**Logic**: Read text → split into words → explode array to rows → group by word → count. **Code**: `from pyspark.sql import SparkSession; from pyspark.sql import functions as F; spark = SparkSession.builder.appName("WordCount").getOrCreate(); df =...
This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Altimetrik, Infosys. 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.
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.
Logic: Read text → split into words → explode array to rows → group by word → count. Code: from pyspark.sql import SparkSession; from pyspark.sql import functions as F; spark = SparkSession.builder.appName("WordCount").getOrCreate(); df = spark.read.text("path/to/file.txt"); words = df.select(F.explode(F.split(F.col("value"), "\\s+")).alias("word")); word_counts = words.groupBy("word").count(); word_counts.show(). Scalability: For large files, ensure enough partitions (spark.read.text uses default parallelism); repartition by key if aggregation is skewed. Cost implication: Wide shuffle on groupBy; consider reduceByKey semantics with RDD for partial aggregation to reduce shuffle size. Use coalesce or repartition to control output partitions.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 2 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.