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Mapreduce Algorithms

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Mapreduce Algorithms

  1. 1. Mapreduce Algorithms O'Reilly Strata Conference, London UK, October 1st 2012 Amund Tveit - -
  2. 2. Background ● Been blogging about Mapreduce Algorithms in Academic Papers since since Oct 2009 (1st Hadoop World) 1. papers/ 2. academic-papers-updated/ 3. academic-papers-may-2010-update/ 4. academic-papers-4th-update-may-2011/ 5. academic-papers-5th-update-%E2%80%93-nov-2011/ ● Atbrox works on IR-related Hadoop and cloud projects ● My prior experience: Google (software infrastructure and mobile news), PhD in Computer Science
  3. 3. TOC 1. Brief introduction to Mapreduce Algorithms 2. Overview of a few Recent Mapreduce Algorithms in Papers 3. In-Depth look at a Mapreduce Algorithm 4. Recommendations for Designing Mapreduce Algorithms 5. Appendix - 6th (partial) list of Mapreduce and Hadoop Algorithms in Acemic papers
  4. 4. 1. Brief Introduction to Mapreduce Algorithms
  5. 5. 1.1 So What is Mapreduce? Mapreduce is a concept,method and software for typically batch-based large-scale parallelization. It is inspired by functional programming's map() and reduce() functions Nice features of mapreduce systems include: ● reliably processing job even though machines die (vs MPI,BSP) ● parallelization, e.g. thousands of machines for terasort and petasort Mapreduce was invented by the Google fellows: Jeff Dean Sanjay Ghemawat
  6. 6. 1.2 Mapper function Processes one key and value pair at the time, e.g. ● word count ○ map(key: uri, value: text): ■ for word in tokenize(value) ■ emit(word, 1) # found 1 occurence of word ● inverted index ○ map(key: uri, value: text): ■ for word in tokenize(value) ■ emit(word, key) # word and uri pair
  7. 7. 1.3 Reducer function Reducers processes one key and all values that belong to it (as received and aggregated from the map function), e.g. ● word count ○ reduce(key: word type, value: list of 1s): ■ emit(key, sum(value)) ● inverted index ○ reduce(key: word type, value: list of URIs): ■ # perhaps transformation of value, e.g. encoding ■ emit(key, value) // e.g. to a distr. hash table
  8. 8. 1.4 Mapreduce Pan Patterns
  9. 9. 1.4 Pattern 1 - Data Reduction ● Word Count ● Machine Learning (e.g. training models) ● Probably the most common way of using mapreduce
  10. 10. 1.5 Pattern 2 - Transformation ● Sorting (e.g. Terasort and Petasort)
  11. 11. 1.6 Pattern 3 - Data Increase ● Decompression ● Annotation, e.g. traditional indexing pipeline
  12. 12. 2. Examples of recently published use and development of Mapreduce Algorithms
  13. 13. 2.1 Machine Learning - ILP ● Problem: Automatically find (induce) rules from examples and knowledge base ● Paper: ○ Data and Task Parallelism in ILP using Mapreduce (IBM Research India This follows Pan Pattern 1 - Data Reduction - output is a set of rules from a (typically larger) set of examples and knowledge base
  14. 14. 2.1 Machine Learning - ILP - II Example Input: Example Result:
  15. 15. 2.2 Finance - Trading Problem: Optimize Algorithmic Trading Paper: ○ Optimizing Parameters of Algorithm Trading Strategies using Mapreduce (EMC-Greenplum Research China et. al) This follows Pan Pattern 1 - Data Reduction - output is the set of best parameter sets for algorithmic trading. Note that during map phase there is increase in data, i.e. creation of permutations of possible parameters
  16. 16. 2.3 Software Engineering Problem: Automatically generate unit test code to increase test coverage and offload developers Paper: ○ A Parallel Genetic Algorithm Based on Hadoop Mapreduce for the Automatic Generation of JUnit Test Suites (University of Salerno, Italy) This (probably) follows Pan Pattern 1, 2 and 3, i.e. - assumably - fixed amount of chromosomes (i.e. transformation), collection unit tests are being evolved and the combined lengths of unit tests evolved might increase or decrease compared to the original input.
