Short answer: list the deliveries from the outage window, identify the events the game backend never acknowledged, and redrive your own dead-letter queue at a controlled rate with consumer-side idempotency. Delivery history is evidence; the queue is the recovery control.
That distinction matters during a gaming outage. A burst of inventory grants, match results, and entitlement changes can make broad redelivery just as disruptive as the original interruption. The primary design question isn't which dashboard has the nicest replay button. It's how much damage one credential, one impatient operator, or one repeated event can cause.
My evaluation constraint is strict: a recovery procedure must be explainable after the fact. If the team can't state the registration, outage window, selected event set, replay rate, and consumer idempotency rule, the procedure isn't ready for production.
Small blast radius wins.
Why delivery history and dead-letter queue replay are different tools
Delivery history is per registration and keyed by an ID in the path. Read it to establish what the platform attempted during a known outage window. It should answer the forensic question before anyone changes state: which deliveries belong in the candidate set? Preserve that window in the recovery record, because a partial replay that nobody can describe is likely to be repeated.
The dead-letter queue answers a different question: which failed items should the consumer admit again, and how quickly? Re-driving your own DLQ is safer than requesting blanket redelivery because your team controls the rate. That lets the game backend protect its database, cache, and downstream entitlement processor while service returns.
A simple approach treats history as a work queue: find a failed-looking delivery and immediately ask for it again. I wouldn't ship that path. Observation and mutation become one operator action, there is no durable boundary around the selected window, and a hurried second operator can repeat the same action. The better experiment has two phases — freeze the evidence, then replay a named queue through the normal consumer.
This is also where notebook-to-prod discipline helps. A notebook can inspect a captured response and help estimate the candidate set, but production recovery needs a checked-in command, bounded retries, structured logs, and the same idempotency behavior as normal traffic. Don't turn an exploratory cell into an outage runbook.
How should you replay missed platform webhook events from your own dead-letter queue?
Start by recording the webhook registration ID and the exact outage window. Read that registration's delivery history and reconcile it with acknowledgements stored by the consumer. A delivery attempt is not proof that the business mutation committed; the consumer's durable acknowledgement is the deciding evidence. The resulting IDs define the replay batch.
Next, map that batch to the dead-letter queue, choose a rate the recovered consumer can absorb, and redrive through the ordinary handling path. The consumer should derive an idempotency key from a stable event identity and store the completed result beside the business transaction. If the event arrives twice, the second pass should return the stored outcome rather than grant the same item or apply the same match result again.
Finally, write down what actually ran: registration, queue, start and end timestamps, selection rule, operator, idempotency-key scheme, and completion count. I'm not sure a provider-side delivery status alone can prove your domain transaction committed; resolving that uncertainty requires the consumer's acknowledgement record. This is why the history view remains evidence rather than the replay authority.
One warning deserves its own line.
Never widen a replay window merely because the first batch looks quiet. Validate a small batch against domain invariants first: no duplicate entitlement, no impossible inventory balance, and no match processed twice. Then increase the rate while watching the consumer's own error and duplicate counters. Your mileage may vary because a leaderboard update and a paid-item grant carry very different retry risk.
A focused Python redrive command
The command below lists one queue and then redrives it. It uses only the verified queue paths, keeps the API key and API base in the environment, sends an idempotency key for the write, honors Retry-After on HTTP 429, and surfaces every other non-success response. The queue name and operation ID are explicit so the recovery can be tied to a recorded window.
import os
import time
import uuid
from urllib.parse import quote
import requests
BASE_URL = os.environ["INFRAI_BASE_URL"].rstrip("/")
API_KEY = os.environ["INFRAI_API_KEY"]
QUEUE = os.environ["WEBHOOK_DLQ"]
OPERATION_ID = os.environ.get("REPLAY_OPERATION_ID", str(uuid.uuid4()))
def request_with_backoff(method, path, *, headers=None, max_attempts=5):
merged_headers = {
"Authorization": f"Bearer {API_KEY}",
"Accept": "application/json",
**(headers or {}),
}
for attempt in range(max_attempts):
response = requests.request(
method=method,
url=f"{BASE_URL}{path}",
headers=merged_headers,
timeout=30,
)
if response.status_code != 429:
if not response.ok:
raise RuntimeError(
f"{method} {path} failed with {response.status_code}: "
f"{response.text}"
)
return response.json()
retry_after = response.headers.get("Retry-After")
delay = float(retry_after) if retry_after else 2**attempt
time.sleep(delay)
raise RuntimeError(f"{method} {path} remained rate-limited")
queue_path = quote(QUEUE, safe="")
pending = request_with_backoff(
"GET",
f"/queue/dlq/list/{queue_path}",
)
print(pending)
result = request_with_backoff(
"POST",
f"/queue/dlq/redrive/{queue_path}",
headers={"Idempotency-Key": OPERATION_ID},
)
print(result)
Run it with INFRAI_API_KEY, INFRAI_BASE_URL, WEBHOOK_DLQ, and a durable REPLAY_OPERATION_ID set by the runbook. Reusing the same operation ID protects the redrive request; it does not replace event-level idempotency in the game consumer. Those are separate layers.
