Some records are missing after window function

[asurendran]

While loading data from one layer to another layer using pyspark window function, I noticed that some data is missing. This is happening if the data is huge. It's not happening for small quantity. Does anyone come across this issue before?

[MadhuB]

@asurendran Missing data with PySpark window functions on large datasets often stems from incorrect data partitioning (leading to incomplete window calculations) and/or data skew (causing executor overload or failures). Memory limitations and network issues can also contribute. 

Can you elaborate little more?

[asurendran]

I have a dataframe with key, eff date, end date... I want to use a window function with lag option to populate previous end date... I am partitioning by the key and order by the effective date. But I am seeing count diference.

[MadhuB]

Before applying the window function, try repartitioning your DataFrame based on the key (or the salted key). This can help distribute the data more evenly across the executors.

from pyspark.sql import Window
from pyspark.sql.functions import lag

# Repartition DataFrame
df = df.repartition("key")

# Define window specification
window_spec = Window.partitionBy("key").orderBy("eff_date")

# Add previous end date
df = df.withColumn("prev_end_date", lag("end_date", 1).over(window_spec))

# Show the result
df.show()

[asurendran]

Thanks Madhu! Will try this.

[asurendran]

I tried repartitioning and renaming dataframe name for each transformation. Still it's showing missing records. Please let me know if you have any other suggestion.

[asurendran]

Is there a way caching the dataframe helps to fix this issue?

[MadhuB]

Caching is for performance optimization but it may not work, if there is a problem lies in the logic of your window function, data skew, or data inconsistencies. 

I would recommend to try with a memory optimized cluster to see how it goes.