How do you optimize Spark jobs? What happens internally when you run a Spark job?

When you submit a Spark job, the driver program translates the high‑level API into a Directed Acyclic Graph (DAG) of stages. Each stage contains tasks that can run in parallel across executors. Spark first partitions the input data (based on the partitioner or hash of keys) and then executes narrow transformations (map, filter) locally on each partition. When a wide transformation (join, reduceByKey, groupByKey) is encountered, Spark triggers a shuffle: data is partitioned and written to disk or memory, then read by downstream stages. Internally, the scheduler assigns tasks to executors, balancing load and respecting data locality. Memory management is handled by the Unified Memory Manager, which splits memory between execution and storage. Optimizations include broadcast joins (small dataset is sent to all executors), partition pruning (skipping partitions that don’t match filter predicates), and caching (persisting RDDs/DataFrames). Spark’s Catalyst optimizer rewrites SQL queries into efficient physical plans, applying predicate pushdown, column pruning, and cost‑based optimizations. Dynamic allocation can scale the number of executors up or down based on workload. Monitoring the Spark UI and logs helps identify bottlenecks such as skewed partitions, excessive shuffles, or GC pauses. By tuning partition size, memory fractions, and using appropriate join strategies, you can significantly reduce runtime and resource consumption.