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MapReduce and Data Management

MapReduce and Data Management. Based on slides from Jimmy Lin’s lecture slides (http://www.umiacs.umd.edu/~jimmylin/cloud-2010-Spring/index.html) (licensed under Creation Commons Attribution 3.0 License)

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MapReduce and Data Management

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  1. MapReduce and Data Management Based on slides from Jimmy Lin’s lecture slides (http://www.umiacs.umd.edu/~jimmylin/cloud-2010-Spring/index.html) (licensed under Creation Commons Attribution 3.0 License) I used also some ideas from chapter 2 from the book by Anand Rajaraman and Jeff Ullman: "Mining of Massive Datasets“ (http://i.stanford.edu/~ullman/mmds.html)

  2. MapReduce Algorithm Design

  3. MapReduce: Recap Programmers must specify: map (k, v) → list(<k’, v’>) reduce (k’, list(v’)) → <k’’, v’’> All values with the same key are reduced together Optionally, also: partition (k’, number of partitions) → partition for k’ Often a simple hash of the key, e.g., hash(k’) mod n Divides up key space for parallel reduce operations combine (k’, v’) → <k’, v’>* Mini-reducers that run in memory after the map phase Used as an optimization to reduce network traffic The execution framework handles everything else…

  4. k1 v1 k2 v2 k3 v3 k4 v4 k5 v5 k6 v6 map map map map a 1 b 2 c 3 c 6 a 5 c 2 b 7 c 8 combine combine combine combine a 1 b 2 c 9 a 5 c 2 b 7 c 8 partition partition partition partition Shuffle and Sort: aggregate values by keys a 1 5 b 2 7 c 2 9 8 reduce reduce reduce r1 s1 r2 s2 r3 s3

  5. “Everything Else” • The execution framework handles everything else… • Scheduling: assigns workers to map and reduce tasks • “Data distribution”: moves processes to data • Synchronization: gathers, sorts, and shuffles intermediate data • Errors and faults: detects worker failures and restarts • Limited control over data and execution flow • All algorithms must expressed in m, r, c, p • You don’t know: • Where mappers and reducers run • When a mapper or reducer begins or finishes • Which input a particular mapper is processing • Which intermediate key a particular reducer is processing

  6. Tools for Synchronization • Cleverly-constructed data structures • Bring partial results together • Sort order of intermediate keys • Control order in which reducers process keys • Partitioner • Control which reducer processes which keys • Preserving state in mappers and reducers • Capture dependencies across multiple keys and values

  7. Basic Hadoop API • Mapper • void map(K1 key, V1 value, OutputCollector<K2, V2> output, Reporter reporter) • void configure(JobConf job) • void close() throws IOException • Reducer/Combiner • void reduce(K2 key, Iterator<V2> values, OutputCollector<K3,V3> output, Reporter reporter) • void configure(JobConf job) • void close() throws IOException • Partitioner • void getPartition(K2 key, V2 value, int numPartitions) *Note: forthcoming API changes…

  8. Data Types in Hadoop Writable Defines a de/serialization protocol. Every data type in Hadoop is a Writable. WritableComprable Defines a sort order. All keys must be of this type (but not values). Concrete classes for different data types. IntWritableLongWritable Text … Binary encoded of a sequence of key/value pairs SequenceFiles

  9. Scalable Hadoop Algorithms: Themes • Avoid object creation • Inherently costly operation • Garbage collection • Avoid buffering • Limited heap size • Works for small datasets, but won’t scale!

  10. Hadoop Map Reduce Example • See the word count example from Hadoop Tutorial: http://hadoop.apache.org/common/docs/current/mapred_tutorial.html#Overview

  11. Basic Cluster Components • One of each: • Namenode (NN) • Jobtracker (JT) • Set of each per slave machine: • Tasktracker (TT) • Datanode (DN)

  12. Putting everything together… namenode job submission node namenode daemon jobtracker tasktracker tasktracker tasktracker datanode daemon datanode daemon datanode daemon Linux file system Linux file system Linux file system … … … slave node slave node slave node

  13. Anatomy of a Job • MapReduce program in Hadoop = Hadoop job • Jobs are divided into map and reduce tasks • An instance of running a task is called a task attempt • Multiple jobs can be composed into a workflow • Job submission process • Client (i.e., driver program) creates a job, configures it, and submits it to job tracker • JobClient computes input splits (on client end) • Job data (jar, configuration XML) are sent to JobTracker • JobTracker puts job data in shared location, enqueues tasks • TaskTrackers poll for tasks • Off to the races…

