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Understanding Hadoop Distributed File System

Learn about HDFS architecture, design goals, core components, data correctness, and rebalancing in this comprehensive guide.

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Understanding Hadoop Distributed File System

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  1. The Hadoop Distributed File System, by DhyubaBorthakurand Related Work Presented by MohitGoenka

  2. The Hadoop Distributed File System: Architecture and Design

  3. Requirement • Need to process Multi Petabyte Datasets • Expensive to build reliability in each application. • Nodes fail every day • Need common infrastructure SECTION TITLE

  4. Introduction • HDFS, Hadoop Distributed File System is designed to run on commodity hardware • Built out by brilliant engineers and contributors from Yahoo, and Facebook and Cloudera and other companies • Has grown into really large project at Apache with significant ecosystem SECTION TITLE

  5. Commodity Hardware • Typically in 2 level architecture • – Nodes are commodity PCs • – 30-40 nodes/rack • – Uplink from rack is 3-4 gigabit • – Rack-internal is 1 gigabit SECTION TITLE

  6. Goals • 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 SECTION TITLE

  7. HDFS Basic Architecture Cluster Membership NameNode 1. filename Secondary NameNode 2. BlckId, DataNodes o SECTION TITLE Client 3.Read data 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 DataNodes

  8. Distributed File System • Single Namespace for entire cluster • Data Coherency • – Write-once-read-many access model • – Client can only append to existing files • Files are broken up into blocks • – Typically 128 MB block size • – Each block replicated on multiple DataNodes • Intelligent Client • – Client can find location of blocks • – Client accesses data directly from DataNode SECTION TITLE

  9. HDFS Core Architecture SECTION TITLE

  10. NameNode Metadata • Meta-data in Memory • – The entire metadata is in main memory • – No demand paging of meta-data • Types of Metadata • – List of files • – List of Blocks for each file • – List of DataNodes for each block • – File attributes, e.g creation time, replication factor • A Transaction Log • – Records file creations, file deletions. etc SECTION TITLE

  11. Data Node • A Block Server • – Stores data in the local file system (e.g. ext3) • – Stores meta-data of a block (e.g. CRC) • – Serves data and meta-data to Clients • Block Report • – Periodically sends a report of all existing blocks to the NameNode • Facilitates Pipelining of Data • – Forwards data to other specified DataNodes SECTION TITLE

  12. Block Placement • Current Strategy • - One replica on local node • - Second replica on a remote rack • - Third replica on same remote rack • - Additional replicas are randomly placed • Clients read from nearest replica • Would like to make this policy pluggable SECTION TITLE

  13. Data Correctness • Use Checksums to validate data • – Use CRC32 • File Creation • – Client computes checksum per 512 byte • – DataNode stores the checksum • File access • – Client retrieves the data and checksum from DataNode • – If Validation fails, Client tries other replicas SECTION TITLE

  14. NameNode Failure • A single point of failure • Transaction Log stored in multiple directories • - A directory on the local file system • - A directory on a remote file system (NFS/CIFS) • Need to develop a real HA solution SECTION TITLE

  15. Data Pipelining • Client retrieves a list of DataNodes on which to place replicas of a block • Client writes block to the first DataNode • The first DataNode forwards the data to the next DataNode in the Pipeline • When all replicas are written, the Client moves on to write the next block in file SECTION TITLE

  16. Rebalancer • Goal: % disk full on DataNodes should be similar • Usually run when new DataNodes are added • Cluster is online when Rebalancer is active • Rebalancer is throttled to avoid network congestion • Command line tool SECTION TITLE

  17. Hadoop Map / Reduce • The Map-Reduce programming model • – Framework for distributed processing of large data sets • – Pluggable user code runs in generic framework • Common design pattern in data processing • cat * | grep | sort | unique -c | cat > file • input | map | shuffle | reduce | output • Natural for: • – Log processing • – Web search indexing • – Ad-hoc queries SECTION TITLE

  18. Data Flow Web Servers Scribe Servers SECTION TITLE Network Storage Oracle RAC Hadoop Cluster MySQL

  19. Basic Operations • Listing files • - ./bin/hadoopfs –ls • Writing files • - ./bin/hadoopfs –put • Running Map Reduce Jobs • - mkdirinput • - cpconf/*.xml input • - cat output/* SECTION TITLE

  20. Hadoop Ecosystem Projects • HBase • - Big Table • HIVE • - Built on Facebook, provides SQL interface • Chukwa • - Log Processing • Pig • - Scientific data analysis language • Zookeeper • - Distributed Systems management SECTION TITLE

  21. Limitatons • The gigabytes to terabytes of data this system handles can only be scaled down to limited threshold • Due to this threshold being very high, the system is limited in a lot of ways • It hampers the efficiency of the system during large computations or parallel data exchange SECTION TITLE

  22. JSON Interface to Control HDFS An Open Source Project by MohitGoenka

  23. JSON Interface to Control HDFS An Open Source Project by MohitGoenka

  24. JSON • JSON (JavaScript Object Notation) is a lightweight data-interchange format • Can be easily read and written by humans • Can be easily parsed by machines • Written in text format • Similar conventions as existing programming languages SECTION TITLE

  25. JSON Data • It is based on two structures: • A collection of name/value pairs • An ordered list of values • Concept: Use the light-weighted nature of JSON data to automate command execution on HDFS interface SECTION TITLE

  26. Goal • Designing a JSON interface to control HDFS • Development of two modules: • For writing into the system • For reading from the system SECTION TITLE

  27. Outcome • User can specify execution commands directly in the JSON file along with data • Only data gets stored into the system • Commands are deleted from the file after execution SECTION TITLE

  28. Sources and Acknowledgements

  29. Sources • DhurbaBorthakur, Apache Hadoop Developer, Facebook Data Infrastructure • MateiZaharia, Cloudera / Facebook / UC Berkeley RAD Lab • Devaraj Das, Yahoo! Inc. Bangalore and Apache Software Foundation • HDFS Java API: • - http://hadoop.apache.org/core/docs/current/api/ • HDFS source code: • - http://hadoop.apache.org/core/version_control.html SECTION TITLE

  30. Acknowledgements • Professor Chris Mattmann for guidance as and when reqired • Hossein (Farshad) Tajalli for his continued support and help throughout the project • All my classmates for providing valuable inputs throughout the work, especially through their presentations SECTION TITLE

  31. That’s All Folks! SECTION TITLE

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