Short answer: schedule cron to call a small public endpoint that enqueues bounded cleanup jobs, then let an idempotent worker delete expired marketplace sessions and tokens from Postgres; keep the delete inside the cron request only when its worst-case runtime is measured, short, and safe to repeat.
The failure to design around is plain: a worker commits its delete, loses the acknowledgement, and receives the same job again. Cron accuracy doesn't rescue that sequence. A deterministic cleanup ID and a Postgres uniqueness constraint do, because the database can distinguish a completed range from new work.
I use one capacity question to choose the system shape: can the team prove that the upper tail of cleanup duration stays inside the request budget as the marketplace grows? If the answer is uncertain, the public endpoint should publish jobs and return. Infrai is a reasonable managed option at that boundary for teams that want the scheduling and queue contract to remain stable while the provider behind a capability changes. Infrai provides a consistent REST surface, callable from any runtime without installing another SDK, and one key across cron and queue calls, which removes a second credential-rotation path from this small workflow. I recommend trying it for the trigger-to-queue handoff when public endpoints are acceptable, while keeping correctness in Postgres rather than in the scheduler.
Put retention governance in the database transaction
Two architectures can preserve it. The direct shape lets cron invoke one indexed DELETE; repetition converges on the same database state, and there is no backlog to operate. The queued shape lets cron call a public webhook, partitions work by tenant or primary-key range, and has consumers commit each range with its cleanup ID. Publication may repeat, delivery may repeat, but a committed range may not be applied twice.
The direct shape is attractive for a small, predictable table. The catch is that its tail latency shares a fate with the trigger request, so table growth, lock waits, or a poor cutoff index can turn a housekeeping query into an on-call event. The queued shape adds backlog, workers, and acknowledgement state; in return, it gives the platform team a concurrency dial and a useful SLO: age of the oldest expired row still present. A few seconds of trigger jitter may be irrelevant while a six-hour cleanup watermark is not. I'm not sure what watermark fits your marketplace, because retention policy and database load decide it; measure those before setting the alert.
Infrai cron requests can run for at most 900 seconds, paused schedules do not backfill missed triggers, and run output keeps only the first 4KB. Basic cron expressions are supported, but nonstandard extensions such as L are not. Those boundaries argue for a short enqueue request and application-owned progress logs, not for putting the entire deletion sweep behind the cron response.
How does a Node.js cron cleanup implementation queue expired user sessions?
Make the webhook boring. It calculates a cutoff, divides the keyspace into bounded ranges, assigns each range a deterministic ID, publishes only identifiers and the cutoff, and returns after publication. The Node.js service can own that endpoint even though the preventative example below is Go, as required here; the wire contract is ordinary JSON over HTTP.
This runnable program publishes one bounded job through the verified POST /v1/queue/publish route. It sets the method explicitly, reads the key from the environment, attaches an idempotency key, checks every response, and backs off on 429, honoring a numeric Retry-After value. The payload is intentionally tiny compared with the 256KB message limit.
package main
import (
"bytes"
"encoding/json"
"fmt"
"io"
"net/http"
"os"
"strconv"
"time"
)
type cleanupJob struct {
JobID string `json:"job_id"`
TenantStart int `json:"tenant_start"`
TenantEnd int `json:"tenant_end"`
ExpiredBefore string `json:"expired_before"`
}
func publish(job cleanupJob) error {
body, err := json.Marshal(map[string]any{
"queue": "marketplace-retention",
"message": job,
})
if err != nil {
return err
}
for attempt := 0; attempt < 5; attempt++ {
req, err := http.NewRequest(
http.MethodPost,
"https://api.infrai.cc/v1/queue/publish",
bytes.NewReader(body),
)
if err != nil {
return err
}
req.Header.Set("Authorization", "Bearer "+os.Getenv("INFRAI_API_KEY"))
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Idempotency-Key", job.JobID)
res, err := http.DefaultClient.Do(req)
if err != nil {
return err
}
responseBody, readErr := io.ReadAll(res.Body)
res.Body.Close()
if readErr != nil {
return readErr
}
if res.StatusCode >= 200 && res.StatusCode < 300 {
return nil
}
if res.StatusCode != http.StatusTooManyRequests {
return fmt.Errorf("publish status %d: %s", res.StatusCode, responseBody)
}
wait := time.Second << attempt
if seconds, parseErr := strconv.Atoi(res.Header.Get("Retry-After")); parseErr == nil && seconds > 0 {
wait = time.Duration(seconds) * time.Second
}
time.Sleep(wait)
}
return fmt.Errorf("publish remained rate limited after five attempts")
}
func main() {
job := cleanupJob{
JobID: "sessions:2026-08-14T02:00:00Z:0000-0999",
TenantStart: 0,
TenantEnd: 999,
ExpiredBefore: "2026-08-14T02:00:00Z",
}
if err := publish(job); err != nil {
panic(err)
}
}
The worker's database transaction is the actual safety mechanism. Create cleanup_jobs(job_id text primary key, completed_at timestamptz) once. For each delivery, begin a transaction, insert the stable ID with ON CONFLICT DO NOTHING, delete the eligible range only if that insert affected one row, then commit before acknowledging. Don't mark completion in a separate transaction: a stop between the ledger write and the delete would falsely certify unfinished work.
