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.
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.
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
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.
both cache() and persist() are useful for avoiding costly re computation of RDDs and improve performance by avoiding costly re computation of RDDs.
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
RDDs provide two methods for changing the number of partitions: repartition() and coalesce(). These methods allow you to control the partitioning of your RDD
RDD pair functions consist of key-value pairs. These pair functions provide powerful operations for data transformation and aggregation based on keys.
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…
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…
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…