Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Code**: ```python from pyspark.sql.functions import countDistinct, to_date logins = spark.read.parquet("/logins") inactive = spark.read.text("/inactive_users").selectExpr("value as user_id") active_logins = logins.join(inactive, "user_id", "left_anti") daily_unique =...
This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Dunnhumby. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, partition, python) will help you answer variations of this question confidently.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones. The expert answer includes a code example that demonstrates the implementation pattern.
Code:
from pyspark.sql.functions import countDistinct, to_date
logins = spark.read.parquet("/logins")
inactive = spark.read.text("/inactive_users").selectExpr("value as user_id")
active_logins = logins.join(inactive, "user_id", "left_anti")
daily_unique = active_logins.groupBy(to_date("login_ts").alias("date")).agg(countDistinct("user_id").alias("unique_users"))
Why left_anti: Excludes rows that match. No shuffle of inactive if small (broadcast).
Scalability Trade-offs: Partition by date. Cache inactive if small and reused.
Cost Implications: Broadcast inactive. Partition pruning on date. Efficient pattern.
Pro-Move: 'left_anti + broadcast(inactive); we process 10M logins in 2 min.' Red Flag: Full join then filter—shuffles entire login table.
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.