1. Hadoop: Distributed Data Processing
Amr Awadallah
Founder/CTO, Cloudera, Inc.
ACM Data Mining SIG
Thursday, January 25th, 2010
Wednesday, January 27, 2010
2. Outline
▪Scaling for Large Data
Processing
▪What is Hadoop?
▪HDFS and MapReduce
▪Hadoop Ecosystem
▪Hadoop vs RDBMSes
▪Conclusion
Amr Awadallah, Cloudera Inc 2
Wednesday, January 27, 2010
3. Current Storage Systems Can’t Compute
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Wednesday, January 27, 2010
4. Current Storage Systems Can’t Compute
Collection
Instrumentation
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Wednesday, January 27, 2010
5. Current Storage Systems Can’t Compute
Storage Farm for Unstructured Data (20TB/day)
Mostly Append
Collection
Instrumentation
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Wednesday, January 27, 2010
6. Current Storage Systems Can’t Compute
Interactive Apps
RDBMS (200GB/day)
ETL Grid
Storage Farm for Unstructured Data (20TB/day)
Mostly Append
Collection
Instrumentation
Amr Awadallah, Cloudera Inc 3
Wednesday, January 27, 2010
7. Current Storage Systems Can’t Compute
Interactive Apps
RDBMS (200GB/day)
ETL Grid
Filer heads are a bottleneck
Storage Farm for Unstructured Data (20TB/day)
Mostly Append
Collection
Instrumentation
Amr Awadallah, Cloudera Inc 3
Wednesday, January 27, 2010
8. Current Storage Systems Can’t Compute
Interactive Apps Ad hoc Queries &
Data Mining
RDBMS (200GB/day)
ETL Grid Non-Consumption
Filer heads are a bottleneck
Storage Farm for Unstructured Data (20TB/day)
Mostly Append
Collection
Instrumentation
Amr Awadallah, Cloudera Inc 3
Wednesday, January 27, 2010
9. The Solution: A Store-Compute Grid
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Wednesday, January 27, 2010
10. The Solution: A Store-Compute Grid
Storage + Computation
Mostly Append
Collection
Instrumentation
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Wednesday, January 27, 2010
11. The Solution: A Store-Compute Grid
Interactive Apps
RDBMS
ETL and
Aggregations
Storage + Computation
Mostly Append
Collection
Instrumentation
Amr Awadallah, Cloudera Inc 4
Wednesday, January 27, 2010
12. The Solution: A Store-Compute Grid
Interactive Apps “Batch” Apps
RDBMS
Ad hoc Queries
ETL and & Data Mining
Aggregations
Storage + Computation
Mostly Append
Collection
Instrumentation
Amr Awadallah, Cloudera Inc 4
Wednesday, January 27, 2010
13. What is Hadoop?
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Wednesday, January 27, 2010
14. What is Hadoop?
▪A scalable fault-tolerant grid operating
system for data storage and processing
Amr Awadallah, Cloudera Inc 5
Wednesday, January 27, 2010
15. What is Hadoop?
▪A scalable fault-tolerant grid operating
system for data storage and processing
▪ Its scalability comes from the marriage of:
▪ HDFS: Self-Healing High-Bandwidth Clustered Storage
▪ MapReduce: Fault-Tolerant Distributed Processing
Amr Awadallah, Cloudera Inc 5
Wednesday, January 27, 2010
16. What is Hadoop?
▪A scalable fault-tolerant grid operating
system for data storage and processing
▪ Its scalability comes from the marriage of:
