Hadoop at Rakuten, 2011/07/06

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Hadoop at Rakuten.

Rakuten Inc. Architect GroupHamba Mitsuharu & Nakagawa Gen 2011/07/06(Wed)

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Today’s Agenda.

Hadoop at Rakuten.

1. Our Profie.2. What is Hadoop?3. Our Current Hadoop System Overview.4. Our Hadoop Usage.5. Our Challenge.6. Our Future Plan.

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Our Profile.

Hadoop at Rakuten.

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From ACT GroupNakagawa GenHamba Mitsuharu

Our Profile.

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Our Profile.

Our Mission

Enhancing Hadoop at Rakuten.

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Our Profile.

Latest Our Tasks.Done.

1.Implementing Ganglia.2.Implementing HA.

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Our Profile.

Latest Our Tasks.Now Handing Over.

1. Keeping Up Our Hadoop Cluster.2. Modifying Our Hadoop Configurations.3. Implementing Scripts for Daily Chores.

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Our Profile.

Latest Our Tasks. Concentrate It!

1.Evaluating The Related Products.

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What is Hadoop?

Hadoop at Rakuten.

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One of The Most PowerfulDistributed Processing for Large Data Sets.

What is Hadoop?

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Distributions.

What is Hadoop?

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Ecosystem.

What is Hadoop?

ETC...

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What is Hadoop?

HDFS : Hadoop Distributed File System.MapReduce :Map & Reduce (Includes Shuffle & Sort) .

HDFS & MapReduce Constitute Hadoop.

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What is Hadoop?

Source : http://horicky.blogspot.com/2008_11_01_archive.html

Input from HDFS.

Output to HDFS. Process by MapReduce.

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What is Hadoop?

Simple Example.

Source : http://techblog.yahoo.co.jp/cat207/cat209/hadoop/

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What is Hadoop?


In Common Case,Combine Several Simple Jobs.

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What is Hadoop?

NameNode & DataNode Constitute HDFS.


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What is Hadoop?

Read & Write on HDFS.

Source : http://hadoop.apache.org/common/docs/current/hdfs_design.html#NameNode+and+DataNodes

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What is Hadoop?

JobTracker & TaskTracker Constitute MapReduce.


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What is Hadoop?

Good & Bad Points of Hadoop.

�Bad…There is SPoF at NameNode.

�Good!Easy to Scale Out System.Easy to Implement Distributed Processing.

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Our Current HadoopSystem Overview.

Hadoop at Rakuten.

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Our Current Hadoop System Overview.

The Cluster Infrastructure. #1For Instance.

Source : http://www.ibm.com/developerworks/linux/library/l-hadoop/

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The Cluster Infrastructure. #2In Our Case.

Switch Switch Switch

Switch

Rack Rack Rack Rack Rack Rack Rack Rack Rack Rack Rack Rack

NN&JTActive

NN&JTStandby

SNN

DN&TT DN&TT DN&TT DN&TT DN&TT DN&TT

DN&TTDN&TTDN&TT

1Gbps1Gbps1Gbps

1Gbps

x10 x10 x10 x10 x10 x10

x3 x3 x3

Client

Others Others Others

x18 x18 x183 Masters & 69 Slaves.

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The Monitoring System.Using Ganglia (& MRTG).Every Time We Easily Can CheckThe Resource Usage,Not Only Each MachineBut As Cluster.

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High Availability.Using DRBD & HeartBeat.

v-host.rakuten.co.jp

eth1

NN JT NN JT

/foo/drbd0 /foo/drbd1 /foo/drbd0 /foo/drbd1

DRBD Sync The Change.

eth0 eth0

eth1

Active Standby

Client

Source : Gen

NN : NameNodeJT : JobTracker

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Our Hadoop Usage.

Hadoop at Rakuten.

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Our Hadoop Usage.

1. Generating Recommend Engine Index.2. Analyzing Redirect Log.3. Calculating AD Targeting Index.4. Measuring AD Effects.5. Analyzing Ichiba Merchandise & Order Info. 6. Calculating Ichiba Product Ranking.7. Analyzing Search Log.

8. Analyzing Rakuten Travel’s Access Log. (Coming Soon...)9. Analyzing Search Word N-gram. (Coming Soon...)

Who Is Using Our Hadoop.

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Our Hadoop Usage.

The Issues of The Previous System.

Purchase

Shop

ITEM

Intermediate

Intermediate

FileFileFileFileFileFile

Marketing

Utility

Previous System

Category NFS

Mail

Unload

Load

Manipulate

1. Need High Cost to Keep Up The RDBMS.2. Need Quite a Lot of Storage Space More & More.3. System Cannot Handle So Many Job Request

Due to Low Performance.Batch Server

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Our Hadoop Usage.

The Effect of The New System.

Purchase

Shop

ITEM

FileFileFileFileFileFile

Marketing

Utility

New System! 1st Step.

Category NFS

Mail

Unload

Load

Manipulate

Batch Serverwith

1. Get Scalable System at Very Low Cost. (80% OFF as Storage.)2. Transaction Time is Dramatically Improved. (50-75% OFF.)

Intermediate

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Our Hadoop Usage.

The Remaining Subject ofThe New System.1. Still Halfway to Aiming DWH.2. The Negative Influence Due to The Migration

from Occupied Environment to Shared Environment.1. Security.2. Sharing Cluster Resource.

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Our Challenge.

Hadoop at Rakuten.

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Our Challenge.

1. Likely to Use Up The HDFS Space.2. Need Much Electlicity Power.3. Share The Cluster Resource Efficiently.4. Need More Network Bandwidth.

The Issues with Our Hadoop.

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Our Future Plan.

Hadoop at Rakuten.

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Our Future Plan.

Considering New Slave Machine.

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Now Looking for a Machine Which has…Low Electric Power Consumption,About 6 Cores CPU x2,About 10TB HDD,About 96GB Memory,& Naturally Compatible With Our Data Center.

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Our Future Plan.

Upgrade from Apache to CDH3.

Source : http://www.quora.com/What-are-the-advantages-of-getting-Apache-Hadoop-from-Cloudera-rather-than-the-Apache-Software-Foundation]

1. A version of Hadoop that has frequent releases (quarterly) that include bug fixes and back ported features (append for HBase, Kerberos security from Y!, etc.).

2. Related projects (Hive, Pig, Oozie, HBase, Flume, Sqoop, etc.) tested together and work as a cohesive system.

3. Simplified installation via Yum / Apt repositories.4. Tighter integration with the OS (init scripts for daemons, installation of things in

common paths, logs in their proper location.).5. A fixed release schedule.6. Support available from Cloudera with SLAs.

Mr.Eric Sammer (Solution Architect at Cloudera) Described the Advantage of Hadoop from Cloudera on Quora.

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Our Future Plan.

Evaluating HBase Using AWS.

Constructing HBase Cluster on Amazon EC2.Doing Evaluation & Verification This Summer!

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Hadoop at Rakuten.

We Need Hadooper Much More!Come With Us!

Need Your Help!

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Thank You.

Hadoop at Rakuten.

Technology

Hadoop at Rakuten, 2011/07/06