Department of Computer Science University of Cyprus EPL646 Advanced Topics in Databases. Lecture 15

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1 Department of Computer Science University of Cyprus EPL646 Advanced Topics in Databases Lecture 15 Big Data Management V (Big-data Analytics / Map-Reduce) Chapter 16 and 19: Abideboul et. Al. Demetris Zeinalipour 15-1

2 EPL646: Part Β Distributed/Web/Cloud DBs/Dstores Lecture Focus (OLTP) (OLAP) Venn Diagram by 451 group 15-2

3 Lecture Outline Introduction to "Big-Data" Analytics Example Scenarios and Architectures. Map-Reduce Programming Model Microsoft's Dryad Programming Model Map-Reduce Counting Problem Map-Reduce Architecture Hadoop JobTracker, Tasktrackers and data-nodes Failure Management Map-Reduce Optimizations Combiners, Compression, In-Memory Shuffling, Speculative Execution Programming Map-Reduce With Languages, PIG and in-the-cloud 15-3

4 Big-data Analytics 15-4

5 Big-data Analytics (Example) We have a large file of words, one word to a line. e.g., analyze web server logs for popular URLs Count the number of times each distinct word appears in the file i.e., sort datafile uniq c Scenario captures essence of MapReduce Great thing is it is naturally parallelizable! 15-5

6 Big-data Analytics 15-6

7 Map-Reduce Programming Model 15-7

8 Map-Reduce Programming Model Another Model: Microsoft Dryad! programming models for writing parallel and distributed programs to scale from a small cluster to a large data-center Higher Level Language: DryadLINQ 15-8

9 Dryad Programming Model Microsoft Dryad! programming models for writing parallel and distributed programs to scale from a small cluster to a large data-center Higher Level Language: DryadLINQ (PIG counterpart) Let us get back to Map-Reduce! 15-9

10 Map-Reduce Problem Count the distinct words in all documents cat *.txt sort uniq -c 1 TB on 1 PC = 2 hours!!! 1TB on 100 PCs = 1min!!! 15-10

11 Map-Reduce Example Example uses 1 mapper / 1 reduce only! M a p S hu ffl e R e d u c e 15-11

12 Map-Reduce Programming Model (dumping) (hashing / sorting) (grouping) 15-12

13 Map-Reduce Architecture (e.g., in Hadoop) HFDS blocks (64MB containing documents) Standard Output (e.g., socket) Hashing HDFS Reading Local Shuffling (of terms) Remote Write (e.g., Socket) HDFS Writing 15-13

14 Map-Reduce Architecture (e.g., in Hadoop) 15-14

15 Map-Reduce Architecture (Processing Remarks) 15-15

16 Map-Reduce Architecture (Failure Management) "ZooKeeper: Wait-free coordination for Internet-scale systems", Hunt et al., USENIX 2010,

17 Map-Reduce Optimizations (Combiners) * Distributive: COUNT, MIN, MAX, SUM AVG, STDDEV (Algebraic) and MEDIAN, RANK (Holistic, all are necessary) 15-18

18 Map-Reduce Optimizations (Compression) * 15-19

19 Map-Reduce Optimizations (Shuffling in Memory) * 15-20

20 Map-Reduce Optimizations (Speculative Execution) * 15-21

21 MapReduce in Hadoop (MR => HADOOP => HBASE) Map-Reduce: a programming model for processing large data sets. Invented by Google! "MapReduce: Simplified Data Processing on Large Clusters, Jeffrey Dean and Sanjay Ghemawat, OSDI'04: Sixth Symposium on Operating System Design and Implementation,San Francisco, CA, December, 2004." Can be implemented in any language (recall javascript Map- Reduce we used in the context of CouchDB). Hadoop: Apache's open-source software framework that supports data-intensive distributed applications Derived from Google's MapReduce + Google File System (GFS) papers. (Input by Yahoo!, Facebook, etc.) Enables applications to work with thousands of computationindependent computers and petabytes of data. Download:

22 MapReduce in Hadoop (Who is driving Hadoop?) 15-23

23 MapReduce in Hadoop (MR => HADOOP => HBASE) Hadoop Project Modules: Hadoop Common: The common utilities that support the other Hadoop modules. Hadoop Distributed File System (HDFS ): A distributed file system that provides highthroughput access to application data. Hadoop YARN (Yet Another Resource Negotiator): A framework for job scheduling and cluster resource management. Hadoop MapReduce (MapReduce v2.0): A YARN-based system for parallel processing of large data sets. Other Hadoop-related projects at Apache include: Avro : A data serialization system. Cassandra : A scalable multi-master database with no single points of failure. Chukwa : A data collection system for managing large distributed systems. HBase (Hadoop Database): A scalable, distributed database that supports structured data storage for large tables. (Next Lectures) Hive : A data warehouse infrastructure that provides data summarization and ad hoc querying. Mahout : A Scalable machine learning and data mining library. Pig : A high-level data-flow language and execution framework for parallel computation. (Next Lectures) ZooKeeper : A high-performance coordination service for distributed applications

24 Programming with Hadoop (with Languages) * Our Focus! 15-25

25 Programming with Hadoop (in the Cloud!) * Our Focus! 15-26

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