▪ HDFS: Self-Healing High-Bandwidth Clustered Storage
▪ MapReduce: Fault-Tolerant Distributed Processing
▪ Operates on unstructured and structured data
Amr Awadallah, Cloudera Inc 5
Wednesday, January 27, 2010
17. What is Hadoop?
▪A scalable fault-tolerant grid operating
system for data storage and processing
▪ Its scalability comes from the marriage of:
▪ HDFS: Self-Healing High-Bandwidth Clustered Storage
▪ MapReduce: Fault-Tolerant Distributed Processing
▪ Operates on unstructured and structured data
▪ A large and active ecosystem (many developers
and additions like HBase, Hive, Pig, …)
Amr Awadallah, Cloudera Inc 5
Wednesday, January 27, 2010
18. What is Hadoop?
▪A scalable fault-tolerant grid operating
system for data storage and processing
▪ Its scalability comes from the marriage of:
▪ HDFS: Self-Healing High-Bandwidth Clustered Storage
▪ MapReduce: Fault-Tolerant Distributed Processing
▪ Operates on unstructured and structured data
▪ A large and active ecosystem (many developers
and additions like HBase, Hive, Pig, …)
▪ Open source under the friendly Apache License
Amr Awadallah, Cloudera Inc 5
Wednesday, January 27, 2010
19. What is Hadoop?
▪A scalable fault-tolerant grid operating
system for data storage and processing
▪ Its scalability comes from the marriage of:
▪ HDFS: Self-Healing High-Bandwidth Clustered Storage
▪ MapReduce: Fault-Tolerant Distributed Processing
▪ Operates on unstructured and structured data
▪ A large and active ecosystem (many developers
and additions like HBase, Hive, Pig, …)
▪ Open source under the friendly Apache License
▪ http://wiki.apache.org/hadoop/
Amr Awadallah, Cloudera Inc 5
Wednesday, January 27, 2010
20. Hadoop History
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Wednesday, January 27, 2010
21. Hadoop History
▪ 2002-2004: Doug Cutting and Mike Cafarella started working
on Nutch
Amr Awadallah, Cloudera Inc 6
Wednesday, January 27, 2010
22. Hadoop History
▪ 2002-2004: Doug Cutting and Mike Cafarella started working
on Nutch
▪ 2003-2004: Google publishes GFS and MapReduce papers
Amr Awadallah, Cloudera Inc 6
Wednesday, January 27, 2010
23. Hadoop History
▪ 2002-2004: Doug Cutting and Mike Cafarella started working
on Nutch
▪ 2003-2004: Google publishes GFS and MapReduce papers
▪ 2004: Cutting adds DFS & MapReduce support to Nutch
Amr Awadallah, Cloudera Inc 6
Wednesday, January 27, 2010
24. Hadoop History
▪ 2002-2004: Doug Cutting and Mike Cafarella started working
on Nutch
▪ 2003-2004: Google publishes GFS and MapReduce papers
▪ 2004: Cutting adds DFS & MapReduce support to Nutch
▪ 2006: Yahoo! hires Cutting, Hadoop spins out of Nutch
Amr Awadallah, Cloudera Inc 6
Wednesday, January 27, 2010
25. Hadoop History
▪ 2002-2004: Doug Cutting and Mike Cafarella started working
on Nutch
▪ 2003-2004: Google publishes GFS and MapReduce papers
▪ 2004: Cutting adds DFS & MapReduce support to Nutch
▪ 2006: Yahoo! hires Cutting, Hadoop spins out of Nutch
▪ 2007: NY Times converts 4TB of archives over 100 EC2s
Amr Awadallah, Cloudera Inc 6
Wednesday, January 27, 2010
26. Hadoop History
▪ 2002-2004: Doug Cutting and Mike Cafarella started working
on Nutch
▪ 2003-2004: Google publishes GFS and MapReduce papers
▪ 2004: Cutting adds DFS & MapReduce support to Nutch
▪ 2006: Yahoo! hires Cutting, Hadoop spins out of Nutch
▪ 2007: NY Times converts 4TB of archives over 100 EC2s
▪ 2008: Web-scale deployments at Y!, Facebook, Last.fm
Amr Awadallah, Cloudera Inc 6
Wednesday, January 27, 2010
27. Hadoop History
▪ 2002-2004: Doug Cutting and Mike Cafarella started working
on Nutch
▪ 2003-2004: Google publishes GFS and MapReduce papers
▪ 2004: Cutting adds DFS & MapReduce support to Nutch
▪ 2006: Yahoo! hires Cutting, Hadoop spins out of Nutch
