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An Introduction to Hadoop

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An Introduction to Hadoop and the MapReduce paradigm. (A presentation that I did in mid-2010.)

An Introduction to Hadoop and the MapReduce paradigm. (A presentation that I did in mid-2010.)

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  • Hello and welcome to An Introduction to Hadoop
  • Before we begin, I’m …
  • Here’s what I want to accomplish in this talk
  • Read this quote. That data is something like 4 exabytes.
  • User-generated content
  • Traditional retailers are creating this data too
  • And government has troves of data too
  • One way to do that analysis is through Hadoop
  • Rackspace for log processing. Netflix for recommendations. LinkedIn for social graph. SU for page recommendations.
  • HDFS cluster/healing. MapReduce
  • Not cheap servers
  • In the cluster there are two kinds of nodes….
  • Let’s talk about HDFS
  • So let’s look at an example: Word Count. WC is the hello world for MapReduce jobs.
  • Transcript

    • 1. An introduction to
    • 2. Hello
      • Processing against a 156 node cluster
      • Certified Hadoop Developer
      • Certified Hadoop System Administrator
    • 3. Goals
      • Why should you care?
      • What is it?
      • How does it work?
    • 4. Data Everywhere “ Every two days now we create as much information as we did from the dawn of civilization up until  2003”
      • Eric Schmidt
      • then CEO of Google
      • Aug 4, 2010
    • 5. Data Everywhere
    • 6. Data Everywhere
    • 7. Data Everywhere
    • 8. The Hadoop Project
      • Originally based on papers published by Google in 2003 and 2004
      • Hadoop started in 2006 at Yahoo!
      • Top level Apache Foundation project
      • Large, active user base, user groups
      • Very active development, strong development team
    • 9. Who Uses Hadoop?
    • 10. Hadoop Components Storage Self-healing high-bandwidth clustered storage Processing Fault-tolerant distributed processing HDFS MapReduce
    • 11. Typical Cluster
      • 3-4000 commodity servers
      • Each server
        • 2x quad-core
        • 16-24 GB ram
        • 4-12 TB disk space
      • 20-30 servers per rack
    • 12. 2 Kinds of Nodes Master Nodes Slave Nodes
    • 13. Master Nodes
      • NameNode
        • only 1 per cluster
        • metadata server and database
        • SecondaryNameNode helps with some housekeeping
      • JobTracker
      • only 1 per cluster
      • job scheduler
    • 14. Slave Nodes
      • DataNodes
        • 1-4000 per cluster
        • block data storage
      • TaskTrackers
      • 1-4000 per cluster
      • task execution
    • 15. HDFS Basics
      • HDFS is a filesystem written in Java
      • Sits on top of a native filesystem
      • Provides redundant storage for massive amounts of data
      • Use cheap(ish), unreliable computers
    • 16. HDFS Data
      • Data is split into blocks and stored on multiple nodes in the cluster
        • Each block is usually 64 MB or 128 MB (conf)
      • Each block is replicated multiple times (conf)
        • Replicas stored on different data nodes
      • Large files, 100 MB+
    • 17. NameNode
      • A single NameNode stores all metadata
      • Filenames, locations on DataNodes of each block, owner, group, etc.
      • All information maintained in RAM for fast lookup
      • Filesystem metadata size is limited to the amount of available RAM on the NameNode
    • 18. SecondaryNameNode
      • The Secondary NameNode is not a failover NameNode
      • Does memory-intensive administrative functions for the NameNode
      • Should run on a separate machine
    • 19. Data Node
      • DataNodes store file contents
      • Stored as opaque ‘blocks’ on the underlying filesystem
      • Different blocks of the same file will be stored on different DataNodes
      • Same block is stored on three (or more) DataNodes for redundancy
    • 20. Self-healing
      • DataNodes send heartbeats to the NameNode
        • After a period without any heartbeats, a DataNode is assumed to be lost
        • NameNode determines which blocks were on the lost node
        • NameNode finds other DataNodes with copies of these blocks
        • These DataNodes are instructed to copy the blocks to other nodes
        • Replication is actively maintained
    • 21. HDFS Data Storage
      • NameNode holds file metadata
      • DataNodes hold the actual data
        • Block size is 64 MB, 128 MB, etc
        • Each block replicated three times
      NameNode foo.txt: blk_1, blk_2, blk_3 bar.txt: blk_4, blk_5 DataNodes blk_1 blk_2 blk_3 blk_5 blk_1 blk_3 blk_4 blk_1 blk_4 blk_5 blk_2 blk_4 blk_2 blk_3 blk_5
    • 22. What is MapReduce?
      • MapReduce is a method for distributing a task across multiple nodes
      • Automatic parallelization and distribution
      • Each node processes data stored on that node (processing goes to the data)
    • 23. Features of MapReduce
      • Fault-tolerance
      • Status and monitoring tools
      • A clean abstraction for programmers
    • 24. JobTracker
      • MapReduce jobs are controlled by a software daemon known as the JobTracker
      • The JobTracker resides on a master node
        • Assigns Map and Reduce tasks to other nodes on the cluster
        • These nodes each run a software daemon known as the TaskTracker
        • The TaskTracker is responsible for actually instantiating the Map or Reduce task, and reporting progress back to the JobTracker
    • 25. Two Parts
      • Developer specifies two functions:
        • map()
        • reduce()
      • The framework does the rest
    • 26. map()
      • The Mapper reads data in the form of key/value pairs
      • It outputs zero or more key/value pairs
      map(key_in, value_in) -> (key_out, value_out)
    • 27. reduce()
      • After the Map phase all the intermediate values for a given intermediate key are combined together into a list
      • This list is given to one or more Reducers
      • The Reducer outputs zero or more final key/value pairs
        • These are written to HDFS
    • 28. map() Word Count map(String input_key, String input_value) foreach word w in input_value emit(w, 1) (1234, “to be or not to be”) (5678, “to see or not to see”) (“to”,1),(“be”,1),(“or”,1),(“not”,1), (“to”,1),(“be”,1), (“to”,1),(“see”,1), (“or”,1),(“not”,1),(“to”,1),(“see”,1)
    • 29. reduce() Word Count reduce(String output_key, List middle_vals) set count = 0 foreach v in intermediate_vals: count += v emit(output_key, count) (“to”, [1,1,1,1]) (“be”,[1,1]) (“or”,[1,1]) (“not”,[1,1]) (“see”,[1,1]) (“to”, 4) (“be”,2) (“or”,2) (“not”,2) (“see”,2)
    • 30. Resources http://hadoop.apache.org/ http://developer.yahoo.com/hadoop/ http://www.cloudera.com/resources/?media=Video
    • 31. Questions?