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Big Data Analytics with Storm, Spark and GraphLab


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A tutorial by Dr. Vijay Srinivas Agneeswaran, Director and Head, Big-data R&D, Innovation Labs, Impetus

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Big Data Analytics with Storm, Spark and GraphLab

  1. 1. 1 Big Data Analytics with Storm, Spark and GraphLab Dr. Vijay Srinivas Agneeswaran Director and Head, Big-data R&D Impetus Technologies Inc.
  2. 2. 2 Contents Big Data Computations •Introduction to ML •Characterization Berkeley data analytics stack •Spark Real-time Analytics with Storm PMML Scoring for Naïve Bayes •PMML Primer •Naïve Bayes Primer GraphLab Hadoop 2.0 (Hadoop YARN) Programming Abstractions
  3. 3. • What is it? • learn patterns in data • improve accuracy by learning • Examples • Speech recognition systems • Recommender systems • Medical decision aids • Robot navigation systems Introduction to Machine Learning 3
  4. 4. • Attributes and their values: • Outlook: Sunny, Overcast, Rain • Humidity: High, Normal • Wind: Strong, Weak • Temperature: Hot, Mild, Cool • Target prediction - Play Tennis: Yes, No Introduction to Machine Learning 4
  5. 5. 5 Introduction to Machine Learning NoStrongHighMildRainD14 YesWeakNormalHotOvercastD13 YesStrongHighMildOvercastD12 YesStrongNormalMildSunnyD11 YesStrongNormalMildRainD10 YesWeakNormalCoolSunnyD9 NoWeakHighMildSunnyD8 YesWeakNormalCoolOvercastD7 NoStrongNormalCoolRainD6 YesWeakNormalCoolRainD5 YesWeakHighMildRainD4 YesWeakHighHotOvercastD3 NoStrongHighHotSunnyD2 NoWeakHighHotSunnyD1 Play TennisWindHumidityTemp.OutlookDay Tom Mitchell, Machine Learning, Tata McGraw Hill Publications.
  6. 6. 6 Introduction to Machine Learning: Decision Trees Outlook Sunny Overcast Rain Humidity High Normal Wind Strong Weak No Yes Yes YesNo
  7. 7. 7 Decision Trees to Random Forests Can we have an ensemble of trees? – random forests Final prediction is the mean (regression) or class with max votes (categorization) Does not need tree pruning for generalization Greater accuracy across domains. Decision trees Pros •Handling of mixed data, Robustness to outliers, Computational scalability cons •Low prediction accuracy, High variance, Size VS Goodness of fit
  8. 8. K-means Clustering 8
  9. 9. 9 Support Vector Machines
  10. 10. 10 Introduction to Machine Learning Machine learning tasks Learning associations – market basket analysis Supervised learning (Classification/regression) – random forests, support vector machines (SVMs), logistic regression (LR), Naïve Bayes Unsupervised learning (clustering) - k-means, sentiment analysis Prediction – random forests, SVMs, LR Data Mining Application of machine learning to large data Knowledge Discovery in Databases (KDD) Credit scoring, fraud detection, market basket analysis, medical diagnosis, manufacturing optimization
