An introduction to

Hello

• Processing against a 156 node cluster

•Certified Hadoop Developer

•Certified Hadoop System Administrator

Goals

•Why should you care?

•What is it?

•How does it work?

Data Everywhere“Every two days now we create as much information as we did from the dawn of civilization up until  2003”

-Eric Schmidtthen CEO of GoogleAug 4, 2010

Data Everywhere

Data Everywhere

Data Everywhere

The Hadoop Project

•Originally based on papers published by Google in 2003 and 2004

•Hadoop started in 2006 at Yahoo!

•Top level Apache Foundation project

•Large, active user base, user groups

•Very active development, strong development team

Who Uses Hadoop?

Hadoop Components

HDFSHDFS

Storage

Self-healinghigh-bandwidth

clustered storage

MapReducMapReducee

Processing

Fault-tolerantdistributedprocessing

Typical Cluster•3-4000 commodity servers

•Each server

•2x quad-core

•16-24 GB ram

•4-12 TB disk space

•20-30 servers per rack

2 Kinds of Nodes

Master Nodes Slave Nodes

Master Nodes•NameNode

•only 1 per cluster

•metadata server and database

•SecondaryNameNode helps with some housekeeping

•JobTracker

• only 1 per cluster

• job scheduler

Slave Nodes• DataNodes

• 1-4000 per cluster

• block data storage

•TaskTrackers

• 1-4000 per cluster

• task execution

HDFS Basics•HDFS is a filesystem written in Java

•Sits on top of a native filesystem

•Provides redundant storage for massive amounts of data

•Use cheap(ish), unreliable computers

HDFS Data•Data is split into blocks and stored on

multiple nodes in the cluster

•Each block is usually 64 MB or 128 MB (conf)

•Each block is replicated multiple times (conf)

•Replicas stored on different data nodes

•Large files, 100 MB+

NameNode•A single NameNode stores all

metadata

•Filenames, locations on DataNodes of each block, owner, group, etc.

•All information maintained in RAM for fast lookup

•Filesystem metadata size is limited to the amount of available RAM on the NameNode

SecondaryNameNode

•The Secondary NameNode is not a failover NameNode

•Does memory-intensive administrative functions for the NameNode

•Should run on a separate machine

Data Node•DataNodes store file contents

•Stored as opaque ‘blocks’ on the underlying filesystem

•Different blocks of the same file will be stored on different DataNodes

•Same block is stored on three (or more) DataNodes for redundancy

Self-healing•DataNodes send heartbeats to the

NameNode

• After a period without any heartbeats, a DataNode is assumed to be lost

• NameNode determines which blocks were on the lost node

• NameNode finds other DataNodes with copies of these blocks

• These DataNodes are instructed to copy the blocks to other nodes

• Replication is actively maintained

HDFS Data Storage•NameNode holds

file metadata

•DataNodes hold the actual data

•Block size is 64 MB, 128 MB, etc

•Each block replicated three times

NameNodefoo.txt: blk_1, blk_2, blk_3bar.txt: blk_4, blk_5

DataNodesblk_1 blk_2

blk_3 blk_5

blk_1 blk_3

blk_4

blk_1 blk_4

blk_5

blk_2 blk_4

blk_2 blk_3

blk_5

What is MapReduce?

•MapReduce is a method for distributing a task across multiple nodes

•Automatic parallelization and distribution

•Each node processes data stored on that node (processing goes to the data)

Features of MapReduce

•Fault-tolerance

•Status and monitoring tools

•A clean abstraction for programmers

JobTracker•MapReduce jobs are controlled by a

software daemon known as the JobTracker

•The JobTracker resides on a master node• Assigns Map and Reduce tasks to other nodes on the cluster

• These nodes each run a software daemon known as the TaskTracker

• The TaskTracker is responsible for actually instantiating the Map or Reduce task, and reporting progress back to the JobTracker

Two Parts

•Developer specifies two functions:

•map()

•reduce()

•The framework does the rest

map()

•The Mapper reads data in the form of key/value pairs

•It outputs zero or more key/value pairs

map(key_in, value_in) ->

(key_out, value_out)

reduce()•After the Map phase all the

intermediate values for a given intermediate key are combined together into a list

•This list is given to one or more Reducers

•The Reducer outputs zero or more final key/value pairs

•These are written to HDFS

map() Word Countmap(String input_key, String input_value) foreach word w in input_value emit(w, 1)

(1234, “to be or not to be”)(5678, “to see or not to see”)

(“to”,1),(“be”,1),(“or”,1),(“not”,1),(“to”,1),(“be”,1), (“to”,1),(“see”,1),(“or”,1),(“not”,1),(“to”,1),(“see”,1)

reduce() Word Countreduce(String output_key, List

middle_vals) set count = 0 foreach v in intermediate_vals: count += v emit(output_key, count)

(“to”, [1,1,1,1])(“be”,[1,1])(“or”,[1,1])(“not”,[1,1])(“see”,[1,1])

(“to”, 4)(“be”,2)(“or”,2)(“not”,2)(“see”,2)

Resources

http://hadoop.apache.org/

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

http://www.cloudera.com/resources/?media=Video

Questions?