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HDFS ( Hadoop Distributed File System)

HDFS ( Hadoop Distributed File System). 2011-10-10 Taejoong Chung, MMLAB. Contents. Introduction Hadoop Distributed File System? Assumption & Goals Mechanism Structure Data Management Maintenance Pros and Cons. HDFS. Hadoop Distributed File System

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HDFS ( Hadoop Distributed File System)

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  1. HDFS (Hadoop Distributed File System) 2011-10-10 Taejoong Chung, MMLAB

  2. Contents • Introduction • Hadoop Distributed File System? • Assumption & Goals • Mechanism • Structure • Data Management • Maintenance • Pros and Cons

  3. HDFS • Hadoop Distributed File System • Started from ‘Nutch’ (open-source search engine project) in 2005 • Java based, Apache top-level project • To save massive data with low cost • Characteristics • User-level distributed file system • Fault-tolerant • Could be deployed on low-cost hardwares

  4. Assumption & Goals • Protection of Failure • Detection of faults and quick, automatic recovery • Consider hardware & software failure • Streaming Data Access • Batch processing rather than interactive use • High throughput of data access rather than low latency of data access

  5. Assumption & Goals - contd 3) Large Data Set • Typical file in HDFS is gigabytes to terabytes • High aggregate data bandwidth scaling to hundreds of nodes. 4) Simple Coherency Model • Write-once-read-many access • File once created, not allowed to modified

  6. Assumption & Goals - contd 5) Migrating Computation into data • Provides interface for applications to move themselves closer to where the data is located 6) Portability • Easily portable from one platfrom to another • Java based

  7. Structure • Master / Slave architecture • NameNode (Master) • Manages the file system namespace • Regulates access to files by clients • Not contain any data files • Unique • DataNode (Slave) • Actual repository • Multiple nodes are required

  8. Conceptual Diagram Namespace(Headquarter) Directory service Block: Piece of data a DataNode: contain multiple blocks of data

  9. Operation • A file is distributed with multiple blocks with multiple duplication over the DataNodes • A file is cut into multiple blocks whose size is 64MB (default) • Each block is replicated over the DataNodes (# of replica: 3, default) • Scheme • Direction to maximize the ‘tolerance’ • Local Tolerance • Inside of rack • Global Tolerance • Outside of rack

  10. Example Command to save files from NameNode Local tolerance: in same rack Global tolerance: outside of rack Rack 2 Rack 3 Rack 1 DataNodes Rack Awareness

  11. Data Maintenance • Each DataNode send ‘Heartbeat’ messages containing ‘Blockreport’ to NameNode • Blockreport • A list of all blocks on a DataNode • Heartbeat • Kinds of ‘Ping’ (I’m alive!) • Receipt of a Hearbeat implies that the DataNodes is functioning properly

  12. Data Management • NameNode manages all data • EditLog • All the transaction is recorded from NameNode • FsImage (File System Image) • To configure the which data blocks are stored in which DataNodes • Key matadata is stored in memory • Heartbeat messages from DataNodes are stored in here

  13. Data Integrity (1) • Safemode • On startup, NameNode receives Heartbeat and Blockreport messages from DataNode • Each block has a specified minimum number of replicas • Under this threshold, re-replication happened • No replication of new data blocks does not occur in this period • This happens regularly

  14. Data Integrity (2) • Data fetched from a DataNode could be corrupted • Checksum algorithms are implemented • Operation • When a client creates an HDFS files, it also create calculated checksum • A client receives a file, it also downloads checksum • Comparing downloaded checksum and another calculated checksum from file, a client could verify the content

  15. Robustness • Data disk failure, heartbeats and re-replication • From heartbeats message, NameNode could check the liveness of DataNode • Cluster rebalancing • If a DataNode have much more data than the others, procedure for redistribution of blocks happened • Data integrity • Checksum • Metadata disk failure • FsImage, EditLog are copied

  16. Pros and Cons • Pros • Powerful mechanism for ‘Fault-Tolerant’ • Easy to deploy • Free • Cons • Single point of failure – NameNode • Not optimized solution • Same magnitude of replication for each block • Not that fast

  17. Download & More Information • Official site • http://hadoop.apache.org/ • Last build at March, 2011 • Korean Dev. • http://www.hadoop.co.kr/ • Last uploaded materials at Oct, 2011

  18. QnA

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