PostgreSQL Queues for Email Risk Reviews

In one signup system I maintained, the hardest part of email risk checks was not the classifier. It was the handoff between the API, the reviewer job, and the audit trail. We had valid reasons to flag domains, aliases, and patterns like facebook temp email, but the operational flow got noisy fast when several workers looked at the same review item at once.

That noise usually shows up in boring ways: duplicate decisions, retries that hide the first failure, and support notes that do not match what the Authentication service actually decided. It sounds small, but it creates a very real maintenance tax. Backend teams dont lose time on the final SQL statement, they lose time on unclear ownership.

Why email risk review queues get noisy

The common setup is simple:

• the signup API writes a pending review row
• a worker scans for pending rows every few seconds
• another worker retries stale work
• dashboards read counts without enough state detail

This works for a while, then gets weird. A domain rule changes, a reviewer deploy lands mid-run, and suddenly nobody is sure whether a decision was skipped, retried, or overwritten. If your team already thinks about data budgets for signup risk checks, the same idea applies here too: keep just enough state to explain decisions, not a giant blob of maybe-useful metadata.

The tricky part is that engineers often treat the queue as a transport concern only. In practice, the queue is also your explanation layer. When a reviewer asks why a signup hit a manual check because of a domain that looked like temp gamil com, the system should answer from stored state, not from team memory. That is where PostgreSQL helps more than people expect.

The PostgreSQL model that aged well for us

The pattern that held up best was a single review table plus an append-only decision log:

create table email_risk_reviews (
  id bigserial primary key,
  user_id bigint not null,
  email_address text not null,
  risk_reason text not null,
  status text not null check (status in ('pending', 'claimed', 'approved', 'blocked')),
  claimed_by text,
  claimed_at timestamptz,
  available_at timestamptz not null default now(),
  created_at timestamptz not null default now(),
  updated_at timestamptz not null default now()
);

create table email_risk_review_events (
  id bigserial primary key,
  review_id bigint not null references email_risk_reviews(id),
  event_type text not null,
  event_payload jsonb not null default '{}'::jsonb,
  created_at timestamptz not null default now()
);

I like this layout because the current state stays cheap to query, while the event table tells the story after the fact. You do not need event sourcing religion here, just enough structure so incidents are reconstructable later. That distinction matters more than it seems, and teams forget it alot.

For risk reasons, keep them narrow and reviewable. Examples:

• domain_on_watchlist
• mx_lookup_failed
• alias_pattern_collision
• manual_rule_match

Once those values drift into free-form text, the whole system starts to rot a bit.

Claim rows with clear ownership

The most useful behavior change was moving from "worker reads pending rows" to "worker claims specific rows." That removes a surprising amount of accidental concurrency.

with next_review as (
  select id
  from email_risk_reviews
  where status = 'pending'
    and available_at <= now()
  order by created_at
  for update skip locked
  limit 1
)
update email_risk_reviews r
set status = 'claimed',
    claimed_by = $1,
    claimed_at = now(),
    updated_at = now()
from next_review
where r.id = next_review.id
returning r.*;

for update skip locked is not magic, but it is a very practical default for this kind of worker. One row gets one owner for one attempt. If the worker crashes, you can requeue the row with a timeout job. If the worker succeeds, you append an event and finalize the status. Clean enough, and pretty boring in a good way.

That boringness is valuable. It also makes it easier to avoid duplicate notification sends in adjacent workflows, because your backend now has a stable record of which review actually finished before downstream mail or account actions begin.

One caution: do not let the worker directly mutate user access in the same transaction that claims the review. Keep the claim step small. Finish the review, write the event, then let the API or a dedicated command handler apply the account change. Mixing those concerns tends to work untill it really doesnt.

Where the authentication boundary should stay

For me, the most maintainable boundary is:

• signup API creates the review row
• reviewer service decides approved or blocked
• authentication service consumes the final decision
• audit tools read both current state and events

This keeps the queue from becoming an all-knowing god object. It is only responsible for review coordination and traceability. The final account policy still belongs to Authentication, where rate limits, user messaging, and lock rules already live.

That separation also keeps typo-heavy evidence from leaking into policy rules. If a support agent notes temp org mail in a ticket, that can exist as review context without becoming a permanent domain rule by accident. Small distinction, but it saves you from some silly cleanups later.

If you need one more field, add a decision_version. When policies change, you can tell which rule set produced which outcome. I skipped that for too long on one service, and the post-incident analysis was more annoying than it needed to be.

Q&A

Should every flagged signup become a queued review?

No. Reserve the queue for cases that are ambiguous or operationally sensitive. Hard blocks and clear allows should stay synchronous when possible, otherwise the API gets slower and the queue becomes a dumping ground.

Do I need Kafka for this?

Not always. If review volume is moderate and the team already runs PostgreSQL well, a relational queue is often enough. Add more infra only when you can point to a real bottleneck, not because the pattern looks more modern.

What should I log for each decision?

At minimum: review id, user id, risk reason, worker id, decision, and policy version. If you cannot answer "who claimed this row and why did it end blocked?" from logs plus tables, the design still needs work.

A review queue like this will never make bad email heuristics good by itself. What it does is make backend behavior legible. For systems that have to explain risky signup decisions later, that legibility is most of the win.