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A six-hour recheck schedule makes a software directory more responsive, but it does not make every listing continuously current. If a source has published a change and the scheduled job succeeds, the directory should usually discover it within one interval; a change made just after a run can wait almost six hours. The delay grows when a run fails, a source has not published the change, an API returns incomplete data, or a downstream directory takes time to process an update. Building the directory taught me to treat the interval as one part of freshness—not as a guarantee—and to record source coverage, missed runs, conflicts, and publication latency separately.
What does “every six hours” actually mean?
In monitoring terminology, the check interval is the time between executions, not a promise that data changes at that frequency. Uptime.com describes frequency this way and links a shorter interval to faster detection when checks run normally: its explanation of check interval and frequency.
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For a directory, a six-hour schedule answers one narrow question: how often does the system attempt to compare a listing with its sources? It does not answer which fields are compared, whether every source responded, or when a corrected value becomes visible to readers.
| Event | What a six-hour schedule can mean | What it cannot establish |
|---|---|---|
| Source changes immediately after a successful run | The next scheduled run may detect it in almost six hours. | The source may not expose the change to your query yet. |
| Source changes immediately before a run | The next run may find it within minutes. | A successful fetch does not prove every field was returned. |
| Scheduled run fails or is skipped | Detection waits for a retry or a later run. | A missing result must not be treated as a healthy, unchanged listing. |
| Directory publishes an update after review | Your system may have submitted the correction already. | Public visibility can lag behind your own database. |
The useful unit is therefore “time from source-visible change to verified public correction,” not simply “six hours.” I keep those timestamps distinct so a slow source or publishing queue is not misdiagnosed as a monitor failure.
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How long can a change remain invisible?
Under ideal conditions, a periodic schedule creates a bounded detection window: a change that becomes queryable just after a successful check waits nearly one interval, while one that appears just before the next check is found quickly. That is a scheduling deduction, not a measured performance result from my directory.
When the source is authoritative but slow
A listing can be correct at its origin and still look old elsewhere. A vendor may batch updates, require moderation, cache an API response, or expose different fields to different endpoints. Until the changed value is available through the source your checker actually queries, another six-hour run has nothing new to discover.
When a publishing target adds its own queue
Business-listing systems illustrate this second delay. Uberall says publishing estimates can vary and that Google may review some changes from a few hours to several days; its troubleshooting guidance asks users to allow 24–72 hours for some updates. Those figures describe Uberall’s business-listing workflows, not a service-level agreement for software directories, but they show why an internal “updated” timestamp is not the same as public visibility. See Uberall’s directory publishing targets and its listing-sync troubleshooting guidance.
When a run is incomplete
An HTTP 200 response can still contain an empty, truncated, or partially authorized result. The checker should record field-level completeness and source response status rather than converting every response into a simple pass. Otherwise, a temporary omission can overwrite a valid description, price, or availability flag.
What did the record teach me about freshness?
The durable lessons were operational rather than numerical. A schedule is useful only when the record shows what ran, what was observed, and what became visible afterward.
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Freshness is evaluated only when a scan runs
Qualytics makes this point with a concrete example: a freshness rule allowing a maximum age of six hours, paired with a daily scan, can leave data stale for much of a day before an alert. The check cannot notice staleness between scans. Its freshness best-practices guidance is about data-quality monitoring, but the principle applies directly to directory records.
For my directory, “last checked” and “last changed” are separate values. A listing checked at noon is not necessarily fresh at 6 p.m.; it is merely known to have matched the queried sources at noon.
Missed checks need a visible state
A missed execution is evidence about the monitor, not evidence that the listing remained unchanged. Agent Service Directory’s methodology reports passed, failed, and missed checks separately and marks results stale after two missed intervals. That is the policy of that service—not an industry rule—and its page notes that its monitoring location and method versions changed on 17 October 2026. Its methodology is available at agentservicedirectory.com/methodology.
