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ZeroHttpPump

ZeroHttpPump breaks a send into bounded cooperative phases so HTTP work does not behave like one large blocking slab. This page explains the payoff and the limits.

Why this page matters

This page explains how ZeroHttpPump fits into the wider ZeroKernel execution model, what problem it is meant to solve, and what trade-off you are actually accepting when you use it in production firmware. The goal is not to treat ZeroHttpPump as an isolated API call, but to understand where it sits inside bounded scheduling, queue discipline, fault visibility, and profile selection.

Read this topic as an operational contract. Start from the smallest working path, wire it into a lean profile first, and only expand into richer routing, diagnostics, or transport state after you can prove that the timing outcome is still worth the extra flash and RAM. That mindset is what keeps ZeroKernel useful on small boards instead of turning it into another bloated abstraction.

The safest pattern is always the same: define the runtime boundary, keep the hot path short, measure the effect with compare scripts, and only then scale complexity. The examples below are not filler; they show the smallest repeatable patterns you can lift into real firmware when you need clean integration instead of ad-hoc loops.

Three practical patterns

WiFi maintenance loop

Keep reconnect pacing separate from sensor cadence so link churn does not poison the hot path.

C++
    ZeroWiFiMaintainer wifi;
wifi.setBackoff(250, 2000);
wifi.tick(linkIsUp, attemptReconnect);
  
HTTP queue step pump

Queue the work first, then drain it cooperatively instead of letting one POST dominate the loop.

C++
    ZeroHttpPump pump;
pump.enqueue(payload);
pump.tick(connectStep, writeStep, finishStep);
  
MQTT publish drain

Bound publish attempts per tick and read the success rate next to queue depth, not in isolation.

C++
    ZeroMqttPump mqtt;
mqtt.enqueue(topicKey, value);
mqtt.tick(connectBroker, publishStep);
  

What to verify while you use it

  • Validate timing before you validate aesthetics. A cleaner API is not a win if fast misses rise.
  • Prefer the smallest profile that still matches the workload, then add optional modules only when the measured payoff is obvious.
  • Keep callbacks and transport steps bounded so watchdog, panic flow, and queue limits remain meaningful.

Common mistakes that make results misleading

  • Do not copy a demo pattern into production firmware without measuring it on the real board and real build profile you plan to ship.
  • Do not read success counters without reading queue depth, timing, and workload label next to them.
  • Do not enable heavier diagnostics and compatibility flags in a lean target just because the defaults looked convenient.

Recommended working sequence

Start from the smallest valid path

Boot the runtime, register the minimum useful task set, and prove that the baseline timing is clean before adding optional layers.

Add one layer, then measure it

Introduce routing, diagnostics, or transport one layer at a time so the cost and payoff remain obvious.

Publish only repeatable results

Update docs, charts, or public claims only after the same workload survives the same validation path more than once.

What it improves

The module reduces how much one send attempt dominates the main loop by pacing phases and retries through a bounded path instead of a single big application callback.

What it does not promise

It cannot make an underlying networking library fully non-blocking. It can only structure the work so the runtime can see, budget, and compare it more cleanly.

How to read results

Read sent_ok, sent_fail, total attempts, and queue depth together. More attempts can raise both ok and fail counts while still improving throughput.

ZeroHttpPump FAQ

Should every ESP node use it?

Use it when HTTP transport is a real source of pressure, not just because the module exists.

What is the safest way to validate this page on real hardware?

Start from the leanest profile that still matches the topic, run the narrowest compare script for this behavior, and only then move to heavier mixed workloads. Do not jump straight to a fully loaded build if the base timing is not yet proven.

When should I stop adding abstraction around this topic?

Stop when the extra layer no longer produces a measurable payoff. If RAM, flash, or execution cost rises while misses, throughput, or recovery do not improve, you are paying complexity without getting runtime value back.