title: Schema Evolution sidebar_position: 8 id: schema_evolution license: | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

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Apache Fory™ supports schema evolution in compatible mode, allowing fields to be added or removed while maintaining compatibility. Compatible mode is enabled by default in both xlang and native mode.

Compatible readers also tolerate selected scalar field type changes when the value is lossless. A matched field can read between bool, str, numeric scalars, and Decimal when the converted value has the same logical value. For example, "true", "false", "0", and "1" can be read as booleans; "123" can be read as a numeric field that can hold 123; numbers and decimals can be read as canonical strings; and numeric widening or narrowing succeeds only when no precision or range is lost.

Scalar conversion is only applied to matched compatible fields, not to root values or collection elements. String-to-number conversion accepts finite ASCII decimal literals without whitespace, a leading +, Unicode digits, underscores, or special values such as NaN and Infinity. Invalid strings, out-of-range values, and lossy conversions fail with pyfory.error.ForyInvalidDataError during deserialization. Optional and nullable fields still compose with these conversions, but reference-tracked scalar type changes are incompatible.

Default Compatible Mode

import pyfory

f = pyfory.Fory()
native_f = pyfory.Fory(xlang=False)

pyfory.dataclass also supports slots=True:

@pyfory.dataclass(slots=True)
class SlotMessage:
    id: int

Schema Evolution Example

import pyfory
from dataclasses import dataclass

# Version 1: Original class
@dataclass
class User:
    name: str
    age: pyfory.Int32

f = pyfory.Fory(xlang=True)
f.register(User, name="User")
data = f.dumps(User("Alice", 30))

# Version 2: Add new field (backward compatible)
@dataclass
class User:
    name: str
    age: pyfory.Int32
    email: str = "unknown@example.com"  # New field with default

# Can still deserialize old data
user = f.loads(data)
print(user.email)  # "unknown@example.com"

Supported Changes

  • Add new fields: With default values
  • Remove fields: Old data with extra fields will be skipped
  • Reorder fields: Fields are matched by name, not position

Same-Schema Class Optimization

Use compatible=False only when the class schema used to deserialize every payload is always the same as the class schema used to serialize it, and you want faster serialization and smaller size. For xlang payloads, set compatible=False only after verifying that every language uses the same schema, or when native types are generated from Fory schema IDL.

f = pyfory.Fory(xlang=False, compatible=False)

For one dataclass, you can opt out of evolution metadata with pyfory.dataclass(evolving=False):

import pyfory

@pyfory.dataclass(evolving=False)
class SameSchemaMessage:
    id: int
    name: str

Best Practices

  1. Always provide default values for new fields
  2. Use name for cross-language compatibility
  3. Test schema changes before deploying
  4. Document schema versions for your team

Related Topics