PySpark Interview Questions (2026)

Practice 45 PySpark interview questions on DataFrames, Spark SQL, joins, shuffles, UDFs, partitioning, caching, streaming, and job debugging.

45 questions with answers

What Is PySpark?

Key Takeaways

  • ✓PySpark is the Python API for Apache Spark, used to process large datasets with distributed execution.
  • ✓Interviews test DataFrames, Spark SQL, transformations, actions, joins, shuffles, UDFs, caching, partitions, and job debugging.
  • ✓Strong answers explain the Spark execution plan behind the Python code.
  • ✓Do not answer PySpark like Pandas. PySpark code runs across executors and needs partition-aware thinking.

PySpark is Apache Spark's Python API for distributed data processing. It lets Python users work with Spark DataFrames, Spark SQL, batch jobs, streaming pipelines, and ML workflows. In interviews, PySpark questions test whether you can write clean DataFrame code and explain how that code runs across driver, executors, partitions, stages, and shuffles.

45Questions with direct answers
3Groups: PySpark basics, coding tasks, production scenarios
7+Code, diagrams, tables, videos, and quiz
45-90 minTypical PySpark interview round length

Watch: Learning PySpark with Databricks

Video: Learning PySpark with Databricks (Databricks, YouTube)

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6 quick questions. Score 70%+ to download your PySpark certificate.

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All Questions on This Page

45 questions
PySpark Advanced Scenarios
  1. 31. A PySpark job fails with driver memory errors. What do you suspect?
  2. 32. A join becomes much slower after a data growth spike. What do you check?
  3. 33. A UDF-heavy pipeline is slow. What changes?
  4. 34. Deduplication changed business counts. How do you respond?
  5. 35. A JSON field changes type and the job fails. What prevents this?
  6. 36. Caching makes the cluster slower. Why?
  7. 37. A streaming PySpark job duplicates output after restart. What is missing?
  8. 38. A candidate uses coalesce(1) before every write. What is the concern?
  9. 39. A job launches thousands of tiny tasks. What do you inspect?
  10. 40. A window function causes spills. What do you check?
  11. 41. A table is partitioned by customer id. Why can this be bad?
  12. 42. A Parquet read scans too much data. What is wrong?
  13. 43. How do you move a PySpark notebook to production?
  14. 44. Why is Pandas thinking risky in PySpark?
  15. 45. What would you challenge in a PySpark design review?

PySpark Fundamentals

Foundational15 questions

Start here. These are the definitions and first-principle checks that open most rounds.

Q1. What is SparkSession in PySpark?

SparkSession is the entry point for PySpark DataFrame, SQL, catalog, and configuration operations.

Most modern PySpark code starts from spark, not separate SQLContext objects.

SparkSession changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Watch a deeper explanation

Video: PySpark tutorial (freeCodeCamp.org, YouTube)

Q2. What is a PySpark DataFrame?

A PySpark DataFrame is a distributed table with named columns and a schema.

Operations build a plan that Spark executes across partitions.

DataFrame changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q3. Why define a schema explicitly?

An explicit schema avoids slow inference, prevents surprise types, and makes bad input easier to catch.

This is useful for CSV, JSON, and streaming sources.

Schema changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q4. Why doesn't PySpark run immediately after a transformation?

PySpark transformations are lazy. Spark records them and runs only when an action is called.

Lazy evaluation lets Spark optimize the full plan.

Lazy evaluation changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Answer partWhat to sayEvidence to mention
DefinitionLazy evaluation in one direct sentence.Official docs or course material
Use caseThe work where it changes a decision.Dataset, model, query, dashboard, or pipeline
RiskWhat breaks when it is misunderstood.Metric, log, test result, or review note

Q5. Which PySpark operations are actions?

count, collect, show, take, write, and foreach trigger Spark execution.

An action creates one or more Spark jobs.

Action changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Watch a deeper explanation

Video: Using Apache Spark to analyze open data (Databricks, YouTube)

Q6. What does withColumn do?

withColumn adds or replaces a column using an expression.

