Hadoop Administration

From Training Material
Jump to: navigation, search

Nobleprog.svg

Hadoop Administration



Title

Hadoop Administration
Author


Tytus Kurek (NobleProg)
Subfooter

Hadoop Administration          

Tytus Kurek (NobleProg)


hat does Hadoop mean?⌘===

HadoopAdministration01.png
https://developers.google.com/hadoop/images/hadoop-elephant.png

"The name my kid gave a stuffed yellow elephant. Short, relatively easy to spell and pronounce, meaningless and not used elsewhere: those are my naming criteria. Kids are good at generating such. Googol is a kid's term."

Doug Cutting, Hadoop Project Creator

Outline⌘

  • First Day:
    • Session I:
      • Introduction to the course
      • Introduction to Big Data and Hadoop
    • Session II:
      • HDFS
    • Session III:
      • YARN
    • Session IV:
      • MapReduce

Outline #2⌘

  • Second Day:
    • Session I:
      • Installation and configuration of the Hadoop in a pseudo-distributed mode
    • Session II:
      • Running MapReduce jobs on the Hadoop cluster
    • Session III:
      • Hadoop ecosystem tools: Pig, Hive, Sqoop, HBase, Flume, Oozie
    • Session IV:
      • Supporting tools: Hue, Cloudera Manager
      • Big Data future: Impala, Tez, Spark, NoSQL

Outline #3⌘

  • Third Day:
    • Session I:
      • Hadoop cluster planning
    • Session II:
      • Hadoop cluster installation and configuration
    • Session III:
      • Hadoop cluster installation and configuration
    • Session IV:
      • Case Studies
      • Certification and Surveys

First Day - Session I⌘


Introduction


HadoopAdministration02.jpg
http://news.nost.org.cn/wp-content/uploads/2013/06/BigDataBigBuildings.jpg

"I think there is a world market for maybe five computers"⌘


Thomas J Watson Sr.jpg
http://upload.wikimedia.org/wikipedia/commons/7/7e/Thomas_J_Watson_Sr.jpg

Thomas Watson, IBM, 1943.

How big are the data we are talking about?⌘


HadoopAdministration11.png
http://www.atkearney.com.tr/documents/10192/698536/FG-Big-Data-and-the-Creative-Destruction-of-Todays-Business-Models-1.png

Foundations of Big Data"⌘


Screen-shot-2012-02-27-at-11.18.56-AM.png
http://blog.softwareinsider.org/wp-content/uploads/2012/02/Screen-shot-2012-02-27-at-11.18.56-AM.png

What is Hadoop?⌘

  • Apache project for storing and processing large data sets
  • Open-source implementation of Google Big Data solutions
  • Components:
    • core:
      • HDFS (Hadoop Distributed File System)
      • YARN (Yet Another Resource Negotiator)
    • noncore:
      • Data processing paradigms (MapReduce, Spark, Impala, Tez, etc.)
      • Ecosystem tools (Pig, Hive, Sqoop, HBase, etc.)
  • Written in Java

Virtualization vs clustering⌘


HadoopAdministration13.jpg

Data storage evolution⌘

  • 1956 - HDD (Hard Disk Drive), now up to 6 TB
  • 1983 - SDD (Solid State Drive), now up to 16 TB
  • 1984 - NFS (Network File System), first NAS (Network Attached Storage) implementation
  • 1987 - RAID (Redundant Array of Independent Disks), now up to ~100 disks
  • 1993 - Disk Arrays, now up to ~200 disks
  • 1994 - Fibre-channel, first SAN (Storage Area Network) implementation
  • 2003 - GFS (Google File System), first Big Data implementation

Big Data and Hadoop evolution⌘

  • 2003 - GFS (Google File System) publication, available here
  • 2004 - Google MapReduce publication, available here
  • 2005 - Hadoop project founded
  • 2006 - Google BigTable publication, available here
  • 2008 - First Hadoop implementation
  • 2010 - Google Dremel publication, available here
  • 2010 - First HBase implementation
  • 2012 - Apache Hadoop YARN project founded
  • 2013 - Cloudera releases Impala
  • 2014 - Apache releases Spark

Hadoop distributions⌘

Hadoop automated deployment tools⌘

Who uses Hadoop?⌘


HadoopAdministration12.png
http://cdn2.hadoopwizard.com/wp-content/uploads/2013/01/hadoopwizard_companies_by_node_size600.png

Where is it all going?⌘

  • At this point we have all the tools to start changing the future:
    • capability of storing and processing any amount of data
    • tools for storing and processing both structured and unstructured data
    • tools for batch and interactive data processing
    • Big Data paradigm has matured
  • BigData future:
    • search engines
    • scientific analysis
    • trends prediction
    • business intelligence
    • artificial intelligence

References⌘

Introduction to the lab⌘

Lab components:

  • Laptop with Windows 8
  • Virtual Machine with CentOS 7.2 on VirtualBox:
    • Hadoop instance in a pseudo-distributed mode
    • Hadoop ecosystem tools
    • terminal for connecting to the GCE
  • GCE (Google Compute Engine) cloud-based IaaS platform
    • Hadoop cluster

VirtualBox⌘

  • VirtualBox 64-bit version (click here to download)
  • VM name: Hadoop Administration
  • Credentials: terminal / terminal (use root account)
  • Snapshots (top right corner)
  • Press right "Control" key to release

GCE⌘

  • GUI: https://console.cloud.google.com/compute/
  • Project ID: check after logging in in the "My First Project" lap
  • Connect the VM to the GCE:
    • sudo /opt/google-cloud-sdk/bin/gcloud auth login
    • follow on-screen instructions
    • sudo /opt/google-cloud-sdk/bin/gcloud config set project [Project ID]
    • sudo /opt/google-cloud-sdk/bin/gcloud components update
    • sudo /opt/google-cloud-sdk/bin/gcloud config list

First Day - Session II⌘


HDFS


HadoopAdministration03.jpg
http://munnamark.files.wordpress.com/2012/03/petabyte1.jpg

Goals and motivation⌘

  • Very large files:
    • file sizes larger than tens of terabytes
    • filesystem sizes larger than tens of petabytes
  • Streaming data access:
    • write-once, read-many-times approach
    • optimized for batch processing
  • Fault tolerance:
    • data replication
    • metadata high availability
  • Scalability:
    • commodity hardware
    • horizontal scalability
  • Resilience:
    • components failures are common
    • auto-healing feature
  • Support for MapReduce data processing paradigm

Design⌘

  • FUSE (Filesystem in USErspace)
  • Non POSIX-compliant filesystem (http://standards.ieee.org/findstds/standard/1003.1-2008.html)
  • Block abstraction:
    • block size: 64 MB
    • block replication factor: 3
  • Benefits:
    • support for file sizes larger than disk sizes
    • segregation of data and metadata
    • data durability
  • Lab Exercise 1.2.1

Daemons⌘

  • Datanode:
    • responsible for storing and retrieving data
    • many per cluster
  • Namenode:
    • responsible for storing and retrieving metadata
    • responsible for maintaining a database of data locations
    • 1 active per cluster
  • Secondary namenode:
    • responsible for checkpointing metadata
    • 1 active per cluster

Data⌘

  • Stored in a form of blocks on datanodes
  • Blocks are files on datanodes' local filesystem
  • Reported periodically to the namenode in a form of block reports
  • Durability and parallel processing thanks to replication

Metadata⌘

  • Stored in a form of files on the namenode:
    • fsimage_[transaction ID] - complete snapshot of the filesystem metadata up to specified transaction
    • edits_inprogress_[transaction ID] - incremental modifications made to the metadata since specified transaction
  • Contain information on HDFS filesystem's structure and properties
  • Copied and served from the namenode's RAM
  • Don't confuse metadata with the database of data locations!
  • Lab Exercise 1.2.2

Metadata checkpointing⌘

  • When does it occur?
    • every hour
    • edits file reaches 64 MB
  • How does it work?

