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MapReduce: simplified data processing on large clusters

MapReduce: simplified data processing on large clusters. Jeffrey Dean and Sanjay Ghemawat Presented By :- Venkataramana Chunduru. AGENDA. GFS MAP REDUCE HADOOP. Motivation. Input data is large. The whole Web, billions of Pages. Lots of machines Use them efficiently.

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MapReduce: simplified data processing on large clusters

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  1. MapReduce: simplified data processing on large clusters Jeffrey Dean and Sanjay Ghemawat Presented By :- Venkataramana Chunduru

  2. AGENDA • GFS • MAP REDUCE • HADOOP

  3. Motivation • Input data is large. • The whole Web, billions of Pages. • Lots of machines • Use them efficiently. • Google needed good Distributed file System • Why not use the existing file systems? • Google’s problems are different from anyone else. • GFS is designed for Google apps and workloads. • Google apps are designed for GFS.

  4. NFS Disadvantages • Network congestion • Heavy disk activity of the NFS server adversely affects the NFS’s performance. • When the client attempts to mount , the client system hangs, although this can be mitigated using a specific mount. • If the server hosting the exportedfile system becomes unavailable due to any reason, no one can access the resource. NFS has security problems because its design assumes a trustednetwork.

  5. GFS Assumptions • High Component failure rates • Inexpensive commodity components fail all the time. • Modest number of huge files. • Just a few million • Each is 100 MB or larger: multi GB files typically • Files are write once ,mostly appended to • Perhaps Concurrently • Large streaming reads.

  6. GFS Design Decisions • Files are stored as chunks. - Fixed size(64 MB). • Reliability through replication. - Each chunk is replicated across 3+ chunkservers • Single master to co ordinate access,keep metadata - Simple centralized management. • No data caching - Little benefit due to large datasets,streaming reads.

  7. GFS Architecture

  8. Single Master • From Distributed systems we know it is a : - Single point of failure. - Scalibility bottleneck. • GFS solutions - Shadow masters - Minimize master involvement • Simple and good enough.

  9. Metadata (1/2) • Global metadata is stored on the master. - File and chunk namespaces. - Mapping from files to chunks. - Locations of each chunk replicas. • All in memory (64bytes/chunk) - Fast - Easily Accessible.

  10. Metadata (2/2) • Master has an operation log for persistent logging of critical metadata updates. - Persistent on local disk - Replicated - Check points for faster recovery.

  11. Deployment in Google • 50 + GFS Clusters • Each with thousands of storage nodes • Managing petabytes of data. • GFS is under big table.

  12. Conclusion of GFS • GFS demonstrates how to support large scale processing workloads on commodity hardware - Designed to tolerate frequent component failures. - Optimized for huge files that are mostly appended and read. - Go for simple solutions. • GFS has met Google's storage needs…. it must be good !!!

  13. Example for MapReduce • Page 1: the weather is good • Page 2: today is good • Page 3: good weather is good.

  14. Map output • Worker 1: • (the 1), (weather 1), (is 1), (good 1). • Worker 2: • (today 1), (is 1), (good 1). • Worker 3: • (good 1), (weather 1), (is 1), (good 1).

  15. Reduce Input • Worker 1: • (the 1) • Worker 2: • (is 1), (is 1), (is 1) • Worker 3: • (weather 1), (weather 1) • Worker 4: • (today 1) • Worker 5: • (good 1), (good 1), (good 1), (good 1)

  16. Reduce Output • Worker 1: • (the 1) • Worker 2: • (is 3) • Worker 3: • (weather 2) • Worker 4: • (today 1) • Worker 5: • (good 4)

  17. MapReduce Architecture

  18. Parallel Execution

  19. Fault Tolerance • Network Failure: • Detect failure via periodic heartbeats • Re-execute completed and in-progress map tasks • Re-execute in progress reduce tasks • Task completion committed through master • Master failure: • Could handle, but don't yet (master failure unlikely)

  20. Refinement • Different partitioning functions. • Combiner function. • Different input/output types. • Skipping bad records. • Local execution. • Status info. • Counters.