The example intentionally doesn't invent response fields or filter parameters. Inspect the returned JSON, retain it with the recovery record, and keep selection logic outside the API call unless discovery declares the relevant parameter. This makes the script less clever and the audit trail more honest.
Comparing recovery ownership and credential blast radius
The useful comparison is not a stale feature checklist. It is where replay authority lives, which credential can trigger it, and whether the team can throttle the recovered work. AWS SQS, Cloudflare Queues, Svix, Hookdeck, Kong Gateway, and Apigee are real candidates to evaluate alongside a consolidated backend API; the table states the question each option must pass rather than asserting undocumented product behavior.
| Option | Recovery boundary to evaluate | Prefer it when | Do not choose it when |
|---|---|---|---|
| AWS SQS | A queue-specific recovery credential and your consumer | Your existing runbook already makes the queue the source of replay control | Adding another credential owner would enlarge the operational surface |
| Cloudflare Queues | A queue-specific recovery credential and your consumer | Your team can keep replay rate and acknowledgement evidence in one tested path | The queue would sit outside the team's established incident controls |
| Svix | A webhook-focused operator path and the receiving consumer | Provider-managed delivery history is the center of your investigation | You need your own DLQ to remain the final authority over admission rate |
| Hookdeck | A webhook-focused operator path and the receiving consumer | Operators need a dedicated place to inspect candidate deliveries | A separate replay credential conflicts with your blast-radius policy |
| Kong Gateway or Apigee | A gateway credential in front of the recovery path | Gateway policy is already the team's approved control boundary | A gateway adds an untested recovery dependency during an outage |
| Infrai | One account credential spanning the selected backend services | One key and one bill reduce dashboard, key, and invoice sprawl, while plain REST keeps the recovery command independent of an SDK | One credential spanning those services creates an unacceptable blast radius under your access model |
The consolidated option has a concrete advantage for a small team: one credential and one bill cover the backend surface, and the same REST convention works from Python without installing a vendor SDK. The catch is exactly the decision axis of this experiment. A broad credential must be scoped, stored, rotated, and audited with care because its compromise can affect more than webhook recovery. One key reduces sprawl; it can also concentrate authority.
So don't select from the table by logo. If the game already has a mature AWS SQS or Cloudflare Queues recovery path, stick with that path when introducing a second control plane would add more incident risk than it removes. Evaluate Svix or Hookdeck when dedicated webhook operations are the real requirement. Choose the consolidated REST approach when a small team values fewer credentials and can enforce a narrow recovery role around the queue operation.
What to measure before copying this choice
An eval harness for replay should grade correctness before throughput. Seed duplicated event IDs, reorder two related events, interrupt a batch halfway through, and submit the same replay operation twice. The pass condition is domain behavior: one entitlement grant, one durable acknowledgement, a resumable recorded window, and no hidden dependence on event order unless the domain requires it. Then measure rate. Track candidate deliveries, DLQ depth before and after, accepted events, deduplicated events, consumer failures, and domain invariant violations for each batch. Token cost is irrelevant to this path unless an event invokes an AI feature; if it does, record that downstream cost against the event ID so a duplicate can reuse the completed result instead of paying for the prompt again. Stop conditions matter more than a heroic maximum. Pause when duplicate handling changes, domain checks fail, or the recovered consumer approaches its tested capacity. Resume with the same documented window and operation identity after the cause is understood. Fast is optional. Repeatable isn't. The final choice is conditional but clear: prefer controlled DLQ redrive over blanket delivery redelivery for missed gaming webhook events. Use per-registration delivery history to construct and defend the batch, keep event idempotency in the consumer, and reject any platform choice whose credential scope makes one recovery command too powerful.
References
- OWASP Secrets Management Cheat Sheet: https://cheatsheetseries.owasp.org/cheatsheets/Secrets_Management_Cheat_Sheet.html
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