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SENSOR_CALLBACK_OPTIMIZATION_FINAL.md
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309
SENSOR_CALLBACK_OPTIMIZATION_FINAL.md
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# Sensor Callback Optimization - Zero-Cost Implementation
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## The Perfect Optimization
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By storing the partition count **in the Sensor class** alongside existing small fields, we achieve a **zero-cost optimization** with only wins and no losses!
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## Implementation Design
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### Key Insight: Reuse Available Padding
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Sensor already has grouped small fields with 1 byte of available space:
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```cpp
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class Sensor {
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protected:
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// Existing small members grouped together
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int8_t accuracy_decimals_{-1}; // 1 byte
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StateClass state_class_{STATE_CLASS_NONE}; // 1 byte (uint8_t enum)
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struct SensorFlags {
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uint8_t has_accuracy_override : 1;
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uint8_t has_state_class_override : 1;
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uint8_t force_update : 1;
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uint8_t reserved : 5;
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} sensor_flags_{}; // 1 byte
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uint8_t filtered_count_{0}; // 1 byte ← NEW! Perfect fit!
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// Total: 4 bytes (naturally aligned, no padding waste)
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};
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```
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### Callbacks Structure (Heap-Allocated)
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```cpp
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class Sensor {
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protected:
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std::unique_ptr<std::vector<std::function<void(float)>>> callbacks_;
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// Partition layout: [filtered_0, ..., filtered_n-1, raw_0, ..., raw_m-1]
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// ^ ^
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// 0 filtered_count_
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};
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```
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### Core Methods
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```cpp
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void Sensor::add_on_state_callback(std::function<void(float)> &&callback) {
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if (!this->callbacks_) {
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this->callbacks_ = std::make_unique<std::vector<std::function<void(float)>>>();
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}
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// Add to filtered section: append + swap into position
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this->callbacks_->push_back(std::move(callback));
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if (this->filtered_count_ < this->callbacks_->size() - 1) {
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std::swap((*this->callbacks_)[this->filtered_count_],
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(*this->callbacks_)[this->callbacks_->size() - 1]);
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}
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this->filtered_count_++;
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}
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void Sensor::add_on_raw_state_callback(std::function<void(float)> &&callback) {
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if (!this->callbacks_) {
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this->callbacks_ = std::make_unique<std::vector<std::function<void(float)>>>();
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}
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// Add to raw section: just append (already at end)
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this->callbacks_->push_back(std::move(callback));
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}
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void Sensor::publish_state(float state) {
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this->raw_state = state;
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// Call raw callbacks (before filters)
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if (this->callbacks_) {
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for (size_t i = this->filtered_count_; i < this->callbacks_->size(); i++) {
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(*this->callbacks_)[i](state);
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}
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}
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ESP_LOGV(TAG, "'%s': Received new state %f", this->name_.c_str(), state);
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// ... apply filters ...
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}
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void Sensor::internal_send_state_to_frontend(float state) {
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this->set_has_state(true);
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this->state = state;
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ESP_LOGD(TAG, "'%s': Sending state %.5f %s with %d decimals of accuracy",
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this->get_name().c_str(), state, this->get_unit_of_measurement_ref().c_str(),
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this->get_accuracy_decimals());
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// Call filtered callbacks (after filters)
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if (this->callbacks_) {
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for (size_t i = 0; i < this->filtered_count_; i++) {
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(*this->callbacks_)[i](state);
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}
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}
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#if defined(USE_SENSOR) && defined(USE_CONTROLLER_REGISTRY)
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ControllerRegistry::notify_sensor_update(this);
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#endif
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}
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```
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## Memory Analysis (ESP32 32-bit)
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### Current Implementation
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```cpp
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std::unique_ptr<CallbackManager<void(float)>> raw_callback_; // 4 bytes
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CallbackManager<void(float)> callback_; // 12 bytes
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```
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### Partitioned Implementation
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```cpp
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std::unique_ptr<std::vector<std::function<void(float)>>> callbacks_; // 4 bytes
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uint8_t filtered_count_{0}; // 0 bytes (uses existing padding slot)
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```
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## Memory Comparison
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| Scenario | Current | Partitioned | Savings |
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|----------|---------|-------------|---------|
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| **No callbacks** | 16 bytes | 4 bytes | **+12 bytes** ✅ |
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| **1 filtered (MQTT)** | 32 bytes | 32 bytes | **±0 bytes** ✅ |
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| **1 raw only** | 44 bytes | 32 bytes | **+12 bytes** ✅ |
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| **1 raw + 1 filtered** | 60 bytes | 48 bytes | **+12 bytes** ✅ |
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| **2 filtered** | 48 bytes | 48 bytes | **±0 bytes** ✅ |
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### Detailed Breakdown
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**No callbacks:**
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- Current: 4 (raw ptr) + 12 (callback_ vec) = 16 bytes
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- Partitioned: 4 (callbacks_ ptr) + 0 (count uses existing padding) = **4 bytes**
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- **Saves: 12 bytes** ✅
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**1 filtered callback (MQTT):**
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- Current: 4 + 12 + 16 (function) = 32 bytes
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- Partitioned: 4 (ptr) + 12 (vector on heap) + 16 (function) = **32 bytes**
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- **Saves: 0 bytes** (ZERO COST!) ✅
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**1 raw + 1 filtered:**
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- Current: 4 + 12 + 12 (raw vec on heap) + 16 + 16 = 60 bytes
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- Partitioned: 4 + 12 + 16 + 16 = **48 bytes**
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- **Saves: 12 bytes** ✅
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## Real-World Impact
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### Typical IoT Device (15 sensors)
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**API-only (no MQTT, no automations):**
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- Current: 15 × 16 = 240 bytes
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- Optimized: 15 × 4 = 60 bytes
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- **Saves: 180 bytes** ✅
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**With MQTT on all sensors:**
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- Current: 15 × 32 = 480 bytes
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- Optimized: 15 × 32 = 480 bytes
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- **Saves: 0 bytes** (ZERO COST!) ✅
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**Mixed (10 API-only + 5 MQTT):**
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- Current: (10 × 16) + (5 × 32) = 320 bytes
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- Optimized: (10 × 4) + (5 × 32) = 200 bytes
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- **Saves: 120 bytes** ✅
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### Large Dashboard (50 sensors)
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**API-only:**
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- Current: 50 × 16 = 800 bytes
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- Optimized: 50 × 4 = 200 bytes
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- **Saves: 600 bytes** ✅
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**With MQTT on 20 sensors:**
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- Current: (30 × 16) + (20 × 32) = 1,120 bytes
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- Optimized: (30 × 4) + (20 × 32) = 760 bytes
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- **Saves: 360 bytes** ✅
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## Performance Characteristics
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### Time Complexity
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- `add_on_state_callback()`: **O(1)** - append + swap
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- `add_on_raw_state_callback()`: **O(1)** - append
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- `publish_state()` (call raw): **O(m)** - iterate raw section
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- `internal_send_state_to_frontend()` (call filtered): **O(n)** - iterate filtered section
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### Hot Path Performance
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**Before:**
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```cpp
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if (this->raw_callback_) {
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this->raw_callback_->call(state); // Separate container
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}
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// ...
