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Outline

Outline. Scaling for Large Data Processing What is Hadoop? HDFS and MapReduce Hadoop Ecosystem Hadoop vs RDBMSes Conclusion. Current Storage Systems Can’t Compute. Ad hoc Queries & Data Mining. Interactive Apps. RDBMS (200GB/day). ETL Grid. Non-Consumption.

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Outline

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  1. Outline • Scaling for Large Data Processing • What is Hadoop? • HDFS and MapReduce • Hadoop Ecosystem • Hadoop vsRDBMSes • Conclusion

  2. Current Storage Systems Can’t Compute Ad hoc Queries & Data Mining Interactive Apps RDBMS (200GB/day) ETL Grid Non-Consumption Filer heads are a bottleneck Storage Farm for Unstructured Data (20TB/day) Mostly Append Collection Instrumentation

  3. The Solution: A Store-Compute Grid Interactive Apps “Batch” Apps RDBMS Ad hoc Queries & Data Mining ETL and Aggregations Storage + Computation Mostly Append Collection Instrumentation

  4. 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/

  5. 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) • September 2009: Doug Cutting joins Cloudera

  6. Hadoop Design Axioms • System Shall Manage and Heal Itself • Performance Shall Scale Linearly • Compute Should Move to Data • Simple Core, Modular and Extensible

  7. HDFS: Hadoop Distributed File System Block Size = 64MB Replication Factor = 3 Cost/GB is a few ¢/month vs $/month

  8. MapReduce: Distributed Processing

  9. MapReduce Example for Word Count SELECT word, COUNT(1) FROM docs GROUP BY word; cat *.txt | mapper.pl | sort | reducer.pl > out.txt (docid, text) (words, counts) Map 1 (sorted words, counts) Reduce 1 (sorted words, sum of counts) Output File 1 Split 1 Be, 5 “To Be Or Not To Be?” Be, 30 Be, 12 Reduce i (sorted words, sum of counts) Output File i (docid, text) Map i Split i Be, 7 Be, 6 Shuffle Reduce R (sorted words, sum of counts) Output File R (docid, text) Map M (sorted words, counts) (words, counts) Split N

  10. Hadoop High-Level Architecture Hadoop Client Contacts Name Node for data or Job Tracker to submit jobs Name Node Maintains mapping of file blocks to data node slaves Job Tracker Schedules jobs across task tracker slaves Data Node Stores and serves blocks of data Task Tracker Runs tasks (work units) within a job Share Physical Node

  11. Apache Hadoop Ecosystem ETL Tools BI Reporting RDBMS Hive (SQL) Sqoop Pig (Data Flow) MapReduce (Job Scheduling/Execution System) (Streaming/Pipes APIs) HBase(key-value store) Zookeepr (Coordination) Avro (Serialization) HDFS(Hadoop Distributed File System)

  12. Use The Right Tool For The Right Job Relational Databases: Hadoop: When to use? • Affordable Storage/Compute • Structured or Not (Agility) • Resilient Auto Scalability When to use? • Interactive Reporting (<1sec) • Multistep Transactions • Interoperability

  13. 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

  14. 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-world-nyc

  15. Cloudera Desktop

  16. 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.

  17. Contact Information AmrAwadallah 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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