Put repeatable documents
on a visible schedule
Schedule recurring document jobs and web captures in any timezone, run on-demand tasks, or process spreadsheets row by row with complete audit logs.
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Define what runs, when it runs, and where it goes
Save a specific source and revision
Select a template, web URL, or raw HTML along with your output format and render options. Revision IDs are tracked on every execution for full auditability.
Set cadence, timezone, and state
Set recurring cadences with timezone support. Pausing keeps your setup, and resuming starts at the next due time.
Choose retention and delivery
Store files in cloudlayer.io or send them directly to your cloud buckets. Each step tracks status for clear retries.
Use one saved design across three run modes
The scheduler provides 3 run modes: on demand, recurring, and once per spreadsheet row. Each mode can reference the same template or capture design. Keep the source, bindings, and output options together, then choose the trigger that matches the workflow instead of maintaining a separate template format for each mode.
Run it now
Start a recorded execution from the application or API. Check the saved revision and representative data first. Then test the destination before enabling recurrence.
Run it on a schedule
Combine cadence with a named timezone, calculated next run, and enabled or paused state. Each occurrence creates an execution record whether it succeeds or fails.
Run it once per row
Upload a spreadsheet, map columns to fields, inspect a bounded preview, and start one document per accepted row. Each row keeps its own result, so completed work stays visible.
Check the next occurrences in the schedule timezone
A local clock time is meaningful only with a timezone and calendar rule. Change the cadence or zone below to inspect the next three calculated occurrences before saving the schedule.
Runs every monday at 06:00, London time.
- In London Where you are
- Next Mon 24 Aug, 06:00 Mon 24, 05:00
- +1 Mon 31 Aug, 06:00 Mon 31, 05:00
- +2 Mon 7 Sept, 06:00 Mon 7, 05:00
The example uses browser timezone rules to display the chosen local time. Production schedule evaluation uses the saved timezone. Test dates around daylight-saving transitions when a skipped or repeated local hour could affect the workflow.
Separate render failure from delivery failure
A scheduled execution can fail before an artifact exists, or delivery can fail after a successful render. Store both states so recovery begins at the failed boundary.
Execution state remains visible
Store the trigger, timestamps, state, and failure category beside successful runs. A missing artifact is then tied to an explicit execution outcome.
Storage delivery has its own attempts
Track storage delivery separately from generation. Each attempt records its own count and idempotency key. The destination still decides how duplicate writes behave.
Error details redact secrets
Retain a safe error summary for diagnosis without placing storage credentials or other secret values into the visible record.
The artifact has a durable identity
Use the stored artifact locator rather than treating one expiring download URL as the permanent identity of the result.
The timer is the smallest part of a scheduler
Calling a render endpoint from a timer is straightforward. Reliable unattended work needs more. It needs a queue, concurrency limits, timezones, run records, retries, pause state, safe credentials, and a recovery path.
Those controls answer what a bare timer cannot. They show which revision ran and why an output is missing. They also separate a completed artifact from failed delivery, manage due work that collides, and define the local time of the next occurrence.
Bind each execution to a recorded revision
An unattended run may succeed with a template that changed after review. Store the revision ID with each artifact. Later edits do not erase the meaning of earlier run history.
Store the source revision and run settings with the execution. Editing the design creates a later revision rather than changing the record of completed runs. History should show the saved run settings, not substitute the template's present state.
- The execution keeps its configuration. Historical inspection shows the saved settings rather than today's template state.
- An edit creates a later revision. It does not rewrite the configuration recorded on completed executions.
- Future runs use the selected revision policy. Review the next occurrence after publishing a template change.
Keep enough execution data to explain the artifact
The file alone cannot explain why it exists, which inputs and revision produced it, or why delivery failed. The execution record connects those operational facts to the artifact.
- What triggered it
- Record whether an API call, schedule occurrence, or batch row started the execution. Two identical outputs may still belong to different source events.
- When it started and finished
- Execution timestamps show queueing and processing independently of a storage object's modified time. Use them to measure where elapsed time accumulated.
- Whether the cache answered
- The cache disposition distinguishes a reused result from a new fetch and render. That difference matters when investigating source changes and usage.
- Where the file went
- Track artifact identity and delivery state while redacting destination credentials. A successful render and successful external upload remain separate outcomes.
- Why it failed, in a category
- Stable categories separate source loading, rendering, timeout, and destination problems. Safe diagnostic detail can then point the operator to the boundary that needs correction.
Generate one document for each accepted spreadsheet row
A row-driven run replaces a custom loop with explicit column mapping, bounded preview, and per-row results. Invalid input remains attached to its source row for correction.
Map columns to template fields
Choose which source column supplies each binding. Match by recorded column identity rather than relying only on the spreadsheet's physical order.
Preview a bounded sample
Render selected rows before starting the full run. Include long values, missing optional fields, and repeated data instead of checking only the shortest first row.
Keep row outcomes separate
Record acceptance, render, and failure against each source row. A rejected or failed row does not erase successful outputs from other rows.
Reuse the saved template
The spreadsheet supplies data to the same design used by on-demand and scheduled runs. Keep one layout and test the row mapping as a separate input contract.
Queue schedules that become due together
Recurring work often clusters on round hours, weekdays, and month end. The scheduler must apply a bounded concurrency policy instead of starting an unbounded browser workload.
Accepted executions wait when the active count reaches the plan limit. Record queued, started, and finished timestamps so delay is visible. Queueing controls resource pressure; it does not promise an exact start time or prevent every downstream timeout.
Use a timeout suited to the work type
Page capture and long-document rendering may need different limits. A bounded timeout keeps stuck work from holding capacity forever. When exceeded, record a timeout category and the relevant execution context instead of leaving a silent gap.