  14. Input File Input File InputSplit InputSplit InputSplit InputSplit InputSplit InputFormat RecordReader RecordReader RecordReader RecordReader RecordReader Mapper Mapper Mapper Mapper Mapper Intermediates Intermediates Intermediates Intermediates Intermediates Source: redrawn from a slide by Cloduera, cc-licensed

  15. Mapper Mapper Mapper Mapper Mapper Intermediates Intermediates Intermediates Intermediates Intermediates Partitioner Partitioner Partitioner Partitioner Partitioner (combiners omitted here) Intermediates Intermediates Intermediates Reducer Reduce Reducer Source: redrawn from a slide by Cloduera, cc-licensed

  16. Reducer Reduce Reducer RecordWriter RecordWriter RecordWriter OutputFormat Output File Output File Output File Source: redrawn from a slide by Cloduera, cc-licensed

  17. Input and Output • InputFormat: • TextInputFormat • KeyValueTextInputFormat • SequenceFileInputFormat • … • OutputFormat: • TextOutputFormat • SequenceFileOutputFormat • …

  18. Shuffle and Sort in Hadoop • Probably the most complex aspect of MapReduce! • Map side • Map outputs are buffered in memory in a circular buffer • When buffer reaches threshold, contents are “spilled” to disk • Spills merged in a single, partitioned file (sorted within each partition): combiner runs here • Reduce side • First, map outputs are copied over to reducer machine • “Sort” is a multi-pass merge of map outputs (happens in memory and on disk): combiner runs here • Final merge pass goes directly into reducer

  19. Shuffle and Sort Mapper intermediate files (on disk) mergedspills (on disk) Reducer Combiner circular buffer (in memory) Combiner other reducers spills(on disk) other mappers

  20. Hadoop Workflow 1. Load data into HDFS 2. Develop code locally 3. Submit MapReduce job 3a. Go back to Step 2 Hadoop Cluster You 4. Retrieve data from HDFS

  21. On Amazon: With EC2 0. Allocate Hadoop cluster 1. Load data into HDFS EC2 2. Develop code locally 3. Submit MapReduce job 3a. Go back to Step 2 Your Hadoop Cluster You 4. Retrieve data from HDFS 5. Clean up! Uh oh. Where did the data go?

  22. On Amazon: EC2 and S3 Copy from S3 to HDFS S3(Persistent Store) EC2(The Cloud) Your Hadoop Cluster Copy from HFDS to S3

  23. Graph Algorithms in MapReduce • G = (V,E), where • V represents the set of vertices (nodes) • E represents the set of edges (links) • Both vertices and edges may contain additional information

  24. Graphs and MapReduce • Graph algorithms typically involve: • Performing computations at each node: based on node features, edge features, and local link structure • Propagating computations: “traversing” the graph • Key questions: • How do you represent graph data in MapReduce? • How do you traverse a graph in MapReduce?

  25. Representing Graphs • G = (V, E) • Two common representations • Adjacency matrix • Adjacency list

  26. Adjacency Matrices Represent a graph as an n x n square matrix M n = |V| Mij = 1 means a link from node i to j 2 1 3 4

  27. Adjacency Matrices: Critique Advantages: Amenable to mathematical manipulation Iteration over rows and columns corresponds to computations on outlinks and inlinks Disadvantages: Lots of zeros for sparse matrices Lots of wasted space

  28. Adjacency Lists 1: 2, 4 2: 1, 3, 4 3: 1 4: 1, 3 Take adjacency matrices… and throw away all the zeros

  29. Adjacency Lists: Critique Advantages: Much more compact representation Easy to compute over outlinks Disadvantages: Much more difficult to compute over inlinks

  30. Finding the Shortest Path • Consider simple case of equal edge weights • Solution to the problem can be defined inductively • Here’s the intuition: • Define: b is reachable from a if b is on adjacency list of a • DISTANCETO(s) = 0 • For all nodes p reachable from s, DISTANCETO(p) = 1 • For all nodes n reachable from some other set of nodes M, DISTANCETO(n) = 1 + min(DISTANCETO(m), mM)

  31. Visualizing Parallel BFS n7 n0 n1 n2 n3 n6 n5 n4 n8 n9

  32. From Intuition to Algorithm Data representation: Key: node n Value: d (distance from start), adjacency list (list of nodes reachable from n) Initialization: for all nodes except for start node, d =  Mapper: m adjacency list: emit (m, d + 1) Sort/Shuffle Groups distances by reachable nodes Reducer: Selects minimum distance path for each reachable node Additional bookkeeping needed to keep track of actual path

