Big Data and Apache Hadoop s MapReduce

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1 Big Data and Apache Hadoop s MapReduce Michael Hahsler Computer Science and Engineering Southern Methodist University January 23, 2012 Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

2 Table of Contents 1 Introduction 2 Hadoop Distributed File System 3 MapReduce 4 More Examples Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

3 Single Machine CPU Memory Disk Typical set up for data processing/mining! What are the problems with big data? Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

4 Big Data Challenge Data Sources Internet and social networks Sensors and science Needed Infrastructure Scale to thousands of CPUs Run on cheap commodity hardware (fault-tolerant hardware is expensive!) Automatically handle data replication and node failure Data distribution and load balancing Easy to implement solutions (thinking in terms of parallel computing is hard!) Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

5 Hardware: Cluster Architecture Switch Backbone Switch Rack 1... Switch Rack n CPU Memory CPU CPU... Memory Memory... CPU Memory Disk Disk Disk Disk Node failure! Rack contains nodes Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

6 Software: Apache Hadoop What is Apache Hadoop? A software framework that supports data-intensive (petabytes) distributed applications under a free license....inspired by Google s MapReduce and Google File System (GFS). Hadoop provides: Distributed file system HDFS API to work with MapReduce Job configuration and scheduling Track progress and utilization Written in the Java programming language. Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

7 What Problem can be solved with Hadoop? Characteristics Processing can easily be made in parallel (simple computations) Process large amounts of unstructured data Running batch jobs is acceptable Examples Creating statistics (word counting) Searching (distributed grep) Sorting Indexing (postings list) Document clustering Graph algorithms (E.g. pagerank) Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

8 Who uses Hadoop? Adobe Amazon.com AOL Facebook Google IBM Microsoft NY Times Yahoo!... Source Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

9 Table of Contents 1 Introduction 2 Hadoop Distributed File System 3 MapReduce 4 More Examples Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

10 The Hadoop Distributed File System (HDFS) Source: Files are split into large block size: 64 MB (typical fs has 4 kb) Replication (2-3x on different racks) Fault-tolerance Master node stores meta information Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

11 Table of Contents 1 Introduction 2 Hadoop Distributed File System 3 MapReduce 4 More Examples Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

12 MapReduce Basic Idea 1 Apply a map function to each input element and emit key/value pairs. map(k1, v1) list(k2, v2) 2 Summarize the results for each key using a reduce function. reduce(k2, list(v2)) (k2, v3) The user only has to specify the map and reduce functions and the framework takes care of the rest! The user does not have to think about concurrency, load balancing, data distribution, fault-tolerance! Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

13 Example: Word Counting Count how often each word appears in a large number of documents. 1 void map ( String name, String document ){ 2 // name: document name 3 // document : document contents 4 for each word w in document : 5 EmitIntermediate ( w, 1 ) 6 } 7 8 void reduce ( String word, Iterator partialcounts ){ 9 // word: a word 10 // partialcounts : a list of aggregated partial counts 11 int sum = 0 ; 12 for each pc in partialcounts : 13 sum += ParseInt ( pc ) ; 14 Emit ( word, AsString ( sum ) ) ; 15 } Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

14 Example: Word Counting the quick brown fox the lazy old dog the quick brown dog the, 1 quick, 1 brown, 1 fox, 1 the, 1 lazy, 1 old, 1 dog, 1 the, 1 quick, 1 brown, 1 dog, 1 brown, 1 brown, 1 dog, 1 dog, 1 fox, 1 lazy, 1 old, 1 quick, 1 quick, 1 the, 1 the, 1 the,1 split map shuffle/sort reduce brown, 2 dog, 2 fox, 1 lazy, 1 old, 1 quick, 2 the, 3 Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

15 Execution of a MapReduce Job Source: Jeffrey Dean and Sanjay Ghemawat, MapReduce: Simplified Data Processing on Large Clusters, OSDI, Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

16 Fault-tolerance Switch Backbone Switch Rack 1... Switch Rack n Master reschedules Task 1 here CPU Memory Disk ping... Task CPU1 Memory Block Disk 1 CPU Memory Block Disk 1... CPU Memory Disk Master Node failure! Rack contains nodes Master re-executes tasks for failed workers. Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

17 Locality Switch Backbone Switch Rack 1... Schedule Task which needs block 2 here or at least on this rack! Switch Rack n CPU Memory Disk CPU CPU... Memory Memory... Block Disk 1 Block Disk 2 CPU Memory Disk Master Rack contains nodes Schedule map tasks near to the data to preserve network bandwidth. Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

18 More Properties Load Balancing: Subdivide work in many small tasks ( # of workers). Since the master dynamically assigns tasks to idle nodes, this automatically provides load balancing. Chaining: MapReduce operations can be chained (output of one operation is input for another operation) to solve more complicated computations. Backup tasks: Reduce phase can only start after all map tasks are finished (use backup tasks to avoid stragglers ). Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

19 Table of Contents 1 Introduction 2 Hadoop Distributed File System 3 MapReduce 4 More Examples Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

20 Example: Distributed Grep Map task? Reduce task? Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

21 Example: Creating an Inverted Index Map task? Reduce task? Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

22 Related Projects Apache HBase: open source, non-relational, distributed database modeled after Google s BigTable Apache Hive: data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. Provides a SQL-like query language called HiveQL. Apache Pig: is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs. Apache Mahout: free implementations of distributed or otherwise scalable machine learning algorithms on the Hadoop platform. Apache Lucene: a high-performance, full-featured text search engine library written entirely in Java. Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

23 Reading Jeffrey Dean and Sanjay Ghemawat, MapReduce: Simplified Data Processing on Large Clusters The Apache Hadoop Project Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, / 23

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