▪ 2007: NY Times converts 4TB of archives over 100 EC2s
▪ 2008: Web-scale deployments at Y!, Facebook, Last.fm
▪ April 2008: Yahoo does fastest sort of a TB, 3.5mins over 910
nodes
Amr Awadallah, Cloudera Inc 6
Wednesday, January 27, 2010
28. Hadoop History
▪ 2002-2004: Doug Cutting and Mike Cafarella started working
on Nutch
▪ 2003-2004: Google publishes GFS and MapReduce papers
▪ 2004: Cutting adds DFS & MapReduce support to Nutch
▪ 2006: Yahoo! hires Cutting, Hadoop spins out of Nutch
▪ 2007: NY Times converts 4TB of archives over 100 EC2s
▪ 2008: Web-scale deployments at Y!, Facebook, Last.fm
▪ April 2008: Yahoo does fastest sort of a TB, 3.5mins over 910
nodes
▪ May 2009:
▪ Yahoo does fastest sort of a TB, 62secs over 1460 nodes
▪ Yahoo sorts a PB in 16.25hours over 3658 nodes
Amr Awadallah, Cloudera Inc 6
Wednesday, January 27, 2010
29. Hadoop History
▪ 2002-2004: Doug Cutting and Mike Cafarella started working
on Nutch
▪ 2003-2004: Google publishes GFS and MapReduce papers
▪ 2004: Cutting adds DFS & MapReduce support to Nutch
▪ 2006: Yahoo! hires Cutting, Hadoop spins out of Nutch
▪ 2007: NY Times converts 4TB of archives over 100 EC2s
▪ 2008: Web-scale deployments at Y!, Facebook, Last.fm
▪ April 2008: Yahoo does fastest sort of a TB, 3.5mins over 910
nodes
▪ May 2009:
▪ Yahoo does fastest sort of a TB, 62secs over 1460 nodes
▪ Yahoo sorts a PB in 16.25hours over 3658 nodes
▪ June 2009, Oct 2009: Hadoop Summit (750), Hadoop World
(500)
Amr Awadallah, Cloudera Inc 6
Wednesday, January 27, 2010
30. Hadoop History
▪ 2002-2004: Doug Cutting and Mike Cafarella started working
on Nutch
▪ 2003-2004: Google publishes GFS and MapReduce papers
▪ 2004: Cutting adds DFS & MapReduce support to Nutch
▪ 2006: Yahoo! hires Cutting, Hadoop spins out of Nutch
▪ 2007: NY Times converts 4TB of archives over 100 EC2s
▪ 2008: Web-scale deployments at Y!, Facebook, Last.fm
▪ April 2008: Yahoo does fastest sort of a TB, 3.5mins over 910
nodes
▪ May 2009:
▪ Yahoo does fastest sort of a TB, 62secs over 1460 nodes
▪ Yahoo sorts a PB in 16.25hours over 3658 nodes
▪ June 2009, Oct 2009: Hadoop Summit (750), Hadoop World
(500)
Amr Awadallah, Cloudera Inc 6
▪ September 2009: Doug Cutting joins Cloudera
Wednesday, January 27, 2010
32. Hadoop Design Axioms
1. System Shall Manage and Heal Itself
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Wednesday, January 27, 2010
33. Hadoop Design Axioms
1. System Shall Manage and Heal Itself
2. Performance Shall Scale Linearly
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Wednesday, January 27, 2010
34. Hadoop Design Axioms
1. System Shall Manage and Heal Itself
2. Performance Shall Scale Linearly
3. Compute Should Move to Data
Amr Awadallah, Cloudera Inc 7
Wednesday, January 27, 2010
35. Hadoop Design Axioms
1. System Shall Manage and Heal Itself
2. Performance Shall Scale Linearly
3. Compute Should Move to Data
4. Simple Core, Modular and
Extensible
Amr Awadallah, Cloudera Inc 7
Wednesday, January 27, 2010
36. HDFS: Hadoop Distributed File System
Block Size = 64MB
Replication Factor = 3
Cost/GB is a few ¢/month
vs $/month
Amr Awadallah, Cloudera Inc 8
Wednesday, January 27, 2010
37. HDFS: Hadoop Distributed File System
Block Size = 64MB
Replication Factor = 3
Cost/GB is a few ¢/month
vs $/month
Amr Awadallah, Cloudera Inc 8
Wednesday, January 27, 2010
40. MapReduce Example for Word Count
SELECT word, COUNT(1) FROM docs GROUP BY word;
cat *.txt | mapper.pl | sort | reducer.pl > out.txt
Split 1
Split i
Split N
Amr Awadallah, Cloudera Inc 10
Wednesday, January 27, 2010
41. MapReduce Example for Word Count
SELECT word, COUNT(1) FROM docs GROUP BY word;
cat *.txt | mapper.pl | sort | reducer.pl > out.txt
(words, counts)
Split 1 (docid, text) Map 1
Be, 5
“To Be
Or Not
To Be?”