HadoopAdministration14.jpg
T. White. "Hadoop: The Definitive Guide"

Read path⌘

  1. A client opens a file by calling the "open()" method on the "FileSystem" object
  2. The client calls the namenode to return a sorted list of datanodes for the first batch of blocks in the file
  3. The client connects to the first datanode from the list
  4. The client streams the data block from the datanode
  5. The client closes the connection to the datanode
  6. The client repeats steps 3-5 for the next block or steps 2-5 for the next batch of blocks, or closes the file when copied

Read path #2⌘


HadoopAdministration15.jpg
T. White. "Hadoop: The Definitive Guide"

Data read preferences⌘

  • Determines the closest datanode to the client
  • The distance is based on the theoretical network bandwidth and latency:
    • 0 - the same node
    • 2 - the same rack
    • 4 - the same data center
    • 6 - different data centers
  • Rack Topology feature has to be configured prior

Write path⌘

  1. A client creates a file by calling the "create()" method on the "DistributedFileSystem" object
  2. The client calls the namenode to create the file with no blocks in the filesystem namespace
  3. The client calls the namenode to return a list of datanodes to store replicas of a batch data blocks
  4. The client connects to the first datanode from the list
  5. The client streams the data block to the datanode
  6. The datanode connects to the the second datanode from the list, streams the data block, and so on
  7. The datanodes acknowledge the receiving of the data block
  8. The client repeats steps 4-5 for the next blocks or steps 3-5 for the next batch of blocks, or closes the file when written
  9. The client notifies the namenode that the file is written

Write path #2⌘


HadoopAdministration16.jpg
T. White. "Hadoop: The Definitive Guide"

Data write preferences⌘

  • 1st copy - on the client (if running datanode daemon) or on randomly selected non-overloaded datanode
  • 2nd copy - on a datanode in a different rack in the same data center
  • 3rd copy - on randomly selected non-overloaded datanode in the same rack
  • 4th and further copies - on randomly selected non-overloaded datanaode

Namenode High Availability⌘

  • Namenode - SPOF (Single Point Of Failure)
  • Manual namenode recovery process may take up to 30 minutes
  • Namenode HA feature available since 0.23 release
  • High Availability type: active / standby
  • Based on: QJM (Quorum Journal Manager) and ZooKeeper
  • Failover type: manual or automatic with ZKFC (ZooKeeper Failover Controller)
  • STONITH (Shot The Other Node In The Head)
  • Fencing methods
  • Secondary namenode functionality moved to the standby namenode

Namenode High Availability #2⌘


HadoopAdministration17.jpg
http://www.binospace.com/wp-content/uploads/2013/07/slide-10-1024_%E5%89%AF%E6%9C%AC.jpg

Journal Node⌘

  • Relatively lightweight
  • Usually collocated on the machines running namenode or resource manager daemons
  • Changes to the "edits" file must be written to a majority of journal nodes
  • Usually deployed in 3, 5, 7, 9, etc.

Namenode Federation⌘

  • Namenode memory limitations
  • Namenode federation feature available since 0.23 release
  • Horizontal scalability
  • Filesystem partitioning
  • ViewFS
  • Federation of HA pairs
  • Lab Exercise 1.2.3
  • Lab Exercise 1.2.4

Namenode Federation #2⌘


HadoopAdministration18.jpg
E. Sammer. "Hadoop Operations"

Interfaces⌘

  • Underlying interface:
    • Java API - native Hadoop access method
  • Additional interfaces:
    • CLI
    • C interface
    • HTTP interface
    • FUSE

URIs⌘

  • [path] - local HDFS filesystem
  • file://[path] - local HDFS filesystem
  • hdfs://[namenode]/[path] - remote HDFS filesystem
  • http://[namenode]/[path] - HTTP
  • viewfs://[path] - ViewFS

Java interface⌘

  • Reminder: Hadoop is written in Java
  • Destined to Hadoop developers rather than Hadoop administrators
  • Based on the "FileSystem" class
  • Reading data:
    • by streaming from Hadoop URIs
    • by using Hadoop API
  • Writing data:
    • by streaming to Hadoop URIs
    • by appending to files

Command-line interface⌘

  • Shell-like commands
  • All begin with the "hdfs" keyword
  • No current working directory
  • Example: Overriding the replication factor:
hdfs dfs -setrep [RF] [file]
  • All commands available here

Base commands⌘

  • hdfs dfs -mkdir [URI] - creates a directory
  • hdfs dfs -rm [URI] - removes the file
  • hdfs dfs -rmr [URI] - removes the directory
  • hdfs dfs -cat [URI] - displays a content of the file
  • hdfs dfs -ls [URI] - displays a content of the directory
  • hdfs dfs -cp [source URI] [destination URI] - copies the file or the directory
  • hdfs dfs -mv [source URI] [destination URI] - moves the file or the directory
  • hdfs dfs -du [URI] - displays a size of the file or the directory

Base commands #2⌘

  • hdfs dfs -chmod [mode] [URI] - changes permissions of the file or the directory
  • hdfs dfs -chown [owner] [URI] - changes the owner of the file or the directory
  • hdfs dfs -chgrp [group] [URI] - changes the owner group of the file or the directory
  • hdfs dfs -put [source path] [destination URI] - uploads the data into HDFS
  • hdfs dfs -getmerge [source directory] [destination file] - merges files from directory into a single file
  • hadoop archive -archiveName [name] [source URI]* [destination URI] - creates a Hadoop archive
  • hadoop fsck [URI] - performs an HDFS filesystem check
  • hadoop version - displays a Hadoop version

HTTP interface⌘

  • HDFS HTTP ports:
    • 50070 - namenode
    • 50075 - datanode
    • 50090 - secondary namenode
  • Access methods:
    • direct
    • via proxy

FUSE⌘

  • Reminder:
    • HDFS is a user-space filesystem
    • HDFS is a non-POSIX-compliant filesystem
  • Fuse-DFS module
  • Allows HDFS to be mounted under the Linux FHS (Filesystem Hierarchy Standard)
  • Allows Unix tools to be used against the HDFS
  • Documentation: src/contrib/fuse-dfs

Quotas⌘

  • Count quotas vs space quotas:
    • count quotas - limit a number of files inside of the HDFS directory
    • space quotas - limit a post-replication disk space utilization of the HDFS directory
  • Setting quotas:
hadoop dfsadmin -setQuota [count] [path]
hadoop dfsadmin -setSpaceQuota [size] [path]
  • Removing quotas:
hadoop dfsadmin -clrQuota [path]
hadoop dfsadmin -clrSpaceQuota [path]
  • Viewing quotas:
hadoop fs -count -q [path]

Limitations⌘

  • Low-latency data access:
    • tens of milliseconds
    • alternatives: HBase, Cassandra
  • Lots of small files
    • limited by namenode's physical memory
    • inefficient storage utilization due to large block size
  • Multiple writers, arbitrary file modifications:
    • single writer
    • append operations only

First Day - Session III⌘


YARN


CCAH-07.jpg
http://th00.deviantart.net/fs71/PRE/f/2013/185/c/9/spinning_yarn_by_chaosfissure-d6byoqu.jpg

Legacy architecture⌘

  • Historically, there had been only one data processing paradigm for Hadoop - MapReduce
  • Hadoop with MRv1 architecture consisted of two core components: HDFS and MapReduce
  • MapReduce component was responsible for cluster resources management and MapReduce jobs execution
  • As other data processing paradigms have become available, Hadoop with MRv2 (YARN) was developed

Goals and motivation⌘

  • Segregation of services:
    • single MapReduce service for resource and job management purpose
    • separate YARN services for resource and job management purpose
  • Scalability:
    • MapReduce framework hits scalability limitations in clusters consisting of 5000 nodes
    • YARN framework doesn’t hit scalability limitations in clusters consisting of 40000 nodes
  • Flexibility:
    • MapReduce framework is capable of executing MapReduce jobs only
    • YARN framework is capable of executing any jobs

Daemons⌘

  • ResourceManager:
    • responsible for cluster resources management
    • consists of a scheduler and an application manager component
    • 1 active per cluster
  • NodeManager:
    • responsible for node resources management, jobs execution and tasks execution
    • provides containers abstraction
    • many per cluster
  • JobHistoryServer:
    • responsible for serving information about completed jobs
    • 1 active per cluster