Chaining many withColumn calls can create large plans. select can be cleaner for many columns.

withColumn changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

python
from pyspark.sql import functions as F
clean = orders.withColumn('net_amount', F.col('amount') - F.col('discount'))

Q7. Why use PySpark functions instead of plain Python functions?

PySpark column functions build expressions that Spark can optimize and execute on workers.

Plain Python functions often need UDFs and extra serialization.

Column functions changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q8. What is a partition in PySpark?

A partition is a slice of a distributed DataFrame processed by one task.

Partition count affects parallelism, task overhead, and output file count.

Partition changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q9. What causes a shuffle in PySpark?

A shuffle happens when data must be redistributed across executors, often for joins, groupBy, distinct, and repartition.

Shuffles are expensive because they use network and disk.

Shuffle changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q10. What is a broadcast join in PySpark?

A broadcast join sends a small DataFrame to every executor to avoid shuffling the large DataFrame.

It is useful for small dimension tables that fit memory.

Broadcast join changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q11. What is a UDF in PySpark?

A UDF is a user-defined function that lets Python logic run on Spark data.

Use UDFs only when built-in functions do not cover the need.

UDF changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q12. What are window functions used for?

Window functions calculate values across related rows without collapsing rows like groupBy does.

Common examples include rank, row_number, lag, lead, and rolling metrics.

Window function changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q13. When should you cache a DataFrame?

Cache a DataFrame when it is reused and expensive to recompute.

Unpersist after use to release executor memory.

Cache changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Watch a deeper explanation

Video: Fundamentals of Apache Spark and Azure Databricks (Microsoft Developer, YouTube)

Q14. How are repartition and coalesce different?

repartition can increase or decrease partitions with a shuffle. coalesce usually reduces partitions with less data movement.

Use them based on parallelism and output file targets.

Repartition and coalesce changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q15. What does PySpark Structured Streaming add?

It lets DataFrame-style queries process streaming data with triggers, checkpoints, state, watermarks, and streaming sinks.

Streaming interviews test recovery and late data handling.

Structured Streaming changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Back to question list

PySpark Practical Interview Questions

Intermediate15 questions

These questions test whether you can apply the topic to real data, real code, and messy constraints.

Q16. How would you read and clean a large CSV in PySpark?

Define a schema, read with options, trim or cast columns, validate required fields, and write cleaned data to columnar storage.

Avoid schema inference for large or messy files.

read and clean CSV changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q17. How do you deduplicate events?

Use a stable event id or business key with dropDuplicates, or use a window to keep the latest row per key.

Define what duplicate means before writing code.

deduplicate events changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q18. Write a PySpark pattern for daily revenue.

Filter valid orders, group by order date, aggregate amount, and write by date partition.

Make the job rerunnable for a date range.

aggregate revenue changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

python
daily = (orders
  .filter(F.col('status') == 'paid')
  .groupBy(F.to_date('created_at').alias('order_date'))
  .agg(F.sum('amount').alias('revenue')))

Q19. How do you join a large fact table with a small dimension table?

Broadcast the small dimension when it fits memory, select only needed columns, and verify the physical plan.

If it does not fit memory, tune partitioning and check skew.

join facts and dimensions changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q20. How do you handle null values in PySpark?

Use fillna, coalesce, when, filters, or validation rules depending on the field meaning.

Do not fill business-critical nulls without owner approval.

handle nulls changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q21. How do you parse a JSON string column?

Use from_json with an explicit schema, then select nested fields.

Explicit schemas keep parsing predictable.

parse JSON column changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q22. How do you replace a Python UDF?

Use built-in functions, SQL expressions, higher-order functions, or pandas UDF only when the vectorized path is justified.

Built-ins usually get better optimization.

avoid Python UDF changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q23. How do you write partitioned Parquet safely?

Choose a common filter column such as date, control partition count, write in Parquet, and validate row counts before publish.

Too many partitions create small files.

write partitioned data changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q24. How do you debug data skew?

Check task duration, input sizes, shuffle read, and key frequency.