  11. 11. 11 Big Data ComputationsComputations/Operations Giant 1 (simple stats) is perfect for Hadoop 1.0. Giants 2 (linear algebra), 3 (N- body), 4 (optimization) Spark from UC Berkeley is efficient. Logistic regression, kernel SVMs, conjugate gradient descent, collaborative filtering, Gibbs sampling, alternating least squares. Example is social group-first approach for consumer churn analysis [2] Interactive/On-the-fly data processing – Storm. OLAP – data cube operations. Dremel/Drill Data sets – not embarrassingly parallel? Deep Learning Artificial Neural Networks Machine vision from Google [3] Speech analysis from Microsoft Giant 5 – Graph processing – GraphLab, Pregel, Giraph [1] National Research Council. Frontiers in Massive Data Analysis . Washington, DC: The National Academies Press, 2013. [2] Richter, Yossi ; Yom-Tov, Elad ; Slonim, Noam: Predicting Customer Churn in Mobile Networks through Analysis of Social Groups. In: Proceedings of SIAM International Conference on Data Mining, 2010, S. 732-741 [3] Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc'Aurelio Ranzato, Andrew W. Senior, Paul A. Tucker, Ke Yang, Andrew Y. Ng: Large Scale Distributed Deep Networks. NIPS 2012: 1232-1240
  12. 12. Iterative ML Algorithms [CB09] C. Bunch, B. Drawert, M. Norman, Mapscale: a cloud environment for scientific computing, Technical Report, University of California, Computer Science Department, 2009. What are iterative algorithms? • Those that need communication among the computing entities • Examples – neural networks, PageRank algorithms, network traffic analysis Conjugate gradient descent • Commonly used to solve systems of linear equations • [CB09] tried implementing CG on dense matrices • DAXPY – Multiplies vector x by constant a and adds y. • DDOT – Dot product of 2 vectors • MatVec – Multiply matrix by vector, produce a vector. Communication Overhead • 1 MR per primitive – 6 MRs per CG iteration, hundreds of MRs per CG computation, leading to 10 of GBs of communication even for small matrices. Other iterative algorithms • fast fourier transform, block tridiagonal
  13. 13. 13 ML realizations: 3 Generational view Generation First Generation Second Generation Third Generation Examples SAS, R, Weka, SPSS in native form Mahout, Pentaho, Revolution R, SAS In- memory Analytics (Hadoop) Spark, HaLoop, GraphLab, Pregel, SAS In-memory Analytics (Greenplum/Teradata), Giraph, Golden ORB, Stanford GPS, ML over Storm Scalability Vertical Horizontal (over Hadoop) Horizontal (Beyond Hadoop) Algorithms Available Huge collection of algorithms Small subset – sequential logistic regression, linear SVMs, Stochastic Gradient Descent, k-means clustering, Random Forests etc. Much wider – including Conjugate Gradient Descent (CGD), Alternating Least Squares (ALS), collaborative filtering, kernel SVM, belief propagation, matrix factorization, Gibbs sampling etc. Algorithms Not Available Practically Nothing Vast no. – Kernel SVMs, Multivariate Logistic Regression, Conjugate Gradient Descent, ALS etc. Multivariate logistic regression in general form, K-means clustering etc. – work in progress to expand the set of algorithms available. Fault- Tolerance Single point of failure Most tools are FT, as they are built on top of Hadoop FT – HaLoop, Spark Not FT – Pregel, GraphLab, Giraph Giants All 7 giants – for small data sets Giants 1, and 2. Spark – giant 2, 3 and 4. GraphLab – giant 5. Vijay Srinivas Agneeswaran, Pranay Tonpay and Jayati Tiwari, “Paradigms for Realizing Machine Learning Algorithms”, Big Data Journal (Libertpub), 1(4), 207-214.
