Introduction: RDD Bottlenecks & DataFrames
"Explore RDD architectural bottlenecks (black-box data structures, Py4J serialization, GC bloat) and master Spark SQL DataFrames powered by the Catalyst Optimizer and Project Tungsten."
What You'll Master
RDD Bottlenecks
Why opaque objects, lack of predicate pushdown, & Py4J serialization slow down RDDs.
DataFrame API
Structured columns, schema enforcement, & declarative SQL DSL operations.
Catalyst Optimizer
Analysis, logical optimization, physical planning, & whole-stage code generation.
Project Tungsten
Off-heap memory management, cache-aware data structures, & zero-GC execution.
Learning Path & Course Syllabus
4-stage query compilation: Unresolved Logical Plan → Optimized Logical Plan → Physical Plans → RDD Code Generation.
Comparing Python RDD execution vs PySpark DataFrame `groupBy()` aggregations on a real dataset.
Tracing PySpark DataFrame transformations and verifying query plan output using `.explain(True)`.
FAANG-style questions covering Catalyst optimization rules, Tungsten binary format, and DataFrame vs Dataset trade-offs.
What's Included in This Module
| Dimension | RDD | DataFrame |
|---|---|---|
| Data Structure | Unstructured Objects (Black Box) | Structured Named Columns & Schema |
| Optimization Engine | None (Manual Developer Optimization) | Catalyst Optimizer & Project Tungsten |
| PySpark Performance | Slow (Py4J & Pickle Serialization) | Identical to Scala (Native Bytecode) |