Fix with salting, hot-key split, broadcast, or adaptive skew handling where available.

debug skew changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q25. What do you inspect in df.explain?

Look for scans, filters, exchanges, joins, broadcast, pushed filters, and adaptive plan changes.

Exchange means data movement.

explain a plan changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Watch a deeper explanation

Video: Spark Structured Streaming concepts (Databricks, YouTube)

Q26. How do you stream Kafka data with PySpark?

Read the Kafka stream, parse values, apply schema, process with event time if needed, write to a sink, and set a checkpoint.

Streaming jobs need restart safety.

stream from Kafka changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q27. How do you handle late streaming data?

Use event time, watermarks, windowing, and a business rule for how late is too late.

Watermarks trade completeness for state size.

late data changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q28. How do you test PySpark transformations?

Create small input DataFrames, run the transformation, compare sorted outputs, and test edge cases.

Keep business logic in functions that accept and return DataFrames.

test PySpark code changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q29. How do you reduce small output files?

Control partitions before write, compact output, and avoid over-partitioning by high-cardinality columns.

Small files hurt later reads.

reduce small files changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q30. How do you parameterize a PySpark job?

Pass date ranges, paths, modes, and environment through arguments or config, then log resolved values.

Parameters make reruns and backfills safer.

job parameters changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Back to question list

PySpark Advanced Scenarios

Advanced15 questions

Advanced rounds test trade-offs, failure modes, and whether the decision can hold up under production pressure.

Q31. A PySpark job fails with driver memory errors. What do you suspect?

collect, toPandas, huge logs, or large broadcast data may be pulling too much data to the driver.

Keep large work distributed.

driver out of memory changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q32. A join becomes much slower after a data growth spike. What do you check?

Check table sizes, skew, stats, broadcast threshold, partition counts, and shuffle read.

The old join plan may no longer fit.

slow join changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q33. A UDF-heavy pipeline is slow. What changes?

Replace UDFs with built-in expressions where possible and inspect serialization overhead.

Python UDFs are often the hidden cost.

UDF bottleneck changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q34. Deduplication changed business counts. How do you respond?

The dedupe key, ordering rule, source duplicates, late arrivals, and expected business definition matters.

Deduping without the business rule is dangerous.

wrong counts changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q35. A JSON field changes type and the job fails. What prevents this?

Schema validation, source contracts, raw capture, alerts, and compatibility rules.

Store raw data so failed parses can be replayed.

schema mismatch changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q36. Caching makes the cluster slower. Why?

Cached data may not fit memory or may evict useful blocks, causing spill and recomputation.

Cache only reused expensive data.

cache pressure changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q37. A streaming PySpark job duplicates output after restart. What is missing?

Idempotent sink writes or correct checkpoint management may be missing.

Recovery depends on source, checkpoint, and sink behavior.

stream restart duplicates changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q38. A candidate uses coalesce(1) before every write. What is the concern?

It serializes output through one partition and can create a bottleneck.

Use it only for small single-file outputs.

coalesce misuse changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q39. A job launches thousands of tiny tasks. What do you inspect?

Input file count, partition count, and small files are likely issues.

Task overhead can dominate work.

too many tasks changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q40. A window function causes spills. What do you check?

Check partition keys, ordering, data skew, row width, and whether the calculation can be pre-aggregated.

Window operations can be expensive on large groups.

window memory changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q41. A table is partitioned by customer id. Why can this be bad?

High-cardinality partitions create too many files and directories.

Date is often a better top-level analytics partition.

bad partition column changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q42. A Parquet read scans too much data. What is wrong?

Partition pruning or predicate pushdown may not be working, or the query may select too many columns.

Check explain output and file layout.

slow read changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q43. How do you move a PySpark notebook to production?

Extract functions, parameterize inputs, add tests, logging, retries, validation, and scheduled execution.

A notebook is a prototype unless it is operated.

notebook to production changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q44. Why is Pandas thinking risky in PySpark?

PySpark code builds distributed plans. Local row-by-row mental models lead to collect calls and slow UDFs.

Think in columns and partitions.

