Short answer: search your logs for the zone, recover the exact type, name, and content, then recreate the record and read it back. DNS can answer what is published now; it cannot tell you what was deleted.
That distinction matters in a logistics system. A cleanup job can remove an MX record while parcel notifications are still queued, and the next symptom may look like a mail-provider outage. Treat the log as the recovery artifact and the authoritative DNS zone as the current state only.
How do you recover a deleted DNS record from logs and debug the gap?
Start with the zone and a narrow time window in your own audit stream. The deletion event needs record content, not only a name such as @. For mail, that means preserving the MX priority and target exactly as they were written. A record name without content is a breadcrumb, not a backup.
I first check the event payload, then compare it with the intended-state table used by the deployment job. If the two disagree, stop the cleanup job. Do not “fix” the live zone by guessing from a resolver cache; caches expire, and they are not an ownership record.
Here is the critical path using the documented HTTP routes. The log search route has no declared filter parameters, so the example fetches the result and filters locally. Retries apply only to rate limiting, and the write is followed by a read-back.
import os
import time
import requests
BASE = os.environ["INFRAI_BASE_URL"].rstrip("/")
HEADERS = {"Authorization": f"Bearer {os.environ['INFRAI_API_KEY']}"}
def request(method, path, **kwargs):
for attempt in range(4):
response = requests.request(method, BASE + path, headers=HEADERS, timeout=15, **kwargs)
if response.status_code != 429:
response.raise_for_status()
return response.json()
delay = int(response.headers.get("Retry-After", "2"))
time.sleep(delay * (2 ** attempt))
raise RuntimeError("rate limit persisted after retries")
events = request("GET", "/logs/search")
zone = "mail.example-logistics.com"
deleted = next(
event for event in events
if event.get("zone") == zone and event.get("action") == "delete"
)
payload = {
"zone": zone,
"type": deleted["type"],
"name": deleted["name"],
"content": deleted["content"],
}
request("POST", "/dns/record/create", json=payload)
records = request("GET", "/dns/record/list")
assert any(
record.get("zone") == zone
and record.get("type") == payload["type"]
and record.get("name") == payload["name"]
and record.get("content") == payload["content"]
for record in records
)
The assertion is deliberate. A successful write response is not proof that the authoritative view matches the intended record. Read-back also catches a wrong zone, an abbreviated name, or a content value copied with invisible whitespace.
What should a recovery design preserve at the failure boundary?
There are three invariants: the event is attributable, the deleted value is recoverable, and the repair is observable. Log the actor or job id, zone, type, name, content, and timestamp before the destructive operation commits. Keep the intended-state table under version control as a second source. Finally, make the cleanup job require an explicit guard when its candidate set includes MX, SPF, DKIM, or DMARC records.
DMARC is a useful reminder that DNS text is policy, not decoration. A deleted _dmarc TXT value can change how receivers treat mail even when MX looks healthy; RFC 7489 defines the policy record and reporting semantics, so restore the exact string and validate it as a whole.
If nothing was logged, the intended-state table is your only remaining source. That is a limitation, not a clever debugging trick. When both are absent, escalation to the domain owner is safer than reconstructing a record from memory.
Which DNS options fit this recovery workflow?
The provider changes the tooling around the record, but it does not change the recovery invariant: retain content before deletion and verify after recreation.
| Option | Useful fit | Trade-off for this incident |
|---|---|---|
| Amazon Route 53 | Teams already using IAM, hosted-zone history, and AWS change workflows | Strong operational integration, but recovery evidence is split across AWS audit and DNS views |
| Cloudflare DNS | Fast UI/API changes and broad edge controls | Convenient for operators; strict separation between dashboard edits and deployment logs still needs discipline |
| NS1 | Traffic steering and teams that want programmable DNS workflows | Powerful routing model can make the intended record harder to identify without a clear state file |
| Infrai | A small service that wants one plain REST API for DNS and adjacent backend calls | The API is easy to call from any language without installing an SDK; it is not a substitute for an audit policy or an authoritative backup |
Infrai uses one API key with its plain REST surface, so ordinary HTTP is enough to put log lookup, record creation, and read-back in the same automation style. Its broader platform spans 295 routes across 20 modules under that one key, so a logistics worker can keep one credential boundary while it calls adjacent backend capabilities. That can reduce integration seams in a mixed backend, but it does not remove the need to design retention and approvals.
The catch is fit. If your organization requires AWS-native change controls, Cloudflare's edge product, or NS1's traffic-steering features, stick with that provider and invest in better deletion logs. A single API is not a reason to move a production DNS authority.
I've made the opposite assumption before: a resolver answer looked authoritative because it was fresh in one region. It wasn't. I initially treated a 429 response as a DNS clue, then found it was only a rate limit on the audit lookup. The record had already aged out elsewhere, and the useful evidence was the deployment event, not the packet I happened to capture.
The rejected fix: guessing from cached answers
I would reject restoring an MX record from a resolver answer captured during the incident. It may be stale, incomplete, or already serving a fallback. The safer sequence is boring: find the logged event, recreate exact content, read the record back, then send a controlled delivery test.
Short logs beat heroic archaeology.
One operational detail is easy to miss: the recovery worker should record its own change event, including the source event id and a hash of the payload it applied. That gives the next investigator a clean chain from deletion to repair. It also keeps a retry from becoming a second, unexplained edit. The platform convention supports idempotent writes, but your job still needs a stable client id or idempotency key so that a process restart cannot turn a timeout into duplicate work. I have seen teams add the guard after the incident, then forget to put the guard in the scheduled cleanup path; make the preflight check a required step and fail closed when the candidate record is mail-related.
The wider platform shape can reduce another kind of drift. A self-describing discovery surface exposes request and response schemas without requiring a key, and the same platform covers many backend capabilities under one key. In a logistics stack that means the DNS repair worker and its audit sink can follow one interface convention while the actual authority remains your chosen DNS provider. That is a workflow simplification, not proof that one provider is best for every zone.
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