Apache Spark - Beyond Basics and Cracking Job Interviews

Understand how Spark runs on a Hadoop YARN or Kubernetes cluster, with a driver, executors, and containers, and how PySpark uses Py4J to run JVM and Python workloads.

Learn how to submit spark apps with spark-submit and configure master, deploy mode, conf, and driver/executor resources on a cluster.

Explore spark deploy modes with spark-submit, including cluster mode and client mode, and see how the driver and executors are managed by YARN in your cluster.

Learn how Spark jobs arise from actions, distinguish transformations (narrow vs wide dependencies), and follow execution through stages, shuffle, tasks, and executor slots.

Learn how the Spark SQL engine processes queries from dataframes and SQL, through analysis, logical optimization, physical planning, and code generation for efficient execution.

Analyze Spark architecture quiz questions, identifying correct and incorrect statements about the driver and executors, cluster mode versus client mode, stages, tasks, slots, and cluster managers.

Practice this video review of quiz 2 questions on Spark stages, shuffle, and cluster manager roles. It explains DAGs, fault tolerance via RDD lineage, and in-memory Spark performance vs Hadoop.

Explain how spark driver and executor memory are allocated on a yarn cluster. Detail heap, overhead, and configurations like spark.driver.memory and spark.executor.memoryOverhead that determine total container memory.

Learn how spark memory management splits the JVM heap into reserved memory, the spark memory pool, and the storage and executor pools. Explore the unified memory manager and off-heap options.

Enable Spark AQE to dynamically adjust shuffle partitions, join strategies, and skew joins by computing statistics during shuffle, optimizing performance and resource usage.

Spark AQE dynamically switches from sort-merge to broadcast hash join by runtime shuffle statistics, with AQE and the local shuffle reader reducing network traffic.

Discover how Spark adaptive query execution handles skewed joins by dynamically splitting skewed partitions and enabling skew-join optimization with AQE.

Learn how dynamic partition pruning in Spark 3.0 speeds queries by pruning partitions via a broadcast dimension table and injecting subqueries into the fact table.

Learn how to cache Spark dataframes in storage memory, compare cache and persist, choose storage levels, and balance memory, disk, and deserialization to optimize performance.

Explore how Spark dataframe repartition and repartitionByRange distribute data through hash or range, trigger shuffles, and influence partition counts, with coalesce reducing partitions without shuffling.

Apply partitioning and join hints in Spark SQL and dataframes to optimize partitioning and joins, including coalesce, repartition, repartition_by_range, rebalance, and broadcast joins, though Spark may not apply them.

Leverage Spark broadcast variables to share a lookup dataset with a user-defined function. Understand the difference between closures and broadcasts, lazy serialization, and the role of memory in broadcast joins.

Use Spark accumulators as global mutable counters to track bad records during data fixes, reducing shuffles and ensuring accurate counts via actions.

Enable Spark speculative execution to duplicate slow tasks on another node and finish a stage faster, and tune interval, multiplier, completion fraction, minimum time, and hard limit to balance overhead.

Learn how Spark scheduler allocates resources across applications on a shared cluster. Compare static and dynamic allocation, and how idle and backlog timeouts govern executor release and request.

Explore Spark scheduling within an application, balancing sequential and parallel jobs, and using FIFO or fair schedulers to manage resource contention. See how pools and the Spark UI reflect scheduling.

Develop modular Spark applications with a lib/transformations module and two unit-tested functions, load_survey_df and count_by_country, using unittest in local and notebook environments.

Practice quiz solutions review spark shuffle basics, map side repartitioning, partition aggregation, and the differences between transformations and actions for data skew, coalesce, and repartition scenarios.

Master Spark optimization techniques through a quiz-driven walkthrough of spark.speculation, memory management with spark.memory.fraction, adaptive query execution, dynamic partition pruning, and join strategy hints.