Benchmarking MapReduce-Style Parallel Computing

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BenchmarkingMapReduce-StyleParallel Computing
Randal E. Bryant Carnegie Mellon University
http//www.cs.cmu.edu/bryant
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Programming with MapReduce

• Background
• Developed at Google for aggregating web data
• Dean Ghemawat MapReduce Simplified Data
  Processing on Large Clusters, OSDI 2004
• Strengths
• Easy way to write scalable parallel programs
• Powerful programming model
• Beyond web search applications
• Runtime system automatically handles many of the
  challenges of parallel programming
• Scheduling, load balancing, fault tolerance

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Overall Execution Model

• General Form
• Input
• Large set of files
• Compute
• Aggregate information
• Output
• Files containing aggregations

• Example Word Count Index
• Input
• 1010 cached web pages
• Stored on cluster of 1000 machines, each with own
  local disk
• Compute
• Index of words with occurrence counts
• Output
• File containing count for each word

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MapReduce Programming

• Map
• Function generating keyword/value pairs from
  input file
• E.g., word/count for each word in document
• Reduce
• Function aggregating values for single keyword
• E.g.,Sum word counts

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MapReduce Implementation

• (Somewhat naïve implementation)
• Map
• Spawn mapping task for each input file
• Execute on processor local to file
• Generate file for each keyword/value
• Shuffle
• Redistribute files by hashing keywords K gt Ph(K)
• Reduce
• Spawn reduce task for each keyword
• On processor to which keyword hashes Ph(K)

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Appealing Features

• Ease of Programming
• Programmer provides only two functions
• Express in terms of computation over data, not
  detailed execution on system
• Robustness
• Tolerant to failures of disks, processors,
  network
• Source files stored redundantly
• Runtime monitor detects and reexecutes failed
  tasks
• Dynamic scheduling automatically adapts to
  resource limitations

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Tolerating Failures

• Dean Ghemawat, OSDI 2004
• Sorting 10 million 100-byte records with 1800
  processors
• Proactively restart delayed computations to
  achieve better performance and fault tolerance

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Our Data-Driven World

• Science
• Data bases from astronomy, genomics, natural
  languages, seismic modeling,
• Humanities
• Scanned books, historic documents,
• Commerce
• Corporate sales, stock market transactions,
  census, airline traffic,
• Entertainment
• Internet images, Hollywood movies, MP3 files,
• Medicine
• MRI CT scans, patient records,

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Big Data Computing Beyond Web Search

• Application Domains
• Rely on large, ever-changing data sets
• Collecting maintaining data is major effort
• Computational Requirements
• Extract information from large volumes of raw
  data
• Hypothesis
• Can apply MapReduce style computation to many
  other application domains
• Give it a Try!
• Hadoop Open source implementation of parallel
  file system MapReduce

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Q1 Workload Characteristics

• Hardware
• 1000s of nodes
• Each with processor(s), disk(s), network
  interface
• High-speed, local network using commodity
  technology
• E.g., gigabit ethernet with switches
• Data Organization
• Distributed file system providing uniform name
  space and redundant storage
• Computation
• Each task executed as separate process with file
  I/O
• Rely on file system for data transfer

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Q2 Hardware/Software Challenges

• Performance Issues
• Disk bandwidth limitations
• ? 3.6 hours to read data from 1TB disk
• Data transfer across network
• Process file I/O overhead
• Runtime Issues
• Detecting and mitigating effects of failed
  components

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Q3 Benchmarking Challenges

• Generalizing Results
• Beyond specific data set cluster configuration
• Performance depends on many different factors
• Can we predict how program will scale?
• Identifying Bottlenecks
• Many interacting parts to system
• Evaluating Robustness
• Creating realistic failure modes

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Q4 University Contributions

• Currently Industry ahead of universities
• Dealing with massive data sets
• Computing at very large scale
• Developing new programming/runtime approaches
• Google, Yahoo!, Microsoft
• University Role
• More open and systematic inquiry
• Apply to noncommercial problems
• Extend and improve programming model and
  notations
• Expose students to emerging styles of computing

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Background Information

• Data-Intensive Supercomputing The case for
  DISC
• Tech Report CMU-CS-07-128
• Available from http//www.cs.cmu.edu/bryant