Processing Large Multiline Files in Spark: Strategies and Best Practices

Handling large, multiline files can be a tricky yet essential task when working with different types of data from different source.

Key Challenges:

1. Handling Inconsistent Delimiters: The files had multiple delimiters, making it challenging to parse fields correctly.
2. Dealing with Multiline Records: Some records spanned multiple lines, requiring combining rows based on context.
3. Scalability: Processing data efficiently when dealing with files > 6GB in size.

Use of monotonically_increasing_id():

• To assign unique IDs to each row, which helps when combining rows or managing multiline records.

from pyspark.sql import functions as F
from pyspark.sql import Window

raw_df = spark.read.text(filePath).withColumnRenamed("value", "line")
raw_df = raw_df.withColumn("unique_id", F.monotonically_increasing_id())

        

Handling Multiline Records with lead and lag:

• By leveraging window functions, combined rows intelligently where necessary.
• This approach ensures data integrity while managing multiline records:

window_spec = Window.orderBy("unique_id")
combined_df = raw_df.withColumn("next_line", F.lead("line", 1).over(window_spec))
combined_df = combined_df.withColumn(
    "combined_line",
    F.when(F.col("flag") == 1, F.concat_ws("|", F.col("line"), F.col("next_line"))).otherwise(F.col("line"))
)
        

Flagging Corrupted Records:

• To identify incomplete or corrupt records, I used conditional flags:

processed_df = combined_df.withColumn(
    "is_corrupt",
    F.when(F.size(F.split(F.col("line"), "\\|")) < expected_field_count, True).otherwise(False)
)
        

Efficiently handling large multiline files in PySpark requires a combination of window functions, smart filtering, and conditional transformations.