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PYSPARK โ€ข LESSON 194

High-Volume Natural Key Deduplication

How do we reconstruct the latest snapshot of account records from an out-of-order changelog using window ranking?

Productionโฑ 3 Minutesโญ 1650 XP
๐Ÿค” THE QUESTION

How do we reconstruct the latest snapshot of account records from an out-of-order changelog using window ranking?

๐Ÿ’ก WHAT IS IT?

Deduplicating changelog updates by natural account ID ordered by effective timestamp descending to isolate current state.

๐ŸŽฏ WHAT IS IT USED FOR?

Enterprise account master data reconstruction and handling at-least-once message delivery.

๐Ÿ’ป EXAMPLE
from pyspark.sql.window import Window
from pyspark.sql.functions import col, row_number

history_window = Window.partitionBy("account_id").orderBy(col("effective_ts").desc())

current_account_state = raw_account_updates \
    .withColumn("version_rank", row_number().over(history_window)) \
    .filter(col("version_rank") == 1) \
    .drop("version_rank")

๐ŸŽฏ Mission Objectives

Practice typing production-grade PySpark code for High-Volume Natural Key Deduplication.

  • โœ“ Partition history by account ID
  • โœ“ Rank versions by effective timestamp
  • โœ“ Isolate latest current account state