  21. What’s • Framework for running applications on large clusters of commodity hardware • Scale: petabytes of data on thousands of nodes • Include • Storage: HDFS • Processing: MapReduce • Support the Map/Reduce programming model • Requirements • Economy: use cluster of comodity computers • Easy to use • Users: no need to deal with the complexity of distributed computing • Reliable: can handle node failures automatically

  22. Whats Hadoop ..Contd. • Hadoop is a software platform that lets one easily write and run applications that process vast amounts of data. • Here's what makes Hadoop especially useful: • Scalable • Economical • Efficient • Reliable

  23. HDFS • Hadoop implements MapReduce, using the Hadoop Distributed File System (HDFS) (see figure below.) • MapReduce divides applications into many small blocks of work. HDFS creates multiple replicas of data blocks for reliability, placing them on compute nodes around the cluster. MapReduce can then process the data where it is located. • Hadoop has been demonstrated on clusters with 2000 nodes. The current design target is 10,000 node clusters.

  24. Hadoop Cluster DFS Block 1 DFS Block 1 DFS Block 1 MAP DFS Block 2 DFS Block 2 MAP Reduce DFS Block 2 MAP DFS Block 3 DFS Block 3 DFS Block 3 Hadoop Architecture Data Data data data data data Data data data data data Data data data data data Data data data data data Data data data data data Data data data data data Data data data data data Data data data data data Data data data data data Data data data data data Data data data data data Data data data data data Results Data data data data Data data data data Data data data data Data data data data Data data data data Data data data data Data data data data Data data data data Data data data data

  25. Sample Hadoop Code • Sample text-files as input: • $ bin/hadoop dfs -ls /usr/joe/wordcount/input/ /usr/joe/wordcount/input/file01 /usr/joe/wordcount/input/file02 $ bin/hadoop dfs -cat /usr/joe/wordcount/input/file01Hello World, Bye World! $ bin/hadoop dfs -cat /usr/joe/wordcount/input/file02Hello Hadoop, Goodbye to hadoop. • Run the application: • $ bin/hadoop jar /usr/joe/wordcount.jar org.myorg.WordCount /usr/joe/wordcount/input /usr/joe/wordcount/output • Output: • $ bin/hadoop dfs -cat /usr/joe/wordcount/output/part-00000 Bye 1 Goodbye 1 Hadoop, 1 Hello 2 World! 1 World, 1 hadoop. 1 to 1

  26. Contd… • Notice that the inputs differ from the first version we looked at, and how they affect the outputs. • Now, lets plug-in a pattern-file which lists the word-patterns to be ignored, via the DistributedCache. • $ hadoop dfs -cat /user/joe/wordcount/patterns.txt \. \, \! to • Run it again, this time with more options: • $ bin/hadoop jar /usr/joe/wordcount.jar org.myorg.WordCount -Dwordcount.case.sensitive=true /usr/joe/wordcount/input /usr/joe/wordcount/output -skip /user/joe/wordcount/patterns.txt • As expected, the output: • $ bin/hadoop dfs -cat /usr/joe/wordcount/output/part-00000 Bye 1 Goodbye 1 Hadoop 1 Hello 2 World 2 hadoop 1

  27. Contd… • Run it once more, this time switch-off case-sensitivity: • $ bin/hadoop jar /usr/joe/wordcount.jar org.myorg.WordCount -Dwordcount.case.sensitive=false /usr/joe/wordcount/input /usr/joe/wordcount/output -skip /user/joe/wordcount/patterns.txt • Sure enough, the output: • $ bin/hadoop dfs -cat /usr/joe/wordcount/output/part-00000 bye 1 goodbye 1 hadoop 2 hello 2 world 2

  28. Hadoop • HDFS assumes that hardware is unreliable and will eventually fail. • Similar to RAID level except -HDFS can replicate data across several machines • Provides Fault tolerance • Extremely high capacity storage

  29. Hadoop • “Moving Computation is cheaper than moving data” • HDFS is said to be rack aware.

  30. Who uses Hadoop? • ‘Facebook’ uses Hadoop to analyze user behavior and the effectiveness of ads on the site. • The tech team at ‘The New York Times’ rented computing power on ‘Amazon’s’ cloud and used Hadoop to convert 11 million archived articles, dating back to 1851, to digital and searchable documents. They turned around in a single day a job that otherwise would have taken months.”

