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Google MapReduce

Google MapReduce. Simplified Data Processing on Large Clusters Jeff Dean, Sanjay Ghemawat Google, Inc. Presented by Conroy Whitney 4 th year CS – Web Development http://labs.google.com/papers/mapreduce.html. Outline. Motivation MapReduce Concept Map? Reduce?

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Google MapReduce

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  1. Google MapReduce Simplified Data Processing on Large Clusters Jeff Dean, Sanjay Ghemawat Google, Inc. Presented by Conroy Whitney 4th year CS – Web Development http://labs.google.com/papers/mapreduce.html

  2. Outline • Motivation • MapReduce Concept • Map? Reduce? • Example of MapReduce problem • Reverse Web-Link Graph • MapReduce Cluster Environment • Lifecycle of MapReduce operation • Optimizations to MapReduce process • Conclusion • MapReduce in Googlicious Action

  3. Motivation: Large Scale Data Processing • Many tasks composed of processing lots of data to produce lots of other data • Want to use hundreds or thousands of CPUs ... but this needs to be easy! • MapReduce provides • User-defined functions • Automatic parallelization and distribution • Fault-tolerance • I/O scheduling • Status and monitoring

  4. Programming Concept • Map • Perform a function on individual values in a data set to create a newlist of values • Example: square x = x * x map square [1,2,3,4,5] returns [1,4,9,16,25] • Reduce • Combine values in a data set to create a new value • Example: sum = (each elem in arr, total +=) reduce [1,2,3,4,5] returns 15 (the sum of the elements)‏

  5. Example: Reverse Web-Link Graph • Find all pages that link to a certain page • Map Function • Outputs <target, source> pairs for each link to a target URL found in a source page • For each page we know what pages it links to • Reduce Function • Concatenates the list of all source URLs associated with a given target URL and emits the pair: <target, list(source)> • For a given web page, we know what pages link to it

  6. Additional Examples • Distributed grep • Distributed sort • Term-Vector per Host • Web Access Log Statistics • Document Clustering • Machine Learning • Statistical Machine Translation

  7. Performance Boasts • Distributed grep • 1010 100-byte files (~1TB of data)‏ • 3-character substring found in ~100k files • ~1800 workers • 150 seconds start to finish, including ~60 seconds startup overhead • Distributed sort • Same files/workers as above • 50 lines of MapReduce code • 891 seconds, including overhead • Best reported result of 1057 seconds for TeraSort benchmark

  8. Typical Cluster • 100s/1000s of Dual-Core, 2-4GB Memory • Limited internal bandwidth • Temporary storage on local IDE disks • Google File System (GFS)‏ • Distributed file system for permanent/shared storage • Job scheduling system • Jobs made up of tasks • Master-Scheduler assigns tasks to Worker machines

  9. Execution Initialization • Split input file into 64MB sections (GFS)‏ • Read in parallel by multiple machines • Fork off program onto multiple machines • One machine is Master • Master assigns idle machines to either Map or Reduce tasks • Master Coordinates data communication between map and reduce machines

  10. Map-Machine • Reads contents of assigned portion of input-file • Parses and prepares data for input to map function (e.g. read <a /> from HTML)‏ • Passes data into map function and saves result in memory (e.g. <target, source>)‏ • Periodically writes completed work to local disk • Notifies Master of this partially completed work (intermediate data)‏

  11. Reduce-Machine • Receives notification from Master of partially completed work • Retrieves intermediate data from Map-Machine via remote-read • Sorts intermediate data by key (e.g. by target page)‏ • Iterates over intermediate data • For each unique key, sends corresponding set through reduce function • Appends result of reduce function to final output file (GFS)‏

  12. Worker Failure • Master pings workers periodically • Any machine who does not respond is considered “dead” • Both Map- and Reduce-Machines • Any task in progress gets needs to be re-executed and becomes eligible for scheduling • Map-Machines • Completed tasks are also reset because results are stored on local disk • Reduce-Machines notified to get data from new machine assigned to assume task

  13. Skipping Bad Records • Bugs in user code (from unexpected data) cause deterministic crashes • Optimally, fix and re-run • Not possible with third-party code • When worker dies, sends “last gasp” UDP packet to Master describing record • If more than one worker dies over a specific record, Master issues yet another re-execute command • Tells new worker to skip problem record

  14. Backup Tasks • Some “Stragglers” not performing optimally • Other processes demanding resources • Bad Disks (correctable errors) • Slow down I/O speeds from 30MB/s to 1MB/s • CPU cache disabled ?! • Near end of phase, schedule redundant execution of in-process tasks • First to complete “wins”

  15. Locality • Network Bandwidth scarce • Google File System (GFS)‏ • Around 64MB file sizes • Redundant storage (usually 3+ machines)‏ • Assign Map-Machines to work on portions of input-files which they already have on local disk • Read input file at local disk speeds • Without this, read speed limited by network switch

  16. Conclusion • Complete rewrite of the production indexing system • 20+ TB of data • indexing takes 5-10 MapReduce operations • indexing code is simpler, smaller, easier to understand • Fault Tolerance, Distribution, Parallelization hidden within MapReduce library • Avoids extra passes over the data • Easy to change indexing system • Improve performance of indexing process by adding new machines to cluster

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