Knowledge Discovery and Data Mining 1 (VO) ( )

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1 Knowledge Discovery and Data Mining 1 (VO) ( ) Map-Reduce Denis Helic KTI, TU Graz Oct 24, 2013 Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

2 Big picture: KDDM Probability Theory Linear Algebra Map-Reduce Information Theory Statistical Inference Mathematical Tools Infrastructure Knowledge Discovery Process Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

3 Outline 1 Motivation 2 Large Scale Computation 3 Map-Reduce 4 Environment 5 Map-Reduce Skew Slides Slides are partially based on Mining Massive Datasets course from Stanford University by Jure Leskovec Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

4 Map-Reduce Motivation Today s data is huge Challenges How to distribute computation? Distributed/parallel programming is hard Map-reduce addresses both of these points Google s computational/data manipulation model Elegant way to work with huge data Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

5 Motivation Single node architecture CPU Memory Data fits in memory Machine learning, statistics Classical data mining Memory Disk Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

6 Motivation Motivation: Google example 20+ billion Web pages Approx. 20 KB per page Approx TB for the whole Web Approx hard drives to store the Web A single computer reads MB/s from disk Approx. 4 months to read the Web with a single computer Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

7 Motivation Motivation: Google example Takes even more time to do something with the data E.g. to calculate the PageRank If m is the number of the links on the Web Average degree on the Web is approx. 10, thus m To calculate PageRank we need per iteration step m multiplications We need approx iteration steps Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

8 Motivation Motivation: Google example Today a standard architecture for such problems is emerging Cluster of commodity Linux nodes Commodity network (ethernet) to connect them 2 10 Gbps between racks 1 Gbps within racks Each rack contains nodes Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

9 Cluster architecture Motivation Switch Switch Switch CPU CPU CPU CPU Memory Memory Memory Memory Memory Disk Memory Disk Memory Disk Memory Disk Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

10 Motivation Motivation: Google example 2011 estimation: Google had approx. 1 million machines report-google-uses-about servers/ Other examples: Facebook, Twitter, Amazon, etc. But also smaller examples: e.g. Wikipedia Single source shortest path: m + n time complexity, approx Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

11 Large Scale Computation Large scale computation Large scale computation for data mining on commodity hardware Challenges How to distribute computation? How can we make it easy to write distributed programs? How to cope with machine failures? Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

12 Large Scale Computation Large scale computation: machine failures One server may stay up 3 years (1000 days) The failure rate per day: p = 10 3 How many failures per day if we have n machines? Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

13 Large Scale Computation Large scale computation: machine failures One server may stay up 3 years (1000 days) The failure rate per day: p = 10 3 How many failures per day if we have n machines? Binomial r.v. Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

14 Large Scale Computation Large scale computation: machine failures PMF of a Binomial r.v. p(k) = ( ) n (1 p) n k p k k Expectation of a Binomial r.v. E[X ] = np Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

15 Large Scale Computation Large scale computation: machine failures n = 1000, E[X ] = 1 If we have 1000 machines we lose one per day n = , E[X ] = 1000 If we have 1 million machines (Google) we lose 1 thousand per day Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

16 Large Scale Computation Coping with node failures: Exercise Exercise Suppose a job consists of n tasks, each of which takes time T seconds. Thus, if there are no failures, the sum over all compute nodes of the time taken to execute tasks at that node is nt. Suppose also that the probability of a task failing is p per job per second, and when a task fails, the overhead of management of the restart is such that it adds 10T seconds to the total execution time of the job. What is the total expected execution time of the job? Example Example from Mining Massive Datasets. Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

17 Large Scale Computation Coping with node failures: Exercise Failure of a single task is a Bernoulli r.v. with parameter p The number of failures in n tasks is a Binomial with parameter n and p PMF of a Binomial r.v. p(k) = ( ) n (1 p) n k p k k Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

18 Large Scale Computation Coping with node failures: Exercise PMF The time until the first failure of a task is a Geometric r.v. with parameter p p(k) = (1 p) k 1 p Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

19 Large Scale Computation Coping with node failures: Exercise If we go to very fine scales we can approximate a Geometric r.v. with an Exponential r.v. with λ = p The time (T ) until a task fails is distributed exponentially PDF f (t; λ) = { λe λt, t 0 0, t < 0 CDF F (t; λ) = { 1 e λt, t 0 0, t < 0 Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

20 Large Scale Computation Coping with node failures: Exercise Expected execution time of a task Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

21 Large Scale Computation Coping with node failures: Exercise Expected execution time of a task E[T ] = P S T S + P F (E[T L ] + T R + E[T ]) P S = 1 P F P F = 1 e λts P S = e λts Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

