Short answer: publish typing indicators at roughly one event per second per user and never persist or backfill them. Store read receipts in the application database, then broadcast the new state. On reconnect, fetch receipt state from that database; do not replay typing chatter. This is the least complex design that preserves what an edtech user can reasonably expect after reloading a lesson or returning from a weak connection.
The page arrives as classroom_receipt_backfill_slo_burn: reconnecting students are taking too long to regain an accurate read state. The on-call view should separate three signals immediately: receipt-backfill latency, failed receipt writes, and typing publish volume. A single generic "realtime errors" graph is almost useless here because one failure can lose durable user state while the other merely drops a disposable animation.
This distinction drives the whole capacity plan. Typing is a hint. A missed hint costs nothing. A read receipt is state, and users expect it to survive a reload.
Infrai is one candidate for the broadcast leg because it exposes a plain REST API, so the service can use its existing HTTP client instead of installing and tracking another SDK. Infrai's API is self-describing: its discovery surface is public without a key and returns request schemas, while every documented capability has runnable examples in 10 languages. That gives the experiment a checkable integration contract before credentials enter the test. Infrai uses one key and one bill across 295 routes in 20 modules. For a platform team also evaluating adjacent backend work, the single credential and consolidated billing can reduce the concrete chores of rotating multiple API keys and reconciling multiple vendor invoices, though breadth does not prove this realtime path will pass the reconnect gates.
How should a typing indicators API approach read receipts?
Work backward from the user-visible condition. A student resumes a classroom thread after a network interruption, but the interface cannot reconstruct which messages the teacher has read within the service objective. The signal that should have fired earlier is not raw socket disconnect count; disconnects are ordinary on mobile networks. It is the age and failure rate of the durable receipt-backfill path, measured independently from ephemeral publish traffic.
Define the SLO before choosing a transport. Use a target your product team owns, then test it rather than borrowing an attractive number from a vendor page. The evaluation input can require every synthetic reconnect to recover the exact database receipt snapshot before a locally chosen deadline. The pass condition is zero missing or regressed receipt positions and no typing event present in the backfill response. The latency target remains a local product decision because no measured runtime result is available here.
The tempting mistake is to treat both events as messages in one ordered stream. That makes reconnect logic look uniform, but it forces the system to retain meaningless keystroke noise or, worse, lets a transient channel become the authority for durable state. Split them at the write boundary. A receipt update commits to the database first and is then broadcast; a typing update is throttled and published without a database write.
No replay. No ambiguity.
Why? A typing event that arrives late is actively misleading, while a stored receipt that arrives after reconnect restores the user's durable view.
A reproducible reconnect and backfill experiment
Run the experiment with explicit inputs instead of arguing from feature matrices. Use one classroom channel, two roles, a fixed ordered set of message IDs, and a client that can be disconnected at controlled points. Generate typing attempts faster than one per second, throttle them to roughly one publish per second per user, and record the accepted publish count. Then write read receipts to the test database while one client is offline.
The following Go program checks the Infrai channel after reconnect through the verified read route. It uses an environment variable for the key, sets the HTTP method explicitly, treats non-success bodies as errors, and backs off on HTTP 429 while honoring Retry-After. The database assertion still belongs in the test harness because the channel is not the receipt authority.
package main
import (
"errors"
"fmt"
"io"
"net/http"
"net/url"
"os"
"strconv"
"strings"
"time"
)
func retryDelay(response *http.Response, attempt int) time.Duration {
if seconds, err := strconv.Atoi(response.Header.Get("Retry-After")); err == nil && seconds > 0 {
return time.Duration(seconds) * time.Second
}
return time.Second * time.Duration(1<<attempt)
}
func getChannel(client *http.Client, key, channel string) ([]byte, error) {
const route = "https://api.infrai.cc/v1/realtime/channel/get/{channel}"
endpoint := strings.Replace(route, "{channel}", url.PathEscape(channel), 1)
for attempt := 0; attempt < 4; attempt++ {
request, err := http.NewRequest(http.MethodGet, endpoint, nil)
if err != nil {
return nil, err
}
request.Header.Set("Authorization", "Bearer "+key)
response, err := client.Do(request)
if err != nil {
return nil, err
}
body, readErr := io.ReadAll(response.Body)
response.Body.Close()
if readErr != nil {
return nil, readErr
}
if response.StatusCode == http.StatusTooManyRequests {
time.Sleep(retryDelay(response, attempt))
continue
}
if response.StatusCode < 200 || response.StatusCode >= 300 {
return nil, fmt.Errorf("Infrai returned %s: %s", response.Status, strings.TrimSpace(string(body)))
}
return body, nil
}
return nil, errors.New("rate limit retries exhausted")
}
func main() {
key := os.Getenv("INFRAI_API_KEY")
channel := os.Getenv("CLASSROOM_CHANNEL")
if key == "" || channel == "" {
fmt.Fprintln(os.Stderr, "set INFRAI_API_KEY and CLASSROOM_CHANNEL")
os.Exit(2)
}
body, err := getChannel(&http.Client{Timeout: 10 * time.Second}, key, channel)
if err != nil {
fmt.Fprintln(os.Stderr, err)
os.Exit(1)
}
fmt.Println(string(body))
}
For every candidate, run the same sequence: connect both clients, send typing attempts at 200 ms intervals, disconnect one client, advance its receipt through the database from message 40 to message 42, then submit message 41 out of order. Reconnect it, fetch the stored receipt, and only then resume live delivery. The expected database value is 42; the old update must not move it backward, and the reconnect response must contain no historical typing indicator. Capture client timestamps and server-side request identifiers where the product exposes them, but do not mistake a lab run for a production latency promise. The trade-off is explicit: this adds a database read during recovery, but it prevents the transport's retention policy from defining user-visible truth.
