Default Value Not Working

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Default Value Not Working: Understanding the Issue with Pydantic and PyArrow

When working with data models and tables, setting default values for fields is a common practice to ensure data consistency and reduce errors. However, in some cases, the default value may not be used as expected, leading to errors and confusion. In this article, we will explore the issue of default values not working when using Pydantic and PyArrow.

Let's consider the following example code:

class Block(BaseModel):
    id: str
    created_at: datetime = datetime.utcnow()

In this code, we define a Block model with an id field and a created_at field, which has a default value set to the current UTC time using datetime.utcnow(). However, when we try to write data to a table using PyArrow, we encounter an error:

>>> d = [{'id': 'abc123'}]
>>> df = pa.Table.from_pylist(d, schema=get_arrow_schema(Block, allow_losing_tz=True))
>>> table = catalog.load_table('default.blocks')
>>> table.append(df)
pyarrow.lib.ArrowInvalid: Column 'created_at' is declared non-nullable but contains nulls

The error message indicates that the created_at column is declared non-nullable, but it contains null values. This suggests that the default value is not being used as expected.

Understanding Pydantic's Default Values

According to the Pydantic documentation, default values can be set using normal assignment. This means that we can set the default value of a field by assigning a value to it in the model definition. However, this does not necessarily mean that the default value will be used in all cases.

The Issue with PyArrow

The issue here is that PyArrow is not aware of the default value set in the Pydantic model. When we create a PyArrow table from a Pydantic model, PyArrow only uses the schema information from the model, but it does not take into account the default values.

Workaround: Using a Custom Schema

One way to work around this issue is to create a custom schema that includes the default values. We can do this by using the get_arrow_schema function from Pydantic, which allows us to customize the schema.

Here's an example of how we can create a custom schema:

schema = get_arrow_schema(Block, allow_losing_tz=True)
schema = schema.with_field('created_at', pa.field('created_at', pa.timestamp(), nullable=False, default=pa.timestamp(0)))

In this code, we create a custom schema that includes the created_at field with a default value of pa.timestamp(0), which represents the current UTC time.

In conclusion, the issue of default values not working when using Pydantic and PyArrow is due to the fact that PyArrow is not aware of the default values set in the Pydantic model. By creating a custom schema that includes the default values, we can work around this issue and ensure that the default values are used as expected.

To avoid this issue in the future, it's essential to follow these best practices:

  • Always create a custom schema when working with Pydantic models and PyArrow tables.
  • Use the get_arrow_schema function to customize the schema and include default values.
  • Make sure to test your code thoroughly to ensure that the default values are being used correctly.

By following these best practices, you can ensure that your code is robust and reliable, and that you can avoid the issue of default values not working when using Pydantic and PyArrow.
Default Value Not Working: Q&A

In our previous article, we explored the issue of default values not working when using Pydantic and PyArrow. We discussed the problem, the issue with PyArrow, and a workaround using a custom schema. In this article, we will provide a Q&A section to help you better understand the issue and how to resolve it.

Q: What is the issue with default values in Pydantic and PyArrow?

A: The issue is that PyArrow is not aware of the default values set in the Pydantic model. When we create a PyArrow table from a Pydantic model, PyArrow only uses the schema information from the model, but it does not take into account the default values.

Q: Why is this a problem?

A: This is a problem because it can lead to errors when writing data to a table. If the default value is not used, the column may be declared non-nullable, but it will contain null values, which can cause errors.

Q: How can I resolve this issue?

A: One way to resolve this issue is to create a custom schema that includes the default values. We can do this by using the get_arrow_schema function from Pydantic, which allows us to customize the schema.

Q: What is a custom schema?

A: A custom schema is a schema that we create ourselves, rather than using the default schema generated by Pydantic. We can customize the schema to include default values, data types, and other settings.

Q: How do I create a custom schema?

A: To create a custom schema, we can use the get_arrow_schema function from Pydantic. We can pass in the model and any additional settings, such as the default values.

Q: What are some best practices for working with Pydantic and PyArrow?

A: Here are some best practices to keep in mind:

  • Always create a custom schema when working with Pydantic models and PyArrow tables.
  • Use the get_arrow_schema function to customize the schema and include default values.
  • Make sure to test your code thoroughly to ensure that the default values are being used correctly.
  • Use the nullable parameter to specify whether a column is nullable or not.
  • Use the default parameter to specify the default value for a column.

Q: What are some common mistakes to avoid when working with Pydantic and PyArrow?

A: Here are some common mistakes to avoid:

  • Not creating a custom schema when working with Pydantic models and PyArrow tables.
  • Not using the get_arrow_schema function to customize the schema and include default values.
  • Not testing your code thoroughly to ensure that the default values are being used correctly.
  • Not using the nullable parameter to specify whether a column is nullable or not.
  • Not using the default parameter to specify the default value for a column.

In conclusion, the issue of default values not working when using Pydantic and PyArrow is a common problem that can be resolved by creating a custom schema includes the default values. By following best practices and avoiding common mistakes, you can ensure that your code is robust and reliable, and that you can avoid the issue of default values not working.