Apache Spark Execution Flow Explained

Published 2026-07-08 in PySpark

Apache Spark Execution Flow Explained One of the most common Spark questions is: "What happens internally when you run a Spark job?" Here's the complete execution flow: 1️⃣ Application Submission The user submits the application using "spark-submit", a Databricks Notebook, or a scheduled job. 2️⃣ Driver Initialization The Driver creates the "SparkSession" and "SparkContext", initializing the Spark application. 3️⃣ Resource Allocation The Cluster Manager allocates CPU, memory, and launches Executors on worker nodes. 4️⃣ Executor Registration The Executors start on worker nodes and register themselves with the Driver. 5️⃣ Logical Plan Creation The Spark SQL Parser converts the DataFrame/SQL code into an unresolved logical plan, and the Analyzer resolves table names, columns, and data types to produce a logical plan. 6️⃣ Query Optimization The Catalyst Optimizer applies optimization rules such as predicate pushdown, column pruning, constant folding, and join optimization to generate an optimized logical plan. 7️⃣ Physical Planning The Spark Planner generates one or more physical execution plans from the optimized logical plan, and Cost-Based Optimization (CBO) selects the most…

More PySpark articles · All collections · Practice challenges