Apache hadoop distributed file system

Apache Hadoop File System and its Usage in Facebook and apache hadoop file system commands
Dr.DavisHardy Profile Pic
Published Date:22-07-2017
Your Website URL(Optional)
Apache Hadoop FileSystem and its Usage in Facebook Dhruba Borthakur Project Lead, Apache Hadoop Distributed File System dhrubaapache.org Presented at Indian Institute of Technology November, 2010 http://www.facebook.com/hadoopfs Outline   Introduction   Architecture of Hadoop Distributed File System (HDFS)   Usage of Hadoop in Facebook  Data Warehouse  mySQL Backups  Online application storage Who Am I?   Apache Hadoop FileSystem (HDFS)  Project Lead   Core contributor since Hadoop’s infancy   Facebook (Hadoop, Hive, Scribe)   Yahoo (Hadoop in Yahoo Search)   Veritas (San Point Direct, Veritas File System)   IBM Transarc (Andrew File System)   Univ of Wisconsin Computer Science Alumni (Condor Project) A Confluence of Trends Fault Tolerance Open Data format File System Flexible Schema Queryable Database Archival Store Never Delete Data HADOOP: A Massively Scalable Queryable Store and Archive Hadoop, Why?   Need to process Multi Petabyte Datasets   Data may not have strict schema   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 Is Hadoop a Database?   Hadoop triggered upheaval in Database Research   “A giant step backward in the programming paradigm”, Dewitt et el   “DBMS performance outshines Hadoop” – Stonebraker, Dewitt, SIGMOD 2009   Parallel Databases   A few scales to 200 nodes and about 5 PB   Primary design goal is “performance”   Requires homogeneous hardware   Anomalous behavior is not well tolerated:  A slow network can cause serious performance degradation  Most queries fail when one node fails   Scalability and Fault Tolerance: Hadoop to the rescue Hadoop History   Dec 2004 – Google GFS paper published   July 2005 – Nutch uses MapReduce   Feb 2006 – Starts as a Lucene subproject   Apr 2007 – Yahoo on 1000-node cluster   Jan 2008 – An Apache Top Level Project   May 2009 – Hadoop sorts Petabyte in 17 hours   Aug 2010 – World’s Largest Hadoop cluster at Facebook  2900 nodes, 30+ PetaByte Who uses Hadoop?   Amazon/A9   Facebook   Google   IBM   Joost   Last.fm   New York Times   PowerSet   Veoh   Yahoo What is Hadoop used for?   Search   Yahoo, Amazon, Zvents   Log processing   Facebook, Yahoo, ContextWeb. Joost, Last.fm   Recommendation Systems   Facebook   Data Warehouse   Facebook, AOL   Video and Image Analysis   New York Times, Eyealike Commodity Hardware Typically in 2 level architecture – Nodes are commodity PCs – 20-40 nodes/rack – Uplink from rack is 4 gigabit – Rack-internal is 1 gigabit Goals of HDFS   Very Large Distributed File System – 10K nodes, 1 billion files, 100 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 3. Read/write data HDFS Architecture NameNode Cluster Membership Secondary NameNode Client DataNodes NameNode : Maps a file to a file-id and list of DataNodes DataNode : Maps a block-id to a physical location on disk SecondaryNameNode: Periodic merge of Transaction log 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 - 256 MB block size – Each block replicated on multiple DataNodes   Intelligent Client – Client can find location of blocks – Client accesses data directly from DataNode 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 DataNode   A Block Server – Stores data in the local file system (e.g. ext3) – Stores meta-data of a block (e.g. CRC32) – Serves data and meta-data to Clients - Periodic validation of checksums   Block Report – Periodically sends a report of all existing blocks to the NameNode   Facilitates Pipelining of Data – Forwards data to other specified DataNodes 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   Pluggable policy for placing block replicas   Co-locate datasets that are often used together   http://hadoopblog.blogspot.com/2009/09/hdfs-block-replica-placement-in-your.html Data Pipelining   Client writes block to the first DataNode   The first DataNode forwards the data to the next DataNode in the Pipeline, and so on   When all replicas are written, the Client moves on to write the next block in file 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)   This is a problem with 24 x 7 operations   AvatarNode comes to the rescue NameNode High Availability: Challenges Client   DataNodes send block location information to only one Client retrieves block location from NameNode NameNode   NameNode needs block locations Primary NameNode in memory to serve clients   The in-memory metadata for 100 Block location message “yes, I million files could be 60 GB, huge have blockid 123” DataNodes