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Version: dev

Troubleshooting

This page covers common issues and their solutions.

Common Issues​

ImportError with Format Features​

# Solution: Install Row format support
pip install pyfory[format]

# Or install from source with format support
pip install -e ".[format]"

Slow Serialization Performance​

# Check if Cython acceleration is enabled
import pyfory
print(pyfory.ENABLE_FORY_CYTHON_SERIALIZATION)  # Should be True

# If False, Cython extension may not be compiled correctly
# Reinstall with: pip install --force-reinstall --no-cache-dir pyfory

Cross-Language Compatibility Issues​

# Use explicit type registration with consistent naming
f = pyfory.Fory(xlang=True)
f.register(MyClass, typename="com.package.MyClass")  # Use same name in all languages

Circular Reference Errors or Duplicate Data​

# Enable reference tracking
f = pyfory.Fory(ref=True)  # Required for circular references

# Example with circular reference
class Node:
    def __init__(self, value):
        self.value = value
        self.next = None

node1 = Node(1)
node2 = Node(2)
node1.next = node2
node2.next = node1  # Circular reference

data = f.dumps(node1)
result = f.loads(data)
assert result.next.next is result  # Circular reference preserved

Schema Evolution Not Working​

# Enable compatible mode for schema evolution
f = pyfory.Fory(xlang=True, compatible=True)

# Version 1: Original class
@dataclass
class User:
    name: str
    age: int

f.register(User, typename="User")
data = f.dumps(User("Alice", 30))

# Version 2: Add new field (backward compatible)
@dataclass
class User:
    name: str
    age: int
    email: str = "[email protected]"  # New field with default

# Can still deserialize old data
user = f.loads(data)
print(user.email)  # "[email protected]"

Type Registration Errors in Strict Mode​

# Register all custom types before serialization
f = pyfory.Fory(strict=True)

# Must register before use
f.register(MyClass, type_id=100)
f.register(AnotherClass, type_id=101)

# Or disable strict mode (NOT recommended for production)
f = pyfory.Fory(strict=False)  # Use only in trusted environments

Debug Mode​

Set environment variable BEFORE importing pyfory to disable Cython for debugging:

import os
os.environ['ENABLE_FORY_CYTHON_SERIALIZATION'] = '0'
import pyfory  # Now uses pure Python implementation

# This is useful for:
# 1. Debugging protocol issues
# 2. Understanding serialization behavior
# 3. Development without recompiling Cython

Error Handling​

Handle common serialization errors gracefully:

import pyfory
from pyfory.error import TypeUnregisteredError, TypeNotCompatibleError

fory = pyfory.Fory(strict=True)

try:
    data = fory.dumps(my_object)
except TypeUnregisteredError as e:
    print(f"Type not registered: {e}")
    # Register the type and retry
    fory.register(type(my_object), type_id=100)
    data = fory.dumps(my_object)
except Exception as e:
    print(f"Serialization failed: {e}")

try:
    obj = fory.loads(data)
except TypeNotCompatibleError as e:
    print(f"Schema mismatch: {e}")
    # Handle version mismatch
except Exception as e:
    print(f"Deserialization failed: {e}")

Development Setup​

git clone https://github.com/apache/fory.git
cd fory/python

# Install dependencies
pip install -e ".[dev,format]"

# Run tests
pytest -v -s .

# Run specific test
pytest -v -s pyfory/tests/test_serializer.py

# Format code
ruff format .
ruff check --fix .