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Scaling Hadoop Applications - Talk by Milind Bhandarkar, Linked In

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- 1. Scaling Hadoop Applications<br />Milind Bhandarkar<br />Distributed Data Systems Engineer<br />[mbhandarkar@linkedin.com]<br />[twitter: @techmilind]<br />http://www.flickr.com/photos/theplanetdotcom/4878812549/<br />
- 2. About Me<br />http://www.linkedin.com/in/milindb<br />Founding member of Hadoop team at Yahoo! [2005-2010]<br />Contributor to Apache Hadoop & Pig since version 0.1<br />Built and Led Grid Solutions Team at Yahoo! [2007-2010]<br />Center for Development of Advanced Computing (C-DAC), National Center for Supercomputing Applications (NCSA), Center for Simulation of Advanced Rockets, Siebel Systems, Pathscale Inc. (acquired by QLogic), Yahoo!, and LinkedIn<br />http://www.flickr.com/photos/tmartin/32010732/<br />
- 3. Outline<br />Scalability of Applications<br />Causes of Sublinear Scalability<br />Best practices<br />Q & A<br />http://www.flickr.com/photos/86798786@N00/2202278303/<br />
- 4. Importance of Scaling<br />Zynga’s Cityville: 0 to 100 Million users in 43 days ! (http://venturebeat.com/2011/01/14/zyngas-cityville-grows-to-100-million-users-in-43-days/)<br />Facebook in 2009 : From 150 Million users to 350 Million ! (http://www.facebook.com/press/info.php?timeline)<br />LinkedIn in 2010: Adding a member per second ! (http://money.cnn.com/2010/11/17/technology/linkedin_web2/index.htm)<br />Public WWW size: 26M in 1998, 1B in 2000, 1T in 2008 (http://googleblog.blogspot.com/2008/07/we-knew-web-was-big.html)<br />Scalability allows dealing with success gracefully !<br />http://www.flickr.com/photos/palmdiscipline/2784992768/<br />
- 5. Explosion of Data<br />(Data-Rich Computing theme proposal. J. Campbell, et al, 2007)<br />http://www.flickr.com/photos/28634332@N05/4128229979/<br />
- 6. Hadoop<br />Simplifies building parallel applications<br />Programmer specifies Record-level and Group-level sequential computation<br />Framework handles partitioning, scheduling, dispatch, execution, communication, failure handling, monitoring, reporting etc.<br />User provides hints to control parallel execution<br />
- 7. Scalability of Parallel Programs<br />If one node processes k MB/s, then N nodes should process (k*N) MB/s<br />If some fixed amount of data is processed in T minutes on one node, the N nodes should process same data in (T/N) minutes<br />Linear Scalability<br />http://www-03.ibm.com/innovation/us/watson/<br />
- 8. Reduce Latency<br />Minimize program<br />execution time<br />http://www.flickr.com/photos/adhe55/2560649325/<br />
- 9. Increase Throughput<br />Maximize data processed per unit time<br />http://www.flickr.com/photos/mikebaird/3898801499/<br />
- 10. Three Equations<br />http://www.flickr.com/photos/ucumari/321343385/<br />
- 11. Amdahl’s Law<br />http://www.computer.org/portal/web/awards/amdahl<br />
- 12. Multi-Phase Computations<br />If computation C is split into N different parts, C1..CN<br />If partial computation Ci can be speeded up by a factor of Si<br />http://www.computer.org/portal/web/awards/amdahl<br />
- 13. Amdahl’s Law, Restated<br />http://www.computer.org/portal/web/awards/amdahl<br />
- 14. MapReduce Workflow<br />http://www.computer.org/portal/web/awards/amdahl<br />
- 15. Little’s Law<br />http://sloancf.mit.edu/photo/facstaff/little.gif<br />
- 16. Message Cost Model<br />http://www.flickr.com/photos/mattimattila/4485665105/<br />
- 17. Message Granularity<br />For Gigabit Ethernet<br />α = 300 μS<br />β = 100 MB/s<br />100 Messages of 10KB each = 40 ms<br />10 Messages of 100 KB each = 13 ms<br />http://www.flickr.com/photos/philmciver/982442098/<br />
