DataEngPrep.tech
QuestionsPracticeAI CoachDashboardPacksBlog
ProLogin
Home/Questions/Spark/Big Data/What determines the maximum parallelism achievable in Databricks?

What determines the maximum parallelism achievable in Databricks?

Spark/Big Datamedium0.4 min read

**Factors**: (1) **Total cores**—executors × cores per executor. (2) **Input partitions**—spark.sql.files.maxPartitionBytes (default 128MB); 1TB = ~8K partitions max. (3) **Shuffle partitions**—spark.sql.shuffle.partitions (default 200). (4) **Cluster size**—autoscale min/max....

🤖 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
partitionsparksql

Why This Question Matters

This medium-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 (partition, spark, sql) will help you answer variations of this question confidently.

How to Approach This

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.

Expert Answer
85 words

Factors: (1) Total cores—executors × cores per executor. (2) Input partitions—spark.sql.files.maxPartitionBytes (default 128MB); 1TB = ~8K partitions max. (3) Shuffle partitions—spark.sql.shuffle.partitions (default 200). (4) Cluster size—autoscale min/max. (5) External—Kafka partitions, source parallelism.

Formula: Effective parallelism = min(partition_count, total_cores). Bounded by slowest stage.

Why It Matters: Over-provisioning cores = waste. Under-provisioning = underutilization. Match partitions to cores.

Scalability Trade-offs: Too many partitions = scheduler overhead. Too few = few tasks, idle cores.

Cost Implications: Right parallelism = lower runtime; wrong = pay for idle or slow.

dataengprep.techdataengprep.techdataengprep.techdataengprep.tech
dataengprep.techdataengprep.techdataengprep.techdataengprep.tech
dataengprep.techdataengprep.techdataengprep.techdataengprep.tech
dataengprep.techdataengprep.techdataengprep.techdataengprep.tech
dataengprep.techdataengprep.techdataengprep.techdataengprep.tech
dataengprep.techdataengprep.techdataengprep.techdataengprep.tech

Want feedback on your answer?

Paste your answer to this question and our AI Coach scores it, finds gaps, and shows you the FAANG-level version.

Try Answer Analyzer →
Want all answers as a PDF for offline study?
1,863 questions across 7 categories — Interview Packs →

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

Companies that ask this Spark/Big Data question

TCS interview questions →

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 →