Performance Guide¶

Tips for optimizing performance when using the Bluetooth SIG Standards Library.

Performance Characteristics¶

Typical Performance¶

UUID resolution: ~10 μs
Simple characteristic parse: ~50 μs
Complex characteristic parse: ~200 μs
Memory footprint: Minimal (< 10 MB)

Bottlenecks¶

The library is optimized for parsing speed. Typical bottlenecks are:

BLE I/O operations - Reading from device (milliseconds)
Network latency - For remote devices
Connection management - Device discovery and pairing

The parsing itself is rarely the bottleneck.

Optimization Tips¶

1. Reuse Translator Instance¶

# ✅ Good - create once, reuse
translator = BluetoothSIGTranslator()
for data in sensor_readings:
    result = translator.parse_characteristic(uuid, data)

# ❌ Bad - creating new instances
for data in sensor_readings:
    translator = BluetoothSIGTranslator()  # Wasteful
    result = translator.parse_characteristic(uuid, data)

2. Batch Operations¶

When reading multiple characteristics, batch the BLE operations:

# ✅ Good - batch read, then parse
async with BleakClient(address) as client:
    # Read all characteristics at once
    battery_data = await client.read_gatt_char("2A19")
    temp_data = await client.read_gatt_char("2A6E")
    humidity_data = await client.read_gatt_char("2A6F")

    # Parse offline
    battery = translator.parse_characteristic("2A19", battery_data)
    temp = translator.parse_characteristic("2A6E", temp_data)
    humidity = translator.parse_characteristic("2A6F", humidity_data)

# ❌ Bad - connect/disconnect for each
for uuid in uuids:
    async with BleakClient(address) as client:
        data = await client.read_gatt_char(uuid)
        result = translator.parse_characteristic(uuid, data)

3. Use Direct Characteristic Classes¶

For repeated parsing of the same characteristic type:

# ✅ Good - direct characteristic access
from bluetooth_sig.gatt.characteristics import BatteryLevelCharacteristic

battery_char = BatteryLevelCharacteristic()
for data in battery_readings:
    value = battery_char.decode_value(data)

# ⚠️ Slower - goes through translator
translator = BluetoothSIGTranslator()
for data in battery_readings:
    result = translator.parse_characteristic("2A19", data)

4. Avoid Unnecessary Conversions¶

# ✅ Good - use bytearray directly
data = bytearray([85])
result = translator.parse_characteristic("2A19", data)

# ❌ Bad - unnecessary conversion
data = bytes([85])
result = translator.parse_characteristic("2A19", bytearray(data))

Profiling¶

To identify bottlenecks in your application:

import cProfile
import pstats
from bluetooth_sig import BluetoothSIGTranslator

def profile_parsing():
    translator = BluetoothSIGTranslator()
    data = bytearray([75])

    # Run many iterations
    for _ in range(10000):
        translator.parse_characteristic("2A19", data)

# Profile
cProfile.run('profile_parsing()', 'stats.prof')

# View results
stats = pstats.Stats('stats.prof')
stats.sort_stats('cumulative')
stats.print_stats(10)

Memory Optimization¶

Registry Caching¶

The registry is loaded once and cached. This is automatic and requires no configuration.

Cleanup¶

For long-running applications, there's minimal memory accumulation. No special cleanup needed.

Concurrent Operations¶

The library is thread-safe for reading operations:

import asyncio
from concurrent.futures import ThreadPoolExecutor

translator = BluetoothSIGTranslator()

def parse_in_thread(uuid, data):
    return translator.parse_characteristic(uuid, data)

# Parallel parsing (though rarely needed)
with ThreadPoolExecutor(max_workers=4) as executor:
        futures = [
            executor.submit(parse_in_thread, uuid, data)
            for uuid, data in sensor_data.items()
        ]
        results = [
            f.result() for f in futures
        ]

Note: Parsing is so fast that parallelization rarely provides benefits. Focus on parallelizing BLE I/O operations instead.

Real-World Performance¶

In typical applications:

import time

translator = BluetoothSIGTranslator()

# Time 1000 parses
start = time.perf_counter()
for _ in range(1000):
    translator.parse_characteristic("2A19", bytearray([75]))
elapsed = time.perf_counter() - start

print(f"1000 parses in {elapsed:.3f}s")
print(
    f"Average: {elapsed * 1000:.1f}μs per parse"
)
# Typical output: "1000 parses in 0.050s" (50μs avg)

The parsing overhead is negligible compared to BLE operations (typically 10-100ms per read).

Recommendations¶

Focus on BLE optimization - Connection management, batching
Reuse translator instances - Create once, use many times
Profile your application - Identify real bottlenecks
Don't over-optimize - Parsing is already fast