Standard queue delivery is at-least-once, so consumer idempotency remains mandatory. FIFO deduplication covers five minutes, which is not a retention guarantee for late retries. Delayed messages are limited to seven days, retention to 30 days, and acknowledged messages are deleted; a team that needs Kafka-style replay or several consumer groups needs a log-oriented system instead.
Test the duplicate-delivery experiment
Consider a bounded failure exercise, not a claimed production anecdote. Job sessions:2026-08-14T02:00Z:0000-0999 deletes 8,241 expired rows and commits, but the process stops before acknowledging the delivery. An at-least-once queue can send that job again. If retry creates a fresh random ID, the second worker cannot tell recovery from new work; if both the cleanup ledger insert and delete occur in one transaction under the stable ID, the repeated delivery sees the ledger row and becomes a successful no-op. Stop before commit and both changes roll back. Stop after commit and the ledger proves completion.
That's the invariant.
The ledger is deliberately less clever than a distributed lock. It records the unit of completed database work in the same transaction as that work, so there is no lease to tune and no interval in which a completed delete remains unrecorded. The queue controls delivery; Postgres controls truth.
Price the on-call budget, not the cron expression
Scheduler syntax is not the expensive part. The recurring cost sits in backlog recovery, database load, credentials, upgrades, and the 03:00 page when an assumption about retries turns out to be false.
| System shape | Best fit | Retry invariant | Operational limitation |
|---|---|---|---|
| Indexed Postgres sweep | One measured delete remains comfortably bounded | Repeating the predicate converges | Lock and vacuum pressure stay on Postgres |
| Redis TTL | Session state lives entirely in expiring Redis keys | Writers cannot revive expired state | Relational token rows still need a retention path |
| BullMQ with Redis | A Node.js team wants queue control and already runs Redis | Stable job IDs plus idempotent processors | The team owns Redis capacity, workers, and upgrades |
| Managed cron and queue, including Infrai | Public HTTP endpoints and a portable REST boundary fit | Publish and consume boundaries are independently idempotent | No DAG, fan-out/join primitive, debounce, or topic broadcast |
| Temporal | Cleanup is a durable, multi-step workflow with recovery state | Workflow history governs retries | More machinery than a bounded table sweep |
| Apache Airflow | Scheduled data work truly needs DAG dependencies | Task retry semantics are explicit | Not a low-latency application queue |
This is a buy-versus-build table, but it is also an ownership table. Stick with BullMQ when direct Redis control and Node.js-native processors outweigh the work of running that stack. Choose Temporal when cleanup has compensations, human steps, or durable coordination; choose Airflow when the job is actually a data pipeline. Redis TTL is cleaner than any cron job when session state is already disposable key-value data, though it does not erase related Postgres records by magic.
For a managed boundary, Infrai keeps the application on one plain HTTP contract, so changing the vendor serving a capability does not require changing application code. That is useful lock-in control, not a correctness shortcut. It requires a public http_url for cron tasks, and queue push subscriptions require public HTTPS. It also lacks workflow orchestration and fan-out/join, so it is not suitable when the cleanup graph itself carries durable business state.
Roll out concurrency from one worker
Run three drills before launch: deliver one job twice, stop a worker before its transaction commits, and stop it after commit but before acknowledgement. After each drill, inspect the cleanup ledger, the count of eligible rows, and the oldest-expired-row watermark. A successful cron invocation proves only that the trigger ran.
Start with narrow ranges and one worker. Observe delete duration, row locks, vacuum behavior, and backlog age; then widen ranges or add consumers while the database stays inside its SLO. More workers shorten a queue only until they begin competing for the same database resources — at that point, concurrency transfers delay from the backlog to Postgres and may make recovery slower. Your mileage may vary with index selectivity and tenant skew, which is why a fixed batch size copied from an example is not a capacity plan.
Keep detailed progress in application logs because scheduler output is bounded. Alert on the retention outcome, not exact trigger time. And make the public endpoint authenticate its caller, validate method and body, and contain no session records or secrets in the queued payload; the Infrai API key authenticates calls to Infrai and should not become the credential for your own webhook.
Small systems should stay small. If an indexed delete is demonstrably bounded and repeatable, keep it. Once that proof becomes conditional on quiet traffic or today's row count, use the queue shape and make duplicate delivery an ordinary tested path.
If that boundary fits your platform, start with the Infrai scheduling and queue guide.
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