hadoop operations basic

Hadoop Operations - BasicHafizur RahmanApril 4, 2013

Agenda

● Why Hadoop● Hadoop Architecture● Hadoop Installation● Hadoop Configuration● Hadoop DFS Command● What's next

Challenges at Large Scale

● Single node can't handle due to limited resource○ Processor time, Memory, Hard drive space, Network

bandwidth○ Individual hard drives can only sustain read speeds between

60-100 MB/second, so multicore does not help that much

● Multiple nodes needed, but probability of failure increases○ Network failure, Data transfer failure, Node failure○ Desynchronized clock, Lock○ Partial failure in distributed atomic transaction

Hadoop Approach (1/4)

● Data Distribution○ Distributed to all the nodes in the cluster○ Replicated to several nodes


● Move computation to the data○ Whenever possible, rather than moving data for

processing, computation is moved to the node that contains the data

○ Most data is read from local disk straight into the CPU, alleviating strain on network bandwidth and preventing unnecessary network transfers

○ This data locality results in high performance


● MapReduce programming model ○ Run as isolated process


● Isolated execution○ Communication between nodes is limited and done

implicitly○ Individual node failures can be worked around by

restarting tasks on other nodes■ No message exchange needed by user task■ No roll back to pre-arranged checkpoints to

partially restart the computation■ Other workers continue to operate as though

nothing went wrong

Hadoop Environment

High-level Hadoop architecture

HDFS (1/2)

● Storage component of Hadoop● Distributed file system modeled after GFS● Optimized for high throughput● Works best when reading and writing large files

(gigabytes and larger)● To support this throughput HDFS leverages unusually

large (for a filesystem) block sizes and data locality optimizations to reduce network input/output (I/O)

HDFS (2/2)

● Scalability and availability are also key traits of HDFS, achieved in part due to data replication and fault tolerance

● HDFS replicates files for a configured number of times, is tolerant of both software and hardware failure, and automatically re-replicates data blocks on nodes that have failed

HDFS Architecture

MapReduce (1/2)

● MapReduce is a batch-based, distributed computing framework modeled

● Simplifies parallel processing by abstracting away the complexities involved in working with distributed systems○ computational parallelization○ work distribution○ dealing with unreliable hardware and software

MapReduce (2/2)

MapReduce Logical Architecture

● Name Node● Secondary

Name Node● Data Node● Job Tracker● Task Tracker

Hadoop Installation

● Local mode○ No need to communicate with other nodes, so it

does not use HDFS, nor will it launch any of the Hadoop daemons

○ Used for developing and debugging the application logic of a MapReduce program

● Pseudo Distributed Mode○ All daemons running on a single machine○ Helps to examine memory usage, HDFS

input/output issues, and other daemon interactions● Fully Distributed Mode

Hadoop ConfigurationFile name Description

hadoop-env.sh ● Environment-specific settings go here.● If a current JDK is not in the system path you’ll want to come here to configure your

JAVA_HOME

core-site.xml ● Contains system-level Hadoop configuration items○ HDFS URL○ Hadoop temporary directory○ script locations for rack-aware Hadoop clusters

● Override settings in core-default.xml: http://hadoop.apache.org/common/docs/r1.0.0/core-default.html.

hdfs-site.xml ● Contains HDFS settings○ default file replication count○ block size○ whether permissions are enforced

● Override settings in hdfs-default.xml: http://hadoop.apache.org/common/docs/r1.0.0/hdfs-default.html

mapred-site.xml ● Contains HDFS settings○ default number of reduce tasks○ default min/max task memory sizes○ speculative execution

● Override settings in mapred-default.xml: http://hadoop.apache.org/common/docs/r1.0.0/mapred-default.html

InstallationPseudo Distributed Mode

● Setup public key based login○ ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa○ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

● Update the following configuration○ hadoop.tmp.dir and fs.default.name at core-site.

xml○ dfs.replication at hdfs-site.xml○ mapred.job.tracker at mapred-site.xml

● Format NameNode○ bin/hadoop namenode -format

● Start all daemons○ bin/start-all.sh

Hands On

● HDFS Commands○ http://hadoop.apache.org/docs/r0.18.1

/hdfs_shell.html● Execute example

○ Wordcount● Web Interface

○ NameNode daemon: http://localhost:50070/○ JobTracker daemon: http://localhost:50030/○ TaskTracker daemon: http://localhost:50060/

● Hadoop Job Command

Hadoop FileSystemFile System

URI Scheme

Java Impl. (all under org.apache.hadoop)

Description

Local file fs.LocalFileSystem Filesystem for a locally connected disk with client-side checksums

HDFS hdfs hdfs.DistributedFileSystem Hadoop’s distributed filesystem

WebHDFS webhdfs hdfs.web.WebHdfsFileSystem

Filesystem providing secure read-write access to HDFS over HTTP

S3 (native) s3n fs.s3native.NativeS3FileSystem

Filesystem backed by Amazon S3

S3 (block based)

s3 fs.s3.S3FileSystem Filesystem backed by Amazon S3, which stores files in blocks (much like HDFS) to overcome S3’s 5 GB file size limit.

