Explain the architecture of Apache Spark and discuss common optimizations. Where would you write logic for a new project and why do we prioritize solving first?
💡 Model Answer
Apache Spark follows a master‑worker model. The driver program runs the main() method, creates a SparkContext, and submits jobs to a cluster manager (YARN, Mesos, or Standalone). Executors run on worker nodes and perform tasks. Spark builds a Directed Acyclic Graph (DAG) of stages; each stage contains tasks that operate on partitions of data. Optimizations include the Catalyst optimizer for query planning, Tungsten execution engine for memory management, partition pruning, broadcast joins, caching/persisting RDDs, and shuffle avoidance. Logic for a new project is written in the driver program using Spark APIs (DataFrame, Dataset, RDD). Solving first means understanding the problem, defining the data flow, and designing the DAG before coding, which reduces debugging and improves performance.
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