Design⌘


HadoopAdministration20.jpg
http://hadoop.apache.org/docs/r2.3.0/hadoop-yarn/hadoop-yarn-site/YARN.html

YARN job stages⌘

  1. A client retrieves an application ID from the resource manager
  2. The client calculates input splits and writes the job resources (e.g. jar file) into HDFS
  3. The client submits the job by calling the submitApplication() method on the resource manager
  4. The resource manager allocates a container for the job execution purpose and launches the application master process inside the container
  5. The application master process initializes the job and retrieves job resources from HDFS
  6. The application master process requests the resource manager for containers allocation
  7. The resource manager allocates containers for tasks execution purpose
  8. The application master process requests node managers to launch JVMs inside the allocated containers
  9. Containers retrieve job resources and data from HDFS
  10. Containers periodically report progress and status updates to the application master process
  11. The client periodically polls the application master process for progress and status updates

YARN job stages #2⌘


CCAH-2.png
http://pravinchavan.files.wordpress.com/2013/04/yarn-2.png

ResourceManager high availability⌘

  • ResourceManager - SPOF
  • High Availability type: active / passive
  • Based on: ZooKeeper
  • Failover type: manual or automatic with ZKFC
  • ZFCK daemon embedded in the ResourceManager code
  • STONITH
  • Fencing methods

Resources configuration⌘

  • Allocated resources:
    • physical memory
    • virtual cores
  • Resources configuration (yarn-site.xml):
    • yarn.nodemanager.resource.memory-mb - available memory in megabytes
    • yarn.nodemanager.resource.cpu-vcores - available number of virtual cores
    • yarn.scheduler.minimum-allocation-mb - minimum allocated memory in megabytes
    • yarn.scheduler.minimum-allocation-vcores - minimum number of allocated virtual cores
    • yarn.scheduler.increment-allocation-mb - increment memory in megabytes into which the request is rounded up
    • yarn.scheduler.increment-allocation-vcores - increment number of virtual cores into which the request is rounded up
  • Lab Exercise 1.3.1

First Day - Session IV⌘


MapReduce


HadoopAdministration04.jpg
http://cacm.acm.org/system/assets/0000/2020/121609_CACMpg73_MapReduce_A_Flexible.large.jpg

Goals and motivation⌘

  • Automatic parallelism and distribution of work:
    • developers are required to only write simple map and reduce functions
    • distribution and parallelism are handled by the MapReduce framework
  • Data locality:
    • computation operations are performed on data local to the computing node
    • data transfer over the network is reduced to an absolute minimum
  • Fault tolerance:
    • jobs high availability
    • tasks distribution
  • Scalability:
    • horizontal scalability

Design⌘

  • Fully automated parallel data processing paradigm
  • Jobs abstraction as an atomic unit
  • Jobs operate on key-value pairs
  • Job components:
    • map tasks
    • reduce tasks
  • Benefits:
    • simplicity of development
    • fast data processing times

MapReduce by analogy⌘

  • Lab Exercise 1.4.1

Input splits vs HDFS blocks⌘

423321.image0.jpg
http://media.wiley.com/Lux/21/423321.image0.jpg

Sample data⌘

  • http://academic.udayton.edu/kissock/http/Weather/gsod95-current/allsites.zip
 1             1             1995         82.4
 1             2             1995         75.1
 1             3             1995         73.7
 1             4             1995         77.1
 1             5             1995         79.5
 1             6             1995         71.3
 1             7             1995         71.4
 1             8             1995         75.2
 1             9             1995         66.3
 1             10            1995         61.8

Sample map function⌘

  • Function:
#!/usr/bin/perl
use strict;
my %results;
while (<>)
{
    $_ =~ m/.*(\d{4}).*\s(\d+\.\d+)/;
    push (@{$results{$1}}, $2);
}
foreach my $year (sort keys %results)
{
    print "$year: ";
    foreach my $temperature (@{$results{$year}})
    {
   	 print "$temperature ";
    }
    print "\n";
}
  • Function output:
year: temperature1 temperature2 ...

Sample reduce function⌘

  • Function:
#!/usr/bin/perl
use List::Util qw(max);
use strict;
my %results;
while (<>)
{
    $_ =~ m/(\d{4})\:\s(.*)/;
    my @temperatures = split(' ', $2);
    @{$results{$1}} = @temperatures;
}
foreach my $year (sort keys %results)
{
    my $temperature = max(@{$results{$year}});
    print "$year: $temperature\n";
}
  • Function output:
year: temperature

Schufle and sort phase⌘

HadoopAdministration19.png
http://blog.cloudera.com/wp-content/uploads/2014/03/ssd1.png

Map task in details⌘

  • Map function:
    • takes key-value pair
    • returns key-value pair
  • Input key-value pair: file - input split
  • Output key-value pair: defined by the map function
  • Output key-value pairs are sorted by the key
  • Partitioner assigns each key-value pair to a partition

Reduce task in details⌘

  • Reduce function:
    • takes key-value pair
    • returns key-value pair
  • Input key-value pair: returned by the map function
  • Output key-value pair: final result
  • Input is received in the shuffle and sort phase
  • Each reducer is assigned to one or more partitions
  • Reducer starts copying the data from partitions as soon as they are written

Combine function⌘

  • Executed by the map task
  • Executed just after the map function
  • Most often the same as the reduce function
  • Lab Exercise 1.4.2

Java MapReduce⌘

  • MapReduce jobs are written in Java
  • org.apache.hadoop.mapreduce namespace:
    • Mapper class and map() function
    • Reducer class and reduce() function
  • MapReduce jobs are executed by the hadoop program
  • Sample Java MapReduce job available here [[1]]
  • Sample Java MapReduce job execution:
hadoop [MapReduce program path] [input data path] [output data path]

Streaming⌘

  • Uses hadoop-streaming.jar class
  • Supports all languages capable of reading from the stdin and writing to the stdout
  • Sample streaming MapReduce job execution:
hadoop jar [hadoop-streaming.jar path] \
-input [input data path] \
-output [output data path] \
-mapper [map function path] \
-reducer [reduce function path] \
-combiner [combiner function path] \
-file [attached files path]

Pipes⌘

  • Supports all languages capable of reading from and writing to network sockets
  • Mostly applicable for C and C++ languages
  • Sample piped MapReduce job execution:
hadoop pipes \
-D hadoop.pipes.java.recordreader=true
-D hadoop.pipes.java.recordwriter=true
-input [input data path] \
-output [output data path] \
-program [MapReduce program path]
-file [attached files path]

Limitations⌘

If one woman can have a baby in nine months, nine women should be able to have a baby in one month.

  • Batch processing system:
    • MapReduce jobs run on the order of minutes or hours
    • no support for low-latency data access
  • Simplicity:
    • optimized for full table scan operations
    • no support for data access optimization
  • Too low-level:
    • raw data processing functions
    • alternatives: Pig, Hive
  • Not all algorithms can be parallelised:
    • algorithms with shared state
    • algorithms with dependent variables

Second Day - Session I⌘


Pseudo-distributed mode


HadoopAdministration06.jpg
http://www.cpusage.com/blog/wp-content/uploads/2013/11/distributed-computing-1.jpg

Hadoop installation in pseudo-distributed
mode⌘



  • Lab Exercise 2.1.1
  • Lab Exercise 2.1.2
  • Lab Exercise 2.1.3
  • Lab Exercise 2.1.4

Second Day - Session II⌘


Running MapReduce jobs


HadoopAdministration07.jpg
http://news.nost.org.cn/wp-content/uploads/2013/06/BigDataBigBuildings.jpg

Running MapReduce jobs in
pseudo-distributed mode⌘



  • Lab Exercise 2.2.1
  • Lab Exercise 2.2.2
  • Lab Exercise 2.2.3
  • Lab Exercise 2.2.4
  • Lab Exercise 2.2.5
  • Lab Exercise 2.2.6

