The document discusses the family of Hadoop projects. It describes the history and origins of Hadoop, starting with Doug Cutting's work on Nutch and the implementation of Google's papers on MapReduce and the Google File System. It then summarizes several major Hadoop sub-projects, including HDFS for storage, MapReduce for distributed processing, HBase for structured storage, and Hive for data warehousing. For each project, it provides a brief overview of the architecture, data model, and programming interfaces.

1. The Family of Hadoop
Nham Xuan Nam
nhamxuannam [at] gmail.com
http://namnham.blogspot.com
Barcamp Saigon, December 13 2009

2. Content
 History
 Sub-projects
 HDFS
 Map Reduce
 HBase
 Hive

3. 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.

4. 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.

5. 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.

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

7. Sub-projects

9. Architecture

10. Data Model
 File stored as blocks (default size: 64M)
 Reliability through replication
– Each block is replicated to several datanodes

11. 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.

12. Data Flow

13. Data Flow

14. 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

16. Programming Model

17. 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

18. Data Flow

19. WordCount Example
File01: File02:
Hello Barcamp Hello Everyone Hello Hadoop Hello Everyone
<_, Hello Barcamp Hello Everyone> <_, Hello Hadoop Hello Everyone>
<Hello, 2> <Hello, 2>
<Barcamp, 1> <Hadoop, 1>
<Everyone, 1> <Everyone, 1>
<Barcamp, [1]>
<Hadoop, [1]>
<Hello, [2,2]>
<Everyone, [1,1]>
<Barcamp, 1>
<Hadoop, 1>
<Hello, 4>
<Everyone, 2>

20. 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.

21. MapReduce in Hadoop

23. 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.

24. 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)

25. Data Model

26. Architecture

27. 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.

28. Accessibility
 Client API
org.apache.hadoop.hbase
.client.*
 HBase Shell
$ bin/hbase shell
hbase>
 Web Interface

30. 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,...

31. 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

32. Architecture

33. 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

34. 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

35. Hive @ Facebook

36. The End
Thank you!