jobtracker

Questions Guide 1.hadoop1.x Retrofit If it is two jobtraker, what do you think solves the problem. 2.hadoop1.x Retrofit if it's two jobtraker, you don't think there's a problem. 3. What do you think of hadoop2.x yarn? In view of my interview mentioned this question: the difference between hadoop1.x and hadoop2.x, the beginning there are a lot of vague places, hope to see this article, can have a basic understanding of Hadoop. Reprinted from Http://www.aboutyun.com/forum.php?mod=viewthreadtid=197

will analyze the second part of mapreduce, from how mapreduce is distributed ######################################## ####################################### The previous mapreduce demo can only run on one machine. Now it is time to run it in a distributed manner. After a simple study of the mapreduce running process and filesystem, I try to start with the configuration to see how to make hadoop run mapreduce on both machines at the same time.First, check back here.String tracker = Conf. Get ("

, InterruptedException,ClassNotFoundException { if (state == JobState.DEFINE) { submit(); } if (verbose) { jobClient.monitorAndPrintJob(conf, info); } else { info.waitForCompletion(); } return isSuccessful();} When a new job is created, the job's jobstate state = jobstate. define, so the submit method will be executed in the above Code. After the submit is returned, different methods will be executed according to the verbose parameter true or false. The specific

This article mainly analyzes the following two points:Directory:1.MapReduce Job Run ProcessProcess of shuffle and sequencing in 2.Map, reduce tasksBody:1.MapReduce Job Run ProcessThe following is a process I draw with visio2010:Process Analysis:1. Start a job on the client.2. Request a job ID to Jobtracker.3. Copy the resource files required to run the job to HDFs, including the jar files packaged by the MapReduce program, the configuration files, and

-namenode.out Starting jobtracker, logging to/home/hadoop/hadoop-1.2.1/libexec/../logs/hadoop-hadoop-jobtracker-namenode.out Datanode2: starting tasktracker, logging to/home/hadoop/hadoop-1.2.1/libexec/../logs/hadoop-hadoop-tasktracker-datanode2.out Datanode1: starting tasktracker, logging to/home/hadoop/hadoop-1.2.1/libexec/../logs/hadoop-hadoop-tasktracker-datanode1.out Hadoop @ namenode :~ Hadoop-1.2.1 $

of 64MB by default. When a user uploads a large file to HDFS, the file is sliced into blocks, stored separately into different DataNode, and, in order to ensure reliable data, writes the same block to several lines (default is 3, which can be configured) different DataNode On The process of storing this file after cutting is transparent to the user.MapReduce ArchitectureLike HDFS, Hadoop MapReduce uses the Master/slave (M/s) architecture, specifically. It consists mainly of the following compon

Error HandlingThere are three main errors in the following types:1. Task Tasks2, Jobtracker failure3, Tasktracker failureTask Tasks1. When the code in the map or reduce sub-task throws an exception , the JVM process sends an error report to the service Tasktracker process before exiting. Tasktracker will mark this (task attempt) Taskattempt as failed state, releasing a slot to run another task .2. For a flow task, if the stream process exits with a no

1. Analyze the MapReduce job running mechanism 1). Typical MapReduce -- MapReduce1.0 There are four independent entities throughout the process Client: Submit MapReduce JobTracker: Coordinates job running TaskTracker: The task after the job is divided. HDFS: used to share job files between other entities The overall running figure is as follows: A. Submit A job The runJob of JobClient is a convenient method for creating a JobClient instance and

Detailed description of the work principle of mapreduce Preface:Some time ago, our cloud computing team learned about the knowledge of Hadoop, and we all actively did and learned a lot of things. But after school, everyone is busy with their own things, cloud computing is not too much movement. hehe ~ But recently in Hu boss's call, our cloud computing team rallied, hope that everyone still aloft "cloud in hand, follow me" slogan Fight down. This blog post is the witness of our team's "Restart c