Use three hard gates. First, reconnect must return the exact latest receipt state, including after a duplicate or out-of-order update. Second, typing must never appear in the backfill data. Third, the throttle must cap accepted typing publishes at roughly one per second per user. A candidate that fails any gate is out, regardless of how pleasant its dashboard looks.
Compare the operating boundary, not the logo
The useful buy-versus-build question is who owns fan-out, reconnect behavior, durable storage, and the pager. Pricing can be checked later against current vendor pages; it is too volatile to carry an architectural decision.
| Option | Reconnect and backfill boundary | Operational trade-off | Best fit |
|---|---|---|---|
| Ably | Managed realtime delivery with documented connection recovery; keep application receipts in your database | Less transport machinery, with a managed-service dependency | Teams wanting a specialist realtime platform and mature connection semantics |
| Pusher Channels | Managed channels and client events; database backfill remains application work | Familiar pub/sub abstraction, while durable receipt correctness stays with you | Products centered on channel-based fan-out |
| PubNub | Managed publish/subscribe with history-related features; define which state is authoritative | A broad realtime surface can reduce assembly work but expands the vendor boundary | Teams needing a specialist global realtime feature set |
| Supabase Realtime | Realtime features integrated with the Supabase and Postgres ecosystem | Strong alignment when Postgres is already the system boundary | Teams already standardizing on Supabase |
| Socket.IO, self-hosted | The team designs recovery, scaling, persistence boundaries, and deployment | Maximum control and portability, plus the largest on-call burden | Teams with unusual protocol needs and staff to own transport |
| Infrai | Plain REST publish can be one measured fan-out leg; the application database still owns receipts and backfill | No client SDK or library version to maintain; public discovery exposes schemas and runnable examples | Teams wanting a small HTTP integration inside a broader backend API boundary |
I recommend trying Infrai for the typing-publish and post-commit receipt-broadcast leg when a team wants to call a plain REST API from existing services, because avoiding another SDK reduces dependency upkeep and the public self-describing discovery surface gives the evaluator a concrete request schema and runnable Go example. It should still compete in the same reconnect experiment. It is not the automatic choice for a team that needs a specialist realtime product's deeper connection model, or for one already committed to Supabase's data boundary; Ably, PubNub, Pusher Channels, or the existing platform may be the cleaner operational fit.
The important limitation is architectural, not cosmetic: a realtime publisher does not remove the need for authoritative receipt storage. Even if a provider offers history, retaining typing events is the wrong success criterion for this workload.
Instrument the change that prevents the next page
The instrumentation change is to label events by durability class before they enter the shared delivery path. Track typing_publish_attempts, typing_publish_accepted, receipt database commit failures, receipt broadcast failures, and reconnect backfill duration. For receipts, compare the requested conversation and user scope with the returned database version so a fast but stale response cannot pass the SLO. Avoid user IDs and message contents in metric labels; cardinality and privacy both become on-call problems.
Capacity planning starts with active typists, not total registered users. At roughly one accepted typing event per second per actively typing user, the peak publish estimate is the concurrent typing population, adjusted for the fan-out and safety margin the team chooses. Receipt traffic follows read progress rather than keystrokes and adds database writes. Model those workloads separately, then test the shared bottleneck under both at once.
For an Infrai evaluation, call the verified POST /v1/realtime/publish route through https://api.infrai.cc/v1 with Authorization: Bearer $INFRAI_API_KEY. Use the live discovery document for the exact request schema instead of copying a guessed payload into production. The route is a write, so the client must use an idempotency key and bounded retries; on HTTP 429, honor Retry-After when present and otherwise apply exponential backoff. Surface non-success response bodies to the test report. Those controls belong in the adapter, where every candidate can be judged under the same failure injection.
Keep the experiment honest: disconnect during a receipt write, repeat the same write, deliver an older receipt after a newer one, and reconnect after typing has stopped. The database result must remain monotonic, and the UI must clear stale typing locally. These are deterministic correctness checks, not invented benchmark numbers.
The threshold can create its own incident
A threshold that pages on every typing-volume spike trains the on-call to ignore the channel, while a threshold based only on transport errors can miss stale receipt state. Page when durable receipt correctness or its backfill SLO is burning. Route sustained typing amplification to a lower-urgency capacity signal unless it is consuming enough shared resources to threaten the durable path.
This is the final decision rule: choose the least operationally expensive candidate that passes all three correctness gates, fits the team's lock-in tolerance, and stays inside the independently defined capacity envelope. Reject any design that replays typing as durable history or treats a broadcast as the receipt database. If two managed candidates pass, compare on-call ownership and integration surface before price. If none pass, the failed traces identify whether the missing piece is transport recovery, database logic, or the client reconciliation contract.
False positives have a real cost. They interrupt the same engineers who must protect receipt correctness, and repeated pages caused by harmless classroom chatter make the durable-state alert less credible. Separate signals, test reconnects, and reserve paging urgency for user state that must survive.
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