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Home/Questions/Spark/Big Data/Write a PySpark job that calculates the number of unique users who logged in per day, but exclude any logins from inactive users listed in a separate file.

Write a PySpark job that calculates the number of unique users who logged in per day, but exclude any logins from inactive users listed in a separate file.

Spark/Big Datamedium0.3 min readPremium

**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 =...

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Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Dunnhumby
Key Concepts Tested
joinpartitionpythonsparksql

Why This Question Matters

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.

How to Approach This

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.

Expert Answer
58 wordsIncludes code

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.

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