I use the same conceptual separation: healthy, changed, failed, incomplete, and not-run are different outcomes. A stale badge should explain which condition caused it, so an editor does not publish confidence that the evidence does not support.
Coverage determines the meaning of “every listing”
“Every listing” can mean every URL in the database, every field on each listing, or every upstream source attached to each listing. Those are different coverage claims. A six-hour job that checks only names and URLs cannot support a statement that prices, licensing, screenshots, integrations, or availability are current.
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Source latency is part of the result
The record needs a source-observed timestamp and a directory-public timestamp. If the source changed at 09:10, the checker detected it at 12:00, and the public page changed at 14:30, those are three events with three owners. Collapsing them into one “updated” time hides where the delay occurred.
Which listing fields should a six-hour recheck cover?
The right field set depends on what readers use to choose software and how frequently each value changes. I classify fields by risk and volatility before assigning a cadence.
- Identity: product name, canonical URL, publisher, and ownership. A mismatch here can attach facts to the wrong product.
- Availability: whether the product is operating, accepting customers, regionally available, or discontinued.
- Pricing: displayed plans, billing period, currency, free-tier limits, and whether a price is promotional. Store the source wording and retrieval time.
- Capabilities: integrations, platforms, deployment model, supported languages, and core features. These often change less frequently than pricing but matter to filtering.
- Evidence links: official documentation, changelogs, status pages, and release notes. A broken or redirected link is a material quality issue even when the description is unchanged.
- Editorial fields: summary, tags, screenshots, and comparison notes. These may require human review rather than automatic replacement.
For each field, define an authority order. An official pricing page may outrank an old marketplace profile; a status page may be authoritative for an outage but not for long-term product availability. When two sources disagree, retain both observations, flag the conflict, and route the field for review instead of choosing whichever response arrived last.
How should a monitor handle failed or missed runs?
A dependable workflow makes failure explicit and recoverable. The following sequence is more important than the exact scheduler used.
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- Create an immutable run record. Store scheduled time, start and finish time, software version, source identifiers, response codes, and the number of listings and fields attempted.
- Use bounded retries. Retry transient network errors and rate limits with backoff. Do not retry permanent authorization or “not found” responses indefinitely; classify them for investigation.
- Keep the previous value on incomplete evidence. If a response omits a field that was previously present, mark that field unverified instead of replacing it with blank text.
- Compare normalized values. Normalize whitespace, URL redirects, currency formatting, and case before deciding that a material change occurred. Preserve the raw source value for audit.
- Emit separate outcomes. Record passed, changed, failed, incomplete, and missed states. A scheduler outage should be visible even if the last stored listing still looks plausible.
- Apply a stale threshold. Choose a policy appropriate to your risk. The two-missed-interval rule published by Agent Service Directory is an example to evaluate, not a universal default.
- Alert on patterns, not noise. Escalate repeated source failures, sudden changes across many listings, conflicting authorities, and a growing backlog of unreviewed updates.
- Make publication idempotent. Replaying a successful run should not duplicate listings, reset editorial timestamps, or erase a manual correction.
These controls let an editor distinguish “nothing changed” from “we did not obtain usable evidence.” That distinction is the main protection against a directory that appears stable only because its monitor is failing quietly.
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How do I design the six-hour recheck process step by step?
Start with a written contract for what the job promises. Then implement the smallest workflow that can prove whether it kept that promise.
- Inventory the scope. Export the listing IDs, fields, and sources included in the job. Record exclusions, regional variants, and listings that require manual review.
- Define material change. Decide which differences trigger an editorial task, an automatic update, or no action. For example, a changed canonical URL may block publication, while punctuation in a description may not.
- Assign a cadence by volatility. Use six hours for fields where a same-day change matters; use a slower schedule for stable archival metadata if the cost and source limits justify it. Keep the cadence documented per source or field.
- Fetch with provenance. Save the exact source URL or API resource, retrieval timestamp, response status, and parser version. Respect rate limits and authentication boundaries.
