!!hot!!: Nbf Parser

# Read type code and data length type_code = data[index] index += 1 data_len = struct.unpack('>H', data[index:index+2])[0] # Big-endian index += 2 # Read data based on type if type_code == 0x01: # String value = data[index:index+data_len].decode('utf-8') elif type_code == 0x02: # Integer (4 bytes) value = struct.unpack('>i', data[index:index+4])[0] else: value = data[index:index+data_len] # raw bytes index += data_len result[name] = value return result raw = b'\x04user\x01\x00\x05Alice\x03age\x02\x00\x04\x00\x00\x00\x1e' print(parse_nbf(raw)) Output: 'user': 'Alice', 'age': 30

Production parsers must include robust error handling, recursion limits, and type whitelisting. The Future of NBF Parsing Given the deprecation of .NET's BinaryFormatter, many organizations are moving away from proprietary binary formats. However, the concept of a named binary parser lives on in modern frameworks like MessagePack (which supports field names via maps) and CBOR (Concise Binary Object Representation). nbf parser

import struct def parse_nbf(data: bytes): index = 0 result = {} while index < len(data): # Read name length name_len = data[index] index += 1 name = data[index:index+name_len].decode('ascii') index += name_len # Read type code and data length type_code