Second Day - Session III⌘


Hadoop ecosystem


HadoopAdministration05.jpg
http://www.imaso.co.kr/data/article/1(2542).jpg

Pig - Goals and motivation⌘

  • Developed by Yahoo
  • Simplicity of development:
    • however, MapReduce programs themselves are simple, designing complex regular expressions may be challenging and time consuming
    • Pig offers much reacher data structures for pattern matching purpose
  • Length and clarity of the source code:
    • MapReduce programs are usually long and comprehensible
    • Pig programs are usually short and understandable
  • Simplicity of execution:
    • MapReduce programs require compiling, packaging and submitting
    • Pig programs can be executed ad-hoc from an interactive shell

Pig - Design⌘

  • Pig components:
    • Pig Latin - scripting language for data flows
      • each program is made up of series of transformations applied to the input data
      • each transformation is made up of series of MapReduce jobs run on the input data
    • Pig Latin programs execution environment:
      • launched from a single JVM
      • execution distributed over the Hadoop cluster

Pig - relational operators⌘

  • LOAD - loads the data from a filesystem or other storage into a relation
  • STORE - saves the relation to a filesystem or other storage
  • DUMP - prints the relation to the console
  • FILTER - removes unwanted rows from the relation
  • DISTINCT - removes duplicate rows from the relation
  • SAMPLE - selects a random sample from the relation
  • JOIN - joins two or more relations
  • GROUP - groups the data in a single relation
  • ORDER - sorts the relation by one or more fields
  • LIMIT - limits the size of the relation to a maximum number of tuples
  • and more

Pig - mathematical and logical operators⌘

  • literal - constant / variable
  • $n - n-th column
  • x + y / x - y - addition / substraction
  • x * y / x / y - multiplication / division
  • x == y / x != y - equals / not equals
  • x > y / x < y - greater then / less then
  • x >= y / x <= y - greater or equal then / less or equal then
  • x matches y - pattern matching with regular expressions
  • x is null - is null
  • x is not null - is not null
  • x or y - logical or
  • x and y - logical and
  • not x - logical negation

Pig - types⌘

  • int - 32-bit signed integer
  • long - 64-bit signed integer
  • float - 32-bit floating-point number
  • double - 64-bit floating-point number
  • chararray - character array in UTF-16 format
  • bytearray - byte array
  • tuple - sequence of fields of any type
  • map - set of key-value pairs:
    • key - character array
    • value - any type

Pig - Exercises⌘

  • Lab Exercise 2.3.1

Hive - Goals and Motivation⌘

  • Developed by Facebook
  • Data structurization:
    • HDFS stores data in an unstructured format
    • Hive stores data in a structured format
  • SQL interface:
    • HDFS does not provide the SQL interface
    • Hive provides an SQL-like interface

Hive - Design⌘

  • Metastore:
    • stores Hive metadata
    • RDBMS database:
      • embedded: Derby
      • local: MySQL, PostgreSQL, etc.
      • remote: MySQL, PostgreSQL, etc.
  • HiveQL:
    • used to read data by accepting queries and translating them to a series of MapReduce jobs
    • used to write data by uploading them into HDFS and updating the Metastore
  • Interfaces:
    • CLI
    • Web GUI
    • ODBC
    • JDBC

Hive - Data Types⌘

  • TINYINT - 1-byte signed integer
  • SMALLINT - 2-byte signed integer
  • INT - 4-byte signed integer
  • BIGINT - 8-byte signed integer
  • FLOAT - 4-byte floating-point number
  • D0UBLE - 8-byte floating-point number
  • BOOLEAN - true/false value
  • STRING - character string
  • BINARY - byte array
  • MAP - an unordered collection of key-value pairs

Hive - SQL vs HiveQL⌘

Feature SQL HiveQL
Updates UPDATE, INSERT, DELETE INSERT OVERWRITE TABLE
Transactions Supported Not Supported
Indexes Supported Not supported
Latency Sub-second Minutes
Multitable inserts Partially supported Supported
Create table as select Partially supported Supported
Views Updatable Read-only

Hive - Exercises⌘

  • Lab Exercise 2.3.2

Sqoop - Goals and Motivation⌘

  • Cooperation with RDBMS:
    • RDBMS are widely used for data storing purpose
    • need for importing / exporting data from RDBMS to HDFS and vice versa
  • Data import / export flexibility:
    • manual data import / export using HDFS CLI, Pig or Hive
    • automatic data import / export using Sqoop

Sqoop - Design⌘

  • JDBC - used to connect to the RDBMS
  • SQL - used to retrieve / populate data from / to RDBMS
  • MapReduce - used to retrieve / populate data from / to HDFS
  • Sqoop Code Generator - used to create table-specific class to hold records extracted from the table

Sqoop - Import process⌘


HadoopAdministration25.jpg
T. White. "Hadoop: The Definitive Guide"

Sqoop - Export process⌘


HadoopAdministration26.jpg
T. White. "Hadoop: The Definitive Guide"

Sqoop - Exercises⌘

  • Lab Exercise 2.3.3

HBase - Goals and Motivation⌘

  • Designed by Google (BigTable)
  • Developed by Apache
  • Interactive queries:
    • MapReduce, Pig and Hive are batch processing frameworks
    • HBase is a real-time processing framework
  • Data structurization:
    • HDFS stores data in an unstructured format
    • HBase stores data in a semi-structured format

HBase - Design⌘

  • Runs on the top of HDFS
  • Used to be concidered a core Hadoop component
  • Key-value NoSQL database
  • Columnar storage
  • Metadata stored in the -ROOT- and .META. tables
  • Data can be accessed interactively or using MapReduce

HBase - Architecture⌘

  • Tables are distributed across the cluster
  • Tables are automatically partitioned horizontally into regions
  • Tables consist of rows and columns
  • Table cells are versionde (by a timestamp by default)
  • Table cell type is an uninterpreted array of bytes
  • Table rows are sorted by the row key which is the table's primary key
  • Table columns are grouped into column families
  • Table column families must be defined on the table creation stage
  • Table columns can be added on the fly if the column family exists

HBase - Exercises⌘

  • Lab Exercise 2.3.4

Flume - Goals and motivation⌘

  • Streaming data:
    • HDFS does not have any built-in mechanisms for handling streaming data flows
    • Flume is designed to collect, aggregate and move streaming data flows into HDFS
  • Data queueing:
    • When writing directly to HDFS data are lost during spike periods
    • Flume is designed to buffer data during spike periods
  • Guarantee of data delivery:
    • Flume is designed to guarantee a delivery of the data by using a single-hop message delivery semantics

Flume - Design⌘

What is Flume? Flume is a frontend to HDFS

  • Event - unit of data that Flume can transport
  • Flow - movement of the events from a point of origin to their final destination
  • Client - an interface implementation that operates at the point of origin of the events
  • Agent - an independent process that hosts flume components such as sources, channels and sinks
  • Source - an interface implementation that can consume the events delivered to it and deliver them to channels
  • Channel - a transient store for the events, which are delivered by the sources and removed by sinks
  • Sink - an interface implementation that can remove the events from the channel and transmit them further

Flume - Design #2⌘


CCAH-3.png
http://archive.cloudera.com/cdh/3/flume-ng-1.1.0-cdh3u4/images/UserGuide_image00.png

Flume - Closer look⌘

  • Flume sources:
  • Sink types:
    • regular - transmit the event to another agent
    • terminal - transmit the event to its final destination
  • Flow pipeline:
    • from one source to one destination
    • from multiple sources to one destination
    • from one source to multiple destinations
    • from multiple sources to multiple destinations

Oozie - Goals and motivation⌘

  • Hadoop jobs execution:
    • Hadoop clients execute Hadoop jobs from CLI using hadoop command
    • Oozie provides web-based GUI for Hadoop jobs definition and execution
  • Hadoop jobs management:
    • Hadoop doesn't provide any built-in mechanism for jobs management (e.g. forks, merges, decisions, etc.)
    • Oozie provides Hadoop jobs management feature based on a control dependency DAG

Oozie - Design⌘

What is Oozie? Oozie is a frontend to Hadoop jobs

  • Oozie - workflow scheduling system
  • Oozie workflow
    • control flow nodes:
      • define the beginning and the end of the workflow
      • provide a mechanism of controlling the workflow execution path
    • action nodes:
      • are mechanisms by which the workflow triggers an execution of Hadoop jobs
      • supported job types include Java MapReduce, Pig, Hive, Sqoop and more