  14. 14. 14 Contents Big Data Computations •Introduction to ML •Characterization Berkeley data analytics stack •Spark Real-time Analytics with Storm Hadoop 2.0 (Hadoop YARN) PMML Scoring for Naïve Bayes •PMML Primer •Naïve Bayes Primer GraphLab Programming Abstractions
  15. 15. 15 Data Flow in Spark and Hadoop
  16. 16. 16 Berkeley Big-data Analytics Stack (BDAS)
  17. 17. BDAS: Use Cases 17 Ooyala Uses Cassandra for video data personalization. Pre-compute aggregates VS on-the- fly queries. Moved to Spark for ML and computing views. Moved to Shark for on-the-fly queries – C* OLAP aggregate queries on Cassandra 130 secs, 60 ms in Spark Conviva Uses Hive for repeatedly running ad-hoc queries on video data. Optimized ad-hoc queries using Spark RDDs – found Spark is 30 times faster than Hive ML for connection analysis and video streaming optimization. Yahoo Advertisement targeting: 30K nodes on Hadoop Yarn Hadoop – batch processing Spark – iterative processing Storm – on-the-fly processing Content recommendation – collaborative filtering
  18. 18. BDAS: Spark [MZ12] Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, and Ion Stoica. 2012. Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation (NSDI'12). USENIX Association, Berkeley, CA, USA, 2-2. Transformations/Actions Description Map(function f1) Pass each element of the RDD through f1 in parallel and return the resulting RDD. Filter(function f2) Select elements of RDD that return true when passed through f2. flatMap(function f3) Similar to Map, but f3 returns a sequence to facilitate mapping single input to multiple outputs. Union(RDD r1) Returns result of union of the RDD r1 with the self. Sample(flag, p, seed) Returns a randomly sampled (with seed) p percentage of the RDD. groupByKey(noTasks) Can only be invoked on key-value paired data – returns data grouped by value. No. of parallel tasks is given as an argument (default is 8). reduceByKey(function f4, noTasks) Aggregates result of applying f4 on elements with same key. No. of parallel tasks is the second argument. Join(RDD r2, noTasks) Joins RDD r2 with self – computes all possible pairs for given key. groupWith(RDD r3, noTasks) Joins RDD r3 with self and groups by key. sortByKey(flag) Sorts the self RDD in ascending or descending based on flag. Reduce(function f5) Aggregates result of applying function f5 on all elements of self RDD Collect() Return all elements of the RDD as an array. Count() Count no. of elements in RDD take(n) Get first n elements of RDD. First() Equivalent to take(1) saveAsTextFile(path) Persists RDD in a file in HDFS or other Hadoop supported file system at given path. saveAsSequenceFile(path) Persist RDD as a Hadoop sequence file. Can be invoked only on key-value paired RDDs that implement Hadoop writable interface or equivalent. foreach(function f6) Run f6 in parallel on elements of self RDD.
  19. 19. Representation of an RDD 19 Information HadoopRDD FilteredRDD JoinedRDD Set of partitions 1 per HDFS block Same as parent 1 per reduce task Set of dependencies None 1-to-1 on parent Shuffle on each parent Function to compute data set based on parents Read corresponding block Compute parent and filter it Read and join shuffled data Meta-data on location (preferredLocaations) HDFS block location from namenode None (parent) None Meta-data on partitioning (partitioningScheme) None None HashPartitioner
  20. 20. Some Spark(ling) examples Scala code (serial) var count = 0 for (i <- 1 to 100000) { val x = Math.random * 2 - 1 val y = Math.random * 2 - 1 if (x*x + y*y < 1) count += 1 } println("Pi is roughly " + 4 * count / 100000.0) Sample random point on unit circle – count how many are inside them (roughly about PI/4). Hence, u get approximate value for PI. Based on the PS/PC = AS/AC=4/PI, so PI = 4 * (PC/PS).
  21. 21. Some Spark(ling) examples Spark code (parallel) val spark = new SparkContext(<Mesos master>) var count = spark.accumulator(0) for (i <- spark.parallelize(1 to 100000, 12)) { val x = Math.random * 2 – 1 val y = Math.random * 2 - 1 if (x*x + y*y < 1) count += 1 } println("Pi is roughly " + 4 * count / 100000.0) Notable points: 1. Spark context created – talks to Mesos1 master. 2. Count becomes shared variable – accumulator. 3. For loop is an RDD – breaks scala range object (1 to 100000) into 12 slices. 4. Parallelize method invokes foreach method of RDD. 1 Mesos is an Apache incubated clustering system –