  17. 17. 2.3 Software Engineering - II Figure from "EvoTest: Test Case Generation using Genetic Programming and Software Analysis"
  18. 18. 3. In-Depth look at a Mapreduce Algorithm
  19. 19. 3.1 The Challenge ● Task: ○ Build a low-latency key-value store for disk or SSD ● Features: ○ Low startup time ■ i.e. no/little pre-loading of (large) caches to memory ○ Prefix-search ■ i.e. support searching for both all prefixes of a key as well as the entire key ○ Low-latency ■ i.e. reduce number of disk/SSD seeks, e.g. by increase probability of disk cache hits ○ Static/Immutable data - write once, read many
  20. 20. 3.2 A few Possible Ways 1. Binary Search or Interpolation Search within a file of sorted keys and then look up value ~ lg(N) or lg(lg(N)) 2. Prefix-algorithms mapped to file, e.g. 1. Trie, 2. Ternary search tree 3. Patricia Tree ~ O(k)
  21. 21. 3.3 Overall Approach 1. Scale - divide key,value data into shards 2. Build patricia tree per shard and store all key, values for later 3. Prepare trees to have placeholder (short) value for each key 4. Flatten each patricia tree to a disk-friendly and byte-aligned format fit for random access 5. Recalculate file addresses in each patricia tree to be able to store the actual values 6. Create final patricia tree with values on disk
  22. 22. 3.4 Split data with mapper 1. Scale - divide key,value data into shards map(key, value): # e.g. simple - hash(first char), or use a classifier # personalization etc. shard_key = shard_function(key, value) out_value = (key,value) emit shard_key, out_value
  23. 23. 3.5 Init and run reduce() 2. Build one patricia tree per (reduce) shard reduce_init(): # called once per reducer before it starts self.patricia = Patricia() self.tempkeyvaluestore = TempKeyValueStore reducer(shard_key, list of key_value pairs): for (key, value) in list of key_value pairs: self.tempkeyvaluestore[key] = value
  24. 24. 3.6 Reducer cont. 3. Prepare trees to have placeholder values (=key) for each key reduce_final(): # called once per reducer after all reduce() for key, value in self.tempkeyvaluestore: self.patricia.add(key, key) # key == value for now
  25. 25. 3.7 Flatten patricia tree for disk 4. Flatten each patricia tree to a disk-friendly and byte- aligned format fit for random access reduce_final(): # continued from 3. # num 0s below constrains addressable size of shard file self.firstblockaddress = "00000000000000" # create mapping from dict of dicts to a linear file self.flatten_patricia(self.patricia, parent=self.firstblockaddress) # self.recalculate_patricia_tree_for_actual_values() self.output_patricia_tree_with_actual_values()
  26. 26. 3.8 Mapreduce Approach - 5 Generated file format below, and corresponding patricia tree to the right 00000000038["", {"r": "00000000038"}] 00000000060["", {"om": "00000000098", "ub": "00000000290"}] 00000000062["", {"ulus": "00000000265", "an": "00000000160"}] 00000000059["", {"e": "00000000219", "us": "00000000242"}] 00000000023["one", ""] 00000000023["two", ""] 00000000025["three", ""] 00000000059["", {"ic": "00000000456", "e": "00000000349"}] 00000000059["", {"r": "00000000432", "ns": "00000000408"}] 00000000024["four", ""] 00000000024["five", ""] 00000000063["", {"on": "00000000519", "undus": "00000000542"}] 00000000023["six", ""] 00000000025["seven", ""]
  27. 27. 4. Recommendations for Designing Mapreduce Algorithms
  28. 28. Mapreduce Patterns Map() and Reduce() methods typically follow patterns, a recommended way of representing such patterns are: extracting and generalize code skeleton fingerprints based on: 1. loops: e.g. "do-while", "while", "for", "repeat-until" => "loop" 2. conditions: e.g. "if", "exception" and "switch" => "condition" 3. emits: e.g. outputs from map() => reduce() or IO => "emit" 4. emit data types: e.g. string, number, list (if known) map(key, value): reduce(key, values): loop # over tokenized value emit # key = word, emit # key=word, val=1 or uri # value = sum(values) or # list of URIs
  29. 29. General Mapreduce Advice Performance 1. IO/moving data is expensive - use compression and aggr. 2. Use combiners, i.e. "reducer afterburners" for mappers 3. Look out for skewness in key distribution, e.g. zipfs law 4. Use the right programming language for the task 5. Balance work between mappers and reducers - http: // hadoopmapreduce-a-python-example/ Cost, Readability & Maintainability 6. Mapreduce = right tool? (seq./parallel/iterative/realtime) 7. E.g. Crunch, Pig, Hive instead of full Mapreduce code? 8. Split job into sequence of mapreduce jobs, e.g. with cascading, mrjob etc.