22 Large Scale Computation Coping with node failures: Exercise After simplifying, we get: E[T ] = T S + P F 1 P F (E[T L ] + T R ) P F 1 P F = 1 e λts e λt S = e λt S 1 (1) E[T ] = T S + (e λt S 1)(E[T L ] + T R ) Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

23 Large Scale Computation Coping with node failures: Exercise E[T L ] =? What is PDF of T L T L models time lost because of failure Time is lost if and only if a failure occurs before the task finishes, i.e. we know that within [0, T S ] a failure has occurred Let this be an event B Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

24 Large Scale Computation Coping with node failures: Exercise What is P(B)? P(B) = F (T S ) = 1 e λt S Our T L is now a r.v. conditioned on event B I.e. we are interested in the probability of event A (failure occurs at time t < T S ) given that B occurred P(A) = λe λt Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

25 Large Scale Computation Coping with node failures: Exercise P(A B) = P(A B) P(B) What is A B? A: failure occurs at time t < T S B: failure occurs within [0, T S ] A B: failure occurs at time t < T S A B = A Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

26 Large Scale Computation Coping with node failures: Exercise P(A B) = P(A) P(B) PDF of a r.v. T L f (t) = λe λt 1 e λt S Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

27 Large Scale Computation Coping with node failures: Exercise Expectation E[T L ]: E[T L ] = tf (t)dt E[T L ] = = TS 0 λe λt t 1 e λt dt S 1 1 e λt S TS 0 tλe λt dt (2) Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

28 Large Scale Computation Coping with node failures: Exercise E[T L ] = 1 1 e λt S = 1 λ T S e λt S 1 ] [ te λt e λt T S λ 0 E[T ] = T S + (e λt S 1)( 1 λ T S e λt S 1 + T R ) = (e λt S 1)( 1 λ + T R) (3) Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

29 Large Scale Computation Coping with node failures: Exercise For a single task: E[T ] = (e pt 1)( 1 p + 10T ) For n tasks: E[T ] = n(e pt 1)( 1 p + 10T ) Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

30 Large Scale Computation Coordination Using this information we can improve scheduling We can also optimize checking for node failures Check-pointing strategies Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

31 Large Scale Computation Large scale computation: data copying Copying data over network takes time Bring data closer to computation I.e. process data locally at each node Replicate data to increase reliability Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

32 Solution: Map-reduce Map-Reduce Storage infrastructure Distributed file system Google: GFS Hadoop: HDFS Programming model Map-reduce Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

33 Storage infrastructure Map-Reduce Problem: if node fails how to store a file persistently Distributed file system Provides global file namespace Typical usage pattern Huge files: several 100s GB to 1 TB Data is rarely updated in place Reads and appends are common Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

34 Distributed file system Map-Reduce Chunk servers File is split into contiguous chunks Typically each chunk is MB Each chunk is replicated (usually 2x or 3x) Try to keep replicas in different racks Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

35 Distributed file system Map-Reduce Master node Stores metadata about where files are stored Might be replicated Client library for file access Talks to master node to find chunk servers Connects directly to chunk servers to access data Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

36 Distributed file system Map-Reduce Reliable distributed file system Data kept in chunks spread across machines Each chunk replicated on different machines Reliable distributed file system Seamless recovery from node failures Bring computation directly to data Chunk servers also used as computation nodes Seamless recovery from disk or machine failure C 0 C 1 C 5 C 2 D 0 C 5 C 1 C 3 C 2 D 0 C 5 D 1 C 0 C 5 D 0 C 2 Chunk server 1 Chunk server 2 Chunk server 3 Chunk server N Bring computation directly to the data! Figure: Figure from slides by Jure Leskovec Chunk servers also serve as compute servers 1/8/2013 Denis Helic (KTI, TU Graz) Jure Leskovec, Stanford CS246: Mining KDDM1 Massive Datasets, Oct 24, / 8233

37 Map-Reduce Programming model: Map-reduce Running example We want to count the number of occurrences for each word in a collection of documents. In this example, the input file is a repository of documents, and each document is an element. Example Example is meanwhile a standard Map-reduce example. Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

38 Map-Reduce Programming model: Map-reduce words input_file sort uniq -c Three step process 1 Split file into words, each word on a separate line 2 Group and sort all words 3 Count the occurrences Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

39 Map-Reduce Programming model: Map-reduce This captures the essence of Map-reduce Split Group Count Naturally parallelizable E.g. split and count Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

