How Each API Transforms Data Internally

The Core Mechanism: When you write Spark code, you're not executing operations immediately. You're building a Directed Acyclic Graph (DAG) that describes what transformations to apply. How Spark handles this DAG differs dramatically between the three APIs. RDD Execution Path With RDDs, each transformation like map or filter creates a node in the DAG representing a function that takes partition data as input. At execution time, Spark's scheduler figures out which transformations can run in the same stage (operations before a shuffle) and which require new stages (operations after a shuffle). Because Spark has no semantic information about what your functions do, it uses generic Java or Python serialization to move records between executors. Every object is materialized as a full JVM object in memory, with all the overhead that entails: object headers, pointers, garbage collection pressure. For example, storing 100 million simple records as JVM objects might consume 8 to 12 gigabytes just for object overhead, before counting the actual data. DataFrame and Dataset Execution Path These APIs work fundamentally differently. When you write DataFrame operations like select, filter, and join, Spark constructs a logical plan tree representing your query. The Catalyst optimizer then applies rule based transformations. It pushes filters down close to data sources, prunes columns you never use, reorders joins based on estimated table sizes, and chooses broadcast joins when one side is under a few hundred megabytes. After optimization, the logical plan converts to a physical plan specifying concrete operators: hash aggregate, sort merge join, or broadcast hash join. This physical plan then uses Tungsten's compact binary row format instead of JVM objects. Whole Stage Code Generation The most powerful trick is whole stage code generation. Instead of interpreting a sequence of operators row by row with virtual dispatch overhead, Spark compiles portions of your physical plan into tight bytecode loops. For a query that filters, projects, and aggregates, the generated code becomes essentially a hand written loop that reads binary rows, applies the filter condition inline, extracts needed fields, and updates the hash table, all without creating intermediate objects. This can be 5 to 10 times faster than interpreted execution. How Datasets Add Type Safety Datasets introduce encoders that map between your domain types and Spark's internal binary format. During most of the pipeline, Spark operates on the binary form to keep optimizer benefits. Only at boundaries where your user code needs actual objects does it materialize them using the encoder. This is why Datasets can approach DataFrame performance while preserving compile time type checking. The optimizer still sees a structured query plan, not opaque functions.