PySpark in 7 Days (bonus1)

PySpark – Advanced Topics (Data Engineer Perspective)

This is the final and advanced-level post of the Learn PySpark series. You have already covered Beginner → Intermediate. This post focuses on production-grade, interview-level, real-world PySpark expected from a Senior Data Engineer.

TL;DR – What You Will Learn

• How Spark actually executes your code (DAG, stages, tasks)
• Catalyst Optimizer & Tungsten engine (why Spark is fast)
• Advanced joins, skew handling, and salting
• Incremental processing & watermarking
• Delta Lake, MERGE, SCD patterns
• Structured Streaming fundamentals
• Error handling, idempotency, and production best practices
• How to explain PySpark architecture in interviews

---

1. How Spark Really Executes Your Code (DAG)

Every PySpark job is converted into a Directed Acyclic Graph (DAG). Understanding this separates coders from data engineers.

• Transformations → Lazy (select, filter, join)
• Actions → Trigger execution (show, write, count)
• DAG is split into stages at shuffle boundaries
• Stages are executed as tasks on executors

Interview Insight:
If a job is slow, always ask: Where is the shuffle happening?

2. Catalyst Optimizer (Why Spark SQL Is Fast)

Catalyst is Spark’s query optimization engine. It applies both logical and physical optimisations.

• Predicate pushdown
• Column pruning
• Join reordering
• Cost-based optimisation (CBO)

df.explain(True)

Always use explain() to understand execution plans.

3. Tungsten Engine (Memory & CPU Optimisation)

• Off-heap memory management
• Binary row format
• Whole-stage code generation

This is why PySpark can still be fast despite using Python.

4. Advanced Join Strategies

Broadcast Join

from pyspark.sql.functions import broadcast fact_df.join( broadcast(dim_df), "key", "inner" )

Skew Join (Salting)

Used when one key dominates the data.

from pyspark.sql.functions import rand, concat, lit df_salted = df.withColumn( "salted_key", concat(df.key, lit("_"), (rand()*10).cast("int")) )

Interview Tip:
If you say “salting” confidently, you instantly sound senior.

5. Incremental Processing (Watermarks)

Never reprocess full data in production unless required.

• Use timestamps or IDs
• Track last processed watermark

df.filter(col("updated_at") > last_processed_ts)

6. Delta Lake Fundamentals (Critical)

Modern PySpark without Delta is incomplete.

• ACID transactions
• Schema enforcement & evolution
• Time travel
• MERGE operations

MERGE (Upsert)

MERGE INTO target t USING source s ON t.id = s.id WHEN MATCHED THEN UPDATE SET * WHEN NOT MATCHED THEN INSERT *

SCD Type 2 Pattern

• Expire old record
• Insert new version
• Track effective dates

7. Structured Streaming (Advanced Overview)

Spark treats streaming as continuous micro-batches.

• Exactly-once semantics
• Checkpointing
• Event-time processing

df.writeStream \ .format("delta") \ .option("checkpointLocation", "/chk") \ .start("/output")

8. Idempotency & Fault Tolerance

Production pipelines must be safe to re-run.

• Use overwrite + partitioning
• Use MERGE instead of INSERT
• Track batch IDs

9. Logging, Monitoring & Debugging

• Spark UI (stages, tasks, skew)
• Executor memory usage
• Shuffle spill to disk

Golden Rule:
If you cannot explain a Spark UI screenshot, you are not production-ready.

10. How to Explain PySpark in a Senior Interview

Sample Answer:

“I use PySpark to build scalable, fault-tolerant ETL pipelines. I focus on minimising shuffles, using broadcast joins where appropriate, handling data skew, and enforcing schemas. For incremental processing, I use watermarks and Delta Lake MERGE patterns. I rely on Spark UI and execution plans to tune performance.”

What You Have Achieved

• Beginner → Intermediate → Advanced PySpark
• Production-ready mental model
• Interview-ready explanations
• Strong foundation for Databricks & Fabric

Series: Learn PySpark (Complete)
Level: Advanced Data Engineer
Outcome: You now understand PySpark the way Spark executes it, not just how to write it.