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Home/Questions/Spark/Big Data/Convert complex SQL (CTEs, window functions, subqueries) to production-grade PySpark. Discuss when to use spark.sql() vs. DataFrame API, and the implications for testability, partitioning, and execution predictability.

Convert complex SQL (CTEs, window functions, subqueries) to production-grade PySpark. Discuss when to use spark.sql() vs. DataFrame API, and the implications for testability, partitioning, and execution predictability.

Spark/Big Datamedium0.8 min readPremium

Two approaches: spark.sql() for direct translation and DataFrame API for programmatic logic. SQL approach: createOrReplaceTempView, run ANSI-like SQL—fast parity, but string-based, harder to unit test, and execution plan less explicit. DataFrame API: composable, testable (pass...

🤖 Analyze Your Answer
Frequency
Low
Asked at 2 companies
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
DatameticaS&P Global
Interview Pro Tip

Red Flag: Converting all SQL to DataFrame without reason—SQL is often fine for ad-hoc and stable queries. Pro-Move: Use SQL for BI/adhoc; DataFrame API for pipelines with tests and partitioning control.

Key Concepts Tested
partitionpythonsparksqlwindow

Why This Question Matters

This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Datametica, S&P Global. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, python, spark) 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
150 wordsIncludes code

Two approaches: spark.sql() for direct translation and DataFrame API for programmatic logic. SQL approach: createOrReplaceTempView, run ANSI-like SQL—fast parity, but string-based, harder to unit test, and execution plan less explicit. DataFrame API: composable, testable (pass mock DataFrames), explicit transformations. Example—top 3 products by revenue per region:

```python
from pyspark.sql import functions as F
from pyspark.sql.window import Window

# SQL approach
df.createOrReplaceTempView("sales")
result = spark.sql('''
WITH revenue AS (
SELECT region, product_id, SUM(quantity*price) AS revenue
FROM sales GROUP BY region, product_id
)
SELECT * FROM (
SELECT *, ROW_NUMBER() OVER (PARTITION BY region ORDER BY revenue DESC) AS rn
FROM revenue
) WHERE rn <= 3
''')

# DataFrame API
revenue = sales.groupBy("region", "product_id").agg(F.sum(F.col("quantity")*F.col("price")).alias("revenue"))
w = Window.partitionBy("region").orderBy(F.col("revenue").desc())
result = revenue.withColumn("rn", F.row_number().over(w)).filter(F.col("rn") <= 3)
```

Scalability: DataFrame API allows explicit repartition, broadcast hints; SQL relies on optimizer. Cost: SQL is faster to write; DataFrame reduces debugging time and enables CI for logic.

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