Introduction to Hadoop and MapReduce

Introduction to Hadoop and MapReduce

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  Introduction to Hadoopand MapReduce Csaba Toth GDG Fresno Meeting Date: February 6th, 2014 Location: The Hashtag, Fresno
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  Agenda • • • • • Big Data A littlehistory Hadoop Map Reduce Demo: Hadoop with Google Compute Engine and Google Cloud Storage
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  Big Data • Wikipedia:“collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications” • Examples: (Wikibon - A Comprehensive List of Big Data Statistics) – 100 Terabytes of data is uploaded to Facebook every day – Facebook Stores, Processes, and Analyzes more than 30 Petabytes of user generated data – Twitter generates 12 Terabytes of data every day – LinkedIn processes and mines Petabytes of user data to power the "People You May Know" feature – YouTube users upload 48 hours of new video content every minute of the day – Decoding of the human genome used to take 10 years. Now it can be done in 7 days
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  Big Data characteristics •Three Vs: Volume, Velocity, Variety • Sources: – – – – Science, Sensors, Social networks, Log files Public Data Stores, Data warehouse appliances Network and in-stream monitoring technologies Legacy documents • Main problems: – Storage Problem – Money Problem – Consuming and processing the data
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  A Little History TwoSeminar papers: • “The Google File System” - October 2003 http://labs.google.com/papers/gfs.html – describes a scalable, distributed, fault-tolerant file system tailored for data-intensive applications, running on inexpensive commodity hardware, delivers high aggregate performance • “MapReduce: Simplified Data Processing on Large Clusters” - April 2004 http://queue.acm.org/detail.cfm?id=988408 – Describes a programming model and an implementation for processing large data sets. 1. 2. map function that processes a key/value pair to generate a set of intermediate key/value pairs reduce function that merges all intermediate values associated with the same intermediate key
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  Hadoop • Hadoop isan open-source software framework that supports data-intensive distributed applications. • It is written in Java, utilizes JVMs • Named after it’s creator’s (Doug Cutting, Yahoo) son’s toy elephant • Hadoop is managing a cluster of commodity hardware computers. The cluster is composed of a single master node and multiple worker nodes
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  Hadoop vs RDBMS Hadoop/ MapReduce RDBMS Size of data Petabytes Gigabytes Integrity of data Low High (referential, typed) Data schema Dynamic Static Access method Interactive and Batch Batch Scaling Linear Nonlinear (worse than linear) Data structure Unstructured Structured Normalization of data Not Required Required Query Response Time Has latency (due to batch processing) Can be near immediate
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  MapReduce • Hadoop leveragesthe programming model of map/reduce. It is optimized for processing large data sets. • MapReduce is an essential technique to do distributed computing on clusters of computers/nodes. • The goal of map reduce is to break huge data sets into smaller pieces, distribute those pieces to various worker nodes, and process the data in parallel. • Hadoop leverages a distributed file system to store the data on various nodes.
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  MapReduce • It isabout two functions: map and reduce 1. Map Step: – it is about dividing the problem into smaller subproblems. A master node has the job of distributing the work to worker nodes. The worker node just does one thing and returns the work back to the master node. 2. Reduce Step: – Once the master gets the work from the worker nodes, the reduce step takes over and combines all the work. By combining the work you can form some answer and ultimately output.
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  MapReduce – Mapstep • There is a master node and many slave nodes. • The master node takes the input, divides it into smaller sub-problems, and distributes the input to worker or slave nodes. worker node may do this again in turn, leading to a multi-level tree structure. • The worker/slave nodes processes the data into a smaller problem, and passes the answer back to its master node. • Each mapping operation is independent of the others, all maps can be performed in parallel.
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  MapReduce – Reducestep • The master node then collects the answers from the worker or slave nodes. It then aggregates the answers and creates the needed output, which is the answer to the problem it was originally trying to solve. • Reducers can also preform the reduction phase in parallel. That is how the system can process petabytes in a matter of hours.
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  Map, Shuffle, andReduce https://mm-tom.s3.amazonaws.com/blog/MapReduce.png
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  Word count http://blog.jteam.nl/wp-content/uploads/2009/08/MapReduceWordCountOverview1.png
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  Hadoop architecture • • • • Job Tracker TaskTracker Name Node Data Node
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  Figures • Following: somefigures from the book Hadoop: The Definitive Guide, 3rd Edition
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  A client readingdata from HDFS
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  A client writingdata to HDFS
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  Network distance inHadoop
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  MapReduce data flowwith a single reduce task
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  MapReduce data flowwith multiple reduce tasks
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  Hadoop architecture Presentation Layer Web Browser(JS) Data Mining (Pegasus, Mahout) Index, Searches (Lucene) DB drivers (Hive driver) Advanced Query Engine (Hive, Pig) Computing Layer (MapReduce) Storage Layer (HDFS) Data Integration Layer Flume Sqoop Log Data RDBMS
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  Demo • Google ComputeEngine + Google Cloud Storage • Using Ubuntu as a remote control host • Following the tutorial of: – https://github.com/GoogleCloudPlatform/solutions-google-computeengine-cluster-for-hadoop – Hadoop on Google Compute Engine for Processing Big Data: https://www.youtube.com/watch?v=se9vV8eIZME • The example hadoop job is an advanced version of word count in perl or python: the words are sorted by length and abc • Showing also Google Developer Tool web interface
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  References • Google’s tutorial(see github and YouTube link of the Demo) • Tom White: Hadoop: The Definitive Guide, 3rd Edition, Yahoo Press • Lynn Langit’s various presentations and YouTube videos • Dattatrey Sindol: Big Data Basics - Part 1 - Introduction to Big Data • Bruno Terkaly’s presentations (for example Hadoop on Azure: Introduction) • Daniel Jebaraj: Ignore HDInsight at Your Own Peril: Everything You Need to Know
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  Thanks for yourattention!