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MapReduce

MapReduce. Simplified Data Processing On large Clusters Jeffery Dean and Sanjay Ghemawat. Outline Introduction Programming Model Implementation Refinement Performance Related work Conclusions. Introduction What is the purpose? The abstraction. Input Data. Intermediate Key/value.

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MapReduce

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  1. MapReduce Simplified Data Processing On large Clusters Jeffery Dean and Sanjay Ghemawat

  2. Outline • Introduction • Programming Model • Implementation • Refinement • Performance • Related work • Conclusions

  3. Introduction • What is the purpose? • The abstraction Input Data Intermediate Key/value Map Reduce Output File

  4. Programming model • Map • Reduce • Example

  5. Programming model • Real example: make an index

  6. Programming Model • More example • Distributed grep • Count of URL Access Frequency • Reverse Web-link Graph • Term Vector per host • Inverted index • Distributed sort

  7. Implementation • Execution overview

  8. Implementation • Master data structure • Fault tolerance • Worker failure • Master failure • Semantics in the Presence of Failures • Locality • Task Granularity • Back Tasks

  9. Refinements • Partitioning Function • Ordering Guarantees • Combiner Function • Input and Out Types • Side-effect • Skipping Bad Records • Local Execution • Status Information • Counters

  10. Performance • Cluster Configuration • 1800machines • Each 2GHz Intel Xeon processors • 4GB memory • 2*160GB IDE disk • 1 Gbps Ethernet • Arranged in two-level tree-shaped

  11. Performance • Grep • Scan through 1010 100-byte records • Search a relatively rare three-character pattern (occur in 92,337 records) • Data transfer rate over time • The entrie computation takes approximately 150s Peaks at over 30GB/s 1764workers assigned

  12. Performance • Sort • Sorts 1010 100-byte records • Modeled after TeraSort benchmark • Extract a 10-byte sorting key Normal execution 200 tasks killed No backup

  13. Performance • Sort • Input rate is less than for grep • There is a delay • The rate: input > shuffle > output • Effect of backup tasks • Machine failures

  14. Related Work • Restricted programming models • Parallel processing compare to • Bulk Synchronous Programming & MPI primitive • Backup task mechanism compare to • Charlotte System • Sorting facility compare to • NOW-Sort

  15. Related Work • Sending data over distributed queue compare to • River • Programming model compare to • BAD-FS

  16. Conclusion • What is the reason for the sucess of MapReduce? • Easy to use • Problem are easily expressible • Scales to large cluster • Learned from this work • Restriction the programming • Network bandwidth is a scarce resource • Redundant execution

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