40 Map-Reduce Programming model: Map-reduce Sequentially read a lot of data Map: extract something that you care about (key, value) Group by key: sort and shuffle Reduce: Aggregate, summarize, filter or transform Write the result Outline Outline is always the same: Map and Reduce change to fit the problem Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

41 tasks, so all key-value Map-Reduce pairs with the same key wind up at the same Reduce task. Programming model: Map-reduce 3. The Reduce tasks work on one key at a time, and combine all the values associated with that key in some way. The manner of combination of values is determined by the code written by the user for the Reduce function. Figure 2.2 suggests this computation. Input chunks Key value pairs (k,v) Keys with all their values (k, [v, w,...]) Combined output Map tasks Group by keys Reduce tasks Figure 2.2: Schematic of a map-reduce computation Figure: Figure from the book: Mining massive datasets The Map Tasks We view input files for a Map task as consisting of elements, which can be any type: a tuple or a document, for example. A chunk is a collection of elements, and no element is stored across two chunks. Technically, all inputs Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

42 Map-Reduce Programming model: Map-reduce Input: a set of (key, value) pairs (e.g. key is the filename, value is a single line in the file) Map(k, v) (k, v ) Takes a (k, v) pair and outputs a set of (k, v ) pairs There is one Map call for each (k, v) pair Reduce(k, (v ) ) (k, v ) All values v with same key k are reduced together and processed in v order There is one Reduce call for each unique k Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

43 Map-Reduce Programming model: Map-reduce Big Document Map Group by key Reduce Star Wars is an American epic space opera franchise centered on a film series created by George Lucas. The film series has spawned an extensive media franchise called the Expanded Universe including books, television series, computer and video games, and comic books. These supplements to the two film trilogies... (Star, 1) (Wars, 1) (is, 1) (an, 1) (American, 1) (epic, 1) (space, 1) (opera, 1) (franchise, 1) (centered, 1) (on, 1) (a, 1) (film, 1) (series, 1) (created, 1) (by, 1) (Star, 1) (Star, 1) (Wars, 1) (Wars, 1) (a, 1) (a, 1) (a, 1) (a, 1) (a, 1) (a, 1) (film, 1) (film, 1) (film, 1) (franchise, 1) (series, 1) (series, 1) (Star, 2) (Wars, 2) (a, 6) (film, 3) (franchise, 1) (series, 2) Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

44 Map-Reduce Programming model: Map-reduce map(key, value): // key: document name // value: a single line from a document foreach word w in value: emit(w, 1) Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

45 Map-Reduce Programming model: Map-reduce reduce(key, values): // key: a word // values: an iterator over counts result = 0 foreach count c in values: result += c emit(key, result) Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

46 Map-reduce computation Environment Map-reduce environment takes care of: 1 Partitioning the input data 2 Scheduling the program s execution across a set of machines 3 Performing the group by key step 4 Handling machine failures 5 Managing required inter-machine communication Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

47 Map-reduce computation Environment MAP: Read input and produces a set of key-value pairs Big document Group by key: Collect all pairs with same key (Hash merge, Shuffle, Sort, Partition) Reduce: Collect all values belonging to the key and output 1/8/2013 Jure Leskovec, Stanford CS246: Mining Massive Datasets, 43 Figure: Figure from the course by Jure Leskovec (Stanford University) Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

48 Map-reduce computation Environment All phases are distributed with many tasks doing the work Figure: Figure from the course by Jure Leskovec (Stanford University) 1/8/2013 Jure Leskovec, Stanford CS246: Mining Massive Datasets, 44 Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

49 Environment Data flow Input and final output are stored in distributed file system Scheduler tries to schedule map tasks close to physical storage location of input data Intermediate results are stored on local file systems of Map and Reduce workers Output is often input to another Map-reduce computation Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

50 Environment Coordination: Master Master node takes care of coordination Task status, e.g. idle, in-progress, completed Idle tasks get scheduled as workers become available When a Map task completes, it notifies the master about the size and location of its intermediate files Master pushes this info to reducers Master pings workers periodically to detect failures Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

51 Environment Map-reduce execution details 2.2. MAP-REDUCE 27 User Program fork fork Master fork assign Map assign Reduce Worker Worker Worker Worker Input Data Worker Intermediate Files Output File Figure 2.3: Overview of the execution of a map-reduce program Figure: Figure from the book: Mining massive datasets executing at a particular Worker, or completed). A Worker process reports to the Master when it finishes a task, and a new task is scheduled by the Master for that Worker process. Each Map task is assigned one or more chunks of the input file(s) and executes on it the code written by the user. The Map task creates a file for Denis Helic (KTI, TUeach Graz) Reduce task on the local disk KDDM1 of the Worker that executes the Map task. Oct 24, / 82

