Apache Spark

In-memory distributed computing engine

Learn about Apache Spark, an in-memory distributed computing engine used for large scale data processing and analytics.

5 minutes 5 Questions

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Big Data Scientist - Apache Spark Example Questions

Test your knowledge of Amazon Simple Storage Service (S3)

Question 1

What is the purpose of the 'textFile()' method in SparkContext?

To create a new text file in Hadoop HDFS To rename a text file in Hadoop HDFS To delete a text file from Hadoop HDFS To write text data to a file from an RDD To read text data from a file into an RDD

Correct Answer: To read text data from a file into an RDD

The textFile() method is used to read text data from a file into an RDD. It takes a path to the file as its argument and returns a new RDD that contains the lines of the file as its elements.

Question 2

What is the purpose of the 'persist()' operation in Spark?

Writes an RDD or DataFrame to an external storage system Filters out specific data from an RDD or DataFrame based on a given condition Aggregates values for each key in an RDD using a specified reduce function Checks the durability of an RDD or DataFrame to ensure data consistency Caches an RDD or DataFrame in memory or disk storage to allow for faster access Randomly shuffles the elements in an RDD or DataFrame

Correct Answer: Caches an RDD or DataFrame in memory or disk storage to allow for faster access

The 'persist()' operation in Spark is used to cache an RDD or DataFrame in memory or disk storage to allow for faster access. Caching an RDD or DataFrame can significantly improve the performance of iterative and interactive algorithms that access the same data multiple times. Once an RDD or DataFrame is persisted in memory, subsequent operations that use that RDD or DataFrame can access the data faster than if it were read from disk or network storage. Persistent storage options include MEMORY_ONLY, MEMORY_ONLY_SER, MEMORY_AND_DISK, DISK_ONLY, etc.

Question 3

What is the purpose of the 'take()' operation in Spark?

Aggregates values for each key in an RDD or DataFrame using a specified reduce function Randomly shuffles the elements in an RDD or DataFrame Returns the specified element of an RDD or DataFrame Sorts the elements in an RDD or DataFrame in ascending order Filters out specific data from an RDD or DataFrame based on a given condition Returns the first n elements of an RDD or DataFrame

Correct Answer: Returns the first n elements of an RDD or DataFrame

The 'take()' operation in Spark is used to return the first n elements of an RDD or DataFrame. This operation is useful for quickly previewing the contents of an RDD or DataFrame, or for retrieving a small subset of data for further processing. The number of elements to return is specified as an argument to the 'take()' method. If no argument is provided, the default value of 1 is used.

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