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Themis: An I/O-Efficient MapReduce (SoCC 2012)

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“Big Data” computing increasingly utilizes the MapReduce programming model for scalable processing of large data collections. Many MapReduce jobs are I/O-bound, and so minimizing the number of I/O operations is critical to improving their performance. In this work, we present Themis, a MapReduce implementation that reads and writes data records to disk exactly twice, which is the minimum amount possible for data sets that cannot fit in memory. In order to minimize I/O, Themis makes fundamentally different design decisions from previous MapReduce implementations. Themis performs a wide variety of MapReduce jobs – including click log analysis, DNA read sequence alignment, and PageRank – at nearly the speed of TritonSort’s record-setting sort performance [29].

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Themis: An I/O-Efficient MapReduce (SoCC 2012)

  1. 1. Themis An I/O-Efficient MapReduce Alex Rasmussen, Michael Conley, Rishi Kapoor, Vinh The Lam, George Porter, AminVahdat* University of California San Diego *& Google, Inc. 1
  2. 2. MapReduce is Everywhere   First published in OSDI 2004   De facto standard for large-scale bulk data processing   Key benefit: simple programming model, can handle large volume of data 2
  3. 3. I/O Path Efficiency   I/O bound, so disks are bottleneck   Existing implementations do a lot of disk I/O – Materialize map output, re-read during shuffle – Multiple sort passes if intermediate data large – Swapping in response to memory pressure   Hadoop Sort 2009: <3MBps per node (3% of single disk’s throughput!) 3
  4. 4. How Low CanYou Go?   Agarwal andVitter: minimum of two reads and two writes per record for out-of-core sort   Systems that meet this lower bound have the “2-IO property”   MapReduce has sort in the middle, so same principle can apply 4
  5. 5. Themis   Goal: Build a MapReduce implementation with the 2-IO property   TritonSort (NSDI ’11): 2-IO sort – World record holder in large-scale sorting   Runs on a cluster of machines with many disks per machine and fast NICs   Performs wide range of I/O-bound MapReduce jobs at nearly TritonSort speeds 5
  6. 6. Outline   Architecture Overview   Memory Management   Fault Tolerance   Evaluation 6
  7. 7. 7 Network Phase One Map and Shuffle
  8. 8. Map Network 8
  9. 9. Map 3 3925 15 Network 9
  10. 10. Map 3 3925 15 1 10 11 20••• ••• 21 30 31 40••• ••• Network 10
  11. 11. 1 10 11 20••• ••• 21 30 31 40••• •••Unsorted Network 11 PhaseTwo Sort and Reduce
  12. 12. 1 10 11 20••• ••• 21 30 31 40••• ••• 1 10 11 20 21 30 31 40••• ••• ••• •••1 10 11 20 21 30 31 40••• ••• ••• ••• Unsorted Network Sort 12
  13. 13. 1 10 11 20••• ••• 21 30 31 40••• ••• 1 10 11 20 21 30 31 40••• ••• ••• •••1 10 11 20 21 30 31 40••• ••• ••• ••• 1 10 11 20 21 30 31 40••• ••• ••• ••• Unsorted Reduce Network Sort 13
  14. 14. 1 10 11 20••• ••• 21 30 31 40••• ••• 1 10 11 20 21 30 31 40••• ••• ••• •••1 10 11 20 21 30 31 40••• ••• ••• ••• 1 10 11 20 21 30 31 40••• ••• ••• ••• Unsorted Reduce Network 1 10 11 20 21 30 31 40••• ••• ••• ••• Sort 14
  15. 15. Reader Byte Stream Converter Mapper Sender Receiver Byte Stream Converter Tuple Demux Chainer Coalescer Writer Network Implementation Details 15 Phase One
  16. 16. Reader Byte Stream Converter Mapper Sender Receiver Byte Stream Converter Tuple Demux Chainer Coalescer Writer Network Implementation Details 16 Phase One Stage
  17. 17. Implementation Details 17 Worker 1 Worker 2 Worker 3 Worker 4
  18. 18. Challenges   Can’t spill to disk or swap under pressure – Themis memory manager   Partitions must fit in RAM – Sampling (see paper)   How does Themis handle failure? – Job-level fault tolerance 18
  19. 19. Outline   Architecture Overview   Memory Management   Fault Tolerance   Evaluation 19
  20. 20. Example    20 A B C
