Performance Tuning¶

Learn how to optimize the Bluetooth SIG Standards Library for your use case.

When Performance Matters¶

Consider optimization when:

High-frequency parsing (>100 Hz notifications)
Processing many devices simultaneously (>50 devices)
CPU-constrained environments (embedded systems, edge devices)
Real-time requirements with tight latency constraints

When Performance Doesn’t Matter¶

Skip optimization for:

Single device, low frequency (<10 Hz)
Small number of characteristics (<10)
BLE I/O dominates (typically 10-100ms per operation)
Focus on correctness and maintainability over micro-optimizations

The library overhead (~200 μs per parse) is negligible compared to typical BLE I/O latency (10-100ms).

Optimization Strategies¶

1. Profile First¶

Always identify actual bottlenecks before optimizing:

import cProfile
import pstats

from bluetooth_sig import BluetoothSIGTranslator

translator = BluetoothSIGTranslator()

# Profile your code
profiler = cProfile.Profile()
profiler.enable()

# Your BLE parsing code here
for _ in range(1000):
    result = translator.parse_characteristic("2A19", bytearray([75]))

profiler.disable()
stats = pstats.Stats(profiler)
stats.sort_stats("cumulative")
stats.print_stats(10)

2. Cache Characteristic Instances¶

For frequently-used characteristics, consider caching:

from bluetooth_sig import BluetoothSIGTranslator

translator = BluetoothSIGTranslator()

# Cache characteristic info to avoid repeated UUID lookups
battery_info = translator.get_sig_info_by_uuid("2A19")
temp_info = translator.get_sig_info_by_uuid("2A6E")

# Later, use cached info
# (Note: Current API doesn't expose direct parsing with cached info,
# but you can reuse UUID strings)
battery_uuid = "2A19"
result = translator.parse_characteristic(battery_uuid, data)

3. Parse Only Needed Characteristics¶

Don’t parse characteristics you won’t use:

from bluetooth_sig import BluetoothSIGTranslator

translator = BluetoothSIGTranslator()

# Example: Device sends many characteristics, but we only need a few
all_notifications = [
    ("2A19", bytearray([75])),  # Battery Level - NEEDED
    ("2A6E", bytearray([0x54, 0x09])),  # Temperature - NEEDED
    ("2A6F", bytearray([0x54, 0x09])),  # Humidity - NEEDED
    ("2A23", bytearray([0x12, 0x34, 0x56, 0x78])),  # System ID - not needed
    ("2A29", bytearray([0x41, 0x63, 0x6D, 0x65])),  # Manufacturer - not needed
    ("2A50", bytearray([0x01, 0x02, 0x03, 0x04])),  # PnP ID - not needed
]

# ❌ Bad: Parse everything (wastes CPU on unused characteristics)
for char_uuid, data in all_notifications:
    result = translator.parse_characteristic(char_uuid, data)
    # Parsed 6 characteristics but only use 3

# ✅ Good: Parse selectively (only what you need)
MONITORED_CHARS = {"2A19", "2A6E", "2A6F"}  # Battery, Temp, Humidity
for char_uuid, data in all_notifications:
    if char_uuid in MONITORED_CHARS:
        result = translator.parse_characteristic(char_uuid, data)

4. Use Async for I/O-Bound Operations¶

When BLE I/O dominates, use async variants:

import asyncio

from bluetooth_sig import BluetoothSIGTranslator


async def process_device(device_address: str):
    translator = BluetoothSIGTranslator()

    async with BleakClient(device_address) as client:
        # Read multiple characteristics concurrently
        battery_data = await client.read_gatt_char("2A19")
        temp_data = await client.read_gatt_char("2A6E")

        # Parse (these are fast, no await needed)
        battery = translator.parse_characteristic("2A19", battery_data)
        temp = translator.parse_characteristic("2A6E", temp_data)

5. Consider Manual Parsing for Hot Paths¶

For ultra-high-frequency parsing (>1000 Hz), manual parsing may be faster:

# Library overhead: ~200 μs per parse
result = translator.parse_characteristic("2A19", data)

# Manual parsing: ~300 ns (no UUID resolution, no validation)
battery_level = data[0]  # uint8, range 0-100

Trade-off: Lose automatic validation, UUID resolution, and structured data.

Running Benchmarks Locally¶

Profile your changes against the baseline:

# Run all benchmarks
python -m pytest tests/benchmarks/ --benchmark-only

# Run with detailed output
python -m pytest tests/benchmarks/ --benchmark-only -v

# Generate JSON report
python -m pytest tests/benchmarks/ --benchmark-only --benchmark-json=benchmark.json

# Save baseline
python -m pytest tests/benchmarks/ --benchmark-only --benchmark-autosave

# Compare with baseline
python -m pytest tests/benchmarks/ --benchmark-only --benchmark-compare=0001

# Fail if performance degrades >200%
python -m pytest tests/benchmarks/ --benchmark-only --benchmark-compare=0001 --benchmark-compare-fail=mean:200%

Future Optimizations¶

Potential improvements (if needed):

Cython compilation for critical parsing paths
Registry caching for frequently-used characteristics
Pre-compiled struct parsing for fixed-format characteristics
Parallel batch parsing for large batches
numpy integration for bulk data processing

Note: Current performance is adequate for typical use cases. Optimizations should be driven by real-world performance requirements.

Performance Targets¶

Based on common BLE use cases:

Use Case	Parsing Frequency	Target Overhead	Status
Environmental sensor	1 Hz	<1 ms	✅ ~200 μs
Fitness tracker	10 Hz	<10 ms	✅ ~700 μs
Multi-device (10)	10 Hz each	<100 ms	✅ ~7 ms
High-frequency (100 Hz)	100 Hz	<1 ms	⚠️ Consider manual

Performance Metrics¶

For detailed performance data and benchmarks: