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 = 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 nbf parser
Whether you are maintaining a legacy system or designing a new binary protocol, the lessons of the NBF parser remain relevant: import struct def parse_nbf(data: bytes): index = 0
A parser would process a byte stream like this: While its heyday was in early
For new projects, avoid creating a custom NBF parser from scratch unless you have extreme performance or legacy requirements. Instead, use established schemas like , FlatBuffers , or Cap'n Proto —they provide similar efficiency with better tooling and security. Conclusion The NBF parser is a specialized but powerful tool for interpreting binary data with named fields. While its heyday was in early .NET remoting and custom embedded systems, understanding its principles—length prefixes, type codes, and secure parsing—is essential for any developer working with low-level data interchange.
→ 0x04 (4 characters) Step 2: Read Name → "user" Step 3: Read Type Code → 0x01 (means string) Step 4: Read Data Length → 0x0005 (5 bytes) Step 5: Read Data → 0x416C696365 ("Alice") Step 6: Emit → result["user"] = "Alice"
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 = 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
Whether you are maintaining a legacy system or designing a new binary protocol, the lessons of the NBF parser remain relevant:
A parser would process a byte stream like this:
For new projects, avoid creating a custom NBF parser from scratch unless you have extreme performance or legacy requirements. Instead, use established schemas like , FlatBuffers , or Cap'n Proto —they provide similar efficiency with better tooling and security. Conclusion The NBF parser is a specialized but powerful tool for interpreting binary data with named fields. While its heyday was in early .NET remoting and custom embedded systems, understanding its principles—length prefixes, type codes, and secure parsing—is essential for any developer working with low-level data interchange.
→ 0x04 (4 characters) Step 2: Read Name → "user" Step 3: Read Type Code → 0x01 (means string) Step 4: Read Data Length → 0x0005 (5 bytes) Step 5: Read Data → 0x416C696365 ("Alice") Step 6: Emit → result["user"] = "Alice"
