Hadoop
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The document provides an overview of Hadoop distributed computing. It discusses how Hadoop uses MapReduce and HDFS to efficiently process large amounts of data across clusters of commodity servers. Key features of Hadoop include scaling to large datasets, handling failures automatically, and providing a simple programming model through MapReduce.
Hadoop
- 1. Overview on HADOOP Distributed Computing RAGHU JULURI Senior Member Technical Staff Oracle India Development Center. 2/7/2011
- 2. Dealing with lots of Data 20 billion web pages * 20 kb =400 TB 1000 hard disks to store web 1 computer can read ~50 MB/sec from disk => 3 months Sol : spread the work over many machines Hardware & Software Software – Communication & Co-ordination , recovery from failure ,status reporting, debugging . Every application need to implement above functionality (Google search (indexing) , page ranking,trends,picasa…) In 2003 Google came up with Map Reduce run time library. 2/7/2011
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- 9. Hadoop, Why? Need to process Multi Petabyte Datasets Expensive to build reliability in each application. Nodes fail every day – Failure is expected, rather than exceptional. – The number of nodes in a cluster is not constant. Need common infrastructure – Efficient, reliable, Open Source Apache License The above goals are same as Condor, but Workloads are IO bound and not CPU bound 2/7/2011
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- 12. HDFS splits user data across servers in a cluster. It uses replication to ensure that even multiple node failures will not cause data loss. 2/7/2011
- 13. Goals of HDFS Very Large Distributed File System – 10K nodes, 100 million files, 10 PB Assumes Commodity Hardware – Files are replicated to handle hardware failure – Detect failures and recovers from them Optimized for Batch Processing – Data locations exposed so that computations can move to where data resides – Provides very high aggregate bandwidth User Space, runs on heterogeneous OS 2/7/2011
- 14. Secondary NameNode Client HDFS Architecture NameNode DataNodes 1. filename 2. BlckId, DataNodes o 3.Read data Cluster Membership Cluster Membership NameNode : Maps a file to a file-id and list of MapNodes DataNode : Maps a block-id to a physical location on disk SecondaryNameNode: Periodic merge of Transaction log 2/7/2011
- 15. MapReduce: Programming Model How now Brown cow How does It work now brown 1 cow 1 does 1 How 2 it 1 now 2 work 1 M M M M R R <How,1> <now,1> <brown,1> <cow,1> <How,1> <does,1> <it,1> <work,1> <now,1> <How,1 1> <now,1 1> <brown,1> <cow,1> <does,1> <it,1> <work,1> Input Output Map Reduce MapReduce Framework 2/7/2011
- 16. MapReduce: Programming Model Process data using special map () and reduce () functions The map() function is called on every item in the input and emits a series of intermediate key/value pairs All values associated with a given key are grouped together The reduce() function is called on every unique key, and its value list, and emits a value that is added to the output 2/7/2011
- 17. MapReduce Benefits Greatly reduces parallel programming complexity Reduces synchronization complexity Automatically partitions data Provides failure transparency Handles load balancing Practical Approximately 1000 Google MapReduce jobs run everyday. 2/7/2011
- 18. MapReduce Examples Word frequency Map doc Reduce <word,3> <word,1> <word,1> <word,1> Runtime System <word,1,1,1> 2/7/2011
- 19. A Brief History Functional programming (e.g., Lisp) map() function Applies a function to each value of a sequence reduce() function Combines all elements of a sequence using a binary operator 2/7/2011
- 20. MapReduce Execution Overview The user program, via the MapReduce library, shards the input data User Program Input Data Shard 0 Shard 1 Shard 2 Shard 3 Shard 4 Shard 5 Shard 6 * Shards are typically 16-64mb in size 2/7/2011
- 21. MapReduce Execution Overview The user program creates process copies distributed on a machine cluster. One copy will be the “Master” and the others will be worker threads. User Program Master Workers Workers Workers Workers Workers 2/7/2011
- 22. MapReduce Resources The master distributes M map and R reduce tasks to idle workers. M == number of shards R == the intermediate key space is divided into R parts Master Idle Worker Message(Do_map_task) 2/7/2011
- 23. MapReduce Resources Each map-task worker reads assigned input shard and outputs intermediate key/value pairs. Output buffered in RAM. Map worker Shard 0 Key/value pairs 2/7/2011
- 24. MapReduce Execution Overview Each worker flushes intermediate values, partitioned into R regions, to disk and notifies the Master process. Master Map worker Disk locations Local Storage 2/7/2011
- 25. MapReduce Execution Overview Master process gives disk locations to an available reduce-task worker who reads all associated intermediate data. Master Reduce worker Disk locations remote Storage 2/7/2011
- 26. MapReduce Execution Overview Each reduce-task worker sorts its intermediate data. Calls the reduce function, passing in unique keys and associated key values. Reduce function output appended to reduce-task’s partition output file. Reduce worker Sorts data Partition Output file 2/7/2011
- 27. MapReduce Execution Overview Master process wakes up user process when all tasks have completed. Output contained in R output files. wakeup User Program Master Output files 2/7/2011
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- 29. Pig Data-flow oriented language “ Pig latin” Datatypes include sets, associative arrays,tuples High-level language for routing data, allows easy integration of Java for complex tasks • Developed at Yahoo! Hive • SQL-based data warehousing app Feature set is similar to Pig – Language is more strictly SQL Supports SELECT, JOIN, GROUP BY, etc. Features for analyzing very large data sets – Partition columns – Sampling – Buckets Developed at Facebook 2/7/2011
- 30. Hbase Column-store database – Based on design of Google BigTable – Provides interactive access to information Holds extremely large datasets (multi-TB) Constrained access model – (key, val) lookup – Limited transactions (only one row) 2/7/2011
- 31. ZooKeeper Distributed consensus engine Provides well-defined concurrent access semantics: – Leader election – Service discovery – Distributed locking / mutual exclusion – Message board / mailboxes 2/7/2011
- 32. Some more projects… Chukwa – Hadoop log aggregation Scribe – More general log aggregation Mahout – Machine learning library Cassandra – Column store database on a P2P backend Dumbo – Python library for streaming Ganglia – distributed monitoring 2/7/2011
- 33. Conclusions Computing with big datasets is a fundamentally different challenge than doing “big compute” over a small dataset • New ways of thinking about problems needed – New tools provide means to capture this – MapReduce, HDFS, etc. can help 2/7/2011
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