  31. Who uses Hadoop? • Besides Yahoo!, many other organizations are using Hadoop to run large distributed computations. Some of them include: • A9.com • Facebook • Fox Interactive Media • IBM • ImageShack • ISI • Joost • Last.fm • Powerset • The New York Times • Rackspace • Veoh

  32. Yahoo! Launches World's Largest Hadoop Production Application • YAHOO! RECENTLY LAUNCHED WHAT WE BELIEVE IS THE WORLDS LARGEST APACHE HADOOP PRODUCTION APPLICATION. THE YAHOO! SEARCH WEBMAP IS A HADOOP APPLICATION THAT RUNS ON A MORE THAN 10,000 CORE LINUX CLUSTER AND PRODUCES DATA THAT IS NOW USED IN EVERY YAHOO! WEB SEARCH QUERY. • THE WEBMAP BUILD STARTS WITH EVERY WEB PAGE CRAWLED BY YAHOO! AND PRODUCES A DATABASE OF ALL KNOWN WEB PAGES AND SITES ON THE INTERNET AND A VAST ARRAY OF DATA ABOUT EVERY PAGE AND SITE. THIS DERIVED DATA FEEDS THE MACHINE LEARNED RANKING ALGORITHMS AT THE HEART OF YAHOO! SEARCH.

  33. Yahoo’s Hadoop • One of Yahoo's Hadoop clusters sorted 1 terabyte of data in 209 seconds, which beat the previous record of 297 seconds in the annual general purpose (daytona) terabyte sort benchmark. The sort benchmark, which was created in 1998 by Jim Gray, specifies the input data (10 billion 100 byte records), which must be completely sorted and written to disk. This is the first time that either a Java or an open source program has won. Yahoo is both the largest user of Hadoop with 13,000+ nodes running hundreds of thousands of jobs a month and the largest contributor, although non-Yahoo usage and contributions are increasing rapidly. • The cluster statistics were: • 910 nodes, 2 quad core Xeons @ 2.0ghz per node • 4 SATA disks per node, 8G RAM per node • 1 gigabit ethernet on each node, 40 nodes per rack • 8 gigabit ethernet uplinks from each rack to the core. • Red Hat Enterprise Linux Server Release 5.1 (kernel 2.6.18) • Sun Java JDK 1.6.0_05-b13

  34. Process Diagram

  35. Map/Reduce Processes • Launching Application – User application code – Submits a specific kind of Map/Reduce job • JobTracker – Handles all jobs – Makes all scheduling decisions • TaskTracker – Manager for all tasks on a given node • Task – Runs an individual map or reduce fragment for a given job – Forks from the TaskTracker

  36. Hadoop Map-Reduce Architecture • Master-Slave architecture • Map-Reduce Master “Jobtracker” – Accepts MR jobs submitted by users – Assigns Map and Reduce tasks to Tasktrackers – Monitors task and tasktracker status, re-executes tasks upon failure • Map-Reduce Slaves “Tasktrackers” – Run Map and Reduce tasks upon instruction from the Jobtracker – Manage storage and transmission of intermediate output

  37. Imp Links • http://public.yahoo.com/gogate/hadoop-tutorial/start-tutorial.html • http://www.youtube.com/watch?v=5Eib_H_zCEY&feature=related • http://www.youtube.com/watch?v=yjPBkvYh-ss&feature=related • http://labs.google.com/papers/gfs-sosp2003.pdf

  38. Thank you !!!!!

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