jobtracker

Transfer from http://weixiaolu.iteye.com/blog/1474172Objective: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 cl

Purpose This article describes how to install, configure, and manage a meaningful hadoop cluster that can scale from a small cluster of several nodes to a large cluster of thousands of nodes. If you want to install Hadoop on a single machine, you can find the details here. Prerequisites Ensure that all required software is installed on each node in your cluster. Get the Hadoop package. Installation Installing a Hadoop cluster typically extracts the installation software to all the machines

is not very good4> If HDFs stores small files in large quantities, it can cause namenode memory pressure to surge.The above four points of fault in the Hadoop2.0 has been resolved, as to how to solve, we will talk in a while, here we first look at the Hadoop1.0 in the MapReduce.MapReduce is a distributed computing framework in Hadoop1.0, consisting of two phases: the mapper phase and the reducer phase, where the user simply implements the map function and the reduce function for distributed com

entire cluster to be used with great pleasure. Let's see how MapReduce works together.the operation mechanism of MapReduceBelow we mainly from two aspects to explore the operation mechanism of MapReduce.First, from the client, Jobtracker, Tasktracker level to analyze the work of MapReduce , its schematic diagram is as follows.As can be seen, the working principle of MapReduce can be divided into the following steps.First step: Start a job on the clie

(Reducer implementation class.class);TenJob.setoutputkeyclass (the data type of the output key.)class); OneJob.setoutputvalueclass (the data type of the output alue.class); AFileinputformat.addinputpath (Job,NewPath (sets the input path for the job); -Fileoutputformat.setoutputpath (Job,NewPath (sets the output path of the job)); - //submit a job to the cluster processing theJob.waitforcompletion (true);Shows the job execution process for mapreducer. The job's WaitForCompletion method internall

Jobtracker when a client makes a request to a Hadoop cluster. Jobtracker with NameNode to distribute the work to the closest possible location to the data it is working with. NameNode is the main system of the file system, providing metadata services to perform data distribution and replication. Jobtracker the Map and Reduce tasks to an available slot on one or

Start, stop, and stop the daemon in Hadoop. Version Hadoop-1.2.1 Script description The start-all.sh starts all Hadoop daemon. Including NameNode, Secondary NameNode, DataNode, JobTracker, TaskTrack The stop-all.sh stops all Hadoop daemon. Including NameNode, Secondary NameNode, DataNode, JobTracker, TaskTrack Start-dfs.sh starts Hadoop HDFS daemon NameNode, SecondaryNameNode and DataNode Stop-dfs.sh stops

1 analyzing the operation mechanism of MapReduce operation1.1 Submission of jobsThe client submits a job to the Jobtracker,jobclient program logic by Jobclient.runjob () as follows:A) Request a new job ID (jobtracker.getnewjobid ()) to Jobtracker;b) Check the output description of the job, if there is a throw error to the client, the input shard of the calculation job;c) Copy the resources required to run t

), block storage mode, etc.Basic framework of 2.HDFS2.1 ArchitectureHDFs one Master-slave mode runs mainly by two types of nodes: one namenode (that is, master) and multiple Datanode (that is, slave), as shown in the framework diagram:2.2 NameNode, DataNode, Jobtracker and Tasktracker Namenode is a master server that manages the namespace and metadata of the entire file system and handles file access requests from outside. Namenode saves

each input file to a Java 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

Basic information of hadoop technology Insider: in-depth analysis of mapreduce architecture design and implementation principles by: Dong Xicheng series name: Big Data Technology series Publishing House: Machinery Industry Press ISBN: 9787111422266 Release Date: 318-5-8 published on: July 6,: 16 webpage:: Computer> Software and program design> distributed system design more about "hadoop technology Insider: in-depth analysis of the mapreduce architecture design and implementation principles. The

; hadoop-0.6.0. All tools used to run hadoop will be present in the directory$ Hadoop_install/hadoop/bin. All configuration files for hadoop will be present in the directory$ Hadoop_install/hadoop/Conf. Startup scripts The$ Hadoop_install/hadoop/binDirectory contains some scripts used to launch hadoop DFS and hadoop MAP/reduce daemons. These are: Start-all.sh-Starts all hadoop daemons, The namenode, datanodes, The jobtracker and tasktrackers. Stop

Generally, one machine in the cluster is specified as namenode, and another machine is specified as jobtracker. These machines areMasters. The remaining Machines serve as datanodeAlsoAs tasktracker. These machines areSlaves Official Address :(Http://hadoop.apache.org/common/docs/r0.19.2/cn/cluster_setup.html) 1 prerequisites Make sure that all required software is installed on each node of your cluster: Sun-JDK, ssh, hadoop Javatm 1.5.x mu

Hadoop Error Handling Mechanism 1. hardware faults Hardware faults refer to jobtracker faults or tasktracker faults. Jobtracker is a single point. If a fault occurs, hadoop cannot handle it yet. Only the most reliable hardware can be used as jobtracker. Jobtracker uses the heartbeat (one minute cycle) signal to che

We have introduced the installation and simple configuration of hadoop in Linux, mainly in standalone mode. The so-called standalone Mode means that no daemon process is required ), all programs are executed on a single JVM. Because it is easier to test and debug mapreduce programs in standalone mode, this mode is suitable for use in the development phase. Here we mainly record the process of configuring the hadoop pseudo distribution mode. The so-called pseudo-distribution mode is to simulate h

running on the same node) to thousands of nodes (where functions are scattered among nodes, to add parallel processing activities ). Figure 1 shows the advanced components of a hadoop cluster. Figure 1. simple demonstration of hadoop cluster architecture A hadoop cluster can be divided into two abstract entities: mapreduce engine and distributed file system. The mapreduce engine can execute map and reduce tasks on the entire cluster and report results. The Distributed File System provide

One, Map task processing1, read the input file contents, parse into key, value pair. For each line of the input file, parse to key, value pair. Each key-value pair is called once to the map function.2, write their own logic, the input key, value processing, converted into a new key, value output.3. Partition the output key and value.4, for different partitions of data, according to key sorting, grouping. The value of the same key is placed in theIn a collection.5. (optional) The data after group

1. Resource management scenarios in Hadoop 1.0Hadoop 1.0 refers to the Apache Hadoop 0.20.x, 1.x, or CDH3 series of Hadoop, the kernel is mainly composed of HDFs and mapreduce two systems, where MapReduce is an offline processing framework, Consists of three parts: the programming model (the old and new API), the runtime Environment (Jobtracker and Tasktracker), and the Data processing engine (Maptask and Reducetask).Hadoop 1.0 Resource Management con

MapReduce Roleclient: Job submission initiator.Jobtracker: Initializes the job, allocates the job, communicates with Tasktracker, and coordinates the entire job.Tasktracker: Maintains Jobtracker communication and performs a mapreduce task on the allocated data fragment. Submit Job• The job needs to be configured before the job is submitted• program code, mainly the MapReduce program written by itself.• Input/output path• Other configurations, such as

-site.xml files at class initialization. In this way, the core-*. xml and hdfs-*. xml files are loaded when namenode is started. The core-*. xml file is loaded by the Configuration class. Load the core-*. xml and mapred-*. xml files After the start-dfs.xml is executed, execute the start-mapred.sh, which is similar to the start-hdf.sh. start-mapred.sh# Start hadoop map reduce daemons. Run this on master node. bin=`dirname "$0"`bin=`cd "$bin"; pwd` if [ -e "$bin/../libexec/hadoop-config.sh" ]; th