jobtracker

Author: Dong | Sina Weibo: XI Cheng understands | reprinted, but the original source and author information and copyright statement must be indicated in the form of a hyperlink. Website: dongxicheng. orgmapreducecdh4-jobtracker-ha everyone knows that HadoopJobTracker has a single point of failure, and there has been no perfect open source solutions. In Hadoop Author: Dong | Sina Weibo: XI Cheng understand | can be reproduced, but must be in the form o

Mapreduce task execution process 5 is the detailed execution flowchart of mapreduce jobs. Figure 5 mapreduce job execution Flowchart 1. Write mapreduce code on the client, configure the job, and start the job. Note that after a mapreduce job is submitted to hadoop, it enters the fully automated execution process. In this process, in addition to the execution status and force termination of the Monitoring Program, the user cannot intervene in the execution process of the job. Therefore, before s

What is the nature of 1.job?2. What is the nature of the task?3. Who manages the namespace of the file system, and what is the role of namespace?What is the role of the 4.Namespace image file (Namespace image) and the Action log file (edit log) file?5.Namenode records the location information of the data nodes in each block in each file, but he does not persist the information, why?6. Does the client pass Namenode when reading or writing a data?What is the relationship between 7.namenode,datanod

JobtrackerJobtracker is a master service, and jobtracker each subtask task that dispatches the job runs on Tasktracker and monitors them, If a failed task is found, rerun it. In general, Jobtracker should be deployed on separate machines.1.2TasktrackerTasktracker is a slaver service that runs on multiple nodes. Tasktracker is responsible for executing each task directly. Tasktracker All need to run on the

The first time you touch Hadoop, the nodes that start Hadoop appear are:NameNodeSecondarynamenodeJobtrackerTasktrackerDataNodeNameNodeThe nodes that are now starting Hadoop appear:SecondarynamenodeNodeManagerResourceManagerNameNodeDataNodeFound in Hadoop now, Jobtracker and Tasktracker disappeared, more NodeManager and ResourceManagerLater, I found that the original Hadoop framework has changed.Below is an introduction to the new Hadoop framework, Yar

Turn from http://langyu.iteye.com/blog/992916 write pretty good! The operation mechanism of MapReduce can be described from many different angles, for example, from the MapReduce running flow, or from the logic flow of the computational model, perhaps some in-depth understanding of the MapReduce operation mechanism will be described from a better perspective, However, there are some things that will not be able to avoid the mapreduce operation mechanism, that is, the instance object that i

When executing a job, Hadoop divides the input data into n split and then launches the corresponding n map programs to process them separately. How the data is divided. How split is dispatched (how to decide which Tasktracker machine the map program for split should run on). How to read the divided data. This is the question to be discussed in this article. Start with a classic MapReduce work flow chart: 1, the operation of mapred procedures; 2, this operation will generate a job, so jobc

parts: Input data, that is, the data to be processed Map-Reduce program, that is, the Mapper and Reducer implemented above JobConf To configure JobConf, you need to have a general understanding of the basic principles of Hadoop job running: Hadoop divides jobs into tasks for processing. There are two types of tasks: map task and reduce task. Hadoop has two types of nodes to control job running: JobTracker and TaskTracker.

key/value pair in a row, the offset of the key, and the value is the row content. The following is the input data of map1: Key1 Value1 0 Hello World Bye World The following is the input data of map2: Key1 Value1 0 Hello Hadoop GoodBye Hadoop2 map output/combine Input The output result of map1 is as follows: Key2 Value2 Hello 1 World 1 Bye 1 World 1 The output result of map2 is as follows: Key2 Value2 Hello 1 Hadoop 1 GoodBye 1 Hadoop 13 combine output The Combiner class combines the values of t

Administrator is responsible for providing an efficient running environment for user jobs. The administrator needs to adjust some key parameter values globally to improve the system throughput and performance. In general, administrators need to provide Hadoop users with an efficient job running environment from four aspects: hardware selection, operating system parameter optimization, JVM parameter optimization, and Hadoop parameter optimization. 1. Hardware Selection the basic features of Hado

stored in several copies on different datanode, to achieve the purpose of fault tolerance and Disaster Tolerance. Namenode is the core of the entire HDFS. It maintains some data structures and records how many files are cut.Block, which can be obtained from the datanode and important information such as the status of each datanode. For more information about HDFS, see the hadoop Distributed File System: architecture and design. Hadoop has a jobtracker