52 Maximizing parallelism Map-Reduce Skew If we want maximum parallelism then Use one Reduce task for each reducer (i.e. a single key and its associated value list) Execute each Reduce task at a different compute node The plan is typically not the best one Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

53 Map-Reduce Skew Maximizing parallelism There is overhead associated with each task we create We might want to keep the number of Reduce tasks lower than the number of different keys We do not want to create a task for a key with a short list There are often far more keys than there are compute nodes E.g. count words from Wikipedia or from the Web Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

54 Map-Reduce Skew Input data skew: Exercise Exercise Suppose we execute the word-count map-reduce program on a large repository such as a copy of the Web. We shall use 100 Map tasks and some number of Reduce tasks. 1 Do you expect there to be significant skew in the times taken by the various reducers to process their value list? Why or why not? 2 If we combine the reducers into a small number of Reduce tasks, say 10 tasks, at random, do you expect the skew to be significant? What if we instead combine the reducers into 10,000 Reduce tasks? Example Example is based on the example from Mining Massive Datasets. Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

55 Maximizing parallelism Map-Reduce Skew There is often significant variation in the lengths of value list for different keys Different reducers take different amounts of time to finish If we make each reducer a separate Reduce task then the task execution times will exhibit significant variance Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

56 Map-Reduce Skew Input data skew Input data skew describes an uneven distribution of the number of values per key Examples include power-law graphs, e.g. the Web or Wikipedia Other data with Zipfian distribution E.g. the number of word occurrences Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

57 Map-Reduce Skew Power-law (Zipf) random variable PMF p(k) = k α ζ(α) k N, k 1, α > 1 ζ(α) is the Riemann zeta function ζ(α) = k=1 k α Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

58 Map-Reduce Skew Power-law (Zipf) random variable Probability mass function of a Zipf random variable; differing α values 0.9 α = α = probability of k k Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

59 Map-Reduce Skew Power-law (Zipf) input data Key size: sum =189681,µ =1.897,σ 2 = Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

60 Map-Reduce Skew Tackling input data skew We need to distribute a skewed (power-law) input data into a number of reducers/reduce tasks/compute nodes The distribution of the key lengths inside of reducers/reduce tasks/compute nodes should be approximately normal The variance of these distributions should be smaller than the original variance If variance is small an efficient load balancing is possible Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

61 Map-Reduce Skew Tackling input data skew Each Reduce task receives a number of keys The total number of values to process is the sum of the number of values over all keys The average number of values that a Reduce task processes is the average of the number of values over all keys Equivalently, each compute node receives a number of Reduce tasks The sum and average for a compute node is the sum and average over all Reduce tasks for that node Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

62 Map-Reduce Skew Tackling input data skew How should we distribute keys to Reduce tasks? Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

63 Map-Reduce Skew Tackling input data skew How should we distribute keys to Reduce tasks? Uniformly at random Other possibilities? Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

64 Map-Reduce Skew Tackling input data skew How should we distribute keys to Reduce tasks? Uniformly at random Other possibilities? Calculate the capacity of a single Reduce task Add keys until capacity is reached, etc. Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

65 Map-Reduce Skew Tackling input data skew We are averaging over a skewed distribution Are there laws that describe how the averages of sufficiently large samples drawn from a probability distribution behaves? In other words, how are the averages of samples of a r.v. distributed? Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

66 Map-Reduce Skew Tackling input data skew We are averaging over a skewed distribution Are there laws that describe how the averages of sufficiently large samples drawn from a probability distribution behaves? In other words, how are the averages of samples of a r.v. distributed? Central-limit Theorem Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

67 Map-Reduce Skew Central-Limit Theorem The central-limit theorem describes the distribution of the arithmetic mean of sufficiently large samples of independent and identically distributed random variables The means are normally distributed The mean of the new distribution equals the mean of the original distribution The variance of the new distribution equals σ2 n, where σ2 is the variance of the original distribution Thus, we keep the mean and reduce the variance Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

68 Central-Limit Theorem Map-Reduce Skew Theorem Suppose X 1,..., X n are independent and identical r.v. with the expectation µ and variance σ 2. Let Y be a r.v. defined as: Y n = 1 n n i=1 X i The CDF F n (y) tends to PDF of a normal r.v. with the mean µ and variance σ 2 for n : lim F 1 y n(y) = e (x µ)2 2σ 2 n 2πσ 2 Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

