Assignment 1 Quiz 5 CSE5BDC T5 2023

Caching only a part of an RDD has no performance benefits. (correct)

Caching data are especially important for the performance of iterative programs.

Caching reduces the amount of disk access and therefore speeds up query execution.

RDDs in Apache Spark are only cached if you explicitly specify that you want the RDD to be cached.

Parquet storage format stores the schema with the data.

Given a dataframe with 100 columns, it is faster to query a single column of the dataframe if the data are stored using the CSV storage format compared to the parquet storage format. (correct)

Parquet storage format stores all values of the same column together.

Given a dataframe with 100 columns, it is faster to query a single column of the dataframe if the data are stored using the parquet storage format compared to the data being stored in the CSV storage format.

Executing queries using SparkSQL DataFrames and DataSets functions are at least as fast as using their RDD counterparts, and often faster.

You can add columns to a dataframe using the withColumn function.

DataSets contain schemas whereas DataFrames do not contain schemas. (correct)

After performing a self-join on a dataframe, the resulting columns will contain duplicate column names.

<p>It allows you to quickly retrieve all data associated with a given value on the partitioned column.</p> Signup and view all the answers

<p>Spark automatically applies query optimisation on a sequence of RDD transformations.</p> Signup and view all the answers

<p>You need to explicitly invoke a combiner in order to enjoy the benefits of reduced data shuffle when using the reduceByKey function.</p> Signup and view all the answers