spark vs mapreduce

users and the number of movies and the number of users who rated the film valnumratings=ratings.count () valnumusers=ratings.map (_._2.user). Distinct (). Count () valnummovies=ratings.map (_._2.product). Distinct (). Count () println ("got" +numRatings+ "ratingsfrom" + numusers+ "users" +numMovies+ "movies") //the sample scoring table with a key value divided into 3 parts, respectively, for training (60%, and adding user ratings), check (20%),and test (20%) //This data is applied multip

Step OneIf not, do not set up the HBase development environment blog, see my next blog.HBase Development Environment Building (Eclipse\myeclipse + Maven)　　Step one, need to add. As follows:In the project name, right-click,Then, write Pom.xml, here not much to repeat. SeeHBase Development Environment Building (Eclipse\myeclipse + Maven)When you are done, write the code, right.Step two some steps after the HBase development environment is built (export exported jar package or Ant mode)Here, do not

Hadoop MapReduce:MapReduce reads the data from disk every time it executes, and then puts the data on the disk after the calculation is complete.Spark Map Reduce:RDD is everything for dev:Basic Concepts:Graph RDD:Spark Runtime:ScheduleDepency Type:Scheduler Optimizations:Event Flow:Submit Job:New Job Instance:Job in Detail:Executor.launchtask:Standalone:Work Flow:Standalone Detail:Driver Application to Clustor:Worker Exception:Executor Exception:Master Exception:Master HA:Hadoopspark

of cores is specified, so the client consumes all cores of the cluster and allocates 500M of memory at each node3. Spark Test 3.1 using Spark-shell testHere we test the Wordcout program that we all know in Hadoop, where the MapReduce implementation wordcout requires map, reduce, and job three parts, and even a single line in

of cores is specified, so the client consumes all cores of the cluster and allocates 500M of memory at each node3. Spark Test 3.1 using Spark-shell testHere we test the Wordcout program that we all know in Hadoop, where the MapReduce implementation wordcout requires map, reduce, and job three parts, and even a single line in

://hive.apache.orgTen HivemallHivemall combines a variety of machine learning algorithms for hive. It includes a number of highly scalable algorithms that can be used for data classification, recursion, recommendation, K nearest neighbor, anomaly detection, and feature hashing.Supported operating systems: Operating system-independent.RELATED Links: Https://github.com/myui/hivemallMahoutAccording to the official website, the Mahout project is designed to "create an environment for rapidly buildin

provide a higher level and richer computational paradigm on the Upper spark.(1) Spark Spark is the core component of the whole bdas, it is a large data distributed programming framework, which not only realizes the MapReduce operator map function and reduce function and calculation model, but also provides richer oper

Original linkWhat is SparkApache Spark is a large data processing framework built around speed, ease of use, and complex analysis. Originally developed in 2009 by Amplab of the University of California, Berkeley, and became one of Apache's Open source projects in 2010.Compared to other big data and mapreduce technologies such as Hadoop and Storm, Spark has the fo

26 Preliminary use of clusterDesign ideas of HDFsL Design IdeasDivide and Conquer: Large files, large batches of files, distributed on a large number of servers, so as to facilitate the use of divide-and-conquer method of massive data analysis;L role in Big Data systems:For a variety of distributed computing framework (such as: Mapreduce,spark,tez, ... ) Provides data storage servicesL Key Concepts: File Cu