GlusterFS glusterfs fs.glusterfs.GlusterFileSystem

Still in betahttps://github.com/gluster/glusterfs/tree/master/glusterfs-hadoop

InstallationFully Distributed Mode

Three different kind of hosts:● master

○ master node of the cluster○ hosts NameNode and JobTracker daemons

● backup○ hosts Secondary NameNode daemon

● slave1, slave2, ...○ slave boxes running both DataNode and TaskTracker

daemons

Hadoop ConfigurationFile Name Description

masters ● Name is misleading and should have been called secondary-masters● When you start Hadoop it will launch NameNode and JobTracker on the local

host from which you issued the start command and then SSH to all the nodes in this file to launch the SecondaryNameNode.

slaves ● Contains a list of hosts that are Hadoop slaves● When you start Hadoop it will SSH to each host in this file and launch the

DataNode and TaskTracker daemons

Recipes

● S3 Configuration● Using multiple disks/volumes and limiting

HDFS disk usage● Setting HDFS block size● Setting the file replication factor

Recipes:S3 Configuration

● Config file: conf/hadoop-site.xml● To access S3 data using DFS command

<property> <name>fs.s3.awsAccessKeyId</name> <value>ID</value></property><property> <name>fs.s3.awsSecretAccessKey</name> <value>SECRET</value></property>

● To use S3 as a replacement for HDFS<property> <name>fs.default.name</name> <value>s3://BUCKET</value></property>

Recipes:Disk Configuration

● Config file: $HADOOP_HOME/conf/hdfs-site.xml● For multiple locations:<property> <name>dfs.data.dir</name> <value>/u1/hadoop/data,/u2/hadoop/data</value></property>

● For limiting the HDFS disk usage, specify reserved space for non-DFS (bytes per volume)

<property><name>dfs.datanode.du.reserved</name><value>6000000000</value></property>

Recipes:HDFS Block Size (1/3)

● HDFS stores files across the cluster by breaking them down into coarser grained, fixed-size blocks

● Default HDFS block size is 64 MB● Affects performance of

○ filesystem operations where larger block sizes would be more effective, if you are storing and processing very large files

○ MapReduce computations, as the default behavior of Hadoop is to create one map task for each data block of the input files


● Option 1: NameNode configuration○ Add/modify dfs.block.size parameter at conf/hdfs-

site.xml○ Block size in number of bytes○ Only the files copied after the change will have the

new block size○ Existing files in HDFS will not be affected

<property> <name>dfs.block.size</name> <value>134217728</value></property>


● Option 2: During file upload○ Applies only to the specific file paths> bin/hadoop fs -Ddfs.blocksize=134217728 -put data.in /user/foo

● Use fsck command> bin/hadoop fsck /user/foo/data.in -blocks -files -locations/user/foo/data.in 215227246 bytes, 2 block(s): ....0. blk_6981535920477261584_1059len=134217728 repl=1 [hostname:50010]1. blk_-8238102374790373371_1059 len=81009518 repl=1 [hostname:50010]

Recipes:File Replication Factor (1/3)

● Replication done for fault tolerance○ Pros: Improves data locality and data access

bandwidth○ Cons: Needs more storage

● HDFS replication factor is a file-level property that can be set per file basis


● Set default replication factor○ Add/Modify dfs.replication property in conf/hdfs-

site.xml○ Old files will be unaffected○ Only the files copied after the change will have the

new replication factor<property> <name>dfs.replication</name> <value>2</value></property>


● Set replication factor during file upload > bin/hadoop fs -D dfs.replication=1 -copyFromLocal non-criticalfile.txt /user/foo

● Change the replication factor of files or file paths that are already in the HDFS○ Use setrep command○ Syntax: hadoop fs -setrep [-R] <path>

> bin/hadoop fs -setrep 2 non-critical-file.txtReplication 3 set: hdfs://myhost:9000/user/foo/non-critical-file.txt

Recipes:Merging files in HDFS

● Use HDFS getmerge command● Syntax:hadoop fs -getmerge <src> <localdst> [addnl]

● Copies files in a given path in HDFS to a single concatenated file in the local filesystem

> bin/hadoop fs -getmerge /user/foo/demofiles merged.txt

Hadoop Operations - Advanced

Example:Advanced Operations

● HDFS○ Adding new data node○ Decommissioning data node○ Checking FileSystem Integrity with fsck○ Balancing HDFS Block Data○ Dealing with a Failed Disk

● MapReduce○ Adding a Tasktracker○ Decommissioning a Tasktracker○ Killing a MapReduce Job○ Killing a MapReduce Task○ Dealing with a Blacklisted Tasktracker

Links

● http://www.michael-noll.com/tutorials/running-hadoop-on-ubuntu-linux-single-node-cluster/

● http://www.michael-noll.com/tutorials/running-hadoop-on-ubuntu-linux-multi-node-cluster/

● http://developer.yahoo.com/hadoop/tutorial/

● http://hadoop.apache.org/docs/r1.0.4/mapred_tutorial.html

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