Oozie - Web GUI⌘


CCAH-4.png
http://www.gruter.com/download_files/cloumon-oozie-01.png

Second Day - Session IV⌘


Supporting tools and Big Data future


CCAH-08.jpg
http://www.datacenterjournal.com/wp-content/uploads/2012/11/big-data1-612x300.jpg

Hue - Goals and motivation⌘

  • Hadoop cluster management:
    • each Hadoop component is managed independently
    • Hue provides centralized Hadoop components management tool
    • Hadoop components are mostly managed from the CLI
    • Hue provides web-based GUI for Hadoop components management
  • Open Source:
    • other cluster management tools (i.e. Cloudera Manager) are payable and closed
    • Hue is free of charge and an open-source tool

Hue - Design⌘

What is Hue? Hue is a frontend to Hadoop components

  • Hue components:
    • Hue server - web application's container
    • Hue database - web application's data
    • Hue UI - web user interface application
    • REST API - for communication with Hadoop components
  • Supported browsers:
    • Google Chrome
    • Mozilla Firefox 17+
    • Internet Explorer 9+
    • Safari 5+
  • Lab Exercise 2.4.1

Hue - Design #2⌘


CCAH-5.jpg
http://www.cloudera.com/content/cloudera-content/cloudera-docs/CDH4/4.3.0/Hue-2-User-Guide/images/huearch.jpg

Cloudera Manager - Goals and motivation⌘

  • Hadoop cluster deployment and configuration:
    • Hadoop components need to be deployed and configured manually
    • Cloudera Manager provides an automatic deployment and configuration of Hadoop components
  • Hadoop cluster management:
    • each Hadoop component is managed independently
    • Cloudera Manager provides a centralized Hadoop components management tool
    • Hadoop components are mostly managed from the CLI
    • Cloudera Manager provides a web-based GUI for Hadoop components management

Cloudera Manager - Design⌘

What is Cloudera Manager? Cloudera Manager is a frontend to Hadoop components

  • Cloudera Manager versions:
    • Express - deployment, configuration, management, monitoring and diagnostics
    • Enterprise - advanced management features and support
  • Cloudera Manager components:
    • Cloudera Manager Server - web application's container and core Cloudera Manager engine
    • Cloudera Manager Database - web application's data and monitoring information
    • Admin Console - web user interface application
    • Agent - installed on every component in the cluster
    • REST API - for communication with Agents
  • Lab Exercise 2.4.2
  • Lab Exercise 2.4.3
  • Lab Exercise 2.4.4

Cloudera Manager - Design #2⌘


CCAH-6.jpeg
http://www.cloudera.com/content/cloudera-content/cloudera-docs/Navigator/1.0/Cloudera-Navigator-Installation-and-User-Guide/images/image1.jpeg

Impala - Goals and Motivation⌘

  • Designed by Google (Dremel)
  • Developed by Cloudera
  • Interactive queries:
    • MapReduce, Pig and Hive are batch processing frameworks
    • Impala is a real-time processing framework
  • Data structurization:
    • HDFS stores data in an unstructured format
    • HBase and Cassandra store data in a semi-structured format
    • Impala stores data in a structured format
  • SQL interface:
    • HDFS does not provide the SQL interface
    • HBase and Cassandra do not provide the SQL interface by default
    • Impala provides the SQL interface

Impala - Design⌘

  • Runs on the top of HDFS or HBase
  • Columnar storage
  • Uses Hive metastore
  • Implements SQL tables and queries
  • Interfaces:
    • CLI
    • web GUI
    • ODBC
    • JDBC

Impala - Daemons⌘

  • Impala Daemon:
    • performs read and write operations
    • accepts queries and returns query results
    • parallelizes queries and distributes the work
  • Impala Statestore:
    • checks health of the Impala Daemons
    • reports detected failures to other nodes in the cluster
  • Impala Catalog Server:
    • relays metadata changes to all nodes in the cluster

Tez - Goals and Motivation⌘

  • Interactive queries:
    • MapReduce is a batch processing framework
    • Tez is a real-time processing framework
  • Job types:
    • MapReduce is destined for key-value-based data processing
    • Tez is destined for DAG-based data processing

Tez - Design⌘

HadoopAdministration27.png
http://images.cnitblog.com/blog/312753/201310/19114512-89b2001bf0444863b5c11da4d5100401.png

Spark⌘

  • Fast and general engine for large-scale data processing.
  • Runs programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.
  • Supports applications written in Java, Scala, Python, R.
  • Combines SQL, streaming, and complex analytics.
  • Runs on Hadoop, Mesos, standalone, or in the cloud.
  • Can access diverse data sources including HDFS, Cassandra, HBase, and S3.

NoSQL⌘

Third Day - Session I⌘


Hadoop cluster planning


HadoopAdministration08.jpg
http://news.fincit.com/wp-content/uploads/2012/07/tangle.jpg

Picking up a distribution⌘

  • Considerations:
    • license cost
    • open / closed source
    • Hadoop version
    • available features
    • supported OS types
    • ease of deployment
    • integrity
    • support

Hadoop releases⌘


HadoopAdministration21.png
http://drcos.boudnik.org/

Why CDH?⌘

  • Free
  • Open source
  • Stable (always based on stable Hadoop releases)
  • Wide range of available features
  • Support for RHEL, SUSE and Debian
  • Easy to deploy
  • Very well integrated
  • Very well supported

Hardware selection⌘

  • Considerations:
    • CPU
    • RAM
    • storage IO
    • storage capacity
    • storage resilience
    • network bandwidth
    • network resilience
    • hardware resilience

Hardware selection #2⌘

  • Master nodes:
    • namenodes
    • resource managers
    • secondary namenode
  • Worker nodes
  • Network devices
  • Supporting nodes

Namenode hardware selection⌘

  • CPU: quad-core 2.6 GHz
  • RAM: 24-96 GB DDR3 (1 GB per 1 million of blocks)
  • Storage IO: SAS / SATA II
  • Storage capacity: less than 1 TB
  • Storage resilience: RAID
  • Network bandwidth: 2 Gb/s
  • Network resilience: LACP
  • Hardware resilience: non-commodity hardware

ResourceManager hardware selection⌘

  • CPU: quad-core 2.6 GHz
  • RAM: 24-96 GB DDR3 (unpredictable)
  • Storage IO: SAS / SATA II
  • Storage capacity: less than 1 TB
  • Storage resilience: RAID
  • Network bandwidth: 2 Gb/s
  • Network resilience: LACP
  • Hardware resilience: non-commodity hardware

Secondary namenode hardware selection⌘

  • Almost always identical to namenode
  • The same requirements for RAM, storage capacity and desired resilience level
  • Very often used as a replacement hardware in case of namenode failure

Worker hardware selection⌘

  • CPU: 2 * 6 core 2.9 GHz
  • RAM: 64-96 GB DDR3
  • Storage IO: SAS / SATA II
  • Storage capacity: 24-36 TB JBOD
  • Storage resilience: none
  • Network bandwidth: 1-10 Gb/s
  • Network resilience: none
  • Hardware resilience: commodity hardware

Network devices hardware selection⌘

  • Downlink bandwidth: 1-10 Gb/s
  • Uplink bandwidth: 10 Gb/s
  • Computing resources: vendor-specific
  • Resilience: by network topology

Supporting nodes hardware selection⌘

  • Backup system
  • Monitoring system
  • Security system
  • Logging system

Cluster growth planning⌘

  • Considerations:
    • replication factor: 3 by default
    • temporary data: 20-30% of the worker storage space
    • data growth: based on trends analysis
    • the amount of data being analised: based on cluster requirements
    • usual task RAM requirements: 2-4 GB
  • Lab Exercise 3.1.1
  • Lab Exercise 3.1.2

Network topologies⌘

  • Hadoop East/West traffic flow design
  • Network topologies:
    • Leaf
    • Spine
  • Inter data center replication
  • Lab Exercise 3.1.3

Leaf topology⌘


HadoopAdministration23.jpg
E. Sammer. "Hadoop Operations"

Spine topology⌘


HadoopAdministration24.jpg
E. Sammer. "Hadoop Operations"

