The term RDD stands for Resilient Distributed Dataset.
What does it mean? Let me explain.
An RDD is a data structure that holds your data records. It shares similarities with DataFrames as they are built on top of RDDs. However, unlike DataFrames, RDD records are language-native objects and do not have a row/column structure or a schema. In simple terms, an RDD is similar to a Scala, Java, or Python collection.
You can create an RDD by reading data from a file. Internally, RDDs are broken down into partitions to form a distributed collection. Like DataFrames, these partitions are then distributed and spread across the executor cores, enabling parallel processing.
RDDs are resilient, which means they are fault tolerant.
How does this work?
RDDs achieve fault tolerance by storing information about how they are created. For instance, let's assume an RDD partition is assigned to an executor core for processing. If the executor fails or crashes, the driver will detect the failure and assign the same RDD partition to another executor core. The new core will reload the RDD partition, utilizing the stored information on how to create and process it. This seamless recovery process makes RDDs resilient, as they can be recreated and reprocessed anywhere within the cluster.
To conclude, Resilient Distributed Datasets (RDDs) are at the heart of Apache Spark's data processing capabilities. By providing fault tolerance, parallel processing, and distributed data manipulation, RDDs enable developers to handle vast amounts of data efficiently. Their ability to recover from failures using lineage information ensures the reliability and robustness of data processing in distributed computing environments. With RDDs, developers can unlock the potential of Spark for processing large-scale datasets, making them an essential tool in big data analytics and processing.
To explore more about it, follow the below official documentation link:https://spark.apache.org/docs/latest/rdd-programming-guide.html
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