talend hadoop

Pre-language: If crossing is a comparison like the use of off-the-shelf software, it is recommended to use the Quickhadoop, this use of the official documents can be compared to the fool-style, here do not introduce. This article is focused on deploying distributed Hadoop for yourself.1. Modify the machine name[[email protected] root]# vi/etc/sysconfig/networkhostname=*** a column to the appropriate name, the author two machines using HOSTNAME=HADOOP0

Hadoop In The Big Data era (1): hadoop Installation If you want to have a better understanding of hadoop, you must first understand how to start or stop the hadoop script. After all,Hadoop is a distributed storage and computing framework.But how to start and manage t

production environment Hadoop large cluster fully distributed mode installation 2013-3-7 Installation Environment Operating platform: Vmware2 Operating system: Oracle Enterprise Linux 5.6 Software version: Hadoop-0.22.0,jdk-6u18 Cluster Architecture: Node,master node (hotel01), slave node (hotel02,hotel03 ...) Host name IP System version

Preface After a while of hadoop deployment and management, write down this series of blog records. To avoid repetitive deployment, I have written the deployment steps as a script. You only need to execute the script according to this article, and the entire environment is basically deployed. The deployment script I put in the Open Source China git repository (http://git.oschina.net/snake1361222/hadoop_scripts ). All the deployment in this article is b

in ~/.ssh/: Id_rsa and id_rsa.pub; These two pairs appear, similar to keys and locks.Append the id_rsa.pub to the authorization key (there is no Authorized_keys file at this moment)$ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys(3) Verify that SSH is installed successfullyEnter SSH localhost. If the display of a native login succeeds, the installation is successful.3. Close the firewall $sudo UFW disableNote: This step is very important, if you do not close, there will be no problem finding D

As you know, Namenode has a single point of failure in the Hadoop system, which has been a weakness for high-availability Hadoop. This article discusses several solution that exist to solve this problem. 1. Secondary NameNode principle: secondary NN periodically reads the editlog from the NN, merging with the image that it stores to form a new metadata image advantage: The earlier version of

Chapter 1 Meet HadoopData is large, the transfer speed is not improved much. it's a long time to read all data from one single disk-writing is even more slow. the obvious way to reduce the time is read from multiple disk once.The first problem to solve is hardware failure. The second problem is that most analysis task need to be able to combine the data in different hardware. Chapter 3 The Hadoop Distributed FilesystemFilesystem that manage storage h

ObjectiveWhat is Hadoop?In the Encyclopedia: "Hadoop is a distributed system infrastructure developed by the Apache Foundation." Users can develop distributed programs without knowing the underlying details of the distribution. Take advantage of the power of the cluster to perform high-speed operations and storage. ”There may be some abstraction, and this problem can be re-viewed after learning the various

Hadoop consists of two parts: Distributed File System (HDFS) Distributed Computing framework mapreduce The Distributed File System (HDFS) is mainly used for the Distributed Storage of large-scale data, while mapreduce is built on the Distributed File System to perform distributed computing on the data stored in the distributed file system. Describes the functions of nodes in detail. Namenode: 1. There is only one namenode in the

Previously introduced me in Ubuntu under the combination of virtual machine Centos6.4 build hadoop2.7.2 cluster, in order to do mapreduce development, to use eclipse, and need the corresponding Hadoop plugin Hadoop-eclipse-plugin-2.7.2.jar, first of all, in the official Hadoop installation package before hadoop1.x with Eclipse Plug-ins, And now with the increase

The main introduction to the Hadoop family of products, commonly used projects include Hadoop, Hive, Pig, HBase, Sqoop, Mahout, Zookeeper, Avro, Ambari, Chukwa, new additions include, YARN, Hcatalog, O Ozie, Cassandra, Hama, Whirr, Flume, Bigtop, Crunch, hue, etc.Since 2011, China has entered the era of big data surging, and the family software, represented by Hadoop