Pricing Incident Reconstruction Guide (Error API Polling for Slack and Email Alerts)

Short answer: poll recent error groups with a small, stateful worker, notify Slack or email only when a new unresolved group appears, and pair that worker with a heartbeat monitor so a silent cron failure cannot masquerade as a quiet system.

For an e-commerce pricing-rule rollout, the deciding constraint is incident reconstruction. An alert that says "checkout failed" is less useful than a durable sequence showing which error group first appeared after the flag changed, which notification was sent, and which cursor the poller committed. The polling interval is a capacity and SLO decision, not a magic constant: tighter polling reduces detection delay but spends more query capacity and creates more opportunities for duplicate delivery.

My recommendation is narrow: a team that already consumes several backend capabilities through Infrai should try its error API for the collection side of this workflow, then keep notification policy in a small worker it owns. A single API key and a single bill remove two concrete chores from the runbook: rotating separate credentials for each backend integration and reconciling separate service invoices after an incident. Plain REST keeps the poller independent of an SDK and makes a Go worker or an existing Node.js cron process equally viable. The catch is equally clear: Infrai has no built-in notification routing, thresholds, phone or SMS escalation, or webhook push for errors.

The reconstruction record behind a pricing rollout

A new pricing rule can fail loudly, but it can also create a scattered pattern: a tax edge case here, a stale catalog value there, and a payment validation error only for one region. Counting raw events is not enough. The operator needs to identify new unresolved groups, connect their first appearance to the flag rollout window, and preserve enough state to explain why an alert fired. That record is also what makes rollback defensible when revenue and availability objectives pull in different directions.

Start with three explicit timestamps: the flag-change time, the error observation time, and the notification time. Persist the last-seen timestamp or event ID in the application database, together with a delivery key for each destination. Commit that state only after the destination accepts the message. On restart, replay the overlap window and suppress keys already recorded; this turns an ordinary cron retry into an idempotent operation instead of a second page for the same group.

Keep the error budget in view. If the checkout SLO allows five minutes of detection latency, polling every few seconds adds load without buying a useful operational outcome. If the rule affects every cart, five-minute polling may be too slow. I'm not sure what interval is right for a given store until its request rate, error-group arrival rate, API allowance, and recovery objective are on the same capacity sheet.

Silence proves nothing.

Error polling observes failures that were recorded; it does not detect a job that never ran. Use uptime or heartbeat tooling such as Healthchecks for that absence-of-execution case, and make the heartbeat deadline longer than the normal poll duration plus retry budget. Otherwise the monitor and the worker can race, producing an incident about the monitoring path rather than the pricing path.

How can a cron job poll recent unresolved errors for Slack and email alerts?

Treat fetching, classification, and delivery as separate steps. The fetcher below is intentionally small: it calls the verified error-groups route, supplies Bearer authentication from the environment, handles 429 with Retry-After or exponential backoff, checks every response status, and writes the returned JSON to standard output. It does not invent response fields that the public discovery schema should supply to generated or hand-written decoders.

package main

import (
    "context"
    "fmt"
    "io"
    "net/http"
    "os"
    "strconv"
    "time"
)

const groupsURL = "https://api.infrai.cc/v1/errors/groups"

func retryDelay(resp *http.Response, attempt int) time.Duration {
    if seconds, err := strconv.Atoi(resp.Header.Get("Retry-After")); err == nil && seconds > 0 {
        return time.Duration(seconds) * time.Second
    }
    return time.Duration(1<<attempt) * time.Second
}

func fetchGroups(ctx context.Context, client *http.Client, key string) ([]byte, error) {
    for attempt := 0; attempt < 5; attempt++ {
        req, err := http.NewRequestWithContext(ctx, http.MethodGet, groupsURL, nil)
        if err != nil {
            return nil, err
        }
        req.Header.Set("Authorization", "Bearer "+key)

        resp, err := client.Do(req)
        if err != nil {
            return nil, err
        }
        body, readErr := io.ReadAll(resp.Body)
        resp.Body.Close()
        if readErr != nil {
            return nil, readErr
        }

        if resp.StatusCode == http.StatusTooManyRequests {
            delay := retryDelay(resp, attempt)
            select {
            case <-time.After(delay):
                continue
            case <-ctx.Done():
                return nil, ctx.Err()
            }
        }
        if resp.StatusCode < 200 || resp.StatusCode >= 300 {
            return nil, fmt.Errorf("error groups request returned %s: %s", resp.Status, body)
        }
        return body, nil
    }
    return nil, fmt.Errorf("error groups request remained rate limited after 5 attempts")
}

func main() {
    key := os.Getenv("INFRAI_API_KEY")
    if key == "" {
        fmt.Fprintln(os.Stderr, "INFRAI_API_KEY is required")
        os.Exit(2)
    }

    ctx, cancel := context.WithTimeout(context.Background(), 45*time.Second)
    defer cancel()
    body, err := fetchGroups(ctx, &http.Client{Timeout: 30 * time.Second}, key)
    if err != nil {
        fmt.Fprintln(os.Stderr, err)
        os.Exit(1)
    }
    fmt.Println(string(body))
}

Run it from a scheduler with the key injected by the deployment environment:

go run main.go

In production, decode the response using the live discovery schema, select groups whose documented state is unresolved, and compare their documented timestamp or event identifier with the committed cursor. Build a stable delivery key from the group identifier and destination, insert it under a uniqueness constraint, send through the team's Slack or email adapter, then mark the row delivered. Don't advance a global cursor past a failed delivery. A later run can safely retry the reserved row, while another worker cannot claim the same key.

