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of different data copies.MapReduce ArchitectureThe MR Framework consists of a single jobtracker running on the master node and Tasktracker running on each cluster from the node. The primary node is responsible for scheduling all tasks that make up a job, which are distributed across different slave nodes. The primary node monitors their execution and re-executes previously failed tasks. The slave node is responsible only for the tasks assigned by the
Part I: How MapReduce worksMapReduce Roleclient: Job submission initiator.Jobtracker: Initializes the job, allocates the job, communicates with Tasktracker, and coordinates the entire job.Tasktracker: Performs a mapreduce task on the allocated data fragment by maintaining jobtracker communication through the heartbeat heartbeat.Submit Job• The job needs to be configured before the job is submitted• program code, mainly the MapReduce program written by
date : September 9, 2013 Reference:"1" Hadoop Technology Insider-deep analysis of MapReduce architecture design and implementation principles Dong Xicheng "2" Hadoop 1.0.0 source "3" Hadoop Technology Insider-deep analysis of Hadoop Common and HDFS architecture design and implementation principles Cai Bin Chen Yiping The life cycle of a mapreduce job is broadly divided into 5 stages "1": 1.job submission and initialization2. Task scheduling and Monitoring 3. Task run Environment Prepar
map-Reduce cluster, the main task is to monitor the resources of the machine where the machine is located (the resource indicates "How many map-tasks and how many reduce-tasks can be started on the local machine ", the upper limit of MAP/reduce tasks on each machine is configured when a cluster is created). In addition, tasktracker monitors the running status of the task on the current machine. Tasktracker needs to send this information to jobtracker
1. Steps to implement partitioning: 1.1 First analyze the specific business logic, determine how many partitions 1.2 first write a class, it inherits Org.apache.hadoop.mapreduce.Partitioner This class 1.3 overrides public int Getpartition This method, according to the specific logic, read the database or configuration return the same number 1.4 in the main method set Partioner class, Job.setpartitionerclass (Datapartitioner.class) ; 1.5 Set the number of reducer, job.setnumreducetasks (6);2. Sor
Previously in Hadoop 1.0, Jobtracker has done two main functions: resource management and Job control. In a scenario where the cluster size is too large, jobtrackerThe following deficiencies exist:1) Jobtracker single point of failure.2) The jobtracker is subjected to great access pressure, which affects the expansibility of the system.3) Calculation frameworks o
class for the Mapper function. If this parameter is not specified, the default value is string.4. define the main function, define a job in it, and run it. Then the task is handed over to the system.1. Basic Concept: hadoop HDFS implements Google's GFS file system. namenode runs on the master as the file system and datanode runs on each machine. At the same time, hadoop implements Google mapreduce. jobtracker runs on the master node as the mapreduce
Briefly describe these systems:Hbase–key/value Distributed DatabaseA collaborative system for zookeeper– support distributed applicationsHive–sql resolution Engineflume– Distributed log-collection system First, the relevant environmental description:S1:Hadoop-masterNamenode,jobtracker;Secondarynamenode;Datanode,tasktracker S2:Hadoop-node-1Datanode,tasktracker; S3:Hadoop-node-2Datanode,tasktracker; namenode– the entire HDFs namespace management Ser
1) installation and configuration of the Java environment2) install hadoop Download hadoop-0.20.2.tar.gz from hadoop and decompress tar zxvf hadoop-0.20.0.tar.gz Add in hadoop-env.shExport java_home =/home/heyutao/tools/jdk1.6.0 _ 20Export hadoop_home =/home/heyutao/tools/hadoop-0.20.2Export Path = $ path:/home/heyutao/tools/hadoop-0.20.2/bin Test whether hadoop is successfully installed. bin/hadoop 3) Configure hadoop in a single-host environment A) edit the configuration file 1) Modify CONF/co
In traditional MapReduce, Jobtracker is also responsible for Job Scheduling (scheduling tasks to corresponding tasktracker) and task Progress Management (monitoring tasks, failed restart or slow tasks ). in YARN, Jobtracker is divided into two independent daemprocesses: Resource Manager (resourcemanager) is responsible for managing all resources of the cluster, In traditional MapReduce,
