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Apache Storm - Real Time Analytics
1. Introduction to Real Time
Analytics using Apache Storm
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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
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3. Need of Real Time Analytics
Ret
• Banking - Fraud Transaction Detection
• Telecommunication – Silent Roamers Detection
• Retail- Inventory Dynamic Pricing
• Social Networking- Trending Topics
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4. Growing Interest in Apache Storm
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5. Storm Usecases – Need for Real Time Analytics
Twitter Trends
Responsive Logs
Source: https://github.com/nathanmarz/storm/wiki/Powered-By
Custom Magazine Feeds
Real Time Video Analytics
Enable Clinicians to Make
Medical Decisions
Compare and Display
Real Time Prices
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6. What is Storm ?
ď‚® Apache Storm is a free and open source distributed real-time computation system.
ď‚® Storm makes it easy to reliably process unbounded streams of data.
ď‚® Storm does for real-time processing what Hadoop did for batch processing.
ď‚® Simple, can be used with any programming language.
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7. Understanding the Storm Architecture
Nimbus
Zookeeper
Supervisor
Zookeeper
Zookeeper
Supervisor
Supervisor
Supervisor
Supervisor
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*Covered in module 2 in the course
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8. ZooKeeper
Nimbus ZooKeeper
ZooKeeper
Supervisor
Supervisor
Supervisor
Supervisor
Supervisor
ď‚®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
ď‚®ZooKeeper nodes:
» Coordinates the Storm cluster
ď‚®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
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9. ď‚®The work is delegated to different types of components that are each responsible for a simple specific processing task.
ď‚®The input stream of a Storm cluster is handled by a component called a spout.
ď‚®The spout passes the data to a component called a bolt, which transforms it in some way.
ď‚®A bolt either persists the data in some sort of storage, or passes it to some other bolt.
Storm Topology
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spout
spout
bolt
bolt
bolt
bolt
passes data
passes data
transforms data
data storage
Input Data
Source
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.
http://hortonworks.com/hadoop/storm/
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12. Upcoming Batch for Storm
Start Date:
16th Aug (08:30 PM – 11:30 PM, India Time) / 16th Aug (08:00 AM – 11:00 AM, Pacific Time)
13th Sep (7:00 AM – 10:00 AM, India Time) / 12th Sep (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
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