1. After the client specifies various parameter configurations of the job, it calls the job. waitforcompletion (true) method to submit the job to jobtracker, waiting for the job to complete. Public void submit () throws ioexception, interruptedexception, classnotfoundexception {ensurestate (jobstate. define); // check jobstate status setusenewapi (); // check and set whether to use the new mapreduce API // connect to the

In the previous blog, we introduced the hadoop Job scheduler. We know that jobtracker and tasktracker are the two core parts in the hadoop job scheduling process. The former is responsible for scheduling and dispatching MAP/reduce jobs, the latter is responsible for the actual execution of MAP/reduce jobs and communication between them through the RPC mechanism. The source code of Job Scheduling in hadoop version 0.20.2 is analyzed below. The source c

One of the most important features of Jobtracker is state monitoring, including monitoring of runtime states such as Tasktracker, job, and task, where Tasktracker status monitoring is relatively straightforward, Just record their latest heartbeat report Time and health status (detected by the tasktracker-side monitoring script and send the results to Jobtracker by Heartbeat).Job Description ModelAs shown

Problems with the original Hadoop MapReduce frameworkThe MapReduce framework diagram of the original HadoopThe process and design ideas of the original MapReduce program can be clearly seen: First the user program (Jobclient) submits a job,job message sent to the job Tracker , the job Tracker is the center of the map-reduce framework, and he needs to communicate with the machine in the cluster (HEARTBE at), you need to manage which programs should run on which machines and manage all jo

the custom partition class. In this example, the year is modeled by default. Reduce phase Reducer obtains the intermediate result of mapper output and processes a key range as input. It is set using jobconf. setreducerclass. In this example, the processing is the same as that in combine phase. the maximum temperature of the data transmitted by each er is calculated in the year. Output phase The reducer output format corresponds to the Mapper input format. Of course, the reducer output can be

. Output phase The reducer output format corresponds to the Mapper input format. Of course, the reducer output can be processed as another mapper input. Ii. Details of job run The process of MAP and reduce is described in task running. In fact, many other details are involved from running "hadoop jar. The entire job running process is shown in: As you can see, the mapreduce operation involves four independent entities: Client, used to submit mapreduce jobs.

calculated in the year.Output phase The reducer output format corresponds to the Mapper input format. Of course, the reducer output can be processed as another mapper input.Ii. Details of job run The process of MAP and reduce is described in task running. In fact, many other details are involved from running "hadoop jar. The entire job running process is shown in: As you can see, the mapreduce operation involves four independent entities: Client, used to submit mapreduce jobs.

Mapreduce Working Principles Body:1. mapreduce job running process Process Analysis: 1. Start a job on the client. 2. Request a job ID from jobtracker. 3. Copy the resource files required for running the job to HDFS, including the jar files packaged by the mapreduce program, configuration files, and input Division information calculated by the client. These files are stored in the folder created specifically for this job by

Here we will talk about the limitations of mapreduce V1: Jobtracker spof bottleneck. Jobtracker in mapreduce is responsible for job distribution, management, and scheduling. It must also maintain heartbeat communication with all nodes in the cluster to understand the running status and Resource Status of the machine. Obviously, the unique jobtracker in mapreduc

value is "\tab". As shown, (a 2) output to Part-0, (b 3) output to Part-1, (c 3) output to Part-27. MapReduce Architecture　　Like HDFs, MapReduce is also a master/slave-based architecture, and its architecture diagram is as follows　　　　The MapReduce consists of four components, client, Jobtracker, Tasktracker, and task, which are described in detail in the following four components.1) Client ClientsEach Job will be stored in HDFS using the client class

1. MapReduce-mapping, simplifying programming modelOperating principle:2. The implementation of MapReduce in Hadoop V1 Hadoop 1.0 refers to Hadoop version of the Apache Hadoop 0.20.x, 1.x, or CDH3 series, which consists mainly of HDFs and MapReduce systems, where MapReduce is an offline processing framework consisting of the programming model (the old and new APIs), the runtime Environment (Jobtracker and Tasktracker), and the Data processing engine (