rdd meaning

The transformation operator with data type value can be divided into the following types according to the relationship between input partition and output partition of Rdd transform operator.1) input partition and output partition one-to-one.2) input partition and output partition many-to-one type.3) input partition and output partition Many-to-many types.4) The output partition is an input partition subset type.5) There is also a special type of opera

Reference articlesCoalesce () method and repartition () methodTransformations Repartitionandsortwithinpartitions explanation return source coalesce and repartition explanation return source pipe explanation return source Cartesian explanation return source code cogroup explanation source Code J Oin explanation return Source code Sortbykey interpretation return source code Aggregatebykey interpretation return Source Reducebykey interpretation return Source Groupbykey interpretation return source

1. What is an RDD?The core concept of Rdd:spark is the RDD (resilient distributed dataset), which refers to a read-only, partitioned, elastic, distributed dataset that can be used in all or part of the data set in memory and can be reused across multiple computations.2. Why is RDD generated?(1) The traditional mapreduce has the advantages of automatic fault toler

This article is mainly about the basic operation of the RDD in Spark. The RDD is a data model specific to spark, and when it comes to what elastic distributed datasets are mentioned in the RDD, and what are the non-circular graphs, this article does not unfold these advanced concepts for the time being, and in reading this article, you can think of the

Original link: https://www.zybuluo.com/jewes/note/35032What is an RDD?A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. Represents an immutable (non-modifiable), partitioned collection of elements that can is operated on parallel. This class contains the basic operations available on all RDDs, such as map , filter , and persist .In addition, Org.apache.spark.rdd.PairRDDFunctions contain

Rdd Detailed This article is a summary of the spark Rdd paper, interspersed with some spark's internal implementation summaries, corresponding to the spark version of 2.0. Motivation The traditional distributed computing framework (such as MapReduce) performs computational tasks in which intermediate results are usually stored on disk, resulting in very large IO consumption, especially for various machine

ObjectiveWith spark for a while, but feel still on the surface, the understanding of Spark's rdd is still in the concept, that is, only know that it is an elastic distributed data set, the other is not knownA little slightly ashamed. Below is a note of my new understanding of the RDD.Official introductionElastic distributed data sets. The RDD is a collection of read-only, partitioned records. The

/** Spark SQL Source Code Analysis series Article */Next article spark SQL Catalyst Source Code Analysis physical Plan. This article describes the detailed implementation details of the physical plan Tordd:We all know a SQL, the real run is when you call it the Collect () method will run the spark Job, and finally calculate the RDD. Lazy val Tordd:rdd[row] = Executedplan.execute ()The Spark plan basically consists of 4 types of operations, the Basico

What is an RDD?The RDD is an abstract data structure type in spark, and any data is represented as an rdd in spark. From a programmatic point of view, an RDD can be viewed simply as an array. Unlike normal arrays, the data in the RDD is partitioned, so that data from differe

What is an RDD?The RDD is an abstract data structure type in spark, and any data is represented as an rdd in spark. From a programmatic point of view, an RDD can be viewed simply as an array. Unlike normal arrays, the data in the RDD is partitioned, so that data from differe

This document is edited by Cmd Markdown, the original link: https://www.zybuluo.com/jewes/note/35032What is an RDD?The RDD is an abstract data structure type in spark, and any data is represented as an rdd in spark. From a programmatic point of view, an RDD can be viewed simply as an array. Unlike normal arrays, the da

Spark RDD Scala language programming The Rdd(Resilient distributed Dataset) is an immutable collection of distributed objects, each of which is divided into partitions that run on different nodes of the cluster. The RDD supports two types of operations: conversion (trainsformation) and action , and Spark only lazily calculates the

The RDD is an abstract class that defines methods such as map (), reduce (), but in fact the derived class that inherits the Rdd typically implements two methods: def Getpartitions:array[partition] def compute (thepart:partition, Context:taskcontext): Nextiterator[t] GetPartitions () is used to tell how to partition input.Compute () is used to output all the rows of each partition (the lin

The execution of the Rdd dag is triggered by essentially executing the runjob operation of the submit job through Sparkcontext in the actions operator. The action operator is categorized according to the output space of the action operator: no output, HDFS, Scala collection, and data type.No output foreachThe F function operation is applied to each element in the RDD, instead of the

Spark Rdd coalesce () method and repartition () method, rddcoalesce In Rdd of Spark, Rdd is partitioned. Sometimes you need to reset the number of Rdd partitions. For example, in Rdd partitions, there are many Rdd partitions, but

What is RDD? What is the role of Spark? How to use it? 1. What is RDD? (1) Why does RDD occur? Although traditional MapReduce has the advantages of automatic fault tolerance, load balancing, and scalability, its biggest disadvantage is the adoption of non-circular data stream models, this requires a large number of disk I/O operations in Iterative Computing.

The main contents of this section:first, Dstream and A thorough study of the RDD relationshipA thorough study of the generation of StreamingrddSpark streaming Rdd think three key questions:The RDD itself is the basic object, according to a certain time to produce the Rdd of the object, with the accumulation of time, no

OverviewIn the "in-depth understanding of spark: core ideas and source analysis," a simple introduction of the next Rdd checkpoint, the book is a pity. So the purpose of this article is to check the gaps and improve the contents of this book.Spark's Rdd will save checkpoints after execution, so that when the entire job fails to run again, the successful RDD resul