DataEngPrep.tech
QuestionsPracticeAI CoachDashboardPacksBlog
ProLogin
Home/Questions/Spark/Big Data/How would you read a large file (e.g., 15GB) efficiently in Spark by increasing parallelism?

How would you read a large file (e.g., 15GB) efficiently in Spark by increasing parallelism?

Spark/Big Datahard0.5 min readPremium

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Read 15GB file with parallelism: (1) `spark.read.option('header','true').csv(path)`—Spark partitions by default. (2) Force partitions:...

🤖 Analyze Your Answer
Frequency
Low
Asked at 1 company
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
TCS
Key Concepts Tested
optimizationpartitionsparksql

Why This Question Matters

This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like TCS. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition, spark) will help you answer variations of this question confidently.

How to Approach This

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.

Expert Answer
95 words

Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

Read 15GB file with parallelism: (1) spark.read.option('header','true').csv(path)—Spark partitions by default. (2) Force partitions: spark.read.csv(path).repartition(64) or coalesce(32). (3) For non-splittable formats: read and repartition. (4) Set spark.sql.files.maxPartitionBytes (default 128MB) to control partition size. For 15GB: ~120 partitions at 128MB. Best practice: Match partitions to cluster cores; use Parquet for native splitting; avoid too many tiny partitions.

Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.

This answer is partially locked

Unlock the full expert answer with code examples and trade-offs

Recommended

Start AI Mock Interview

Practice real interviews with AI feedback, track progress, and get interview-ready faster.

  • Unlimited AI mock interviews
  • Instant feedback & scoring
  • Full answers to 1,800+ questions
  • Resume analyzer & SQL playground
Create Free Account

Pro starts at $24/mo - cancel anytime

Just need answers for quick revision?

Download curated PDF interview packs

Interview Packs
1,800+ real interview questions sourced from 5 top companies
AmazonGoogleDatabricksSnowflakeMeta
This answer is in the DE Mastery Vault 2026
1,863 questions with expert answers across 7 categories →

Free: Top 20 SQL Interview Questions (PDF)

Get the most asked SQL questions with expert answers. Instant download.

No spam. Unsubscribe anytime.

Related Spark/Big Data Questions

mediumWhat is the difference between repartition and coalesce in Apache Spark?FreehardWhat is the difference between SparkSession and SparkContext in Spark?FreemediumWhat is the difference between cache() and persist() in Spark? When would you use each?FreemediumWhat is the difference between groupByKey and reduceByKey in Spark?FreemediumWhat is the difference between narrow and wide transformations in Apache Spark? Explain with examples.Free

Want to know if YOUR answer is good enough?

Paste your answer and get instant AI feedback with a FAANG-level improved version.

Analyze My Answer — Free

According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

← Back to all questionsMore Spark/Big Data questions →