Be, 12
Split i (docid, text) Map i
Be, 7
Be, 6
Split N (docid, text) Map M (words, counts)
Amr Awadallah, Cloudera Inc 10
Wednesday, January 27, 2010
42. MapReduce Example for Word Count
SELECT word, COUNT(1) FROM docs GROUP BY word;
cat *.txt | mapper.pl | sort | reducer.pl > out.txt
(words, counts)
Split 1 (docid, text) Map 1 (sorted words, counts)
Be, 5 Reduce 1
“To Be
Or Not
To Be?”
Be, 12
Reduce i
Split i (docid, text) Map i
Be, 7
Be, 6
Shuffle
Reduce R
Split N (docid, text) Map M (words, counts) (sorted words, counts)
Amr Awadallah, Cloudera Inc 10
Wednesday, January 27, 2010
43. MapReduce Example for Word Count
SELECT word, COUNT(1) FROM docs GROUP BY word;
cat *.txt | mapper.pl | sort | reducer.pl > out.txt
(words, counts)
Split 1 (docid, text) Map 1 (sorted words, counts)
Output File
Be, 5 Reduce 1 (sorted words,
sum of counts)
1
“To Be
Or Not Be, 30
To Be?”
Be, 12
Output File i
(sorted words,
Reduce i sum of counts)
Split i (docid, text) Map i
Be, 7
Be, 6
Shuffle Output File
(sorted words, R
Reduce R sum of counts)
Split N (docid, text) Map M (words, counts) (sorted words, counts)
Amr Awadallah, Cloudera Inc 10
Wednesday, January 27, 2010
44. Hadoop High-Level Architecture
Hadoop Client
Contacts Name Node for data
or Job Tracker to submit jobs
Name Node Job Tracker
Maintains mapping of file blocks Schedules jobs across
to data node slaves task tracker slaves
Data Node Task Tracker
Stores and serves Runs tasks (work units)
blocks of data within a job
Share Physical Node
Amr Awadallah, Cloudera Inc 11
Wednesday, January 27, 2010
49. Use The Right Tool For The Right Job
Hadoop: Relational Databases:
Amr Awadallah, Cloudera Inc 13
Wednesday, January 27, 2010
50. Use The Right Tool For The Right Job
Hadoop: Relational Databases:
Amr Awadallah, Cloudera Inc 13
Wednesday, January 27, 2010
51. Use The Right Tool For The Right Job
Hadoop: Relational Databases:
When to use? When to use?
