from pyspark.sql import Window from pyspark.sql.functions import avg, col, row_number df = spark.table("employees").filter("age < 30") dept_avg = df.groupBy("dept_id").agg(avg("salary").alias("dept_avg_sal")) df2 = df.join(dept_avg, "dept_id").filter(col("salary") >...
This medium-level SQL question appears frequently in data engineering interviews at companies like Freight Tiger. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, partition, spark) 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.
from pyspark.sql import Window
from pyspark.sql.functions import avg, col, row_number
df = spark.table("employees").filter("age < 30")
dept_avg = df.groupBy("dept_id").agg(avg("salary").alias("dept_avg_sal"))
df2 = df.join(dept_avg, "dept_id").filter(col("salary") > col("dept_avg_sal"))
window = Window.partitionBy("dept_id").orderBy(col("salary").desc())
df2.withColumn("rn", row_number().over(window)).filter("rn <= 3").select("dept_id", "employee_id", "salary", "age").show() Why: Filter early; compute dept avg once; window for top-N. Scalability: Broadcast dept_avg if small; ensure even partition distribution.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.