The Family of Hadoop

Nham Xuan Nam

nhamxuannam [at] gmail.com

http://namnham.blogspot.com

Barcamp Saigon, December 13 2009

Content

History Sub-projects HDFS Map Reduce HBase Hive

History created by Doug Cutting, the creator of

Lucene.

Lucene: open source index & search library.

Nutch: Lucene-based web crawler.

Jun 2003, there was a successful 100 million page Nutch demo system.

Nutch problem: its architecture could not scale to the billions of pages.

History Oct 2003, Google published the paper “The Google File System”.

In 2004, Nutch team wrote an open source implementation of GFS, called Nutch Distributed File System (NDFS).

Dec 2004, Google published the paper “MapReduce: Simplified Data Processing on Large Clusters”.

In 2005, Nutch team implemented MapReduce in Nutch.

Mid 2005, all the major Nutch algorithms had been ported to run using MapReduce and NDFS.

History Feb 2006, Nutch's NDFS and the MapReduce

implementation formed Hadoop project.

Doug Cutting joined Yahoo!.

Jan 2008, Hadoop became Apache top-level project.

Feb 2008, Yahoo! production search index was generated by a 10,000-core Hadoop cluster.

History

Source: http://wiki.apache.org/hadoop/PoweredBy

Sub-projects

Architecture

Data Model File stored as blocks (default size: 64M)

Reliability through replication

– Each block is replicated to several datanodes

Namenode & Datanodes Namenode (master)

– manages the filesystem namespace

– maintains the filesystem tree and metadata for all the files and directories in the tree.

Datanodes (slaves)

– store data in the local file system

– Periodically report back to the namenode with lists of all existing blocks

Clients communicate with both namenode and datanodes.

Data Flow

Data Flow

Accessibility FileSystem Java API

– org.apache.hadoop.fs.*

Web Interface

Commands for HDFS users

$ hadoop dfs ­mkdir /barcamp

$ hadoop dfs ­ls /barcamp

Commands for HDFS admins

$ hadoop dfsadmin ­report

$ hadoop dfsadmin ­refreshNodes

Programming Model

Programming Model Data is a stream of keys and values

Map

– Input: <key1,value1> pairs from data source

– Output: immediate <key2,value2> pairs

Reduce

– Called once per a key, in sorted order

Input: <key2, list of value2>

Output: <key3,value3> pairs

Data Flow

WordCount ExampleFile01:Hello Barcamp Hello Everyone

File02:Hello Hadoop Hello Everyone

<Hello, 2><Barcamp, 1><Everyone, 1>

<_, Hello Hadoop Hello Everyone><_, Hello Barcamp Hello Everyone>

<Hello, 2><Hadoop, 1><Everyone, 1>

<Barcamp, 1><Hadoop, 1><Hello,  4><Everyone, 2>

<Barcamp, [1]><Hadoop, [1]><Hello, [2,2]><Everyone, [1,1]>

MapReduce in Hadoop JobTracker (master)

– handling all jobs.

– scheduling tasks on the slaves.

– monitoring & re-executing tasks.

TaskTrackers (slaves)

– execute the tasks.

Task

– run an individual map or reduce.

MapReduce in Hadoop

Introduction Nov 2006, Google released the paper “Bigtable: A Distributed Storage System for Structured Data”

BigTable: distributed, column-oriented store, built on top of Google File System.

HBase: open source implementation of BigTable, built on top of HDFS.

Data Model Data are stored in tables of rows and columns.

Cells are ”versioned”

→ Data are addressed by row/column/version key.

Table rows are sorted by row key, the table's primary key.

Columns are grouped into column families.

→ A column name has the form “<family>:<label>”

Tables are stored in regions.

Region: a row range [start-key : end-key)

Data Model

Architecture

Architecture Master Server

– assigns regions to regionservers

– monitors the health of regionservers

– handles administrative funtions

RegionServers– contain regions and handle client read/write requests

Catalog Tables (ROOT and META)– maintain the current list, state, recent history, and

location of all regions.

Accessibility Client APIorg.apache.hadoop.hbase.client.*

HBase Shell$ bin/hbase shellhbase> 

Web Interface

Introduction started at Facebook

an open source data warehousing solution built on top of Hadoop

for managing and querying structured data

Hive QL: SQL-like query language

– compiled into map-reduce jobs

log processing, data mining,...

Data Model Tables

– analogous to tables in RDBMS

– rows are organized into typed columns

– all the data is stored in a directory in HDFS

Partitions

– determine the distribution of data within sub-directories of the table directory

Buckets

– based on the hash of a column in the table

– Each bucket is stored as a file in the partition directory

Architecture

Architecture Metastore

– contains metadata about data stored in Hive.

– stored in any SQL backend or an embedded Derby.

– Database: a namespace for tables

– Table metadata: column types, physical layout,...

– Partition metadata

Compiler

Excution Engine

Shell

Hive Query Language Data Definition (DDL) statements

– CREATE/DROP/ALTER TABLE

– SHOW TABLE/PARTITIONS

Data Manipulation (DML) statements

– LOAD DATA

– INSERT

– SELECT

User Defined functions: UDF/UDAF

Hive @ Facebook

The End

Thank you!