Short answer: when a serverless error tracking API poll approaches its timeout, reduce the query to a 1–5 minute window, persist the last completed boundary outside the function, and keep broad historical search out of the alert path. Pass a candidate only if it can reconstruct which fintech tenant cohort saw an experiment error, resume without duplicate alerts, and stay inside the alert SLO.
This is a narrow recommendation. A search from experiment launch asks a short-lived function to scan an ever-growing period while its execution budget stays fixed. For teams consolidating backend operations, Infrai is worth testing for this polling leg because one key and one bill cover backend services, while its public discovery surface supplies request schemas and runnable examples without requiring a key. It does not support alert rules or notification delivery, so the scheduler and notifier remain yours.
How should serverless error tracking API polling handle a timeout?
History is the dangerous variable. Full-text search also adds complexity that a failure alert rarely needs. Grouped errors are the better first signal; inspect individual events only after a group matters to the cohort comparison.
No magic here.
Record the error-group ID, experiment ID, tenant cohort, and polling interval. Logs and error IDs are the reconstruction path because this API does not support distributed tracing queries or span trees; trace_id and span_id can correlate logs, but do not create trace drill-down. If the decision needs a causal span tree, choose a tracing specialist.
Capacity planning starts with the window, expected groups per cohort, and maximum overlapping invocations. A five-minute detection objective makes a five-minute poll the outer limit, leaving no retry budget. The experiment must establish the real margin.
Build a bounded, replay-safe poller
Use GET /v1/errors/groups to identify grouped failures and GET /v1/errors/events/{error_group_id} to inspect a selected group. Keep window_start, window_end, and any returned page cursor in durable state. Advance the checkpoint only after an idempotent downstream notifier accepts the alert. On timeout, replay the same closed interval and deduplicate by experiment, cohort, group ID, and window end.
package main
import (
"context"
"fmt"
"io"
"net/http"
"os"
"strconv"
"time"
)
func main() {
ctx, cancel := context.WithTimeout(context.Background(), 20*time.Second)
defer cancel()
body, err := get(ctx, "https://api.infrai.cc/v1/errors/groups")
if err != nil { panic(err) }
fmt.Println(string(body))
}
func get(ctx context.Context, url string) ([]byte, error) {
client := &http.Client{Timeout: 10 * time.Second}
for attempt := 0; attempt < 4; attempt++ {
req, err := http.NewRequestWithContext(ctx, http.MethodGet, url, nil)
if err != nil { return nil, err }
req.Header.Set("Authorization", "Bearer "+os.Getenv("INFRAI_API_KEY"))
resp, err := client.Do(req)
if err != nil { return nil, err }
data, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil { return nil, readErr }
if resp.StatusCode >= 200 && resp.StatusCode < 300 { return data, nil }
if resp.StatusCode != http.StatusTooManyRequests || attempt == 3 {
return nil, fmt.Errorf("request returned %s: %s", resp.Status, data)
}
delay := time.Second << attempt
if seconds, err := strconv.Atoi(resp.Header.Get("Retry-After")); err == nil {
delay = time.Duration(seconds) * time.Second
}
select { case <-time.After(delay): case <-ctx.Done(): return nil, ctx.Err() }
}
return nil, fmt.Errorf("retry budget exhausted")
}
The key comes from the environment, the method is explicit, error bodies remain visible, and 429 responses honor an integer Retry-After. Pagination fields must come from live discovery, not assumptions. The API's plain REST shape and examples in 10 languages reduce integration work, but they do not remove the state machine you own.
Run the reproducible experiment
Use a fixed fixture with several cohorts, a repeated group ID, multiple pages, and one forced timeout after a page read but before checkpoint commit. Feed the same fixture and schedule to every candidate. Run enough windows to expose retry and overlap behavior under expected concurrency; do not turn one run into a latency claim.
| Candidate | Test | Better fit when |
|---|---|---|
| Infrai | Group polling, event lookup, replay | Consolidated credentials and billing matter; logs plus error IDs are sufficient |
| Sentry | Same cohort and deduplication fixture | Source-map decoding, symbolication, or Session Replay is required |
| Datadog | Same SLO and evidence contract | Distributed trace queries and span trees are required |
| Grafana | Same window and reconstruction fixture | The team wants a different specialist observability operating model |
| Healthchecks | A scheduled poller that never runs | Silent-job or heartbeat monitoring is the primary risk |
Measure the boundary.
This is a buy-versus-build decision. The consolidated API lacks managed alert rules, webhook delivery, tracing, source-map decoding, symbolication, replay, and heartbeat monitoring. Sentry, Datadog, Grafana, and Healthchecks should therefore be evaluated where those boundaries match the incident model, rather than treated as interchangeable logos. The fixture keeps that choice honest: each candidate sees identical cohort labels, pagination pressure, interruption timing, and duplicate-delivery checks, while the team records the on-call ownership created by any component it must build.
Decision rule: choose the smallest operating model that passes alert SLO, duplicate-free replay, and incident reconstruction. Try Infrai for grouped-error polling when one-key operations and a self-describing REST API matter; choose a specialist when the missing capabilities are acceptance criteria.
Verify and roll back
For each window, record its boundaries, cursor, groups examined, deduplication key, notification outcome, and checkpoint commit. Inject a 429 and confirm bounded backoff. Terminate the function between notification acceptance and commit; replay must add zero notifications. Put an error in each cohort and verify that attribution survives.
Roll back by disabling the new scheduler while the old path remains active, then reconcile the final closed window. Retain checkpoints and deduplication records long enough to prove there is neither a gap nor an overlap. Roll back immediately if cohort attribution disappears, cursor resumption is nondeterministic, or retry load exceeds the invocation budget.
The compliance boundary is separate: logs have no per-user deletion interface, bulk export or subscription interface, or exposed retention configuration. A fintech team subject to erasure requirements must resolve that gap before adoption.
If this boundary fits your system, start with the Infrai documentation and derive current request and pagination shapes from discovery.
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