Hadoop-Quick introduction
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Hadoop-Quick introduction



Overview of Big Data and Hadoop.

Overview of Big Data and Hadoop.



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Hadoop-Quick introduction Hadoop-Quick introduction Presentation Transcript

  • “Data is a precious things and will last longer than the system themselves” – Tim Berners Lee
  • Sandeep Kumar
  • What is Data ? • What is Data ? • And why should we care about it ?
  • What is Big Data ? • Big data is a collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications.
  • Few Examples • Web logs • RFID • Social Data-Facebook, Linkedin, Twitter. • Call Detail Records • Large-Scale e-commerce • Medical Records • Video archives • Atmospheric Science • Astronomy • Feeds • Media & Advertising.
  • What is Big Data ? • Ancestry.com stores around 2.5 petabytes of Data. • The New York Stock Exchange generates about one terabyte of new trade data per day. • The Internet Archive stores around 2 petabytes of data, and is growing at a rate of 20 terabytes per month. (http://archive.org/web/web.php)
  • How to Process The Big Data? • Need to process large datasets (>100TB) • Only reading 100TB of data can be overwhelming • Takes ~11 days to read on a standard computer • Takes a day across a 10GB link (very high end storage solution) • On a single node (@50MB/s) – 23days • On a 1000 node cluster – 33min
  • Not so easy……….. • The challenges are in search, sharing, transfer, visualization etc. • Moving data from storage cluster to computation cluster is not feasible. • In large cluster failure is expected . Computer fails everyday. • Very expensive to build reliability into each application. • massively parallel software running on tens, hundreds, or even thousands of servers • A programmer worries about errors, data motion, communication.
  • What We are looking for.
  • What we are looking for. • A common infrastructure and standard set of tools to handle this complexity. • A Efficient, Reliable fault-tolerant and usable framework.
  • What is Hadoop ? • Its a framework that allows distributed processing of large data sets across clusters of computers. • It is designed to scale up from single servers to thousands of machines. • Its also designed to run on commodity hardware.
  •  Scalable: store and process petabytes, scale by adding HW and added without needing to change data formats.  Economical: 1000s of commodity machines.  Efficient: runs tasks where data is located.  Flexible: Hadoop is schema-less, and can absorb any type of data, structured or not, from any number of sources.  Fault tolerant: When you lose a node, the system redirects work to another location of the data and continues processing without missing a beat. Hadoop is….
  • Hadoop is useful for……. • Batch Data Processing. • Log Processing. • Document Analysis & Indexing. • Text Mining. • Crawl Data Processing. • Highly parallel data intensive distributed applications.
  • Use The Right Tool For The Right Job Hadoop:RDBMS When to use? • Write once read many times. • Structured or Not (Agility) • Batch Processing When to use? • Interactive Reporting (<1sec) • Multistep Transactions • Lots of Inserts/Updates/Deletes
  • Hadoop Terminology……. Node 1
  • Hadoop Terminology……. Node 1 Node 2
  • Hadoop Terminology……. Node 1 Node 2 Node 3
  • Hadoop Terminology……. Node 1 Node 2 . . Node 3 Rack 1
  • Hadoop Terminology……. Node 1 Node 2 . . Node 3 Rack 1 Node 1 Node 2 . . Node 3 Rack 2
  • Hadoop Terminology……. Node 1 Node 2 . . Node 3 Rack 1 Node 1 Node 2 . . Node 3 Rack 2 Node 1 Node 2 . . Node 3 Rack 3
  • Hadoop Terminology……. Node 1 Node 2 . . Node 3 Rack 1 Node 1 Node 2 . . Node 3 Rack 2 Node 1 Node 2 . . Node 3 Rack 3 Hadoop Cluster
  • Hadoop Framework…….
  • Hadoop Nodes……. • HDFS Nodes  NameNode (Master)  DataNode (Slaves)  Checkpoint Node  Secondary NameNode (deprecated)  Backup Node
  • Hadoop Nodes……. • MapReduce nodes  JobTracker (Master)  TaskTracker (Slaves)
  • Hadoop Nodes-Overview
  • Hadoop Nodes-NameNode • Manages the filesystem namespace and metadata • Replicate missing blocks • No data goes through the NameNode • NameNode mainly consists of:  fsimage: Contains a checkpoint copy of the metadata on disk  edit logs: Records all write operations, synchronizes with metadata in RAM after each write  In case of ‘power failure’ on NameNode Can recover using fsimage + edit logs
  • Hadoop Nodes-CheckPoint Node • Periodically creates checkpoints of NameNode filesystem • The Checkpoint node should run on a different machine than the NameNode • Should have same storage requirements as NameNode • There can be many Checkpoint nodes per cluster
  • Hadoop Nodes-BackUp Node • Difference with Checkpoint node is that it keeps and up- to-date copy of metadata in RAM • Same RAM requirements as NameNode • Can only have one Backup node per cluster
  • Hadoop Nodes-Data Node Can be many per Hadoop cluster •Manages blocks with data and serves them to clients •Periodically reports to NameNode the list of blocks it stores •Use inexpensive commodity hardware for this node
  • Hadoop Nodes-Job Tracker One per Hadoop cluster (Multiple namenode can be configured in Hadoop 2.2 or letter version) •Receives job requests submitted by client •Schedules and monitors MapReduce jobs on task trackers
  • Hadoop Nodes-Task Tracker • Can be many per Hadoop cluster • Executes MapReduce operations • Reads blocks from DataNodes
  • Map Reduce It offers: • Operates on key and value pairs. • Two major functions: Map() and Reduce() • Input formats and splits • Number of tasks. • Provides status about jobs to users • Monitors task progress
  • Map Reduce Diagram
  • Map Reduce Architecture.
  • Map Reduce Job. JobTracker client TaskTackers & Datanodes ←4.tasks NameNode 3. Namespace info
  • Input Output . The MapReduce framework operates on <key, value> pairs. It views the input to the job as a set of <key, value> pairs and produces a set of <key, value> pairs as the output of the job.
  • Input Output.. Input and Output types of a MapReduce job: (input) <k1, v1> -> map -> <k2, v2> -> combine -> <k2, v2> -> reduce -> <k3, v3> (output) Reference: http://hadoop.apache.org/docs/r1.0.4/mapred_tutorial.html
  • HDFS Architecture
  • Hadoop Tools……. Hive  It’s a data warehouse system for Hadoop  Providing data summarization, query, and analysis.
  • Hadoop Tools……. • Pig  Its a high-level platform for creating MapReduce programs used with Hadoop.  Developed by Yahoo.
  • Hadoop Tools……. Hbase  Used when needs random, real-time read/write access to your Big Data.  Also used for storing historical data.
  • Hadoop Tools……. • Hue  Its a Web application for interacting with Apache Hadoop. It supports a file browser, job tracker interface, Hive, Pig and more.
  • Hadoop Tools……. • Sqoop  Its a Command-line interface application for transferring data between relational databases and Hadoop.  Microsoft uses a Sqoop-based connector to help transfer data from Microsoft SQL Server databases to Hadoop.
  • Hadoop Tools……. • Flume  Its used for efficiently collecting, aggregating, and moving large amounts of distributed data or log data.
  • Hadoop Tools……. • Flume Model
  • Hadoop in the Enterprise…….
  • There are many tools developed on top of hadoop these days and those are available in market and being used widely in industry. We can get more on it from Cloudera, hortonworks and from Google.com
  • Thanks for your time today.