Performance Tuning & Production Systems
💡 Interview Tip
Focus: Spark UI debugging, Unity Catalog, Photon, CI/CD, cost management, governance
Approach: Every topic starts with simple explanation + analogy, then interview-level depth
MEMORY MAP: PERFORMANCE TUNING = SPARK-FIX
🧠 PERFORMANCE TUNING → SPARK-FIX
PERFORMANCE TUNINGSPARK-FIX
SShuffle (the #1 performance killer)
PPartitioning (too many → small files, too few → OOM)
AAQE (Adaptive Query Execution — auto-fix at runtime)
RResource tuning (executor memory, cores, instances)
KKey skew (one key has 90% of data)
FFile format (Parquet + snappy, avoid CSV/JSON)
IIndexing (Z-ORDER, Liquid Clustering, data skipping)
XeXecution plan (EXPLAIN, Spark UI reading)
Memorize this. When an interviewer asks "how do you tune Spark?",
walk through each letter. It shows a structured mental model.
SECTION 1: SPARK UI DEBUGGING & EXECUTION PLANS
Q1: How do you read a Spark execution plan? Walk through a real example.
Simple Explanation: An execution plan is Spark's "recipe" for running your query. It tells you exactly what Spark will do — which tables to scan, how to join them, where to shuffle data. Reading a plan is like reading a recipe backward: you start at the bottom (raw ingredients) and read up to the final dish.
Analogy: Think of a GPS route. Before you drive, the GPS shows you the full route — highways, turns, toll roads. An execution plan is Spark's GPS route for your query. You read it to spot "toll roads" (shuffles) and "traffic jams" (skew) before the query even runs.
Technical depth:
python — editable
df = spark.table("orders") \
.filter(col("date") == "2025-01-15") \
.join(spark.table("customers"), "customer_id") \
.groupBy("region").agg(sum("amount"))
df.explain(True) # ← True = show all plan levels (parsed, analyzed, optimized, physical)Reading the plan (BOTTOM UP — always start at the bottom):
== Physical Plan ==
*(3) HashA