69 Map-Reduce Skew Central-Limit Theorem Example Practically, it is possible to replace F n (y) with a normal distribution for n > 30 We should always average over at least 30 values Approximating uniform r.v. with a normal r.v. by sampling and averaging Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

70 Central-Limit Theorem Map-Reduce Skew 18 Averages: µ =0.5,σ 2 = Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

71 Central-Limit Theorem Map-Reduce Skew 40 Averages: µ =0.499,σ 2 = Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

72 Map-Reduce Skew Central-Limit Theorem IPython Notebook examples http: //kti.tugraz.at/staff/denis/courses/kddm1/clt.ipynb Command Line ipython notebook pylab=inline clt.ipynb Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

73 Map-Reduce Skew Input data skew We can reduce impact of the skew by using fewer Reduce tasks than there are reducers If keys are sent randomly to Reduce tasks we average over value list lengths Thus, we average over the total time for each Reduce task (Central-limit Theorem) We should make sure that the sample size is large enough (n > 30) Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

74 Map-Reduce Skew Input data skew We can further reduce the skew by using more Reduce tasks than there are compute nodes Long Reduce tasks might occupy a compute node fully Several shorter Reduce tasks are executed sequentially at a single compute node Thus, we average over the total time for each compute node (Central-limit Theorem) We should make sure that the sample size is large enough (n > 30) Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

75 Input data skew Map-Reduce Skew Key size: sum =196524,µ =1.965,σ 2 = Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

76 Input data skew Map-Reduce Skew 4500 Task key size: sum =196524,µ = ,σ 2 = Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

77 Input data skew Map-Reduce Skew Task key averages: µ =1.958,σ 2 = Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

78 Input data skew Map-Reduce Skew Node key size: sum =196524,µ = ,σ 2 = Node key averages: µ =1.976,σ 2 = Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

79 Map-Reduce Skew Input data skew IPython Notebook examples http: //kti.tugraz.at/staff/denis/courses/kddm1/mrskew.ipynb Command Line ipython notebook pylab=inline mrskew.ipynb Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

80 Map-Reduce Skew Combiners Sometimes a Reduce function is associative and commutative Commutative: x y = y x Associative: (x y) z = x (y z) The values can be combined in any order, with the same result The addition in reducer of the word count example is such an operation Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

81 Map-Reduce Skew Combiners When the Reduce function is associative and commutative we can push some of the reducers work to the Map tasks E.g. instead of emitting (w, 1), (w, 1),... We can apply the Reduce function within the Map task In that way the output of the Map task is combined before grouping and sorting Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

82 Combiners Map-Reduce Skew Map Combiner Group by key Reduce (Star, 1) (Wars, 1) (is, 1)... (American, 1) (epic, 1) (space, 1)... (franchise, 1) (centered, 1) (on, 1)... (film, 1) (series, 1) (created, 1) (Star, 2)... (Wars, 2) (a, 6)... (a,3)... (film, 3) (franchise, 1) (series, 2) (Star, 2) (Wars, 2) (a, 6) (a, 3) (a, 4)... (film, 3) (franchise, 1) (series, 2) (Star, 2) (Wars, 2) (a, 13) (film, 3) (franchise, 1) (series, 2) Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

83 Map-Reduce Skew Input data skew: Exercise Exercise Suppose we execute the word-count map-reduce program on a large repository such as a copy of the Web. We shall use 100 Map tasks and some number of Reduce tasks. 1 Do you expect there to be significant skew in the times taken by the various reducers to process their value list? Why or why not? 2 If we combine the reducers into a small number of Reduce tasks, say 10 tasks, at random, do you expect the skew to be significant? What if we instead combine the reducers into 10,000 Reduce tasks? 3 Suppose we do use a combiner at the 100 Map tasks. Do you expect skew to be significant? Why or why not? Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

84 Input data skew Map-Reduce Skew Key size: sum =195279,µ =1.953,σ 2 = Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

85 Input data skew Map-Reduce Skew Task key averages: µ =1.793,σ 2 = Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

86 Map-Reduce Skew Input data skew IPython Notebook examples combiner.ipynb Command Line ipython notebook pylab=inline combiner.ipynb Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82

87 Map-Reduce: Exercise Map-Reduce Skew Exercise Suppose we have an n n matrix M, whose element in row i and column j will be denoted m ij. Suppose we also have a vector v of length n, whose jth element is v j. Then the matrix-vector product is the vector x of length n, whose ith element is given by x i = n m ij v j j=1 Outline a Map-Reduce program that calculates the vector x. Denis Helic (KTI, TU Graz) KDDM1 Oct 24, / 82