spark vs mapreduce

Transferred from:http://blog.csdn.net/colorant/article/details/8255958= = what is = = Target Scope(what to fix) Iterative or duplicate query retrieval on a large-scale, specific data setOfficial definitionAmapreduce-like Cluster Computing framework designed for Low-latency Iterativejobs and interactive with from an interpreterPersonal understandingFirst,Mapreduce-like is to say that, like most distributed computing frameworks,

; Textfile.map (line = Line.split (""). Size). Reduce ((A, B) and if (a > B) a else B) Res3:int = 14 First, the line is mapped into an integer value to produce a new rdd. In this new RDD, use reduce to find the maximum number of words in a row. The parameters of map and reduce are Scala's function strings (closures), and can use any language feature or Scala/java class library. For example, we can easily invoke other function declarations. We use the Math.max () function to make the cod

Elastic distribution Data Set RddThe RDD (resilient distributed Dataset) is the most basic abstraction of spark and is an abstraction of distributed memory, implementing an abstract implementation of distributed datasets in a way that operates local collections. The RDD is the core of Spark, which represents a collection of data that has been partitioned, immutable, and can be manipulated in parallel, with

You are welcome to reprint it. Please indicate the source, huichiro.Summary The previous blog shows how to modify the source code to view the call stack. Although it is also very practical, compilation is required for every modification, which takes a lot of time and is inefficient, it is also an invasive modification that is not elegant. This article describes how to use intellij idea to track and debug spark source code.Prerequisites This document a

The spark version tested in this article is 1.3.1Spark Streaming programming Model:The first step:A StreamingContext object is required, which is the portal to the spark streaming operation, and two parameters are required to build a StreamingContext object:1, Sparkconf object: This object is configured by the Spark program settings, such as the master node of th

Content:1, the traditional spark memory management problem;2, Spark unified memory management;3, Outlook;========== the traditional Spark memory management problem ============Spark memory is divided into three parts:Execution:shuffles, Joins, Sort, aggregations, etc., by default, spark.shuffle.memoryfraction default i

transformation processing, the contents of the dataset are changed, the dataset A is converted to DataSet B, and the contents of the dataset are then normalized to a specific value after action has been processed. Only if there is an action on the RDD, all operation on the RDD and its parent RDD will be submitted to cluster for real execution.From code to dynamic running, the components involved are as shown.New Sparkcontext ("spark://...", "MyJob"

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, not its management will lead to memory overflow, so in batchduration time after performing the Rdd operation, the RDD needs to be managed. 1, Dstream generate Rdd process, dstream in

The MAVEN components are as follows: org.apache.spark spark-streaming-kafka-0-10_2.11 2.3.0The official website code is as follows:Pasting/** Licensed to the Apache software Foundation (ASF) under one or more* Contributor license agreements. See the NOTICE file distributed with* This work for additional information regarding copyright ownership.* The ASF licenses this file to under the Apache License, Version 2.0* (the "License"); You are no

Spark Cluster preview:Official documentation for the spark cluster is described below, which is a typical master-slave structure:Official documentation provides detailed guidance on some of the key points in the spark cluster:The definition of its worker is as follows:It is important to note that the spark driver clust

I. The purpose of this articleStraggler is the hotspot of research, and there are straggler problems in spark. GC problem is one of the most important factors that lead to straggler, in order to understand the straggler problem caused by GC, we need to learn GC problem first and how to monitor the GC of Spark. GC issues are more discussed, and a series of articles is recommended for learning: to become a GC

data, with the increasing scale of data, is the original analysis techniques outdated? The answer is clearly no, the original analytical skills remain valid in the existing analytical dimension, of course, for the new data we want to dig out more interesting and valuable content, this goal can be given to data mining or machine learning to complete.So how can the original data analysts quickly switch to Big Data's platform, to re-learn a script, directly in Scala or Python to write the Rdd. Obv

ProblemHow does Spark's computational model work in parallel? If you have a box of bananas, let three people take home to eat, if not unpacking the box will be very troublesome right, haha, a box, of course, only one person can be carried away. At this time, people with normal IQ know to open the box, pour out bananas, respectively, take three small boxes to reload, and then, each to go home to chew it. Spark and many other distributed computing syste

0. For (stage1, task0), the content of Data Partition 0 to be read is composed of task 0 and partition 0 in Task 1. Now the key problem is converting to (stage_1, task_0). How do I know if (stage_2, task_x) has the corresponding output that belongs to Data Partition 0? The solution to this problem is mapstatus. After each shufflemaptask is executed, a mapstatus will be reported. In mapstatus, it will reflect the Data Partition to which data is written. If data is written, the size is a non-ze

NetEase Big Data Platform Spark technology practice author Wang Jian Zong NetEase's real-time computing requirementsFor most big data, real-time is the important attribute that it should have, the arrival and acquisition of information should meet the requirement of real time, and the value of information needs to be maximized when it arrives at that moment, for example, e-commerce website, the website recommendation system expects to analyze its purc

Y. You are welcome to repost it. Please indicate the source, huichiro.Summary "Spark is a headache, and we need to run it on yarn. What is yarn? I have no idea at all. What should I do. Don't tell me how it works. Can you tell me how to run spark on yarn? I'm a dummy, just told me how to do it ." If you and I are not too interested in the metaphysical things, but are entangled in how to do it, reading this

For more than 90% of people who want to learn spark, how to build a spark cluster is one of the greatest difficulties. To solve all the difficulties in building a spark cluster, jia Lin divides the spark cluster construction into four steps, starting from scratch, without any pre-knowledge, covering every detail of the

1. After installing Spark, enter spark in the bin directory: Bin/spark-shell scala> val textfile = Sc.textfile ("/users/admin/spark/ Spark-1.6.1-bin-hadoop2.6/readme.md ") scala> Textfile.flatmap (_.split (" ")). Filter (!_.isempty). Map ((_,1)). Reducebykey (_+_). Collect (

Label:Scenario: Use spark streaming to receive the data sent by Kafka and related query operations to the tables in the relational database;The data format sent by Kafka is: ID, name, Cityid, and the delimiter is tab.1 Zhangsan 12 Lisi 13 Wangwu 24 3The table city structure of MySQL is: ID int, name varchar1 BJ2 sz3 shThe results of this case are: Select S.id, S.name, S.cityid, c.name from student S joins C

Label:Spark Learning five: Spark SQLtags (space delimited): Spark Spark learns five spark SQL An overview Development history of the two spark Three spark SQL and hive comparison Quad