  30. 30. The End ● Mapreduce Paper Trends (from 2009 => 2012), roughly: ○ Increased use of mapreduce jobflows, i.e. more than one mapreduce in a sequence and also in various types of iterations ■ e.g. the Algorithmic Trading earlier ○ Increased amount of papers published related to semantic web (e.g. RDF) and AI reasoning/inference ○ Decreased (relative) amount of IR and Ads papers
  31. 31. APPENDIX List of Mapreduce and Hadoop Algorithms in Academic Papers - 6th version (partial subset of forthcoming blogpost)
  32. 32. AI: Reasoning & Semantic Web 1. Reasoning with Fuzzy-cL+Ontologies Using Mapreduce 2. WebPIE: A Web-scale parallel inference engine using Mapreduce 3. Towards Scalable Reasoning over Annotated RDF Data Using Mapreduce 4. Reasoning with Large Scale Ontologies in Fuzzy pD* Using Mapreduce 5. Scalable RDF Compression with Mapreduce 6. Towards Parallel Nonmonotonic Reasoning with Billions of Facts
  33. 33. Biology & Medicine 1. A Mapreduce-based Algorithm for Motif Search 2. A MapReduce Approach for Ridge Regression in Neuroimaging Genetic Studies 3. Fractal Mapreduce decomposition of sequence alignment 4. Cloud-enabling Sequence Alignment with Hadoop Mapreduce: A Performance Analysis AI Misc. A MapReduce based Ant Colony Optimization approach to combinatorial optimization problems
  34. 34. Machine Learning 1. An efficient Mapreduce Algorithm for Parallelizing Large- Scale Graph Clustering 2. Accelerating Bayesian Network Parameter Learning Using Hadoop and Mapreduce 3. The Performance Improvements of SPRINT Algorithm Based on the Hadoop Platform Graphs & Graph Theory 4. Large-Scale Graph Biconnectivity in MapReduce 5. Parallel Tree Reduction on MapReduce
  35. 35. Datacubes & Joins 1. Data Cube Materialization and Mining Over Mapreduce 2. Fuzzy joins using Mapreduce 3. Efficient Distributed Parallel Top-Down Computation of ROLAP Data Cube Using Mapreduce 4. V-smart-join: A scalable MapReduce Framework for all-pair similarity joins of multisets and vectors 5. Data Cube Materialization and Mining over MapReduce Finance & Business 6. Optimizing Parameters of Algorithm Trading Strategies using Mapreduce 7. Using Mapreduce to scale events correlation discovery for business processes mining 8. Computational Finance with Map-Reduce in Scala
  36. 36. Mathematics & Statistics 1. GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries 2. Fast Parallel Algorithms for Blocked Dense Matrix Multiplication on Shared Memory Architectures 3. Mr. LDA: A Flexible Large Scale Topic Modelling Package using Variational Inference in MapReduce 4. Matrix chain multiplication via multi-way algorithms in MapReduce