PySpark Interview Preparation Guide

PySpark Interview Preparation Guide

Day 1: PySpark Basics & Core Concepts

• What is PySpark: Python API for Apache Spark used for large-scale data processing.
• Spark Architecture: Consists of Driver, Executors, Cluster Manager.
• RDD vs DataFrame vs Dataset: RDD is low-level, DataFrame is optimized and user-friendly.
• Transformations vs Actions: Transformations are lazy; Actions trigger computation.
• Lazy Evaluation: Optimization mechanism that delays execution until necessary.

Day 2: RDD Operations & DataFrame API

• RDD operations: map, flatMap, filter, reduceByKey.
• DataFrame creation: from RDD or structured data.
• DataFrame methods: select, filter, groupBy, agg, withColumn, drop, cast, alias.
• File formats: CSV, JSON, Parquet reading and writing.

Day 3: Joins, UDFs & SQL in PySpark

• Join types: inner, left, right, outer joins.
• SQL queries: Registering temp views and running SQL on DataFrames.
• UDFs: Create custom transformation logic with User Defined Functions.

Day 4: Window Functions & Complex Operations

• Window Functions: row_number, rank, dense_rank, lead, lag.
• Partitioning: Use of partitionBy and orderBy in window specs.
• Pivot: Reshape DataFrame using pivot/unpivot operations.

Day 5: Performance Tuning & Optimization

• Catalyst Optimizer: Optimizes query plans in Spark SQL.
• Tungsten Engine: Handles memory and binary code optimization.
• Partitioning: Efficient data distribution using repartition and coalesce.
• Caching & Persistence: Store intermediate results in memory or disk.
• Broadcast Join: Used when one dataset is small enough to fit in memory.

Day 6: PySpark with Machine Learning (MLlib)

• MLlib: Spark's machine learning library.
• Pipeline: Chain of Transformers and Estimators.
• VectorAssembler: Combine features into a single vector column.
• StandardScaler: Normalize features.
• Models: LinearRegression, LogisticRegression.

Day 7: Real-time Scenarios + Mock Interview

• Real-time Use Cases: Handling ETL, ingestion pipelines, and optimizations.
• Performance Bottlenecks: Identifying and resolving slow Spark jobs.
• Common Issues: Data skew, large joins, memory pressure.
• Mock Questions: End-to-end project explanation, tuning strategies, troubleshooting steps.

Use this guide to prepare thoroughly for PySpark interviews from basic to advanced levels. Each day is structured for progressive learning and hands-on practice.