Blades, SANs, RAIDs and virtualization⌘

  • Blades considerations:
    • Hadoop is designed to work on the commodity hardware
    • Hadoop cluster scales out rather than up
  • SANs considerations:
    • Hadoop is designed for processing local data
    • HDFS is distributed and shared by design
  • RAIDs considerations:
    • Hadoop is designed to provide data durability
    • RAID introduces additional limitations and overhead
  • Virtualization considerations:
    • Hadoop worker nodes don't benefit from virtualization
    • Hadoop is a clustering solution which is the opposite to virtualization

Hadoop directories⌘

Directory Linux path Grows?
Hadoop install directory /usr/* no
Namenode metadata directory defined by an administrator yes
Secondary namenode metadata directory defined by an administrator yes
Datanode data directory defined by an administrator yes
MapReduce local directory defined by an administrator no
Hadoop log directory /var/log/* yes
Hadoop pid directory /var/run/hadoop no
Hadoop temp directory /tmp no

Filesystems⌘

  • LVM (Logical Volume Manager) - HDFS killer
  • Considerations:
    • extent-based design
    • burn-in time
  • Winners:
    • EXT4
    • XFS
  • Lab Exercise 3.1.4

Fourth Day - Session II and III⌘


Hadoop cluster installation and configuration


HadoopAdministration09.jpeg
http://www.michael-noll.com/blog/uploads/Yahoo-hadoop-cluster_OSCON_2007.jpeg

Underpinning software⌘

  • Prerequisites:
    • JDK (Java Development Kit)
  • Additional software:
    • Cron daemon
    • NTP client
    • SSH server
    • MTA (Mail Transport Agent)
    • RSYNC tool
    • NMAP tool

Hadoop installation⌘

  • Should be performed as the root user
  • Installation instructions are distribution-dependent
  • Installation types:
    • manual installation
    • automated installation
  • Installation sources:
    • packages
    • tarballs
    • source code

Hadoop package content⌘

  • /etc/hadoop - configuration files
  • /etc/rc.d/init.d - SYSV init scripts for Hadoop daemons
  • /usr/bin - executables
  • /usr/include/hadoop - C++ header files for Hadoop pipes
  • /usr/lib - C libraries
  • /usr/libexec - miscellaneous files used by various libraries and scripts
  • /usr/sbin - administrator scripts
  • /usr/share/doc/hadoop - License, NOTICE and README files

CDH-specific additions⌘

  • Binaries for Hadoop Ecosystem
  • Use of alternatives system for Hadoop configuration directory:
    • /etc/alternatives/hadoop-conf
  • Default nofile and nonproc limits:
    • /etc/security/limits.d

Hadoop configuration⌘

  • hadoop-env.sh - environmental variables
  • core-site.xml - all daemons
  • hdfs-site.xml - HDFS daemons
  • mapred-site.xml - MapReduce daemons
  • log4j.properties - logging information
  • masters (optional) - list of servers which run secondary namenode daemon
  • slaves (optional) - list of servers which run datanode and tasktracker daemons
  • fail-scheduler.xml (optional) - fair scheduler configuration and properties
  • capacity-scheduler.xml (optional) - capacity scheduler configuration and properties
  • dfs.include (optional) - list of servers permitted to connect to the namenode
  • dfs.exclude (optional) - list of servers denied to connect to the namenode
  • hadoop-policy.xml - user / group permissions for RPC functions execution
  • mapred-queue-acl.xml - user / group permissions for jobs submission

Hadoop XML files structure⌘

<?xml version="1.0"?>
<configuration>
    <property>
        <name>[name]</name>
        <value>[value]</value>
        (<final>{true | false}</final>)
    </property>
</configuration>

Hadoop configuration⌘

  • Parameter: fs.defaultFS
  • Configuration file: core-site.xml
  • Purpose: specifies the default filesystem used by clients
  • Format: Hadoop URL
  • Used by: NN, SNN, DN, RM, NM, clients
  • Example:
<property>
    <name>fs.defaultFS</name>
    <value>hdfs://namenode.hadooplab:8020</value>
</property>

Hadoop configuration⌘

  • Parameter: io.file.buffer.size
  • Configuration file: core-site.xml
  • Purpose: specifies a size of the IO buffer
  • Guidline: 64 KB
  • Format: integer
  • Used by: NN, SNN, DN, JT, TT, clients
  • Example:
<property>
    <name>io.file.buffer.size</name>
    <value>65536</value>
</property>

Hadoop configuration⌘

  • Parameter: fs.trash.interval
  • Configuration file: core-site.xml
  • Purpose: specifies the amount of time (in minutes) the file is retained in the .Trash directory
  • Format: integer
  • Used by: NN, clients
  • Example:
<property>
    <name>fs.trash.interval</name>
    <value>60</value>
</property>

Hadoop configuration⌘

  • Parameter: ha.zookeeper.quorum
  • Configuration file: core-site.xml
  • Purpose: specifies the list of ZKFCs for automatic jobtracker failover purpose
  • Format: comma separated list of hostname:port pairs
  • Used by: JT
  • Example:
<property>
    <name>ha.zookeeper.quorum</name>
    <value>jobtracker1.hadooplab:2181,jobtracker2.hadooplab:2181</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.name.dir
  • Configuration file: hdfs-site.xml
  • Purpose: specifies where the namenode should store a copy of the HDFS metadata
  • Format: comma separated list of Hadoop URLs
  • Used by: NN
  • Example:
<property>
    <name>dfs.name.dir</name>
    <value>file:///namenodedir,file:///mnt/nfs/namenodedir</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.data.dir
  • Configuration file: hdfs-site.xml
  • Purpose: specifies where the datanode should store HDFS data
  • Format: comma separated list of Hadoop URLs
  • Used by: DN
  • Example:
<property>
    <name>dfs.data.dir</name>
    <value>file:///datanodedir1,file:///datanodedir2</value>
</property>

Hadoop configuration⌘

  • Parameter: fs.checkpoint.dir
  • Configuration file: hdfs-site.xml
  • Purpose: specifies where the secondary namenode should store HDFS metadata
  • Format: comma separated list of Hadoop URLs
  • Used by: SNN
  • Example:
<property>
    <name>fs.checkpoint.dir</name>
    <value>file:///secondarynamenodedir</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.permissions.supergroup
  • Configuration file: hdfs-site.xml
  • Purpose: specifies a group permitted to perform all HDFS filesystem operations
  • Format: string
  • Used by: NN, clients
  • Example:
<property>
    <name>dfs.permissions.supergroup</name>
    <value>supergroup</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.balance.bandwidthPerSec
  • Configuration file: hdfs-site.xml
  • Purpose: specifies the maximum allowed bandwidth for HDFS balancing operations
  • Format: integer
  • Used by: DN
  • Example:
<property>
    <name>dfs.balance.bandwidthPerSec</name>
    <value>100000000</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.blocksize
  • Configuration file: hdfs-site.xml
  • Purpose: specifies the HDFS block size of newly created files
  • Guideline: 64 MB
  • Format: integer
  • Used by: clients
  • Example:
<property>
    <name>dfs.block.size</name>
    <value>67108864</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.datanode.du.reserved
  • Configuration file: hdfs-site.xml
  • Purpose: specifies the disk space reserved for MapReduce temporary data
  • Guideline: 20-30% of the datanode disk space
  • Format: integer
  • Used by: DN
  • Example:
<property>
    <name>dfs.datanode.du.reserved</name>
    <value>10737418240</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.namenode.handler.count
  • Configuration file: hdfs-site.xml
  • Purpose: specifies the number of concurrent namenode handlers
  • Guideline: natural logarithm of the number of cluster nodes, times 20, as a whole number
  • Format: integer
  • Used by: NN
  • Example:
<property>
    <name>dfs.namenode.handler.count</name>
    <value>105</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.datanode.failed.volumes.tolerated
  • Configuration file: hdfs-site.xml
  • Purpose: specifies the number of datanode disks that are permitted to die before failing the entire datanode
  • Format: integer
  • Used by: DN
  • Example:
<property>
    <name>dfs.datanode.failed.volumes.tolerated</name>
    <value>0</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.hosts
  • Configuration file: hdfs-site.xml
  • Purpose: specifies the location of dfs.hosts file
  • Format: Linux path
  • Used by: NN
  • Example:
<property>
    <name>dfs.hosts</name>
    <value>/etc/hadoop/conf/dfs.hosts</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.hosts.exclude
  • Configuration file: hdfs-site.xml
  • Purpose: specifies the location of dfs.hosts.exclude file
  • Format: Linux path
  • Used by: NN
  • Example:
<property>
    <name>dfs.hosts.exclude</name>
    <value>/etc/hadoop/conf/dfs.hosts.exclude</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.nameservices
  • Configuration file: hdfs-site.xml
  • Purpose: specifies virtual namenode names used for the namenode HA and federation purpose
  • Format: comma separated strings
  • Used by: NN, clients
  • Example:
<property>
    <name>dfs.nameservices</name>
    <value>HAcluster1,HAcluster2</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.ha.namenodes.[nameservice]
  • Configuration file: hdfs-site.xml
  • Purpose: specifies logical namenode names used for the namenode HA purpose
  • Format: comma separated strings
  • Used by: DN, NN, clients
  • Example:
<property>
    <name>dfs.ha.namenodes.HAcluster1</name>
    <value>namenode1,namenode2</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.namenode.rpc-address.[nameservice].[namenode]
  • Configuration file: hdfs-site.xml
  • Purpose: specifies namenode RPC address used for the namenode HA purpose
  • Format: Hadoop URL
  • Used by: DN, NN, clients
  • Example:
<property>
    <name>dfs.namenode.rpc-address.HAcluster1.namenode1</name>
    <value>http://namenode1.hadooplab:8020</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.namenode.http-address.[nameservice].[namenode]
  • Configuration file: hdfs-site.xml
  • Purpose: specifies namenode HTTP address used for the namenode HA purpose
  • Format: Hadoop URL
  • Used by: DN, NN, clients
  • Example:
<property>
    <name>dfs.namenode.http-address.[nameservice].[namenode]</name>
    <value>http://namenode1.hadooplab:50070</value>
</property>

