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Apache Storm


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Apache Storm is a free and open source, distributed real-time computation system for processing fast, large streams of data. Storm adds reliable real-time data processing capabilities to Apache Hadoop 2.x. Its effective stream processing capabilities are trusted by Twitter and Yahoo for quickly extracting insights from their Big Data.

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Apache Storm

  1. 1. Introduction to Real Time Analytics using Apache Storm Buy Complete Course at : Batch Starts On: 17th May 07:00 AM , IST / 16th May 06:30 PM, PDT Course Fee: USD 329 / INR (17795 + 12.36% Service tax)** Introductory (15% OFF) Price : USD 280 / INR 15126 For Existing edureka Customers (25% OFF) Price : USD 247/ INR 13346 * Offer expires on 11th May Post your Questions on Twitter on @edurekaIN: #askEdureka
  2. 2. Objectives of this Session • Un • The need for Real Time Analytics - Usecases • How does Storm come to rescue? • Where does Storm fit in Hadoop Framework? • Storm Architecture – Components of Storm • Quiz to reinforce your learning For Queries during the session and class recording: Post on Twitter @edurekaIN: #askEdureka Post on Facebook /edurekaIN
  3. 3. Need of Real Time Analytics Ret • Banking - Fraud Transaction Detection • Telecommunication – Silent Roamers Detection • Retail- Inventory Dynamic Pricing • Social Networking- Trending Topics *Covered in module 5 and 6 in the course @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions
  4. 4. Growing Interest in Apache Storm @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions
  5. 5. Storm Usecases – Need for Real Time Analytics Twitter Trends Responsive Logs Source: Custom Magazine Feeds Real Time Video Analytics Enable Clinicians to Make Medical Decisions Compare and Display Real Time Prices @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions
  6. 6. What is Storm ?  Apache Storm is a free and open source distributed real-time computation system.  Storm makes it easy to reliably process unbounded streams of data.  Storm does for real-time processing what Hadoop did for batch processing.  Simple, can be used with any programming language. @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions
  7. 7. Understanding the Storm Architecture Nimbus Zookeeper Supervisor Zookeeper Zookeeper Supervisor Supervisor Supervisor Supervisor *Covered in module 2 in the course Twitter @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions
  8. 8. ZooKeeper Nimbus ZooKeeper ZooKeeper Supervisor Supervisor Supervisor Supervisor Supervisor Nimbus node (master node, similar to the Hadoop JobTracker): » Uploads computations for execution » Distributes code across the cluster » Launches workers across the cluster » Monitors computation and reallocates workers as needed ZooKeeper nodes: » Coordinates the Storm cluster Supervisor nodes : » Communicates with Nimbus through Zookeeper, starts and stops workers according to signals from Nimbus Storm Components A Storm cluster has 3 sets of nodes 1. Nimbus node 2. Zookeeper nodes 3. Supervisor nodes @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions
  9. 9. The work is delegated to different types of components that are each responsible for a simple specific processing task. The input stream of a Storm cluster is handled by a component called a spout. The spout passes the data to a component called a bolt, which transforms it in some way. A bolt either persists the data in some sort of storage, or passes it to some other bolt. Storm Topology @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions spout spout bolt bolt bolt bolt passes data passes data transforms data data storage Input Data Source
  10. 10. Why Storm is ideal for Real Time Processing Fast – benchmarked as processing one million, 100 byte messages, per second per node. Scalable – with parallel calculations that run across a cluster of machines. Fault-tolerant – when workers die, Storm will automatically restart them. If a node dies, the worker will be restarted on another node. Reliable – Storm guarantees that each unit of data (tuple) will be processed at least once or exactly once. Messages are only replayed when there are failures. Easy to operate – standard configurations are suitable for production on day one. Once deployed, Storm is easy to operate. @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions
  11. 11. MapReduce (Batch) INTERACTIVE (Text) ONLINE (HBase) STORM (Streaming) GRAPH (Giraph) IN-MEMORY (Spark) HPC MPI (OpenMPI) OTHER (Search) (Weave..) Storm in the Hadoop Framework @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions
  12. 12. Upcoming Batch for Storm Start Date: 17th May (07:00 AM – 10:00 AM, India Time) / 16th May (06:30 PM – 09:30 PM, Pacific Time) Curriculum: Module 1: Introduction of Big Data and Storm Module 2: Getting Started with Storm Module 3: Spouts and Bolts Module 4: Trident Topologies Module 5: Real Life Storm Project – 1 Module 6: Real Life Storm Project – 2 Price: Course Fee: USD 329 / INR (17795 + 12.36% Service tax)** Introductory Discount : 15% Discount for Existing Edureka Customers: 25% @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions
  13. 13. Annie’s Question Storm can be used in: - Real-time Processing - Batch Processing - Both
  14. 14. Annie’s Answer Real-time Processing
  15. 15. Annie’s Question Which of them can be a source of Stream? - Spout - Bolt - Both
  16. 16. Annie’s Answer Both
  17. 17. Annie’s Question It is not possible to run Storm process along with MapReduce jobs inside a Hadoop Cluster. - True - False
  18. 18. Annie’s Answer False. With Hadoop 2.0, it is possible.
  19. 19. Annie’s Question A Nimbus Node is similar to TaskTracker Node in Hadoop Cluster. - True - False
  20. 20. Annie’s Answer No. A Nimbus Node is more like a JobTracker Node in Hadoop
  21. 21. Annie’s Question A Storm topology is defined in terms of - Nimbus, Zookeeper, Supervisor nodes - Spout, Bolt - Spout, Bolt, Nimbus, Zookeeper, Supervisor nodes - Spout, Bolt, Zookeeper node
  22. 22. Annie’s Answer Spout and Bolt
  23. 23. Questions? @edurekaIN, Facebook /edurekaIN, use #askEdureka for Questions Buy Complete Course at : Batch Starts On: 17th May 07:00 AM , IST / 16th May 06:30 PM, PDT Course Fee: USD 329 / INR (17795 + 12.36% Service tax)** Introductory (15% OFF) Price : USD 280 / INR 15126 For Existing edureka Customers (25% OFF) Price : USD 247/ INR 13346 * Offer expires on 11th May