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Mapreduce Algorithms OReilly Strata Conference, London UK, October 1st 2012 Amund Tveit email@example.com - twitter.com/atveit http://atbrox.com/about/ - twitter.com/atbrox
Background● Been blogging about Mapreduce Algorithms in Academic Papers since since Oct 2009 (1st Hadoop World) 1. http://atbrox.com/2009/10/01/mapreduce-and-hadoop-academic- papers/ 2. http://atbrox.com/2010/02/12/mapreduce-hadoop-algorithms-in- academic-papers-updated/ 3. http://atbrox.com/2010/05/08/mapreduce-hadoop-algorithms-in- academic-papers-may-2010-update/ 4. http://atbrox.com/2011/05/16/mapreduce-hadoop-algorithms-in- academic-papers-4th-update-may-2011/ 5. http://atbrox.com/2011/11/09/mapreduce-hadoop-algorithms-in- 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
TOC1. Brief introduction to Mapreduce Algorithms2. Overview of a few Recent Mapreduce Algorithms in Papers3. In-Depth look at a Mapreduce Algorithm4. Recommendations for Designing Mapreduce Algorithms5. Appendix - 6th (partial) list of Mapreduce and HadoopAlgorithms in Acemic papers
1.1 So What is Mapreduce?Mapreduce is a concept,method and software for typically batch-basedlarge-scale parallelization. It is inspired by functional programmingsmap() and reduce() functionsNice features of mapreduce systems include: ● reliably processing job even though machines die (vs MPI,BSP) ● parallelization, e.g. thousands of machines for terasort and petasortMapreduce was invented by the Google fellows: Jeff Dean Sanjay Ghemawat
1.2 Mapper functionProcesses 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
1.3 Reducer functionReducers processes one key and all values that belong to it (asreceived 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
1.6 Pattern 3 - Data Increase ● Decompression ● Annotation, e.g. traditional indexing pipeline
2. Examples of recently published use and development of Mapreduce Algorithms
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 et.al)This follows Pan Pattern 1 - Data Reduction - output is a set ofrules from a (typically larger) set of examples and knowledgebase
2.1 Machine Learning - ILP - II Example Input: Example Result:
2.2 Finance - TradingProblem: Optimize Algorithmic TradingPaper: ○ Optimizing Parameters of Algorithm Trading Strategies using Mapreduce (EMC-Greenplum Research China et. al)This follows Pan Pattern 1 - Data Reduction - output is the setof best parameter sets for algorithmic trading. Note that duringmap phase there is increase in data, i.e. creation ofpermutations of possible parameters
2.3 Software EngineeringProblem: Automatically generate unit test code to increase testcoverage and offload developersPaper: ○ 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), collectionunit tests are being evolved and the combined lengths of unittests evolved might increase or decrease compared to theoriginal input.
2.3 Software Engineering - II Figure from "EvoTest: Test Case Generation using Genetic Programming and Software Analysis"
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
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)
3.3 Overall Approach1. Scale - divide key,value data into shards2. Build patricia tree per shard and store all key, values for later3. Prepare trees to have placeholder (short) value for each key4. Flatten each patricia tree to a disk-friendly and byte-aligned format fit for random access5. Recalculate file addresses in each patricia tree to be able to store the actual values6. Create final patricia tree with values on disk
3.4 Split data with mapper 1. Scale - divide key,value data into shardsmap(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
3.5 Init and run reduce()2. Build one patricia tree per (reduce) shardreduce_init(): # called once per reducer before it starts self.patricia = Patricia() self.tempkeyvaluestore = TempKeyValueStorereducer(shard_key, list of key_value pairs): for (key, value) in list of key_value pairs: self.tempkeyvaluestore[key] = value
3.6 Reducer cont.3. Prepare trees to have placeholder values (=key) for each keyreduce_final(): # called once per reducer after allreduce() for key, value in self.tempkeyvaluestore: self.patricia.add(key, key) # key == value for now
3.7 Flatten patricia tree for disk4. Flatten each patricia tree to a disk-friendly and byte-aligned format fit for random accessreduce_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()
4. Recommendations for Designing Mapreduce Algorithms
Mapreduce PatternsMap() and Reduce() methods typically follow patterns, arecommended 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
General Mapreduce AdvicePerformance 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: //atbrox.com/2010/02/08/parallel-machine-learning-for- 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.
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
APPENDIX List of Mapreduce and HadoopAlgorithms in Academic Papers - 6th version (partial subset of forthcoming blogpost)
AI: Reasoning & Semantic Web1. Reasoning with Fuzzy-cL+Ontologies Using Mapreduce2. WebPIE: A Web-scale parallel inference engine using Mapreduce3. Towards Scalable Reasoning over Annotated RDF Data Using Mapreduce4. Reasoning with Large Scale Ontologies in Fuzzy pD* Using Mapreduce5. Scalable RDF Compression with Mapreduce6. Towards Parallel Nonmonotonic Reasoning with Billions of Facts
Biology & Medicine1. A Mapreduce-based Algorithm for Motif Search2. A MapReduce Approach for Ridge Regression in Neuroimaging Genetic Studies3. Fractal Mapreduce decomposition of sequence alignment4. Cloud-enabling Sequence Alignment with Hadoop Mapreduce: A Performance AnalysisAI Misc.A MapReduce based Ant Colony Optimization approach tocombinatorial optimization problems
Machine Learning1. An efficient Mapreduce Algorithm for Parallelizing Large- Scale Graph Clustering2. Accelerating Bayesian Network Parameter Learning Using Hadoop and Mapreduce3. The Performance Improvements of SPRINT Algorithm Based on the Hadoop PlatformGraphs & Graph Theory4. Large-Scale Graph Biconnectivity in MapReduce5. Parallel Tree Reduction on MapReduce
Datacubes & Joins1. Data Cube Materialization and Mining Over Mapreduce2. Fuzzy joins using Mapreduce3. Efficient Distributed Parallel Top-Down Computation of ROLAP Data Cube Using Mapreduce4. V-smart-join: A scalable MapReduce Framework for all-pair similarity joins of multisets and vectors5. Data Cube Materialization and Mining over MapReduceFinance & Business6. Optimizing Parameters of Algorithm Trading Strategies using Mapreduce7. Using Mapreduce to scale events correlation discovery for business processes mining8. Computational Finance with Map-Reduce in Scala
Mathematics & Statistics1. GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries2. Fast Parallel Algorithms for Blocked Dense Matrix Multiplication on Shared Memory Architectures3. Mr. LDA: A Flexible Large Scale Topic Modelling Package using Variational Inference in MapReduce4. Matrix chain multiplication via multi-way algorithms in MapReduce