  21. 21. Memory Management - Goals   If we exceed physical memory, have to swap – OS approach: virtual memory, swapping – Hadoop approach: spill files   Since this incurs more I/O, unacceptable 21
  22. 22. Example – Runaway Stage 22 A B C Too Much Memory!
  23. 23. Example – Large Record    23 A B C Too Much Memory!
  24. 24. Requirements   Main requirement: can’t allocate more than the amount physical memory – Swapping or spilling breaks 2-IO   Provide flow control via back-pressure – Prevent stage from monopolizing memory – Memory allocation can block indefinitely   Support large records   Should have high memory utilization 24
  25. 25. Approach   Application-level memory management   Three memory management schemes – Pools – Quotas – Constraints 25
  26. 26. Pool-Based Memory Management 26 A B C PoolAB PoolBC
  27. 27. Pool-Based Memory Management 27 A B C PoolAB PoolBC
  28. 28. Pool-Based Memory Management   Prevents using more than what’s in a pool   Empty pools cause back-pressure   Record size limited to size of buffer   Unless tuned, memory utilization might be low – Tuning is hard; must set buffer, pool sizes   Used when receiving data from network – Must be fast to keep up with 10Gbps links 28
  29. 29. Quota-Based Memory Management 29 A B C QuotaAC = 1000 300 700
  30. 30. Quota-Based Memory Management   Provides back-pressure by limiting memory between source and sink stage   Supports large records (up to size of quota)   High memory utilization if quotas set well   Size of the data between source and sink cannot change – otherwise leaks   Used in the rest of phase one (map + shuffle) 30
  31. 31. Constraint-Based Memory Management   Single global memory quota   Requests that would exceed quota are enqueued and scheduled   Dequeue based on policy – Current: stage distance to network/disk write – Rationale: process record completely before admitting new records 31
  32. 32. Constraint-Based Memory Management   Globally limits memory usage   Applies back-pressure dynamically   Supports record sizes up to size of memory   Extremely high utilization   Higher overhead (2-3x over quotas)   Can deadlock for complicated graphs, patterns   Used in phase two (sort + reduce) 32
  33. 33. Outline   Architecture Overview   Memory Management   Fault Tolerance   Evaluation 33
  34. 34. FaultTolerance   Cluster MTTF determined by size, node MTTF – Node MTTF ~ 4 months (Google, OSDI ’10) 34 Google 10,000+ Nodes 2 minute MTTF Failures common Job must survive fault Average Hadoop Cluster 30-200 Nodes 80 hour MTTF Failures uncommon OK to just re-run MTTF Small MTTF Large
  35. 35. Why are Smaller Clusters OK?   Hardware trends let you do more with less – Increased hard drive density (32TB in 2U) – Faster bus speeds – 10Gbps Ethernet at end host – Larger core counts   Can store, process petabytes   But is it really OK to just restart on failure? 35
  36. 36. Analytical Modeling   Modeling goal: when is performance gain nullified by restart cost?   Example: 2x improvement – 30 minute jobs: ~4300 nodes – 2.5 hr jobs: ~800 nodes   Can sort 100TB on 52 machines in ~2.5 hours – Petabyte scale easily possible in these regions   See paper for additional discussion 36
  37. 37. Outline   Architecture Overview   Memory Management   Fault Tolerance   Evaluation 37
  38. 38. Workload   Sort: uniform and highly-skewed   CloudBurst: short-read gene alignment (ported from Hadoop implementation)   PageRank: synthetic graphs,Wikipedia   Word count   n-Gram count (5-grams)   Session extraction from synthetic logs 38
  39. 39. Performance 39
  40. 40. Performance 40
  41. 41. Performance 41
  42. 42. Performance vs. Hadoop 42 Application Hadoop Runtime (Sec) Themis Runtime (Sec) Improvement Sort-500G 28881 1789 16.14x CloudBurst 2878 944 3.05x
  43. 43. Summary   Themis – MapReduce with 2-IO property   Avoids swapping and spilling by carefully managing memory   Potential place for job-level fault tolerance   Executes wide variety of workloads at extremely high speed 43
  44. 44. Themis - Questions? 44 Zoom! http://themis.sysnet.ucsd.edu/!

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