  22. 22. Logistic Regression in Spark: Serial Code // Read data file and convert it into Point objects val lines ="data.txt").getLines() val points = => parsePoint(x)) // Run logistic regression var w = Vector.random(D) for (i <- 1 to ITERATIONS) { val gradient = Vector.zeros(D) for (p <- points) { val scale = (1/(1+Math.exp(-p.y*(w dot p.x)))-1)*p.y gradient += scale * p.x } w -= gradient } println("Result: " + w)
  23. 23. Logistic Regression in Spark // Read data file and transform it into Point objects val spark = new SparkContext(<Mesos master>) val lines = spark.hdfsTextFile("hdfs://.../data.txt") val points = => parsePoint(x)).cache() // Run logistic regression var w = Vector.random(D) for (i <- 1 to ITERATIONS) { val gradient = spark.accumulator(Vector.zeros(D)) for (p <- points) { val scale = (1/(1+Math.exp(-p.y*(w dot p.x)))-1)*p.y gradient += scale * p.x } w -= gradient.value } println("Result: " + w)
  24. 24. Logistic Regression: Spark VS Hadoop 24
  25. 25. 25
  26. 26. 26 Contents Big Data Computations •Introduction to ML •Characterization Berkeley data analytics stack •Spark Real-time Analytics with Storm PMML Scoring for Naïve Bayes •PMML Primer •Naïve Bayes Primer GraphLab Hadoop 2.0 (Hadoop YARN) Programming Abstractions
  27. 27. 27 Real-time Analytics with Storm
  28. 28. Solution to Internet Traffic Analysis Use Case
  29. 29. 29 Contents Big Data Computations •Introduction to ML •Characterization Berkeley data analytics stack •Spark Real-time Analytics with Storm PMML Scoring for Naïve Bayes •PMML Primer •Naïve Bayes Primer GraphLab Hadoop 2.0 (Hadoop YARN) Programming Abstractions
  30. 30. PMML Primer 30 Predictive Model Markup Language Developed by DMG (Data Mining Group) XML representation of a model. PMML offers a standard to define a model, so that a model generated in tool-A can be directly used in tool-B. May contain a myriad of data transformations (pre- and post-processing) as well as one or more predictive models.
  31. 31. Naïve Bayes Primer 31 Normalization Constant Likelihood Prior A simple probabilistic classifier based on Bayes Theorem Given features X1,X2,…,Xn, predict a label Y by calculating the probability for all possible Y value
  32. 32. PMML Scoring for Naïve Bayes 32 Wrote a PMML based scoring engine for Naïve Bayes algorithm. This can theoretically be used in any framework for data processing by invoking the API Deployed a Naïve Bayes PMML generated from R into Storm / Spark and Samza frameworks Real time predictions with the above APIs
  33. 33. 33 Header •Version and timestamp •Model development environment information Data Dictionary •Variable types, missing valid and invalid values, Data Munging/Transformation •Normalization, mapping, discretization Model •Model specifi attributes •Mining Schema •Treatment for missing and outlier values •Targets •Prior probability and default •Outputs •List of computer output fields •Post-processing •Definition of model architecture/parameters.
  34. 34. <DataDictionary numberOfFields="4"> <DataField name="Class" optype="categorical" dataType="string"> <Value value="democrat"/> <Value value="republican"/> </DataField> <DataField name="V1" optype="categorical" dataType="string"> <Value value="n"/> <Value value="y"/> </DataField> <DataField name="V2" optype="categorical" dataType="string"> <Value value="n"/> <Value value="y"/> </DataField> <DataField name="V3" optype="categorical" dataType="string"> <Value value="n"/> <Value value="y"/> </DataField> </DataDictionary> (ctd on the next slide) PMML Scoring for Naïve Bayes 34
