Explain Spark RDD Storage Levels
persistence refers to the ability to store RDD in memory or on disk. Spark provides different storage levels that determine how RDDs are persisted and stored.
How to Access RDD Broadcast Variable
broadcast variable is a read-only variable that can be shared across all nodes of a cluster. It allows data to be sent to the worker nodes to be reused
How to Access Accumulator Variables
Accumulators are variables that can be updated by tasks running on different nodes in a cluster, their updated values can be accessed by the driver program.
Compare Cache and Persist in Spark
both cache() and persist() are useful for avoiding costly re computation of RDDs and improve performance by avoiding costly re computation of RDDs.
How to Shuffle Partitions in Spark RDD
During the map stage, data is grouped by keys and written to intermediate partitions. In the reduce stage, the data is shuffle and merged to produce the result
How to use Repartition and Coalesce
RDDs provide two methods for changing the number of partitions: repartition() and coalesce(). These methods allow you to control the partitioning of your RDD
How to use Pair Functions in Spark
RDD pair functions consist of key-value pairs. These pair functions provide powerful operations for data transformation and aggregation based on keys.
How to use SparkContext in Spark
In PySpark, SparkContext is a fundamental component that serves as the connection between a Spark cluster and the application code. It represents the entry point for low-level Spark functionality and…
How to use SparkSession in Spark
The SparkSession is the entry point for any Spark functionality in PySpark. It provides a way to interact with Spark and enables the creation of Dataframe and Dataset objects, which…
How to Use Array in PySpark
arrays in PySpark allows you to handle collection of values within a Dataframe column. PySpark provides various functions to manipulate and extract information