Move an existing timed job in stages
An existing script may already create the correct file. Migrate the source, schedule, delivery, and shutdown separately so a difference can be traced to one changed boundary.
Validate the saved design on demand
Run the selected revision with production-like data and compare the artifact. Scheduling adds timing and delivery behavior that still need their own verification.
Run both paths for a defined window
Use separate destination prefixes. Compare the files and run times, then inspect failures and delivery records. Choose a window that covers key calendar cases.
Change the receiving path explicitly
Switch the final destination only after the artifact is accepted. Check permissions and naming first. Then test lifecycle rules, notifications, and repeated writes.
Retire the old worker after observation
Remove its timer and credentials only after the new path has completed the agreed observation window and its operators know where to inspect failures.
The migration transfers the cadence, timezone, queue, run records, delivery attempts, pause state, and credentials. Keep the artifact comparison as evidence that the document path moved correctly.
Recurring document and capture patterns
Scheduling fits work whose source, cadence, and expected destination are known in advance. These patterns differ in content but need the same visible execution and failure state.
A weekly report
Render a saved report from current data before a regular review. Keep the source timestamp and revision with the execution so the numbers can be placed in context.
A recurring page record
Capture a reachable public page on a cadence and retain the execution time, request context, and artifact identity for later comparison.
A period-end batch
Map a spreadsheet of statements, invoices, or certificates into one saved design. Review per-row results and correct only the rows that failed.
A storage handoff
Deliver the artifact to a configured bucket for another system to process. Track upload state and attempts without assuming the destination provides exactly-once consumption.
Deliver to object storage with its policies understood
A scheduled result may need to enter storage without a waiting caller. The destination's permissions, naming, lifecycle, encryption, and event rules become part of the workflow.
The product supports 6 storage destination types. They are Amazon S3 or S3-compatible storage, Azure Blob Storage, Cloudflare R2, Google Cloud Storage, DigitalOcean Spaces, and Backblaze B2. Your account and bucket policies still apply.
What an existing bucket does to a new producer
- Lifecycle rules may remove results. Check expiration and transition policies on the chosen prefix.
- Existing read grants apply. Review who can access objects written under that destination identity and prefix.
- Writers need a naming contract. Avoid collisions by assigning prefixes and keys explicitly rather than assuming one producer.
When Cloudlayer retains the artifact, use its durable account locator to request access. Download URLs may expire and should not become the permanent identifier in your own records.
Retain capture evidence with the execution that created it
A recurring capture may be kept as a record rather than read immediately. Its value depends on tying the artifact to the request context, execution time, and stored attestation instead of relying on the image alone.
A certified capture records what was requested and which artifact came back. It also keeps an attestation with that run. Legal or regulatory use depends on the rules that apply and on how your organization keeps and presents the record.
Use status and run data to locate the failure
Check the run category, safe error detail, destination state, and platform status. A changed source page needs a different response from a rejected upload. Render timeouts and service incidents also have their own recovery paths. Do not collapse them into one failure.
Apply the timeout policy for the work type and record when it is exceeded. Operators can then distinguish a bounded timeout from a missing run or failed destination handoff.
Know what scheduling does not solve
A schedule repeats one saved document or capture task. It does not fix source data, infer business meaning, replay paused dates, or create network access.
It does not validate business truth
The run uses the supplied source and data. Validate amounts, ownership, and business rules in the system responsible for those facts.
It does not interpret change
A cadence creates each artifact. Your application or review workflow decides whether the difference from an earlier result is expected or important.
It does not replay paused occurrences
Resuming calculates the next occurrence. Start any required catch-up work explicitly so its range, revision, and destination are reviewed.
It does not create private-network access
A reachable page may use supplied headers or cookies for an authorized session. Scheduling does not connect the renderer to an otherwise unreachable private network.
Treat these limits as part of the run contract. The system that owns the data should validate and compare it. Start catch-up work explicitly, and design network access separately.
Begin with a small recurring task that has a clear owner
Choose a low-risk task with known inputs, a predictable artifact, and someone who reviews the result. It should exercise the same timezone, delivery, and failure path you expect to use later without making a critical process depend on the first attempt.
A weekly summary, recurring public-page capture, or routine internal handoff can provide that trial. Define who checks the execution history, how long parallel observation lasts, and which failures require action.
Then observe it across several occurrences
Verify several normal runs first. Then test pause and resume, source failure, and delivery failure. Include a daylight-saving boundary when local clock time matters.
Run history should show when work became due and which revision and trigger it used. It should also show whether rendering worked, where delivery stopped, and what happened next.
Questions about scheduled document runs
What happens when a scheduled run fails?
The execution record keeps its failure category and timestamps. Storage delivery has its own attempt count and idempotency key, while webhook delivery makes one attempt. The job remains available through the Jobs API even if notification or storage delivery fails. Safe error details are retained with credential values redacted.
Can I pause a schedule without deleting it?
Yes. Enabled and paused state are separate from cadence, timezone, and design revision. Pausing keeps the configuration. On resume, the scheduler finds the next occurrence. It does not replay every date that passed during the pause.
Which timezone does a schedule run in?
Use the named timezone stored on the schedule. The scheduler applies that zone to the cadence and next-run calculation rather than inferring time from the account or the worker that executes it. Review daylight-saving boundary dates when local clock time matters.
Where does the finished file go?
You can keep the result in the account or configure Amazon S3, Azure Blob Storage, Cloudflare R2, Google Cloud Storage, DigitalOcean Spaces, or Backblaze B2. A storage upload may fail after rendering works. In that case, the Cloudlayer result remains available under its retention rules.
Schedule one reviewed design
and inspect every run
Choose the revision, timezone, cadence, and destination, then test both successful output and recovery before relying on it.
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