Learn more about rdd in spark
RDD (Resilient Distributed Dataset) is called the elastic distributed data set. It is the most basic data abstraction in Spark. It represents an immutable, partitionable, and set of elements that can be calculated in parallel. RDD has the characteristics of a data flow model: automatic fault tolerance, location-aware scheduling, and scalability. RDD allows users to explicitly cache the working set in memory when executing multiple queries. Subsequent queries can reuse the working set, which greatly improves query speed. Today, let's talk briefly about RDD in Spark. The RDD API will be put into the next chapter and then detailed RDD Introduction RDD can be regarded as an object of Spark, which runs in memory itself. For example, reading a file is an RDD, calculating a file is an RDD, and the result set is also an RDD. Different shards, data dependencies, key- Value type map data can be regarded as RDD. (Note: from Baidu Encyclopedia), here, RDD will not say much, just talk abou...