HTX Email Checker API Workflow for Automated Checks

An exchange notification list is never checked once. It decays, so the check has to run again and again, which makes it an automation problem rather than a spreadsheet problem. The API route exists for that reason. But automating an HTX email check has one wrinkle that most pipelines miss: the list predates the current brand. Rows were collected under the Huobi name, merged at least once, and each source batch carried its own campaign ID. If the pipeline discards that lineage on the way through, every downstream question ("which agency file did this dead address come from?") becomes unanswerable. HTX Email Checker is the ZelNum page behind this workflow: HTX Email Checker.

Quick answer

Submit a batch through the API, receive registration status and domain risk per email, and store the result next to the record's original campaign ID and source label. Re-run on a schedule. The output you actually care about is not any single run — it is the movement between runs, especially the risky-domain share.

Design the payload around lineage, not emails alone

The minimum request is a list of addresses. That gets you a result, but a result you cannot investigate later. Send these fields with every record:

The checker returns registration status and domain risk per row. Your storage job should append those to the record together with a checked timestamp, and leave the original fields untouched. One row, two eras, both visible.

The workflow, end to end

1. Extract with lineage. Pull emails plus record_id, campaign_id, and source label from the source of truth. No trimming, no lowercasing — the account has whatever case it has on file.
2. Submit one job per batch. Keep batches at a size you are willing to re-run. A 10,000-row job that dies at 90% is more painful to resume than four 2,500-row jobs.
3. Poll or receive callbacks. At quarterly cadence and low volume, polling is simpler and has fewer failure modes. Webhooks pay off only when a downstream system acts on results within minutes.
4. Store per row, per run. Status, domain risk, checked_at, and the run ID. Never overwrite the previous run in place; the drift between runs is the product.
5. Compare, then alert. The alert worth building: risky-domain share up more than a point or two since last run. That is the leading indicator of notification gaps.

Reading the trend across runs

Here is what a year of quarterly runs on a merged list looks like:

(Treat the numbers shown as worked samples, not benchmark results.)

Two percentage points of risky-domain growth per quarter looks harmless until you read it as an inbox count: on a 10,000-address list, that is 200 more accounts per quarter whose withdrawal notices may not arrive. The invalid column staying flat while the risky column climbs is the signature of an aging merged list — nothing is dying outright, but typo and throwaway domains keep surfacing as registration checks get stricter about what "registered" means.

Idempotency and retries

Two rules that prevent the classic automation disasters. First, make result-processing idempotent: if the same callback or polled result arrives twice, writing it twice must not double-count the risky-domain share or double-charge the repair queue. Key your writes on (record_id, run_id) and upsert. Second, retry unknowns on a schedule rather than immediately. Unknown status clusters around transient provider issues, and a re-run a few days later resolves most of them. Treating unknown as invalid on the first pass suppresses rows that were only sleeping.

What the export has to survive

Run this checklist against your storage design before the first automated run:

• Every result row joins back to its source via record_id, with campaign_id intact.
• Each run is a separate snapshot. You can answer "what did July say about this address?" without excavating.
• Failed and unknown rows are queryable by source batch, so a bad agency import is visible as a cluster, not as noise.
• The checked_at timestamp is stored per row, not per run. Rows in one batch can resolve at different times.

If any of these fail, the automation produces data nobody can audit, which is barely better than no automation at all.

Mistakes to avoid

• Automating the check but not the repair queue. A rising risky-domain trend with no workflow attached to it is a dashboard decoration. The API's job is to hand you a queue; someone still has to work it.
• Normalizing emails before submission. Lowercasing and trimming belong after the check, applied to your copy, never to the submitted value. A preprocessing-induced mismatch is indistinguishable from a real result, and you will not know which one you are looking at.
• Running one giant job. Big jobs turn transient failures into all-nighters. Smaller batches cost the same and recover in minutes.
• Alerting on single-run numbers. A 15% risky-domain share means nothing on its own. The movement from 13% is the signal.

FAQ

What is the output of the HTX email API pipeline?

Notification-readiness over time: how many accounts have a working inbox for withdrawal and risk notices, and how that number moves between runs. The trend is the deliverable; any single run is a sample.

Why keep the Huobi-era campaign ID in the payload?

Because failure clusters by source. When a batch of typo domains shows up, campaign_id tells you within minutes whether it came from one old agency import or spread across the whole list. Without it, you are diffing blind.

How often should the API run?

Quarterly for stable lists, monthly for lists actively being repaired, and always before a security review or after any merge touching the file.

Webhooks or polling?

Polling for most teams. The check runs on a schedule anyway, so minutes of latency cost nothing, and polling has fewer moving parts to make idempotent.

What should the pipeline do with unknown rows?

Re-run them after a few days, automatically, and only escalate the ones that stay unknown across two runs. Unknown is a retry state, not a verdict.

Related ZelNum pages

• HTX Email Checker Use Cases for the way how different job roles read rows.
• Bulk HTX Email Checker: the CSV guide & export format for the CSV-side walkthrough.