Interview Questions and Answers

Lazy evaluation is a strategy in which the execution of operations is delayed until the result is actually needed. This helps in optimizing the execution plan.

Example:

val filteredRDD = inputRDD.filter(x => x > 0)
filteredRDD.count()

Spark achieves fault tolerance through lineage information (DAG) and recomputing lost data from the original source. If a partition of an RDD is lost, Spark can recompute it using the lineage information.

Example:

val resilientRDD = originalRDD.filter(x => x > 0)

The Spark Shuffle operation redistributes data across partitions during certain transformations, such as groupByKey or reduceByKey. It is a costly operation that involves data exchange and can impact performance.

Example:

val groupedRDD = inputRDD.groupByKey()

Spark offers in-memory processing, higher-level abstractions like DataFrames, and iterative processing, making it faster and more versatile than Hadoop MapReduce.

Example:

SparkContext sc = new SparkContext("local", "SparkExample")

Data skewness occurs when certain keys have significantly more data than others. Spark handles it by using techniques like data pre-partitioning or using advanced algorithms like map-side aggregation.

Example:

val skewedData = inputRDD.groupByKey(numPartitions)

Spark aims to schedule tasks on nodes that have a copy of the data to minimize data transfer over the network. This is achieved by using data locality-aware task scheduling.

Example:

sparkConf.set("spark.locality.wait", "2s")