- 18. Alpha-Beta<br />Common Mistake: Assuming that α is constant<br />Scheduling latency for responder<br />Jobtracker/Tasktracker time slice inversely proportional to number of concurrent tasks<br />Common Mistake: Assuming that β is constant<br />Network congestion<br />TCP incast<br />
- 19. Causes of Sublinear Scalability<br />http://www.flickr.com/photos/sabertasche2/2609405532/<br />
- 20. Sequential Bottlenecks<br />Mistake: Single reducer (default)<br />mapred.reduce.tasks<br />Parallel directive<br />Mistake: Constant number of reducers independent of data size<br />default_parallel<br />http://www.flickr.com/photos/bogenfreund/556656621/<br />
- 21. Load Imbalance<br />Unequal Partitioning of Work<br />Imbalance = Max Partition Size – Min Partition Size<br />Heterogeneous workers<br />Example: Disk Bandwidth<br />Empty Disk: 100 MB/s<br />75% Full disk: 25 MB/s <br />http://www.flickr.com/photos/kelpatolog/176424797/<br />
- 22. Over-Partitioning<br />For N workers, choose M partitions, M >> N<br />Adaptive Scheduling, each worker chooses the next unscheduled partition<br />Load Imbalance = Max(Sum(Wk)) – Min(Sum(Wk))<br />For sufficiently large M, both Max and Min tend to Average(Wk), thus reducing load imbalance<br />http://www.flickr.com/photos/infidelic/3238637031/<br />
- 23. Critical Paths<br />Maximum distance between start and end nodes<br />Long tail in Map task executions<br />Enable Speculative Execution<br />Overlap communication and computation<br />Asynchronous notifications<br />Example: deleting intermediate outputs after job completion<br />http://www.flickr.com/photos/cdevers/4619306252/<br />
- 24. Algorithmic Overheads<br />Repeating computation in place of adding communication<br />Parallel exploration of solution space results in wastage of computations<br />Need for a coordinator<br />Share partial, unmaterialized results<br />Consider memory-based small replicated K-V stores with eventual consistency<br />http://www.flickr.com/photos/sbprzd/140903299/<br />
- 25. Synchronization<br />Shuffle stage in Hadoop, M*R transfers<br />Slowest system components involved: Network, Disk<br />Can you eliminate Reduce stage ?<br />Use combiners ?<br />Compress intermediate output ?<br />Launch reduce tasks before all maps finish<br />http://www.flickr.com/photos/susan_d_p/2098849477/<br />
- 26. Task Granularity<br />Amortize task scheduling and launching overheads<br />Centralized scheduler<br />Out-of-band Tasktracker heartbeat reduces latency, but<br />May overwhelm the scheduler<br />Increase task granularity<br />Combine multiple small files<br />Maximize split sizes<br />Combine computations per datum<br />http://www.flickr.com/photos/philmciver/982442098/<br />
- 27. Hadoop Best Practices<br />Use higher-level languages (e.g. Pig)<br />Coalesce small files into larger ones & use bigger HDFS block size<br />Tune buffer sizes for tasks to avoid spill to disk<br />Consume only one slot per task<br />Use combiner if local aggregation reduces size<br />http://www.flickr.com/photos/yaffamedia/1388280476/<br />
- 28. Hadoop Best Practices<br />Use compression everywhere<br />CPU-efficient for intermediate data<br />Disk-efficient for output data<br />Use Distributed Cache to distribute small side-files (<< than input split size)<br />Minimize number of NameNode/JobTracker RPCs from tasks<br />http://www.flickr.com/photos/yaffamedia/1388280476/<br />
- 29. Questions ?<br />http://www.flickr.com/photos/horiavarlan/4273168957/<br />

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