How Spark Executes Your Program?

Your journey begins with writing code in a Spark-supported language like Python, Scala, or Java. This code specifies operations to transform and analyze data, such as filtering rows or aggregating values.

• Key Insight: In Spark, there are two types of operations:
  • Transformations: Instructions to modify the data, such as map or filter. These are lazy, meaning they only describe the task and don’t execute immediately.
  • Actions: Operations like collect or save that trigger the actual execution.

Imagine this step as writing a list of tasks for a factory, detailing how raw materials (your data) should be processed into the final product (your result).

The program you run launches a driver process, which acts as the manager of the entire operation. The driver’s job is to:

• Parse your program.
• Break it down into a series of tasks.
• Distribute those tasks to worker nodes in the cluster.

Think of the driver as the manager of a factory. It oversees the process, plans the workflow, and delegates work to individual workers.

When you submit your program, Spark doesn’t execute it immediately. Instead, it constructs a logical plan:

• Unoptimized Logical Plan: Spark takes your high-level transformations (e.g., filter, groupBy) and arranges them in a raw, step-by-step blueprint.
• Optimized Logical Plan: Spark applies rules to streamline this plan. For example, it pushes filters earlier in the pipeline to reduce the amount of data processed downstream.

This is like drafting a rough blueprint for a factory assembly line, then revising it to minimize effort and maximize efficiency.

Once the logical plan is optimized, Spark generates a physical plan, which outlines the exact steps for execution. This plan considers factors like:

• Data locality: Where the data resides in the cluster.
• Partitioning: How the data is split across nodes for parallel processing.

Imagine turning the blueprint into specific instructions for each worker, detailing where to find the materials and how to assemble the final product.

Next, the driver program divides the physical plan into stages and schedules tasks for execution. Each stage represents a group of parallel tasks that can be executed without dependencies.

• Tasks are assigned to executors running on worker nodes.
• Spark aims to minimize data shuffling (moving data between nodes) to improve performance.

This step is like assigning tasks to factory workers based on their availability and proximity to the materials.

The executors, running on the worker nodes, are responsible for actually processing the data. Each executor:

• Reads a partition of data.
• Applies the transformations specified in the task.
• Writes intermediate results or sends them back to the driver.

Think of this step as the factory workers assembling parts of the product, each working on a specific section of the assembly line.

When an action is triggered, such as collect() or save(), Spark gathers the results from all executors and returns them to the driver or writes them to storage.

This is like the factory manager collecting the final products from the workers and delivering them to the customer.

One of Spark’s superpowers is its ability to recover from failures. If a task fails, Spark can use the DAG (Directed Acyclic Graph)—a lineage of all transformations—to recompute lost data.

Imagine a worker in the factory misplaces a part. Thanks to the detailed blueprint, another worker can recreate the lost piece without starting from scratch.