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When would you use flatten, explode, or collect_list in Spark?

Python/Codingeasy0.4 min readPremium

**Semantics:** explode = one-to-many (array → rows). flatten = array of arrays → single array (no new rows). collect_list = many-to-one (aggregate to array per group). **Use Cases:** explode: normalize nested JSON. flatten: merge nested array before processing. collect_list:...

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Frequency
Low
Asked at 1 company
Category
179
questions in Python/Coding
Difficulty Split
127E|24M|28H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
TCS
Key Concepts Tested
spark

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Expert Answer
84 words

Semantics: explode = one-to-many (array → rows). flatten = array of arrays → single array (no new rows). collect_list = many-to-one (aggregate to array per group).

Use Cases: explode: normalize nested JSON. flatten: merge nested array before processing. collect_list: group by key, collect values (e.g., order items per order_id).

Scalability: explode multiplies rows—risk of skew. collect_list can create huge arrays—monitor group sizes. flatten keeps row count; watch for very nested structures. explode_outer to preserve nulls.

explode('items') # 1 row → N rows
flatten(array_of_arrays)
collect_list('item').alias('items')

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