Spark RDD API By Example - 大数据

RDD is short for Resilient Distributed Dataset. RDDs are the workhorse of the Spark system. As a user, one can consider a RDD as a handle for a collection of individual data partitions, which are the result of some computation.

However, an RDD is actually more than that. On cluster installations, separate data partitions can be on separate nodes. Using the RDD as a handle one can access all partitions and perform computations and transformations using the contained data. Whenever a part of a RDD or an entire RDD is lost, the system is able to reconstruct the data of lost partitions by using lineage information. Lineage refers to the sequence of transformations used to produce the current RDD. As a result, Spark is able to recover automatically from most failures.

All RDDs available in Spark derive either directly or indirectly from the class RDD. This class comes with a large set of methods that perform operations on the data within the associated partitions. The class RDD is abstract. Whenever, one uses a RDD, one is actually using a concertized implementation of RDD. These implementations have to overwrite some core functions to make the RDD behave as expected.

One reason why Spark has lately become a very popular system for processing big data is that it does not impose restrictions regarding what data can be stored within RDD partitions. The RDD API already contains many useful operations. But, because the creators of Spark had to keep the core API of RDDs common enough to handle arbitrary data-types, many convenience functions are missing.

The basic RDD API considers each data item as a single value. However, users often want to work with key-value pairs. Therefore Spark extended the interface of RDD to provide additional functions (PairRDDFunctions), which explicitly work on key-value pairs. Currently, there are four extensions to the RDD API available in spark.