  35. 35. <NaiveBayesModel modelName="naiveBayes_Model" functionName="classification" threshold="0.003"> <MiningSchema> <MiningField name="Class" usageType="predicted"/> <MiningField name="V1" usageType="active"/> <MiningField name="V2" usageType="active"/> <MiningField name="V3" usageType="active"/> </MiningSchema> <Output> <OutputField name="Predicted_Class" feature="predictedValue"/> <OutputField name="Probability_democrat" optype="continuous" dataType="double" feature="probability" value="democrat"/> <OutputField name="Probability_republican" optype="continuous" dataType="double" feature="probability" value="republican"/> </Output> <BayesInputs> (ctd on the next page) PMML Scoring for Naïve Bayes 35
  36. 36. PMML Scoring for Naïve Bayes 36 <BayesInputs> <BayesInput fieldName="V1"> <PairCounts value="n"> <TargetValueCounts> <TargetValueCount value="democrat" count="51"/> <TargetValueCount value="republican" count="85"/> </TargetValueCounts> </PairCounts> <PairCounts value="y"> <TargetValueCounts> <TargetValueCount value="democrat" count="73"/> <TargetValueCount value="republican" count="23"/> </TargetValueCounts> </PairCounts> </BayesInput> <BayesInput fieldName="V2"> * <BayesInput fieldName="V3"> * </BayesInputs> <BayesOutput fieldName="Class"> <TargetValueCounts> <TargetValueCount value="democrat" count="124"/> <TargetValueCount value="republican" count="108"/> </TargetValueCounts> </BayesOutput>
  37. 37. PMML Scoring for Naïve Bayes 37 Definition Of Elements:- DataDictionary : Definitions for fields as used in mining models ( Class, V1, V2, V3 ) NaiveBayesModel : Indicates that this is a NaiveBayes PMML MiningSchema : lists fields as used in that model. Class is “predicted” field, V1,V2,V3 are “active” predictor fields Output: Describes a set of result values that can be returned from a model
  38. 38. PMML Scoring for Naïve Bayes 38 Definition Of Elements (ctd .. ) :- BayesInputs: For each type of inputs, contains the counts of outputs BayesOutput: Contains the counts associated with the values of the target field
  39. 39. Sample Input Eg1 - n y y n y y n n n n n n y y y y Eg2 - n y n y y y n n n n n y y y n y • 1st , 2nd and 3rd Columns: Predictor variables ( Attribute “name” in element MiningField ) • Using these we predict whether the Output is Democrat or Republican ( PMML element BayesOutput) PMML Scoring for Naïve Bayes 39
  40. 40. PMML Scoring for Naïve Bayes 40 • 3 Node Xeon Machines Storm cluster ( 8 quad code CPUs, 32 GB RAM, 32 GB Swap space, 1 Nimbus, 2 Supervisors ) Number of records ( in millions ) Time Taken (seconds) 0.1 4 0.4 7 1.0 12 2.0 21 10 129 25 310
  41. 41. PMML Scoring for Naïve Bayes 41 • 3 Node Xeon Machines Spark cluster( 8 quad code CPUs, 32 GB RAM and 32 GB Swap space ) Number of records ( in millions ) Time Taken ( 0.1 1 min 47 sec 0.2 3 min 35 src 0.4 6 min 40 secs 1.0 35 mins 17 sec 10 More than 3 hrs
  42. 42. 42 Contents Big Data Computations •Introduction to ML •Characterization Berkeley data analytics stack •Spark Real-time Analytics with Storm PMML Scoring for Naïve Bayes •PMML Primer •Naïve Bayes Primer GraphLab Hadoop 2.0 (Hadoop YARN) Programming Abstractions
  43. 43. GraphLab: Ideal Engine for Processing Natural Graphs [YL12] [YL12] Yucheng Low, Danny Bickson, Joseph Gonzalez, Carlos Guestrin, Aapo Kyrola, and Joseph M. Hellerstein. 2012. Distributed GraphLab: a framework for machine learning and data mining in the cloud. Proceedings of the VLDB Endowment 5, 8 (April 2012), 716-727. Goals – targeted at machine learning. •Model graph dependencies, be asynchronous, iterative, dynamic. Data associated with edges (weights, for instance) and vertices (user profile data, current interests etc.). Update functions – lives on each vertex • Transforms data in scope of vertex. • Can choose to trigger neighbours (for example only if Rank changes drastically) • Run asynchronously till convergence – no global barrier. Consistency is important in ML algorithms (some do not even converge when there are inconsistent updates – collaborative filtering). • GraphLab – provides varying level of consistency. Parallelism VS consistency. Implemented several algorithms, including ALS, K-means, SVM, Belief propagation, matrix factorization, Gibbs sampling, SVD, CoEM etc. • Co-EM (Expectation Maximization) algorithm 15x faster than Hadoop MR – on distributed GraphLab, only 0.3% of Hadoop execution time.