Pandas confusion changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Q45. What would you challenge in a PySpark design review?

Ask about data volume, partitioning, join strategy, rerun safety, schema checks, small files, and monitoring.

Those questions reveal production readiness.

senior review changes PySpark decisions by affecting the chosen method, the failure mode, or the validation step. Reliable evidence comes from a metric, test result, query output, log, or review note.

Back to question list

PySpark vs Pandas vs SQL

Candidates often confuse PySpark with local Python data work. The difference is execution: PySpark plans distributed work, while Pandas runs in one Python process.

ToolRuns whereBest useCommon trap
PySparkDistributed Spark clusterLarge data pipelines and Spark SQLUsing collect on large data
PandasOne Python processSmall to medium local analysisAssuming it scales like Spark
Spark SQLSpark SQL engineRelational analytics on large dataMissing the physical plan
Python UDFPython worker with serializationCustom logic when built-ins don't fitBlocking optimization

PySpark answer signals

Good answers Python code connects to Spark execution.

DataFrame code
90 signal
Execution plan
92 signal
Performance
88 signal
Reliability
82 signal
  • DataFrame code: select, filter, join, groupBy
  • Execution plan: jobs, stages, tasks, shuffles
  • Performance: partitions, cache, UDFs, files
  • Reliability: reruns, checkpoints, tests

How to Prepare for a PySpark Interview

Prepare by writing small DataFrame examples and then explaining the physical execution. the question needs both syntax and reasoning.

  • Practice DataFrame operations: select, filter, withColumn, groupBy, joins, windows, and writes.
  • Know lazy evaluation, actions, shuffles, partitions, cache, persist, repartition, and coalesce.
  • Use explain plans and Spark UI language in performance answers.
  • One debugging story about skew, small files, memory, or streaming lag is useful.

PySpark interview prep flow

1Code
DataFrame operations
2Plan
logical and physical plan
3Move
joins and shuffles
4Tune
partitions and cache
5Ship
tests, writes, monitoring

Most rounds reward clear reasoning more than memorized phrasing.

How Strong PySpark Answers Sound

A strong PySpark answer shows the code and the distributed effect. Say which operation triggers a shuffle, why a join may broadcast, when a DataFrame should be cached, and how you would prove the answer with explain output or the Spark UI. Senior candidates also mention schema contracts, idempotent writes, data skew, and avoiding collect on large datasets.

Do not overuse collect

collect pulls distributed data to the driver. Use it only for small inspected results.

Prefer built-in functions

Built-in PySpark functions usually keep Catalyst optimization available. Python UDFs add overhead.

Explain the shuffle

groupBy, distinct, repartition, and many joins move data across executors.

Write rerunnable jobs

Production PySpark jobs need deterministic inputs, safe overwrites or merges, and validation before publish.

Test Yourself: PySpark Quiz

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6 questions, about 4 minutes. Score 70% or higher to earn a shareable certificate.

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Frequently  Asked  Questions

What do PySpark interviews usually ask?

They ask about SparkSession, DataFrames, Spark SQL, joins, shuffles, UDFs, partitions, cache, windows, streaming, and job debugging. Complete coverage has one concrete example, one failure case, and one validation signal beyond the definition.

Is PySpark enough for data engineering interviews?

It is enough for many Spark-heavy roles, but pair it with SQL, Python, data modeling, and pipeline reliability.

What PySpark project should I discuss?

Pick a pipeline where you handled joins, partitions, schema checks, reruns, output files, and a real performance issue.

What separates experienced PySpark candidates?

Experienced candidates explain physical plans, Spark UI evidence, skew, UDF cost, cache trade-offs, and safe production writes. Complete coverage has one concrete example, one failure case, and one validation signal beyond the definition.

Practice PySpark with SQL and Python follow-ups

Use these PySpark questions to prepare your reasoning, then practice live SQL and Python tasks with Hyring's AI Coding Interviewer.

See Hyring AI Coding Interviewer

Sources

Adithyan RKWritten by Adithyan RK
Surya N
Fact-checked by Surya N
Published on: 8 Apr 2026Last updated: 27 Jun 2026
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