV to an SFTP location nightly at 00:30. A SuiteScript 1.0 scheduled script downloads the file, loops every row, loads the item record, updates 14 fields, and upserts a custom record for marketing copy. The job must finish by 02:00. In production it starts at 01:00, reaches row 6,200 by 02:00, then hits the governance limit; the next night it restarts from row 1. Partial updates leave about 1,800 items with new copy but old specifications. The file contains 22 columns, including a pim_id, locale, and effective_date; about 4% of rows are invalid. The team wants to move to SuiteScript 2.1 and cannot add an external database. There are 9,000 active items and 11,000 inactive items; only active items need updating. A full run currently takes 4.5 hours.
1. Why does the SuiteScript 1.0 loop fail, and what execution pattern should replace it?
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Why it fails. The script performs one record load and one record save per row inside a single scheduled execution. Each load and save consumes governance units, and the script has no checkpoint. When governance is exhausted, the execution stops and the next night starts again at row 1, so the same early rows are updated repeatedly while later rows are never reached. The 4.5-hour full run is a symptom of sequential per-record work, not a tuning problem.
Secondary defects. The script processes all 20,000 rows even though only 9,000 active items need updating. It has no concept of an unchanged row, so it rewrites data that did not change. It also has no error queue, so the 4% invalid rows either stop the run or are silently skipped without a record of why.
Pattern to use. Replace the single loop with a staged pipeline. First, land the CSV into a staging custom record in bounded chunks, keyed by pim_id, locale, and effective_date. Then process staging records with Map/Reduce, where the map stage handles a small number of records per invocation and the reduce stage summarizes results. Map/Reduce is designed for large input sets and gives each invocation a fresh governance budget. A scheduled script can orchestrate the stages and enforce the 90-minute window.
Alternative. If Map/Reduce is not appropriate for a particular step, use a chunked scheduled script that processes a fixed number of rows, records a checkpoint, and reschedules itself. That is still better than an unbounded loop, but it requires careful idempotency and checkpoint design.
2. Design the ingestion and update flow with idempotency, invalid rows, and locale handling.
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Ingestion. Download the CSV to the File Cabinet and parse it in bounded chunks. Validate each row before writing: required columns, numeric formats, valid locale, parseable effective_date, and a matching active item. Write valid rows to the staging record and invalid rows to an error record with row number, pim_id, locale, and reason. Do not let one bad row abort the batch.
Idempotency. Give the staging record an external key or a deterministic key built from pim_id, locale, and effective_date. Before updating an item, compare a content hash of the 14 incoming fields with the stored hash. Skip the load and save when nothing changed. If a run is repeated, the same input must produce the same end state and must not create duplicate custom records. Use upsert semantics rather than blind create.
Locale. The file contains multiple locales for the same product. Do not let a fr-CA row overwrite the en-US item name or marketing copy. Store localized content in locale-keyed custom records, or write to locale-specific fields, not to a single shared field. The item master fields that are language-neutral can be updated from the default-locale row only, and that rule should be explicit.
Effective date. Rows with a future effective_date should not be applied early. Either skip them with a status of pending or place them in a dated queue. Rows with a past date should be applied normally. This prevents a seasonal catalog change from going live during the wrong week.
Active items only. Filter inactive items before staging to cut the workload from 20,000 to 9,000 and reduce governance pressure.
3. How do you make the job restartable and observable within the 90-minute window?
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Checkpointing. Record the stage, the last processed staging key, and the run ID in a job-status custom record. Each stage should be able to resume from that checkpoint rather than restarting from the beginning. Because updates are idempotent, reprocessing a small overlap is safe; skipping records is not.
Run orchestration. Start the file download at 00:30 and have the scheduled orchestrator wait for a complete file, not a partial SFTP upload. Use a marker file or a stable file size check. Kick off the staging load, then the Map/Reduce update, then a reduce summary. If a stage exceeds its time budget, stop starting new work and record the run as incomplete rather than letting it die at the governance limit.
Observability. Emit counts per stage: rows read, valid, invalid, unchanged, updated, failed, and skipped due to future effective date. Record start and end times, governance consumed, and the slowest records. Send a summary to the operations channel and write it to the job-status record. Alert on three conditions: invalid rate above an agreed threshold, run incomplete by 02:00, and updated count far outside the normal range.
Recovery. Provide a rerun mode that processes only failed staging keys or a specified run ID. Keep the error records from the failed run so the source of the failure is visible. Do not delete the staging data immediately after success; retain it long enough to investigate a content complaint.
Window control. Measure the new pipeline on a full 9,000-item active set in a sandbox before production. If the run approaches 90 minutes, reduce per-invocation batch size or parallelize the map stage; do not shorten the window by skipping validation.