Hadoop configuration⌘

  • Parameter: topology.script.file.name
  • Configuration file: core-site.xml
  • Purpose: specifies the location of a script for rack-awareness purpose
  • Format: Linux path
  • Used by: NN
  • Example:
<property>
    <name>topology.script.file.name</name>
    <value>/etc/hadoop/conf/topology.sh</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.namenode.shared.edits.dir
  • Configuration file: hdfs-site.xml
  • Purpose: specifies the location of shared directory on QJM with namenode metadata for namenode HA purpose
  • Format: Hadoop URL
  • Used by: NN, clients
  • Example:
<property>
    <name>dfs.namenode.shared.edits.dir</name>
    <value>qjournal://namenode1.hadooplab.com:8485/hadoop/hdfs/HAcluster</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.client.failover.proxy.provider.[nameservice]
  • Configuration file: hdfs-site.xml
  • Purpose: specifies the class used for locating active namenode for namenode HA purpose
  • Format: Java class
  • Used by: DN, clients
  • Example:
<property>
    <name>dfs.client.failover.proxy.provider.HAcluster1</name>
    <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.ha.fencing.methods
  • Configuration file: hdfs-site.xml
  • Purpose: specifies the fencing methods for namenode HA purpose
  • Format: new line-separated list of Linux paths
  • Used by: NN
  • Example:
<property>
    <name>dfs.ha.fencing.methods</name>
    <value>/usr/local/bin/STONITH.sh</value>
</property>

Hadoop configuration⌘

  • Parameter: dfs.ha.automatic-failover.enabled
  • Configuration file: hdfs-site.xml
  • Purpose: specifies whether the automatic namenode failover is enabled or not
  • Format: 'true' / 'false'
  • Used by: NN, clients
  • Example:
<property>
    <name>dfs.ha.automatic-failover.enabled</name>
    <value>true</value>
</property>

Hadoop configuration⌘

  • Parameter: ha.zookeeper.quorum
  • Configuration file: hdfs-site.xml
  • Purpose: specifies the list of ZKFCs for automatic namenode failover purpose
  • Format: comma separated list of hostname:port pairs
  • Used by: NN
  • Example:
<property>
    <name>ha.zookeeper.quorum</name>
    <value>namenode1.hadooplab:2181,namenode2.hadooplab:2181</value>
</property>

Hadoop configuration⌘

  • Parameter: fs.viewfs.mounttable.default.link.[path]
  • Configuration file: hdfs-site.xml
  • Purpose: specifies the mount point of the namenode or namenode cluster for namenode federation purpose
  • Format: Hadoop URL or namenode cluster virtual name
  • Used by: NN
  • Example:
<property>
    <name>fs.viewfs.mounttable.default.link./federation1</name>
    <value>hdfs://namenode.hadooplabl:8020</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.hostname
  • Configuration file: yarn-site.xml
  • Purpose: specifies the location of resource manager
  • Format: hostname:port
  • Used by: RM, NM, clients
  • Example:
<property>
    <name>yarn.resourcemanager.hostname</name>
    <value>resourcemanager.hadooplab:8021</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.nodemanager.local-dirs
  • Configuration file: yarn-site.xml
  • Purpose: specifies where the resource manager and node manager should store application temporary data
  • Format: comma separated list of Hadoop URLs
  • Used by: RM, NM
  • Example:
<property>
    <name>yarn.nodemanager.local-dirs</name>
    <value>file:///applocaldir</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.nodemanager.resource.memory-mb
  • Configuration file: yarn-site.xml
  • Purpose: specifies memory in megabytes available for allocation for the applications
  • Format: integer
  • Used by: NM
  • Example:
<property>
    <name>yarn.nodemanager.resource.memory-mb</name>
    <value>40960</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.nodemanager.resource.cpu-vcores
  • Configuration file: yarn-site.xml
  • Purpose: specifies virtual cores available for allocation for the applications
  • Format: integer
  • Used by: NM
  • Example:
<property>
    <name>yarn.nodemanager.resource.cpu-vcores</name>
    <value>8</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.scheduler.minimum-allocation-mb
  • Configuration file: yarn-site.xml
  • Purpose: specifies minimum amount of memory in megabytes allocated for the applications
  • Format: integer
  • Used by: NM
  • Example:
<property>
    <name>yarn.scheduler.minimum-allocation-mb</name>
    <value>2048</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.scheduler.minimum-allocation-vcores
  • Configuration file: yarn-site.xml
  • Purpose: specifies minimum number of virtual cores allocated for the applications
  • Format: integer
  • Used by: NM
  • Example:
<property>
    <name>yarn.scheduler.minimum-allocation-vcores</name>
    <value>2</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.scheduler.increment-allocation-mb
  • Configuration file: yarn-site.xml
  • Purpose: specifies increment amount of memory in megabytes for allocation for the applications
  • Format: integer
  • Used by: NM
  • Example:
<property>
    <name>yarn.scheduler.increment-allocation-mb</name>
    <value>512</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.scheduler.increment-allocation-vcores
  • Configuration file: yarn-site.xml
  • Purpose: specifies incremental number of virtual cores for allocation for the applications
  • Format: integer
  • Used by: NM
  • Example:
<property>
    <name>yarn.scheduler.increment-allocation-vcores</name>
    <value>1</value>
</property>

Hadoop configuration⌘

  • Parameter: mapreduce.task.io.sort.mb
  • Configuration file: mapred-site.xml
  • Purpose: specifies the size of the circular in-memory buffer used to store MapReduce temporary data
  • Guideline: ~10% of the JVM heap size
  • Format: integer (MB)
  • Used by: NM
  • Example:
<property>
    <name>mapreduce.task.io.sort.mb</name>
    <value>128</value>
</property>

Hadoop configuration⌘

  • Parameter: mapreduce.task.io.sort.factor
  • Configuration file: mapred-site.xml
  • Purpose: specifies the number of files to merge at once
  • Guideline: 64 for 1 GB JVM heap size
  • Format: integer
  • Used by: NM
  • Example:
<property>
    <name>mapreduce.task.io.sort.factor</name>
    <value>64</value>
</property>

