Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Narrow transformations**: Each input partition maps to at most one output partition. No shuffle. Examples: map, filter, flatMap, mapPartitions. **Wide transformations**: Require data from multiple input partitions to produce one output partition. Trigger shuffle. Examples:...
Pro-Move: Explain stage boundaries and pipeline fusion. Red Flag: Not knowing which common ops (e.g., distinct, join) are wide—basic Spark knowledge.
This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Coforge, Delivery Hero, Dunnhumby, and 2 others. 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.
Narrow transformations: Each input partition maps to at most one output partition. No shuffle. Examples: map, filter, flatMap, mapPartitions.
Wide transformations: Require data from multiple input partitions to produce one output partition. Trigger shuffle. Examples: groupByKey, reduceByKey, join, distinct, repartition.
Architectural Logic (Why This Matters): Spark pipelines narrow transformations and executes them in a single stage. Wide transformations force a stage boundary—all prior work is materialized (shuffle write), then a new stage reads (shuffle read). Stage count and shuffle volume drive job latency and cost.
Scalability Trade-offs:
Cost Implications: A pipeline with 5 wide transforms = 5 shuffles. Reordering to 2 wide transforms can cut runtime by 50%+. Partition pruning and predicate pushdown (narrow) reduce data before expensive wide ops.
Examples:
# Narrow: pipelineable within same stage
df.filter(col("age") > 18).select("id", "amount")
# Wide: triggers shuffle; stage boundary
df.groupBy("dept").agg(sum("salary"))
Pro-Move: Explain stage boundaries and pipeline fusion. Red Flag: Not knowing which common ops (e.g., distinct, join) are wide—basic Spark knowledge.
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 5 companies. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.