Apache spark RDD is a very complex and extreme large sets of data which cannot be fit into single node. By nodes, we means how the system operates on the data. So here, we need to fit the data by split into different nodes. This where apache spark partition plays the role. It automatically splits the data into different partitions and fixes into the nodes across the hive. It is very important to understand the parts and characteristics of the partition to achieve a better performance and output without much of errors. We look into some of the basics of the partitioning. Some of them are 1) every node contains one or more partitions, 2) the number of nodes in partition is either too few or few many and hence there it is very important to stay away from partitions as they tend to take more time than it should ideally 3) partitions cannot interlink different machines and hence there is very less cross-over working with multiple data in multiple systems 4) there is only one tasks at a time and each person has to do this at a time and hence there is no multitasking. There are two types of partitioning supported by apache spark. One is hash and the other one is range. Both are used in different types of datasets which needs to operate on a specific type of system. Here again, we need to talk about formats.
Some of the most common big data formats like HDFS file format are chosen to do a certain set of operation of the data set. There are a lot of benefits over choosing a specific type of format. Some of them are easy read and write which is very essential in finishing the processing in a shorter time, easily editable formats without changing the entries in the call and then the final and the most important feature is the storage and compression. These have become very essential as they can be compressed using a MapReduce or even a spark partition.
Processing hive map with the right kind of format become very effective than choosing some random formats and stressing over the output. There are certain kind of characteristics each kind of formats carry with them. Every format is used for a specific purpose. Hive data processing is the most preferred and the most used kind of format.