Daft.lit Window Function: Why Results Differ?

If you've encountered discrepancies in the results of window functions when using daft.lit(1).over(window) compared to DuckDB or PySpark, you're not alone. This article delves into a specific bug report highlighting these differences, offering a detailed explanation and potential solutions. We will explore the intricacies of window functions, compare the behavior across different platforms, and provide insights to ensure consistent results in your data processing pipelines.

Understanding the Bug: Discrepancies in Window Function Results

The core of the issue lies in the inconsistent output when using the daft.lit(1).over(window) function in the Daft data processing framework. Specifically, the results obtained differ from those produced by DuckDB and PySpark, two other popular data processing engines. This inconsistency can lead to significant problems, especially when migrating or comparing data workflows across different platforms. To understand the depth of the problem, we will dissect a code example that highlights the difference.

To truly grasp the scope of this issue, it’s essential to dive into a reproducible example. The following code snippet demonstrates the problem clearly:

import daft
from pyspark.sql import SparkSession
import pyspark.sql.functions as F
from pyspark.sql.window import Window as W

data = {"a": [1, 1, 2], "i": [0, 1, 2]}

print("daft")
print(
 daft.from_pydict(data)
 .with_column(
 "b", daft.functions.count(daft.lit(1)).over(daft.Window().partition_by("a"))
 )
 .collect()
)

session = SparkSession.builder.getOrCreate()
rows = [
 {key: value[i] for key, value in data.items()}
 for i in range(len(data[next(iter(data.keys()))]))
]
df = session.createDataFrame(rows)
df = df.withColumn("b", F.count(F.lit(1)).over(W.partitionBy("a")))
print("pyspark")
df.show()

When you run this code, you'll observe that Daft produces the following output:

daft
╭───────┬───────┬────────╮
│ a ┆ i ┆ b |
│ --- ┆ --- ┆ --- |
│ Int64 ┆ Int64 ┆ UInt64 |
╞═══════╪═══════╪════════╡
│ 1 ┆ 0 ┆ 1 |
├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 1 ┆ 1 ┆ 1 |
├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
│ 2 ┆ 2 ┆ 1 |
╰───────┴───────┴────────╯

However, PySpark yields a different result:

+---+---+---+
| a| i| b|
+---+---+---+
| 1| 0| 2|
| 1| 1| 2|
| 2| 2| 1|
+---+---+---+

The critical difference lies in the b column. In Daft, the count within the window partitioned by a always results in 1, whereas in PySpark, the count correctly reflects the number of rows within each partition. This discrepancy indicates a potential bug in Daft's window function implementation.

The expected behavior, as indicated by the bug report, is that Daft should produce the same results as PySpark (and DuckDB, which also aligns with PySpark in this case). The PySpark output correctly calculates the count within each partition, making it the benchmark for the expected outcome.

This divergence highlights the importance of thorough testing and validation when using different data processing frameworks. It's crucial to ensure that the results are consistent, especially when migrating or comparing workflows.

Deep Dive: Reproducing the Issue and Analyzing the Code

To reproduce this issue, you can execute the provided Python code snippet in an environment with Daft and PySpark installed. The code performs a windowed count operation using daft.lit(1).over(window) and compares the results with those obtained using PySpark. This allows for a direct side-by-side comparison of the outputs.

Let's break down the code step by step:

1. Importing Libraries:

   import daft
   from pyspark.sql import SparkSession
   import pyspark.sql.functions as F
   from pyspark.sql.window import Window as W
   

   This section imports the necessary libraries: daft for Daft dataframes, SparkSession and pyspark.sql.functions for PySpark dataframes, and pyspark.sql.window for window functions in PySpark.

2. Defining the Data:

   data = {"a": [1, 1, 2], "i": [0, 1, 2]}
   

   Here, a dictionary data is defined, representing a simple dataset with two columns, a and i. This data will be used to create dataframes in both Daft and PySpark.

3. Daft Implementation:

   print("daft")
   print(
   daft.from_pydict(data)
   .with_column(
   "b", daft.functions.count(daft.lit(1)).over(daft.Window().partition_by("a"))
   )
   .collect()
   )
   

   This block of code creates a Daft dataframe from the data dictionary. It then adds a new column b by applying a window function. The daft.functions.count(daft.lit(1)).over(daft.Window().partition_by("a")) part calculates the count of rows within each partition defined by the values in column a. The daft.lit(1) ensures that each row contributes 1 to the count. The result is then collected and printed.

4. PySpark Implementation:

   session = SparkSession.builder.getOrCreate()
   rows = [
   {key: value[i] for key, value in data.items()}
   for i in range(len(data[next(iter(data.keys()))]))
   ]
   df = session.createDataFrame(rows)
   

df = df.withColumn("b", F.count(F.lit(1)).over(W.partitionBy("a"))) print("pyspark") df.show() ```

This section sets up a PySpark session and creates a PySpark dataframe from the same `data` dictionary. Similar to the Daft implementation, it adds a new column `b` using a window function. The `F.count(F.lit(1)).over(W.partitionBy("a"))` part performs the same count operation within partitions defined by column `a`. The result is then displayed using `df.show()`.

By running this code, you can clearly see the discrepancy in the b column between the Daft and PySpark outputs. This discrepancy highlights a potential issue with Daft's window function implementation.

Expected Behavior and Implications of the Bug

The expected behavior for the window function is to count the number of rows within each partition specified by the partition_by clause. In this case, the data is partitioned by column a, which has values 1 and 2. For the rows where a is 1, there are two rows, so the count should be 2. For the row where a is 2, there is only one row, so the count should be 1.

The PySpark output correctly reflects this behavior, while Daft incorrectly returns 1 for all rows. This deviation from the expected behavior can have significant implications:

• Incorrect Aggregations: If you rely on window functions for aggregations, such as calculating moving averages or running totals, the results will be inaccurate.
• Data Inconsistencies: The bug can lead to data inconsistencies if you're using Daft in conjunction with other data processing frameworks like PySpark or DuckDB.
• Migration Challenges: Migrating workflows from other platforms to Daft may result in unexpected behavior and require extensive debugging.

Potential Solutions and Workarounds

While this bug requires a fix within the Daft framework, there are potential workarounds you can consider in the meantime:

1. Alternative Aggregation Methods: Depending on your specific use case, you might be able to use alternative aggregation methods that don't rely on window functions. For example, you could group the data and then count the rows within each group.
2. Pre-processing with Other Frameworks: If feasible, you could pre-process the data using PySpark or DuckDB to calculate the windowed counts and then load the results into Daft for further processing.
3. Custom Window Function Implementation: For advanced users, it might be possible to implement a custom window function that provides the correct results. However, this approach requires a deep understanding of Daft's internals and may not be suitable for all users.

Conclusion: Ensuring Data Integrity Across Platforms

In conclusion, the discrepancy in the results of daft.lit(1).over(window) compared to DuckDB and PySpark underscores the importance of verifying the behavior of data processing functions across different platforms. While Daft is a promising framework, this bug highlights the need for thorough testing and validation, especially when using window functions for aggregations.

By understanding the bug, reproducing the issue, and exploring potential workarounds, you can mitigate the impact of this inconsistency on your data workflows. As the Daft community addresses this issue, staying informed and adopting best practices for data validation will be crucial for ensuring data integrity.

For more information on data processing and window functions, you can explore resources like the official Apache Spark documentation on Window Functions.