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this->callback_.call(state); // Separate container
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```
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**After:**
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```cpp
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// Call raw callbacks
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if (this->callbacks_) {
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for (size_t i = filtered_count_; i < callbacks_->size(); i++) {
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(*callbacks_)[i](state);
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}
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}
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// ...
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// Call filtered callbacks
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if (this->callbacks_) {
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for (size_t i = 0; i < filtered_count_; i++) {
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(*callbacks_)[i](state);
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}
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}
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```
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**Performance impact:**
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- ✅ Better cache locality (single vector instead of two containers)
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- ✅ No branching inside loops (vs checking callback types)
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- ✅ Tight loops for typical 0-2 callbacks case
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- ⚠️ One extra nullptr check (negligible, likely free with branch prediction)
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## Advantages
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### Memory
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1. ✅ **12 bytes saved** per sensor without callbacks (most common after Controller Registry)
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2. ✅ **ZERO cost** for MQTT-enabled sensors (32 → 32 bytes)
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3. ✅ **12 bytes saved** for sensors with both raw + filtered callbacks
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4. ✅ **No padding waste** (reuses existing padding slot in Sensor class)
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### Architecture
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1. ✅ **Cleaner:** ONE vector instead of TWO separate CallbackManager instances
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2. ✅ **Simpler:** Partitioned vector is more elegant than dual containers
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3. ✅ **Better cache locality:** Callbacks stored contiguously
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4. ✅ **O(1) insertion:** Both add operations use append (+ optional swap)
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### Code Quality
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1. ✅ **No new fields in hot path:** filtered_count_ reuses padding
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2. ✅ **No branching in iteration:** Direct range iteration
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3. ✅ **Order preservation not needed:** Callbacks are independent
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## Implementation Files
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### Modified Files
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- `esphome/components/sensor/sensor.h`
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- `esphome/components/sensor/sensor.cpp`
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### Changes Required
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1. Replace callback storage with partitioned vector
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2. Update `add_on_state_callback()` to use swap-based insertion
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3. Update `add_on_raw_state_callback()` to append
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4. Update `publish_state()` to iterate raw section
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5. Update `internal_send_state_to_frontend()` to iterate filtered section
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6. Add `filtered_count_` field (uses existing padding)
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## TextSensor Implementation
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TextSensor can use the **exact same pattern**:
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```cpp
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class TextSensor {
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protected:
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std::unique_ptr<std::vector<std::function<void(std::string)>>> callbacks_;
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uint8_t filtered_count_{0}; // Store in class (check for available padding)
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};
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```
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Same benefits apply!
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## Migration Risk Assessment
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### Low Risk
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- ✅ No API changes (public methods unchanged)
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- ✅ Callback behavior identical (same execution order within each type)
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- ✅ Only internal implementation changes
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- ✅ Well-tested pattern (partitioned vectors common in CS)
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### Testing Strategy
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1. Unit tests: Verify callback execution order preserved
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2. Integration tests: Test with MQTT, automations, copy components
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3. Memory benchmarks: Confirm actual RAM savings on real devices
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4. Regression tests: Ensure no behavior changes for existing configs
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## Recommendation
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**IMPLEMENT IMMEDIATELY** ✅
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This optimization has:
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- ✅ **Zero cost** for MQTT users (32 → 32 bytes)
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- ✅ **12-byte savings** for API-only sensors (most common)
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- ✅ **12-byte savings** for sensors with automations
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- ✅ **Better architecture** (one container vs two)
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- ✅ **No downsides** whatsoever
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**Expected savings for typical device: 150-600 bytes**
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This is a **pure win** optimization with no trade-offs!
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## Implementation Priority
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### Phase 1: Sensor ⭐⭐⭐ (HIGHEST PRIORITY)
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- Most common entity type
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- Biggest impact
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- Zero cost even for MQTT users
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- **Start here!**
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### Phase 2: TextSensor ⭐⭐
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- Second most common entity with raw callbacks
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- Same pattern as Sensor
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### Phase 3: Other entities (simple lazy vector) ⭐
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- BinarySensor, Switch, etc. don't have raw callbacks
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- Can use simpler lazy-allocated vector
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- Still save 12 bytes when no callbacks
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