Freshers / Beginner level questions & answers
Ques 1. What is Apache Spark?
Apache Spark is an open-source distributed computing system that provides fast and general-purpose cluster computing for big data processing and analytics.
Example:
SparkContext sc = new SparkContext("local", "SparkExample");
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Ques 2. What is the purpose of the SparkContext in Apache Spark?
SparkContext is the entry point for Spark functionality and represents the connection to the Spark cluster. It coordinates the execution of operations on the cluster.
Example:
val sc = new SparkContext("local", "SparkExample")
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Ques 3. Explain the role of the Spark Driver in a Spark application.
The Spark Driver is the program that runs the main() function and creates the SparkContext. It coordinates the execution of tasks on the Spark Executors and collects results from them.
Example:
object MyApp {
def main(args: Array[String]): Unit = {
val sc = new SparkContext("local", "MyApp")
}
}
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Ques 4. What is the difference between a DataFrame and an RDD in Spark?
A DataFrame is a distributed collection of data organized into named columns, similar to a relational table. An RDD (Resilient Distributed Dataset) is a low-level abstraction representing a distributed collection of objects.
Example:
val df = spark.read.json("/path/to/data.json")
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Intermediate / 1 to 5 years experienced level questions & answers
Ques 5. Explain the difference between Spark transformations and actions.
Transformations are operations that create a new RDD, while actions are operations that return a value to the driver program or write data to an external storage system.
Example:
val mappedRDD = inputRDD.map(x => x * 2)
val result = mappedRDD.reduce((x, y) => x + y)
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Ques 6. What is the significance of Spark's lineage graph (DAG)?
Spark's lineage graph (DAG) is a directed acyclic graph that represents the sequence of transformations and actions on RDDs. It helps in recovering lost data in case of node failure.
Example:
val filteredRDD = inputRDD.filter(x => x > 0)
filteredRDD.toDebugString
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Ques 7. Explain the concept of partitions in Apache Spark.
Partitions are basic units of parallelism in Spark. They represent the logical division of data across the nodes in a cluster, and each partition is processed independently.
Example:
val inputRDD = sc.parallelize(Seq(1, 2, 3, 4, 5), 2)
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Ques 8. What is a Spark Executor and what role does it play in Spark applications?
A Spark Executor is a process responsible for executing tasks on a worker node. Executors are launched at the beginning of a Spark application and run tasks until the application completes or encounters an error.
Example:
spark-submit --master yarn --deploy-mode client --num-executors 3 mySparkApp.jar
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Ques 9. What is the Broadcast variable in Spark and when is it used?
A Broadcast variable is a read-only variable cached on each worker node. It is used to efficiently distribute large read-only data structures, such as lookup tables, to all tasks in a Spark job.
Example:
val broadcastVar = sc.broadcast(Array(1, 2, 3))
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Ques 10. What is the purpose of the Spark SQL module?
Spark SQL is a Spark module for structured data processing. It provides a programming interface for data manipulation using SQL, as well as a DataFrame API for processing structured and semi-structured data.
Example:
val df = spark.sql("SELECT * FROM table")
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Ques 11. How can you persist an RDD in Apache Spark? Provide an example.
You can persist an RDD using the persist() or cache() method. It allows you to store the RDD's data in memory or on disk for faster access.
Example:
val cachedRDD = inputRDD.persist(StorageLevel.MEMORY_ONLY)
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Ques 12. Explain the difference between narrow and wide transformations in Spark.
Narrow transformations involve operations where each input partition contributes to only one output partition. Wide transformations involve operations where multiple input partitions contribute to multiple output partitions.
Example:
Narrow: map, filter
Wide: groupByKey, reduceByKey
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Ques 13. What is the purpose of the Spark Streaming module?
Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. It allows processing real-time data using batch processing capabilities of Spark.
Example:
val streamingContext = new StreamingContext(sparkContext, Seconds(1))
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Ques 14. How does Spark handle data serialization and why is it important?
Spark uses Java's Object Serialization to serialize data between the Spark Driver and Executors. Efficient serialization is crucial for optimizing data transfer and reducing network overhead.
Example:
sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
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Ques 15. What is the purpose of the accumulator in Spark?
An accumulator is a variable that can be added to and is used in Spark to implement counters and sums in a parallel and fault-tolerant manner across distributed tasks.
Example:
val accumulator = sc.longAccumulator("MyAccumulator")
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Ques 16. Explain the concept of Spark DAG (Directed Acyclic Graph).
The Spark DAG represents the logical execution plan of transformations and actions in a Spark application. It is a graph of stages, where each stage contains a sequence of tasks that can be executed in parallel.
Example:
val dag = inputRDD.map(x => x * 2).toDebugString
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Ques 17. Explain the concept of a Spark task.
A task is the smallest unit of work in Spark, representing the execution of a transformation or action on a partition of data. Tasks are scheduled by the Spark Scheduler on Spark Executors.
Example:
val taskResult = executor.runTask(taskID, taskInfo)
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Ques 18. What is the purpose of the Spark MLlib library?
Spark MLlib is Spark's machine learning library, providing scalable implementations of various machine learning algorithms and tools for building and evaluating machine learning models.
Example:
val model = new RandomForestClassifier().fit(trainingData)
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Experienced / Expert level questions & answers
Ques 19. Explain the concept of lazy evaluation in Apache Spark.
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()
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Ques 20. How does Spark handle fault tolerance in RDDs?
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)
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Ques 21. What is the significance of the Spark Shuffle operation?
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()
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Ques 22. What are the advantages of using Spark over Hadoop MapReduce?
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")
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Ques 23. How does Spark handle data skewness in transformations like groupByKey?
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)
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Ques 24. How does Spark handle data locality optimization?
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")
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- What is the purpose of the Spark SQL module?
- Explain the difference between narrow and wide transformations in Spark.