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Cloud Computing: Hadoop
- 1. Data Processing in the Cloud Parand Tony Darugar http://parand.com/say/ [email_address]
- 2. What is Hadoop Flexible infrastructure for large scale computation and data processing on a network of commodity hardware.
- 3. Why? A common infrastructure pattern extracted from building distributed systems Scale Incremental growth Cost Flexibility
- 4. Built-in Resilience to Failure When dealing with large numbers of commodity servers, failure is a fact of life Assume failure, build protections and recovery into your architecture Data level redundancy Job/Task level monitoring and automated restart and re-allocation
- 5. Current State of Hadoop Project Top level Apache Foundation project In production use at Yahoo, Facebook, Amazon, IBM, Fox, NY Times, Powerset, … Large, active user base, mailing lists, user groups Very active development, strong development team
- 6. Widely Adopted A valuable and reusable skill set Taught at major universities Easier to hire for Easier to train on Portable across projects, groups
- 7. Plethora of Related Projects Pig Hive Hbase Cascading Hadoop on EC2 JAQL , X-Trace, Happy, Mahout
- 8. What is Hadoop The Linux of distributed processing.
- 9. How Does Hadoop Work?
- 10. Hadoop File System A distributed file system for large data Your data in triplicate Built-in redundancy, resiliency to large scale failures Intelligent distribution, striping across racks Accommodates very large data sizes On commodity hardware
- 11. Programming Model: Map/Reduce Very simple programming model: Map(anything)->key, value Sort, partition on key Reduce(key,value)->key, value No parallel processing / message passing semantics Programmable in Java or any other language (streaming)
- 12. Processing Model Create or allocate a cluster Put data onto the file system: Data is split into blocks, stored in triplicate across your cluster Run your job: Your Map code is copied to the allocated nodes, preferring nodes that contain copies of your data Move computation to data, not data to computation
- 13. Processing Model Monitor workers, automatically restarting failed or slow tasks Gather output of Map, sort and partition on key Run Reduce tasks Monitor workers, automatically restarting failed or slow tasks Results of your job are now available on the Hadoop file system
- 14. Hadoop on the Grid Managed Hadoop clusters Shared resources improved utilization Standard data sets, storage Shared, standardized operations management Hosted internally or externally (eg. on EC2)
- 15. Usage Patterns
- 16. ETL Put large data source (eg. Log files) onto the Hadoop File System Perform aggregations, transformations, normalizations on the data Load into RDBMS / data mart
- 17. Reporting and Analytics Run canned and ad-hoc queries over large data Run analytics and data mining operations on large data Produce reports for end-user consumption or loading into data mart
- 18. Data Processing Pipelines Multi-step pipelines for data processing Coordination, scheduling, data collection and publishing of feeds SLA carrying, regularly scheduled jobs
- 19. Machine Learning & Graph Algorithms Traverse large graphs and data sets, building models and classifiers Implement machine learning algorithms over massive data sets
- 20. General Back-End Processing Implement significant portions of back-end, batch oriented processing on the grid General computation framework Simplify back-end architecture
- 21. What Next? Dowload Hadoop: http://hadoop.apache.org/ Try it on your laptop Try Pig http://hadoop.apahe.org/pig/ Deploy to multiple boxes Try it on EC2