Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Core logic: read text → split → explode → filter empty → groupBy → count → orderBy desc → limit 10. Code: from pyspark.sql import functions as F; df = spark.read.text("path/to/file.txt"); words = df.select(F.explode(F.split(F.col("value"), "\\s+")).alias("word")); top10 =...
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Capco, Pubmatic. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, spark, sql) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Core logic: read text → split → explode → filter empty → groupBy → count → orderBy desc → limit 10. Code: from pyspark.sql import functions as F; df = spark.read.text("path/to/file.txt"); words = df.select(F.explode(F.split(F.col("value"), "\\s+")).alias("word")); top10 = words.filter(F.length(F.col("word")) > 0).groupBy("word").count().orderBy(F.desc("count")).limit(10). Why \\s+: Handles multiple spaces/tabs; more robust than single space. Scalability: For very large files, ensure enough partitions (coalesce input or repartition after read); reduceByKey equivalent is groupBy+agg. Cost: Single action (limit triggers collect); for distributed top-K without collecting to driver, use Window functions: row_number() over (partition by 1 order by count desc) and filter rank <= 10. Architectural nuance: For multi-file corpus, read as wholeTextFiles or text with glob; partition count affects parallelism. Best practice: Normalize case (lower) and strip punctuation if word identity matters.
Red Flag: Using collect() or limit() that pulls to driver without considering data size. Pro-Move: 'For our 100GB log corpus, we use repartition(200) after split, then aggregate; top-K stays distributed until final limit; we cache the word counts for downstream analytics.'
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 2 companies. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.