• Affordable Storage/ • Interactive Reporting
Compute (<1sec)
• Structured or Not (Agility) • Multistep Transactions
• Resilient Auto Scalability • Interoperability
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52. Economics of Hadoop
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53. Economics of Hadoop
▪ Typical Hardware:
▪ Two Quad Core Nehalems
▪ 24GB RAM
▪ 12 * 1TB SATA disks (JBOD mode, no need for RAID)
▪ 1 Gigabit Ethernet card
Amr Awadallah, Cloudera Inc 14
Wednesday, January 27, 2010
54. Economics of Hadoop
▪ Typical Hardware:
▪ Two Quad Core Nehalems
▪ 24GB RAM
▪ 12 * 1TB SATA disks (JBOD mode, no need for RAID)
▪ 1 Gigabit Ethernet card
▪ Cost/node: $5K/node
Amr Awadallah, Cloudera Inc 14
Wednesday, January 27, 2010
55. Economics of Hadoop
▪ Typical Hardware:
▪ Two Quad Core Nehalems
▪ 24GB RAM
▪ 12 * 1TB SATA disks (JBOD mode, no need for RAID)
▪ 1 Gigabit Ethernet card
▪ Cost/node: $5K/node
▪ Effective HDFS Space:
▪ ¼ reserved for temp shuffle space, which leaves 9TB/node
▪ 3 way replication leads to 3TB effective HDFS space/node
▪ But assuming 7x compression that becomes ~ 20TB/node
Amr Awadallah, Cloudera Inc 14
Wednesday, January 27, 2010
56. Economics of Hadoop
▪ Typical Hardware:
▪ Two Quad Core Nehalems
▪ 24GB RAM
▪ 12 * 1TB SATA disks (JBOD mode, no need for RAID)
▪ 1 Gigabit Ethernet card
▪ Cost/node: $5K/node
▪ Effective HDFS Space:
▪ ¼ reserved for temp shuffle space, which leaves 9TB/node
▪ 3 way replication leads to 3TB effective HDFS space/node
▪ But assuming 7x compression that becomes ~ 20TB/node
Effective Cost per user TB: $250/TB
Amr Awadallah, Cloudera Inc 14
Wednesday, January 27, 2010
57. Economics of Hadoop
▪ Typical Hardware:
▪ Two Quad Core Nehalems
▪ 24GB RAM
▪ 12 * 1TB SATA disks (JBOD mode, no need for RAID)
▪ 1 Gigabit Ethernet card
▪ Cost/node: $5K/node
▪ Effective HDFS Space:
▪ ¼ reserved for temp shuffle space, which leaves 9TB/node
▪ 3 way replication leads to 3TB effective HDFS space/node
▪ But assuming 7x compression that becomes ~ 20TB/node
Effective Cost per user TB: $250/TB
Other solutions cost in the range of $5K to $100K per
user TB
Amr Awadallah, Cloudera Inc 14
Wednesday, January 27, 2010
58. Sample Talks from Hadoop World ‘09
▪ VISA: Large Scale Transaction Analysis
▪ JP Morgan Chase: Data Processing for Financial Services
▪ China Mobile: Data Mining Platform for Telecom Industry
▪ Rackspace: Cross Data Center Log Processing
▪ Booz Allen Hamilton: Protein Alignment using Hadoop
▪ eHarmony: Matchmaking in the Hadoop Cloud
▪ General Sentiment: Understanding Natural Language
▪ Yahoo!: Social Graph Analysis
▪ Visible Technologies: Real-Time Business Intelligence
▪ Facebook: Rethinking the Data Warehouse with Hadoop and
Hive
Slides and Videos at http://www.cloudera.com/hadoop-
Amr Awadallah, Cloudera Inc world-nyc 15
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59. Cloudera Desktop
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60. Conclusion
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Wednesday, January 27, 2010
61. Conclusion
Hadoop is a data grid
operating system which
provides an economically
scalable solution for storing
and processing large amounts
of unstructured or structured
data over long periods of
time.
Amr Awadallah, Cloudera Inc 17
Wednesday, January 27, 2010
62. Contact Information
Amr Awadallah
CTO, Cloudera Inc.
aaa@cloudera.com
http://twitter.com/awadallah
Online Training Videos and Info:
http://cloudera.com/hadoop-
training
http://cloudera.com/blog
http://twitter.com/cloudera
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Wednesday, January 27, 2010
63. (c) 2008 Cloudera, Inc. or its licensors. "Cloudera" is a registered trademark of Cloudera, Inc.. All rights reserved. 1.0
Wednesday, January 27, 2010
