Resolving “Compiled regex exceeds size limit” Error in Pydantic

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What will you learn?

In this tutorial, you will delve into understanding and resolving the “Compiled regex exceeds size limit of 10485760 bytes” error in Pydantic. You will explore the reasons behind this issue and discover effective strategies to handle large regex patterns within your Pydantic models without encountering the size limit error.

Introduction to Problem and Solution

Encountering the “Compiled regex exceeds size limit” error message in Pydantic often occurs when working with extensive regular expressions. Python imposes a memory limit of 10 MB (10485760 bytes) on compiled regex objects, leading to this error. To overcome this limitation, you can break down large regex patterns or utilize external validation libraries for efficient handling.

Code Solution

from pydantic import BaseModel, validator
import re

class MyModel(BaseModel):
    text: str

    @validator('text')
    def check_text(cls, v):
        part1 = r'SomePartOfYourRegex'
        part2 = r'AnotherPartOfYourRegex'

        if not re.match(part1, v) or not re.match(part2,v):
            raise ValueError('Validation failed!')
        return v

# Copyright PHD

Explanation

To address the issue of exceeding the compiled regex size limit in Pydantic, we adopt a modular approach by breaking down extensive regex patterns into smaller components. By using decorators and validators, we can validate different parts of the input data individually while ensuring compliance with Python’s memory constraints.

    1. How does Python enforce the compiled regex size limit? Python monitors memory allocations for objects like compiled regular expressions to maintain efficiency and prevent memory-related issues.

    2. Can I increase the size limit for compiled regular expressions? Directly modifying Python’s limits is complex and discouraged due to compatibility concerns; consider alternative approaches like pattern segmentation.

    3. What are the benefits of breaking down large regex patterns? Breaking down patterns enhances readability, maintainability, facilitates targeted testing, and improves overall code quality.

    4. Is there performance overhead in using multiple smaller regexes compared to one large one? While minor overhead may exist due to additional function calls, it is generally insignificant compared to managing excessively large single patterns.

    5. Are there third-party libraries suited for handling huge patterns? Libraries like regex offer advanced features beyond Python’s re module, catering to specific needs such as recursive patterns or extensive character sets.

    6. Can pre-compiling assist in managing large patterns better? Pre-compilation benefits repeated usage scenarios by saving compilation time but does not directly address memory constraints on individual pattern sizes.

Conclusion

Effectively managing large regular expression patterns in Pydantic involves structuring validation logic creatively. By segmenting validations into smaller components, you can uphold data integrity checks while adhering to Python’s internal constraints on compiled regex sizes.

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