Hadoop configuration⌘

  • Parameter: mapreduce.map.output.compress
  • Configuration file: mapred-site.xml
  • Purpose: specifies whether the output of the map tasks should be compressed or not
  • Format: 'true' / 'false'
  • Used by: NM
  • Example:
<property>
    <name>mapreduce.map.output.compress</name>
    <value>true</value>
</property>

Hadoop configuration⌘

  • Parameter: mapreduce.map.output.compress.codec
  • Configuration file: mapred-site.xml
  • Purpose: specifies the codec used for map tasks output compression
  • Format: Java class
  • Used by: NM
  • Example:
<property>
    <name>mapreduce.map.output.compress.codec</name>
    <value>org.apache.io.compress.SnappyCodec</value>
</property>

Hadoop configuration⌘

  • Parameter: mapreduce.output.fileoutputformat.compress.type
  • Configuration file: mapred-site.xml
  • Purpose: specifies whether the compression should be applied to the values only, key-value pairs or not at all
  • Format: 'RECORD' / 'BLOCK' / 'NONE'
  • Used by: NM
  • Example:
<property>
    <name>mapreduce.output.fileoutputformat.compress.type</name>
    <value>BLOCK</value>
</property>

Hadoop configuration⌘

  • Parameter: mapreduce.reduce.shuffle.parallelcopies
  • Configuration file: mapred-site.xml
  • Purpose: specifies the number of threads started in parallel to fetch the output from map tasks
  • Guideline: natural logarithm of the size of the cluster, times four, as a whole number
  • Format: integer
  • Used by: NM
  • Example:
<property>
    <name>mapreduce.reduce.shuffle.parallelcopies</name>
    <value>5</value>
</property>

Hadoop configuration⌘

  • Parameter: mapreduce.job.reduces
  • Configuration file: mapred-site.xml
  • Purpose: specifies the number of reduce tasks to use for the MapReduce jobs
  • Format: integer
  • Used by: RM
  • Example:
<property>
    <name>mapreduce.job.reduces</name>
    <value>1</value>
</property>

Hadoop configuration⌘

  • Parameter: mapreduce.shuffle.max.threads
  • Configuration file: mapred-site.xml
  • Purpose: specifies the number of threads started in parallel to serve the output of map tasks
  • Format: integer
  • Used by: RM
  • Example:
<property>
    <name>mapreduce.shuffle.max.threads</name>
    <value>1</value>
</property>

Hadoop configuration⌘

  • Parameter: mapreduce.job.reduce.slowstart.completedmaps
  • Configuration file: mapred-site.xml
  • Purpose: specifies when to start allocating reducers as the number of complete map tasks grows
  • Format: float
  • Used by: RM
  • Example:
<property>
    <name>mapreduce.job.reduce.slowstart.completedmaps</name>
    <value>0.5</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.ha.rm-ids
  • Configuration file: yarn-site.xml
  • Purpose: specifies logical resource manager names used for resource manager HA purpose
  • Format: comma separated strings
  • Used by: RM, NM, clients
  • Example:
<property>
    <name>yarn.resourcemanager.ha.rm-ids</name>
    <value>resourcemanager1,resourcemanager2</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.ha.id
  • Configuration file: yarn-site.xml
  • Purpose: specifies preferred resource manager for an active unit
  • Format: string
  • Used by: RM
  • Example:
<property>
    <name>yarn.resourcemanager.ha.id</name>
    <value>resourcemanager1</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.address.[resourcemanager]
  • Configuration file: yarn-site.xml
  • Purpose: specifies resource manager RPC address
  • Format: Hadoop URL
  • Used by: RM, clients
  • Example:
<property>
    <name>yarn.resourcemanager.address.resourcemanager1</name>
    <value>http://resourcemanager1.hadooplab:8032</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.scheduler.address.[resourcemanager]
  • Configuration file: yarn-site.xml
  • Purpose: specifies resource manager RPC address for Scheduler service
  • Format: Hadoop URL
  • Used by: RM, clients
  • Example:
<property>
    <name>yarn.resourcemanager.scheduler.address.resourcemanager1</name>
    <value>http://resourcemanager1.hadooplab:8030</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.admin.address.[resourcemanager]
  • Configuration file: yarn-site.xml
  • Purpose: specifies resource manager RPC address used for the resource manager HA purpose
  • Format: Hadoop URL
  • Used by: RM, clients
  • Example:
<property>
    <name>yarn.resourcemanager.admin.address.resourcemanager1</name>
    <value>http://resourcemanager1.hadooplab:8033</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.resource-tracker.address.[resourcemanager]
  • Configuration file: yarn-site.xml
  • Purpose: specifies resource manager RPC address for Resource Tracker service
  • Format: Hadoop URL
  • Used by: RM, NM
  • Example:
<property>
    <name>yarn.resourcemanager.resource-tracker.address.resourcemanager1</name>
    <value>http://resourcemanager1.hadooplab:8031</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.webapp.address.[resourcemanager]
  • Configuration file: yarn-site.xml
  • Purpose: specifies resource manager address for web UI and REST API services
  • Format: Hadoop URL
  • Used by: RM, clients
  • Example:
<property>
    <name>yarn.resourcemanager.webapp.address.resourcemanager1</name>
    <value>http://resourcemanager1.hadooplab:8088</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.ha.fencer
  • Configuration file: yarn-site.xml
  • Purpose: specifies the fencing methods for resourcemanager HA purpose
  • Format: new-line-separated list of Linux paths
  • Used by: RM
  • Example:
<property>
    <name>yarn.resourcemanager.ha.fencer</name>
    <value>/usr/local/bin/STONITH.sh</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.ha.auto-failover.enabled
  • Configuration file: yarn-site.xml
  • Purpose: enables automatic failover in resource manager HA pair
  • Format: boolean
  • Used by: RM
  • Example:
<property>
    <name>yarn.resourcemanager.ha.auto-failover.enabled</name>
    <value>true</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.recovery.enabled
  • Configuration file: yarn-site.xml
  • Purpose: enables job recovery on resource manager restart or during the failover
  • Format: boolean
  • Used by: RM
  • Example:
<property>
    <name>yarn.resourcemanager.ha.recovery.enabled</name>
    <value>true</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.ha.auto-failover.port
  • Configuration file: yarn-site.xml
  • Purpose: specifies ZKFC port
  • Format: integer
  • Used by: RM
  • Example:
<property>
    <name>yarn.resourcemanager.ha.auto-failover.port</name>
    <value>8018</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.ha.enabled
  • Configuration file: yarn-site.xml
  • Purpose: enables resource manager HA feature
  • Format: boolean value
  • Used by: RM
  • Example:
<property>
    <name>yarn.resourcemanager.ha.enabled</name>
    <value>true</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.store.class
  • Configuration file: yarn-site.xml
  • Purpose: specifies storage for resource manager internal state
  • Format: string
  • Used by: RM
  • Example:
<property>
    <name>yarn.resourcemanager.store.class</name>
    <value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>
</property>

Hadoop configuration⌘

  • Parameter: yarn.resourcemanager.cluster-id
  • Configuration file: yarn-site.xml
  • Purpose: specifies the logical name of YARN cluster
  • Format: string
  • Used by: RM, NM, clients
  • Example:
<property>
    <name>yarn.resourcemanager.cluster-id</name>
    <value>HAcluster</value>
</property>

Hadoop configuration - exercises⌘

  • Lab Exercise 3.2.1
  • Lab Exercise 3.2.2
  • Lab Exercise 3.2.3
  • Lab Exercise 3.3.1
  • Lab Exercise 3.3.2
  • Lab Exercise 3.3.3
  • Lab Exercise 3.3.4
  • Lab Exercise 3.3.5
  • Lab Exercise 3.3.6
  • Lab Exercise 3.3.7
  • Lab Exercise 3.3.8
  • Lab Exercise 3.3.9

Third Day - Session IV⌘


Case Studies, Certification and Surveys


LPIC-102-09.jpg
http://zone16.pcansw.org.au/site/ponyclub/image/fullsize/60786.jpg

Certification and Surveys⌘