1 / 14

IDS594 Special Topics in Big Data Analytics

IDS594 Special Topics in Big Data Analytics. Week2. Hadoop: Big Picture. HDFS: Hadoop Distributed File System. HDFS is a master-slave architecture Master: namenode Slave: datanode (100s or 1000s of nodes) Single namenode and many datanodes Namenode maintains the file system metadata

joel-dawson
Télécharger la présentation

IDS594 Special Topics in Big Data Analytics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. IDS594Special Topics in Big Data Analytics Week2

  2. Hadoop: Big Picture

  3. HDFS: Hadoop Distributed File System • HDFS is a master-slave architecture • Master: namenode • Slave: datanode (100s or 1000s of nodes) • Single namenode and many datanodes • Namenode maintains the file system metadata • Files are split into fixed sized blocks and stored on data nodes (Default 64MB) • Data blocks are replicated for fault tolerance and fast access (Default is 3)

  4. HDFS Architecture • Default placement policy: where to put a given block? • Frist copy is written to the node creating the file (write affinity) • Second copy is written to a datanode within the same rack • Third copy is written to a datanode in a different rack • Objectives: load balancing, fast access, fault tolerance

  5. MapReduce: Hadoop Execution Layer • JobTracker knows everything about submitted jobs • Divides jobs into tasks and decides where to run each task • Continuously communicating with TaskTrackers • TaskTrackers execute task (multiple per node) • Monitors the execution of each task • Continuously sending feedback to JobTracker • MapReduce is a master-slave architecture • Master: JobTracker • Slave: TaskTrackers (100s or 1000s of tasktrackers) • Every datanode is running a TaskTracker

  6. High-level MapReduce Pipeline

  7. Hadoop MapReduce Data Flow

  8. Hadoop Computing Model • Mapper and Reducers consume and produce (key, value) pairs • Users define the data type of the Key and Value • Shuffling and Sorting phase • Map output is shuffled such that all same-key records go the same reducer • Each reducer may receive multiple key sets • Each reducer sorts its records to group similar keys, then process each group

  9. Using Hadoop

  10. Hadoop Configuration and Installation

  11. Online Documentation Windows: http://ebiquity.umbc.edu/Tutorials/Hadoop/00%20-%20Intro.html Mac: http://hadoop.apache.org/docs/stable/single_node_setup.html

More Related