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 adva
creates a table in hive Metastore using the specified pattern
Extract an Avro schema from a set of datafiles using Avro-toolsExtracting Avro schema from a set of data files using the Avro tool
Create a table in the Hive metastore using the Avro file format and an external schema fileCreate a table in hive Metastore using the Avro file format and an external schema file
Improve query performance by creating partitioned tables in the Hive MetastoreCreate partitions in hive Metastore to in
) View HDFs system[[emailprotected] ~] $ hadoop fs -ls /View the Hadoop HDFs file management system through Hadoop fs-ls/commands, as shown in the Linux file system directory. The results shown above indicate that the Hadoop standalone installation was successful. So far, we have not made any changes to the
Hadoop has always been the technology I want to learn, just as the recent project team to do e-mall, I began to study Hadoop, although the final identification of Hadoop is not suitable for our project, but I will continue to study, more and more do not press.The basic Hadoop tutor
To do well, you must first sharpen your tools.
This article has built a hadoop standalone version and a pseudo-distributed development environment starting from scratch. It is illustrated in the following figures and involves:
1. Develop basic software required by hadoop;
2. Install each software;
3. Configure the hadoop standalone mode and run the wordco
Reprinted from http://blessht.iteye.com/blog/2095675Hadoop has always been the technology I want to learn, just as the recent project team to do e-mall, I began to study Hadoop, although the final identification of Hadoop is not suitable for our project, but I will continue to study, more and more do not press.The basic Hadoop
1. Download Hadoop source codeSource code of each Hadoop Member: Just pull it out. Note that only the contents in the trunk directory on SVN are checked-out, for example:Http://svn.apache.org/repos/asf/hadoop/common/trunk,Instead of http://svn.apache.org/repos/asf/hadoop/common,The reason is that the http://svn.apache.
are going to install our Hadoop lab environment on a single computer (virtual machine). If you have not yet installed the virtual machine, please check out the VMware Workstations Pro 12 installation tutorial. If you have not installed the Linux operating system in the virtual machine, please install the Ubuntu or CentOS tutorial under VMware.
The installed mode
return to the packet downstream of the failed node. 2. Specify a new flag for the current block of data that is stored in another normal datanode, and pass the flag to Namenode so that the fault datanode can delete some of the stored data blocks after recovery. 3. Remove the failed data node from the pipeline and write the remaining data blocks to the two normal datanode in the pipeline. Namenode Notice that a new copy is created on the other node when the volume of the replica is insufficient.
Follow the Hadoop installation tutorial _ standalone/pseudo-distributed configuration _hadoop2.6.0/ubuntu14.04 (http://www.powerxing.com/install-hadoop/) to complete the installation of Hadoop, My system is hadoop2.8.0/ubuntu16.
Hadoop Installation
In the fifth step of creating a Hadoop cluster in large data virtualization basics, I want to start by stating that I do not create a cluster through the visual interface provided by BDE. The reason is that our previously deployed Vapp include the BDE Management Server, which is running through a virtual machine. At this point, it has not been able to bind to the Vsphereweb client, thus temporarily unable t
applications in a user-friendly manner to facilitate the diagnosis of their performance. Avro: Data serialization system. Cassandra: Scalable, multi-master database with no single point of failure. Chukwa: Data acquisition system for managing large distributed systems. HBase: A scalable, distributed database that supports large table storage of structured data. (The contents of HBase are described in later chapters) Hive: A data Warehouse infra
-02-06 17:41/user/test_hiveCan see the creation of a folder belonging to HTTPFS. ABC Open File upload a text file from the background test.txt to the/USER/ABC directory, the content isHello world!Access with HTTPFS[[email protected] hadoop-httpfs]# curl-i-x GET "http://xmseapp03:14000/webhdfs/v1/user/abc/test.txt?op=open User.name=httpfs "http/1.1 okserver:apache-coyote/1.1set-cookie:hadoop.auth=" u=httpfsp=httpfst= Simplee=1423574166943s=jtxqijusblvb
Basic Hadoop tutorial
This document uses the Basic Environment configuration of the K-Master server as an example to demonstrate user configuration, sudo permission configuration, network configuration, firewall shutdown, and JDK installation. Follow these steps to complete KVMSlave1 ~ The Basic Environment configuration of the KVMSlave3 server.Development Environment
Hardware environment: Four CentOS 6.5
Installing the Hadoop tutorial on WindowsSee 2010.1.6 www.hadoopor.com/[email protected]1. Installing the JDKInstalling the JRE is not recommended, but it is recommended to install the JDK directly because the JRE can be installed at the same time when the JDK is installed. The development of the MapReduce program and the compilation of Hadoop depend on the JDK,
Excerpt from: http://www.powerxing.com/install-hadoop-cluster/This tutorial describes how to configure a Hadoop cluster, and the default reader has mastered the single-machine pseudo-distributed configuration of Hadoop, otherwise check out the Hadoop installation
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