- Validate completeness. Check required fields, schema shape, content length, and unexpected redirects before comparing values. Quarantine malformed responses.
- Compare and classify. Generate field-level diffs and label each listing as unchanged, changed, failed, incomplete, or missed. Include old and new values for reviewer context.
- Review conflicts. Apply the authority order, request a human decision when sources disagree, and retain the rejected observation in the audit trail.
- Publish with timestamps. Show when the source was observed and when the directory page was last verified. Do not imply that verification time is the source’s own update time.
- Measure the full path. Calculate detection delay, review delay, publication delay, failure rate, missed-run rate, and the percentage of fields covered. Report the measurement period and population.
How should I read the record before claiming the directory is current?
A credible retrospective names its boundaries. For the period being discussed, the record should identify the number and type of listings, the fields and sources checked, the number of scheduled and completed runs, and how exclusions were handled.
| Record item | Question it answers | Qualification to publish |
|---|---|---|
| Run log | Did the schedule execute when expected? | Separate delayed, failed, and skipped executions. |
| Field-level diff log | What actually changed? | State normalization rules and ignored differences. |
| Source timestamps | When was a change visible upstream? | Use source-specific clocks and note missing timestamps. |
| Publication log | When did readers see the correction? | Account for caches, moderation, and queueing. |
| Conflict queue | How often did authorities disagree? | Do not count unresolved conflicts as verified facts. |
Without those records, the defensible statement is limited: the directory was configured to attempt checks every six hours. It is not defensible to claim that every listing was successfully rechecked on that rhythm, that every change was found, or that public pages updated within six hours.
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Is six hours the right cadence for every software directory?
There is no published industry benchmark establishing six hours as the correct interval for software directories. The choice should follow the cost of stale information, expected volatility, source limits, and the harm caused by false updates.
| Directory situation | Cadence approach | Reasoning |
|---|---|---|
| Rapidly changing prices, availability, or status | Short interval for those fields, with alerts on material changes | Staleness can affect purchasing decisions quickly. |
| Mostly stable technical metadata | Longer interval plus event-driven checks from changelogs or releases | Frequent polling may add load without improving accuracy. |
| Sources with strict rate limits | Prioritize high-risk fields and stagger requests | Completeness is better than an interval that causes blocking. |
| Event-driven catalog, such as a launch or migration | Temporarily increase checks around the event, then return to baseline | Cadence should reflect expected change, not habit. |
Dockflow documents one such contextual approach for carrier tracking: checking every six hours near an expected event and every 24 hours during idle stretches. That is an example from its data-freshness documentation, not a prescription for software catalogs; the example is at Dockflow’s data-freshness page.
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What is the practical verdict?
Six-hour rechecks are a sensible operating point when same-day discovery matters and the sources can be queried reliably. The record becomes valuable only when it exposes the entire chain: whether a run happened, which fields and sources were covered, what changed, how conflicts were resolved, and when the correction became public. Treat the interval as a detection target, not a freshness guarantee, and label every claim with the scope and period that the logs actually support.
Frequently Asked Questions
Should a directory remove a listing after one failed check?
Usually no. A transient outage, rate limit, or authentication problem is not proof that the software disappeared; retain the listing, mark the affected evidence unverified, and require a defined sequence of failed or missed checks before considering removal.
How can readers tell whether a listing was recently verified?
Show a verification timestamp together with a short status such as verified, changed, or unable to verify. If different fields come from different sources, expose the field or source scope rather than presenting one date as if it covered everything.
What should happen when two official pages disagree?
Preserve both observations and apply a documented authority rule, such as preferring current pricing documentation over an older marketing page. Send unresolved conflicts to editorial review and keep the decision in an audit log so it can be revisited.
Can event-driven webhooks replace scheduled checks?
Webhooks can reduce delay for sources that reliably emit them, but they do not cover silent edits, dropped events, or sources without webhook support. A periodic reconciliation pass is still useful for detecting missed events and verifying that the public record matches the source.
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