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.
RDD Applications
RDDs (Resilient Distributed Datasets) in PySpark offer several use cases where their characteristics of distributed data processing, fault tolerance, and in-memory processing can provide significant benefits. Here are three use…
How to Use RDD Actions with Example
collect() Action: The collect() action returns all the elements of the RDD as an array to the driver program. # Creating an RDD rdd = spark.sparkContext.parallelize() # Applying collect action…
How to use RDD Transformation with Examples
map() Transformation: The map() transformation applies a specified function to each element of the RDD and returns a new RDD consisting of the transformed elements. # Creating an RDD rdd…