Daft: UDFs And `map_groups` Compatibility Issue

by Alex Johnson 48 views

Introduction

This article delves into a specific issue encountered while using User Defined Functions (UDFs) with the map_groups functionality in the Daft data processing framework. Specifically, the problem arises when attempting to replicate the behavior of daft.udf with newer alternatives like @daft.func or @daft.func.batch. We will explore the bug, the attempted solutions, the resulting errors, and potential workarounds. Understanding these nuances is crucial for effectively leveraging Daft's capabilities for data manipulation and analysis.

The Bug: Incompatibility with New UDFs

The core issue lies in the incompatibility between the new UDF decorators (@daft.func and @daft.func.batch) and the map_groups function. While the older daft.udf seems to work seamlessly, the newer approaches result in errors during the planning phase of the data processing pipeline. This discrepancy poses a challenge for users who are migrating to the latest Daft versions or trying to adopt the recommended UDF practices. Let's examine the details with code examples and error traces.

Reproducing the Issue

To illustrate the bug, we'll use a simplified example derived from the Daft documentation. This example aims to calculate the standard deviation for groups within a DataFrame. Here’s the code snippet that works using the older daft.udf:

import daft, statistics

df = daft.from_pydict({"group": ["a", "a", "a", "b", "b", "b"], "data": [1, 20, 30, 4, 50, 600]})
@daft.udf(return_dtype=daft.DataType.float64())
def std_dev(data):
    return [statistics.stdev(data)]
    
df = df.groupby("group").map_groups(std_dev(df["data"]))
df = df.sort("group")
df.show()

This code groups the DataFrame by the "group" column and then applies the std_dev UDF to calculate the standard deviation for each group. The result is then sorted and displayed. However, attempting to replicate this functionality with @daft.func.batch leads to a different outcome.

Attempt with @daft.func.batch

Intuitively, one might try using @daft.func.batch as it appears to be the closest equivalent to daft.udf for batch processing. Here’s the modified code:

@daft.func.batch(return_dtype=daft.DataType.float64())
def std_dev(data):
    return [statistics.stdev(data)]

However, this approach results in a planning-time error, as demonstrated in the following traceback:

Traceback (most recent call last):
  File "/Users/corygrinstead/Development/Daft/data/scratch.py", line 21, in <module>
    df = df.groupby("group").map_groups(std_dev(df["data"]))
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/corygrinstead/Development/Daft/daft/dataframe/dataframe.py", line 4872, in map_groups
    return self.df._map_groups(udf, group_by=self.group_by)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/corygrinstead/Development/Daft/daft/dataframe/dataframe.py", line 3267, in _map_groups
    builder = self._builder.map_groups(udf, list(group_by) if group_by is not None else None)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/corygrinstead/Development/Daft/daft/logical/builder.py", line 261, in map_groups
    builder = self._builder.aggregate([udf._expr], group_by_pyexprs)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
daft.exceptions.DaftCoreException: DaftError::ValueError Expressions in aggregations must be composed of non-nested aggregation expressions, got __main__.std_dev-bd3434f0-6d7e-4efe-9982-9ff137e8b548(col(data))

The error message, DaftError::ValueError Expressions in aggregations must be composed of non-nested aggregation expressions, indicates that the map_groups function, when used with @daft.func.batch, cannot handle the UDF in the way it expects. This suggests a deeper issue in how Daft's logical planning and execution engine interprets these newer UDFs within the context of aggregations and grouping operations.

Understanding the Error

To dissect this error, it's essential to understand how Daft handles UDFs and aggregations. In Daft, operations like groupby and map_groups often involve constructing an execution plan that optimizes data processing. This plan breaks down the operations into a series of steps that can be efficiently executed in parallel or on distributed systems. When using map_groups, Daft expects the UDF to be compatible with its aggregation framework.

The error message highlights that the expression within the aggregation (i.e., the std_dev UDF) is not a "non-nested aggregation expression." This means that the UDF's internal structure or the way it's being called doesn't align with Daft's expectations for aggregation operations. Essentially, Daft's planner cannot properly integrate the new UDF into its execution plan for map_groups.

Potential Causes and Workarounds

The root cause of this issue likely resides in the internal implementation details of the new UDF decorators and how they interact with Daft's aggregation engine. Possible factors contributing to this behavior include:

  1. Expression Tree Construction: The @daft.func and @daft.func.batch decorators might construct expression trees that are incompatible with the aggregation logic used by map_groups.
  2. Type Handling: Differences in how data types are handled between the old and new UDF mechanisms might lead to mismatches during planning.
  3. Execution Context: The execution context and data flow within map_groups might not be correctly propagated to the new UDFs.

Workarounds and Solutions

While a definitive solution may require updates to Daft's core implementation, several workarounds can be considered:

  1. Using daft.udf (Legacy): As demonstrated earlier, the older daft.udf decorator continues to function correctly with map_groups. If compatibility is paramount, sticking with daft.udf might be a temporary solution.

  2. Applying UDFs Outside map_groups: An alternative approach involves applying the UDF after the grouping operation. This might require reshaping the data or performing additional joins, but it can bypass the direct incompatibility with map_groups. For example:

    import daft, statistics

df = daft.from_pydict({"group": ["a", "a", "a", "b", "b", "b"], "data": [1, 20, 30, 4, 50, 600]})

@daft.func.batch(return_dtype=daft.DataType.float64())
def std_dev(data):
    return [statistics.stdev(data)]

grouped_df = df.groupby("group").agg(daft.col("data").list().alias("data_list"))
grouped_df = grouped_df.with_column("std_dev", std_dev(grouped_df["data_list"]))
grouped_df = grouped_df.sort("group")
grouped_df.show()

    In this workaround, we first aggregate the data into lists within each group and then apply the std_dev UDF to these lists. This approach circumvents the direct use of map_groups with the new UDF.

  3. Exploring Native Daft Functions: Daft provides a rich set of built-in functions for common operations. Before resorting to UDFs, consider whether a native Daft function can achieve the desired result. This can often lead to more efficient and robust code.

Conclusion

The incompatibility between new UDFs (@daft.func and @daft.func.batch) and the map_groups function in Daft presents a notable challenge for data processing workflows. The error, stemming from the aggregation framework's inability to handle these UDFs directly, necessitates careful consideration of alternative approaches. While the legacy daft.udf remains a viable option, workarounds such as applying UDFs outside map_groups or leveraging native Daft functions can provide effective solutions. As Daft continues to evolve, addressing this incompatibility will be crucial for ensuring a seamless and intuitive UDF experience.

For further reading on Daft and its functionalities, consider exploring the official Daft documentation and community resources. You can find more information on User Defined Functions and their usage in various contexts on the Apache Arrow documentation.