PySpark Internals Explained: Essential Guide for New Data Engineers
Behind the scenes of PySpark: decode its architecture, learn how it handles transformations and actions, and optimise your workflows for high-speed data engineering.
Greetings, curious reader,
Many data engineers know how to use PySpark, but few understand how it works. This gap leads to slow performance, inefficient resource usage, and costly mistakes.
PySpark is a distributed computing engine designed for "big data", but without understanding its internals, you won’t unlock its full power.
In this article, you’ll learn:
How PySpark’s architecture distributes workloads across a cluster.
How the driver, cluster manager, and executors work together.
How RDDs, DataFrames, and partitioning impact performance.
How PySpark’s execution model optimises queries.
Why shuffling is a performance bottleneck and how to reduce it.
Understanding internals is the difference between being a mediocre and a stellar data engineer.
🗂️ Resilient Distributed Datasets (RDDs)
🧩 What are RDDs?
Resilient Distributed Datasets (RDDs) are the core data structure in PySpark. RDDs represent an immutable, distributed collection of objects processed across multiple cluster nodes.
RDDs allow for fault-tolerant, parallel processing, making them ideal for large-scale data transformations.
RDDs are based on three fundamental principles:
Resilience – If a partition of an RDD is lost, Spark can recompute it using lineage information.
Distribution – RDDs are automatically partitioned across multiple nodes for parallel processing.
Immutability – Once an RDD is created, it cannot be modified. Instead, transformations create new RDDs.
🔬 How RDDs Work Under the Hood
When an RDD is created, PySpark does not store the entire dataset in memory. Instead, it stores partitions of the dataset across multiple worker nodes. Each partition is processed independently by the executors.
Here’s what happens behind the scenes:
The driver program creates an RDD from a data source.
Spark splits the RDD into partitions and distributes them to executors.
When a transformation is applied, Spark records the transformation but does not execute it immediately (lazy evaluation).
When an action (such as
collect()orcount()) is triggered, Spark executes all transformations in a sequence of stages.If a worker node fails, Spark reconstructs lost partitions using lineage information.