  33. Multiple Iterations Needed Each MapReduce iteration advances the “known frontier” by one hop Subsequent iterations include more and more reachable nodes as frontier expands Multiple iterations are needed to explore entire graph Preserving graph structure: Problem: Where did the adjacency list go? Solution: mapper emits (n, adjacency list) as well

  34. BFS Pseudo-Code

  35. Stopping Criterion • How many iterations are needed in parallel BFS (equal edge weight case)? • Convince yourself: when a node is first “discovered”, we’ve found the shortest path • Now answer the question... • Six degrees of separation?

  36. Graphs and MapReduce • Graph algorithms typically involve: • Performing computations at each node: based on node features, edge features, and local link structure • Propagating computations: “traversing” the graph • Generic recipe: • Represent graphs as adjacency lists • Perform local computations in mapper • Pass along partial results via outlinks, keyed by destination node • Perform aggregation in reducer on inlinks to a node • Iterate until convergence: controlled by external “driver” • Don’t forget to pass the graph structure between iterations

  37. Random Walks Over the Web • Random surfer model: • User starts at a random Web page • User randomly clicks on links, surfing from page to page • PageRank • Characterizes the amount of time spent on any given page • Mathematically, a probability distribution over pages • PageRank captures notions of page importance • One of thousands of features used in web search • Note: query-independent

  38. Given page x with inlinks t1…tn, where C(t) is the out-degree of t  is probability of random jump N is the total number of nodes in the graph PageRank: Defined

  39. Computing PageRank Properties of PageRank Can be computed iteratively Effects at each iteration are local Sketch of algorithm: Start with seed PRi values Each page distributes PRi “credit” to all pages it links to Each target page adds up “credit” from multiple in-bound links to compute PRi+1 Iterate until values converge

  40. Simplified PageRank • First, tackle the simple case: • No random jump factor • No dangling links • Then, factor in these complexities… • Why do we need the random jump? • Where do dangling links come from?

  41. Sample PageRank Iteration (1) Iteration 1 n2 (0.2) n2 (0.166) 0.1 n1 (0.2) 0.1 0.1 n1 (0.066) 0.1 0.066 0.066 0.066 n5 (0.2) n5 (0.3) n3 (0.2) n3 (0.166) 0.2 0.2 n4 (0.2) n4 (0.3)

  42. Sample PageRank Iteration (2) Iteration 2 n2 (0.166) n2 (0.133) 0.033 0.083 n1 (0.066) 0.083 n1 (0.1) 0.033 0.1 0.1 0.1 n5 (0.3) n5 (0.383) n3 (0.166) n3 (0.183) 0.3 0.166 n4 (0.3) n4 (0.2)

  43. PageRank in MapReduce n1 [n2, n4] n2 [n3, n5] n3 [n4] n4 [n5] n5 [n1, n2, n3] Map n2 n4 n3 n5 n4 n5 n1 n2 n3 n1 n2 n2 n3 n3 n4 n4 n5 n5 Reduce n1 [n2, n4] n2 [n3, n5] n3 [n4] n4 [n5] n5 [n1, n2, n3]

  44. PageRank Pseudo-Code

  45. Complete PageRank • Two additional complexities • What is the proper treatment of dangling nodes? • How do we factor in the random jump factor? • Solution: • Second pass to redistribute “missing PageRank mass” and account for random jumps • p is PageRank value from before, p' is updated PageRank value • |G| is the number of nodes in the graph • m is the missing PageRank mass

  46. PageRank Convergence • Alternative convergence criteria • Iterate until PageRank values don’t change • Iterate until PageRank rankings don’t change • Fixed number of iterations • Convergence for web graphs?

  47. Beyond PageRank • Link structure is important for web search • PageRank is one of many link-based features: HITS, SALSA, etc. • One of many thousands of features used in ranking… • Adversarial nature of web search • Link spamming • Spider traps • Keyword stuffing • …

  48. Efficient Graph Algorithms Sparse vs. dense graphs Graph topologies

  49. Power Laws are everywhere! Figure from: Newman, M. E. J. (2005) “Power laws, Pareto distributions and Zipf's law.” Contemporary Physics 46:323–351.

  50. Local Aggregation • Use combiners! • In-mapper combining design pattern also applicable • Maximize opportunities for local aggregation • Simple tricks: sorting the dataset in specific ways

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