  44. 44. GraphLab 2: PowerGraph – Modeling Natural Graphs [1] [1] Joseph E. Gonzalez, Yucheng Low, Haijie Gu, Danny Bickson, and Carlos Guestrin (2012). "PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs." Proceedings of the 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI '12). GraphLab could not scale to Altavista web graph 2002, 1.4B vertices, 6.7B edges. • Most graph parallel abstractions assume small neighbourhoods – low degree vertices • But natural graphs (LinkedIn, Facebook, Twitter) – power law graphs. • Hard to partition power law graphs, high degree vertices limit parallelism. Powergraph provides new way of partitioning power law graphs • Edges are tied to machines, vertices (esp. high degree ones) span machines • Execution split into 3 phases: • Gather, apply and scatter. Triangle counting on Twitter graph • Hadoop MR took 423 minutes on 1536 machines • GraphLab 2 took 1.5 minutes on 1024 cores (64 machines)
  45. 45. 45 Contents Big Data Computations •Introduction to ML •Characterization Berkeley data analytics stack •Spark Real-time Analytics with Storm PMML Scoring for Naïve Bayes •PMML Primer •Naïve Bayes Primer GraphLab Hadoop 2.0 (Hadoop YARN) Programming Abstractions
  46. 46. Hadoop YARN Requirements or 1.0 shortcomings46 R1: Scalability •single cluster limitation R2: Multi-tenancy •Addressed by Hadoop-on- Demand •Security, Quotas R3: Locality awareness •Shuffle of records R4: Shared cluster utilization •Hogging by users •Typed slots R5: Reliability/Availability •Job Tracker bugs R6: Iterative Machine Learning Vinod Kumar Vavilapalli, Arun C Murthy , Chris Douglas, Sharad Agarwal, Mahadev Konar, Robert Evans, Thomas Graves, Jason Lowe , Hitesh Shah, Siddharth Seth, Bikas Saha, Carlo Curino, Owen O'Malley, Sanjay Radia, Benjamin Reed, and Eric Baldeschwieler, “Apache Hadoop YARN: Yet Another Resource Negotiator”, ACM Symposium on Cloud Computing, Oct 2013, ACM Press.
  47. 47. 47 Hadoop YARN Architecture
  48. 48. YARN Internals 48 Application Master •Sends ResourceRequests to the YARN RM •Captures containers, resources per container, locality preferences. YARN RM •Generates tokens and containers •Global view of cluster – monolithic scheduling. Node Manager •Node health monitoring, advertise available resources through heartbeats to RM.
  49. 49. 49 Contents Big Data Computations •Introduction to ML •Characterization Berkeley data analytics stack •Spark Real-time Analytics with Storm PMML Scoring for Naïve Bayes •PMML Primer •Naïve Bayes Primer GraphLab Hadoop 2.0 (Hadoop YARN) Programming Abstractions
  50. 50. Programming Abstractions 50 PMML •XML based representation of the analytical model Spark •Scala collection – over a distributed shared memory system GraphLab •Gather-Apply- Scatter Forge •Domain Specific Language
  51. 51. 51 •Domain specific language approach from Stanford. •Forge [AKS13] – a meta DSL for high performance DSLs. •40X faster than Spark! •OptiML – DSL for machine language Forge: Approach to build high performance Domain Specific Languages [Arvind K. Sujeeth, Austin Gibbons, Kevin J. Brown, HyoukJoong Lee, Tiark Rompf, Martin Odersky, and Kunle Olukotun. 2013. Forge: generating a high performance DSL implementation from a declarative specification. In Proceedings of the 12th international conference on Generative programming: concepts & experiences (GPCE '13). ACM, New York, NY, USA, 145-154.
  52. 52. • Beyond Hadoop Map-Reduce philosophy • Optimization and other problems. • Real-time computation • Processing specialized data structures • PMML scoring • Spark for batch computations • Spark streaming and Storm for real-time. • Allows traditional analytical tools/algorithms to be re- used. Conclusions 52
  53. 53. Thank You! Mail • LinkedIn • Blogs • Twitter •@impetustech