3/12/2024 0 Comments Format koala 103Since Spark/PySpark DataFrame internally stores data in binary there is no need of Serialization and deserialization data when it distributes across a cluster hence you would see a performance improvement. Using RDD directly leads to performance issues as Spark doesn’t know how to apply the optimization techniques and RDD serialize and de-serialize the data when it distributes across a cluster (repartition & shuffling). Spark RDD is a building block of Spark programming, even when we use DataFrame/Dataset, Spark internally uses RDD to execute operations/queries but the efficient and optimized way by analyzing your query and creating the execution plan thanks to Project Tungsten and Catalyst optimizer. In PySpark use, DataFrame over RDD as Dataset’s are not supported in PySpark applications. For Spark jobs, prefer using Dataset/DataFrame over RDD as Dataset and DataFrame’s includes several optimization modules to improve the performance of the Spark workloads.
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