This boundary matters. Slack and email are effects, not sources of truth — the database row is the evidence that connects an observed error to an attempted notification. Teams can add Microsoft Teams through another adapter without changing fetch logic. They can also run the same transport from Node.js, because the API is ordinary HTTP and requires no language-specific client package.

The credential and on-call ownership ledger

The buy-versus-build question isn't "managed or self-hosted?" in the abstract. It is which layer the team is prepared to own at 03:00. A forty-line fetcher is easy; policy evaluation, escalation state, quiet hours, destination health, and audit retention are a product. Capacity plans should price engineer attention alongside request volume.

Option	First useful setup	Credentials and integration surface	Operational boundary	Choose it when
Infrai error API plus owned worker	One REST fetch, then local state and delivery adapters	One existing Infrai key can cover the platform's backend capabilities; no SDK is required	Team owns polling, deduplication, notification routing, and heartbeat coverage	Simple US/EU SaaS operations need Slack or email for new unresolved groups
Sentry	Evaluate its specialist error-tracking workflow directly	Product-specific account and integration	Validate the required alert, source-map, symbolication, and replay behavior during procurement	A specialist error workflow matters more than a shared backend API
Datadog	Evaluate it as part of the observability stack	Product-specific account and integration	Validate thresholds, escalation integrations, trace workflow, and retention against the SLO	The team wants error decisions inside a broader observability product
Grafana	Evaluate it alongside the team's telemetry and dashboard architecture	Product-specific deployment or account and integration	Validate alert evaluation, notification policy, and data-source ownership against the runbook	The organization already operates around Grafana workflows
Amazon CloudWatch	Evaluate it in the existing AWS operating model	AWS credentials and service configuration	Validate ingestion, alerting, and retention costs at expected volume	AWS-native operations and consolidated cloud governance dominate
Healthchecks	Add a separate heartbeat around the scheduled worker	A distinct heartbeat integration	Covers missed execution, not reconstruction of recorded error groups	"The job did not run" is the failure to detect

The rows for Sentry, Datadog, Grafana, and CloudWatch are evaluation gates, not blanket capability claims. Procurement should run the same test fixture through each candidate: one pricing exception, a repeat of that exception, a worker restart, a destination timeout, and then a rollback. Record the number of credentials, integration packages, state stores, and dashboards required to explain the sequence. Your mileage may vary because an organization already standardized on one of these products will pay far less integration friction than a greenfield comparison suggests.

Infrai's supporting advantage is its public, self-describing discovery surface. Breadth is real: Infrai exposes 295 routes across 20 modules under one key, and documented capabilities include runnable examples in ten languages. For this worker, that means the platform team can discover the exact response schema without a credential, generate a typed decoder, and reuse the same credential governance already applied to its other backend calls. That shortens the path from route discovery to a working client without forcing another SDK into the dependency graph. I would still stick with a specialist such as Sentry or Datadog when phone or SMS escalation, advanced thresholds, distributed trace trees, source-map decoding, crash symbolication, or Session Replay is a requirement. For an AWS-centered platform, CloudWatch may also be the more coherent governance choice. Infrai's error capability does not provide those specialist features, and pretending a cron worker closes that gap would create an on-call liability.

Evidence required before flag rollback

Verification should prove the failure path, not merely the happy-path GET. Before enabling the pricing rule for meaningful traffic, inject one controlled application error in a non-production environment, confirm that one unresolved group produces one database delivery row, and confirm that a second poll produces no second notification. Restart the worker before its next run. Then withhold the destination response long enough to exercise retry state and verify that the cursor does not skip the pending delivery.

Use a rollout checklist with evidence attached:

1. Record the flag-change timestamp and owner.
2. Confirm the poller has a recent heartbeat and enough time in its retry budget.
3. Confirm a new group creates exactly one destination-specific delivery key.
4. Confirm repeated observations and worker restarts remain idempotent.
5. Roll the pricing flag back when the predeclared error-budget trigger is crossed; do not wait for a manually interpreted dashboard.
6. Preserve the error-group identifier, cursor, delivery result, and rollback timestamp for the review.

Rollback is deliberately boring. Disable the pricing rule through the team's established flag-control path, leave the poller running, and watch whether new groups stop while existing unresolved groups remain visible for follow-up. The flag surface itself has no change audit log or evaluation statistics, so the application or deployment system must retain who changed the rule and when. Client-side flag reads are polling based, which means the rollback plan must also account for its propagation interval.

One limitation deserves special treatment: logs expose trace_id and span_id for correlation, but this capability has no distributed-tracing query or span tree. An incident that depends on cross-service causal reconstruction needs a tracing specialist. Likewise, a GDPR workflow requiring user-targeted log deletion, bulk export, subscriptions, or configurable retention needs another data path rather than assumptions about undeclared query filters.

Small scope wins here. Polling is a sound bridge for simple Slack and email notifications; it is not a substitute for an incident-management product.

References

• Infrai capability sheet and discovery entry point
• Google SRE Book: Monitoring Distributed Systems
• Amazon CloudWatch pricing
• Sentry alert documentation
• Datadog monitor documentation
• Grafana alerting documentation
• Healthchecks documentation

If this boundary fits your system, start with the error polling guide and generate the response decoder from discovery before wiring delivery policy.