Getting started with Hadoop WordCount Program This article mainly introduces the working principle of MapReduce and explains the WordCount program in detail. 1. MapReduce Working Principle In the book Hadoop in action, we have a good description of the MapReduce computing model. Here we reference it directly:" In Hadoop, there are two machine roles for executing MapReduce tasks: JobTracker and TaskTracker. JobTrac
Install and deploy Apache Hadoop 2.6.0 Note: For this document, refer to the official documentation for the original article. 1. hardware environment There are three machines in total, all of which use the linux system. Java uses jdk1.6.0. The configuration is as follows:Hadoop1.example.com: 172.20.115.1 (NameNode)Hadoop2.example.com: 172.20.1152 (DataNode)Hadoop3.example.com: 172.115.20.3 (DataNode)Hadoop4.example.com: 172.20.115.4Correct resolution between host and IP addressFor Hadoop, in HDF
MapReduce program via Hadoop command(2) jobclient get job id:jobclient contact Jobtracker get a job ID(3) Preparation of jobclient initialization:① copy code, configuration, slice information, etc. to HDFs② partitioning of data based on input data path, block size, and set shard size③ checking the output directory(4) Jobclient Submit job: Jobclient submit job ID and corresponding resource information to each Jobt
file store.3) The client reads the file information.As a distributed file system, HDFs can be used as a reference point in data management:File Block Placement: A block will have three backups, one on the datenode specified by Namenode, and one on Datanode with the specified Datanode not on the same machine, One is the specified datanode on the datanode on the same rack. The purpose of the backup is for data security, in order to take into account the same rack failures, as well as the performa
Sometimes we use it, but we don't know why. Just likeIt may have been natural for the apples to hit us, but Newton discovered the gravitational force of the Earth. OK, hopefully by understanding MapReduce, we can write better examples of MapReduce.Part I: How MapReduce works MapReduce Roleclient: Job submission initiator.Jobtracker: Initializes the job, allocates the job, communicates with Tasktracker, and coordinates the entire job.Tasktracker: Maintains Jo
I. OverviewBased on the analysis of 0.19.1, this article shows some alibaba hadoop optimizations. This article does not involve the jobtracker and nodename metadata. This article mainly describes some logs generated by a task in the computing stage and some log problems.Ii. Brief Introduction to logsWhen all the daemon processes get up (for simplicity, we use the pseudo-distribution mode, which is built on a machine), the general directory structure i
:127.0.0.0 localhost localhost202.197.18.72 dbrg-1 dbrg-1202.197.18.73 dbrg-2 dbrg-2202.197.18.74 dbrg-3 dbrg-3 The/etc/hosts file in dbrg-2 should look like this:127.0.0.0 localhost localhost202.197.18.72 dbrg-1 dbrg-1202.197.18.73 dbrg-2 dbrg-2 As mentioned in the previous study note, for Hadoop, in HDFs's view, nodes are divided into Namenode and Datanode, where Namenode only one, Datanode can be a lot; in MapReduce's view, Nodes are divided into Jobtrack
IP address and the IP address of the Namenode machine to the Hosts file. For example, the/etc/hosts file in dbrg-1 should look like this:127.0.0.0 localhost localhost202.197.18.72 dbrg-1 dbrg-1202.197.18.73 dbrg-2 dbrg-2202.197.18.74 dbrg-3 dbrg-3 The/etc/hosts file in dbrg-2 should look like this:127.0.0.0 localhost localhost202.197.18.72 dbrg-1 dbrg-1202.197.18.73 dbrg-2 dbrg-2 As mentioned in the previous learning note, for Hadoop, in the case of HDFs, nodes are divided into Namenode and Da
The previous article () mentioned the submitJob () method in jobTracker. This method will eventually call listener. jobAdded (job) and register the Job to taskschedded for scheduling. Today, I will continue my research. In Hadoop, the default TaskScheduler is JobQueueTaskScheduler, which adopts the FIFO (first-in-first-out) Principle for scheduling, and FiarScheduler and CapacityTaskScheduler, but hadoop also adds them to the class library). These two
System hadoop namenode-format # Start the Hadoop daemon start-all.shecho-e "\ n ================ ========================================================== ================== "echo" hadoop log dir: $ HADOOP_LOG_DIR "echo" NameNode-http: // $ IP: 50070/"echo" JobTracker-http: // $ IP: 50030/"echo-e" \ n ================================ ========================================================== = "} pseudo dodistributed 2> 1 | tee-a pseudo. logPassphr
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