The document provides an introduction to Apache Storm for streaming distributed processing, highlighting its capabilities in real-time data analytics compared to traditional batch processing systems. It includes a detailed overview of Storm's architecture, components, and a practical 'wordcount' demonstration, illustrating how to set it up and use it effectively. Additionally, it discusses various use cases and patterns for Storm in the context of real-time applications.

1. Introduction to Streaming
Distributed Processing with
Storm
Presenter: Brandon O’Brien
Data Engineer @ Expedia

2. Outline
 Distributed Systems & Batch Processing
 Streaming Processing. Introduce Storm
 WordCount Demo & Setup
 Storm Cluster Architecture
 Storm Topology Architecture
 WordCount Deep Dive
 Discussion and Q&A: Storm Use Cases & Patterns

3. Distributed Systems
 Distribute work across N nodes
 Hadoop Ecosystem
 Batch processing
 Massively parallel (horizontal scale out)
 Problems – data latency, 24 hour batching vs global
client base
 What’s next? Increasing need to move to real time &
streaming processing models

4. Streaming Processing
 Provides near real time views into analytical data sets
and system status. Allows for real time intervention &
response to events
 Streaming frameworks: Spark, Azure Streaming
Analytics, AWS Kinesis+Lambda, Storm
 Created by Nathan Marz, first used at Twitter
 Storm: “Doing for realtime processing what Hadoop did
for batch processing”
 Stream definition: “unbounded sequence of tuples”

5. Storm WordCount Demo
 WordCount Storm Topology
Streams text blobs
Counts word occurrences
Reporting results each 10 seconds
 Getting it running
https://github.com/OpenDataMining/brandonobrien
mvn clean install exec:java -Dexec.mainClass=
"dataclub.storm.TokenCountingTopology”

6. Storm Cluster Architecture
 Core components:
 Zookeeper
 Nimbus
 Supervisors
 Workers/JVM
 Executor/thread
 Component/task (bolts & spouts)
 Scalability – can add supervisors while topologies are running, no
code change required
 Supervisors run Worker JVMs
 Workers run Executor Threads
 Executors run Tasks (instances of Spouts and Bolts)

7. Storm Topology Architecture
 DAG Processing Model
 Directed Acyclic Graph
 Components: Spout & Bolt (benefit: decouple logic from
scalability)
 Tasks (instances of Spouts & Bolts)
 Executors (run Tasks)

8. Storm WordCount Deep Dive
 Topology structure
 Classes
 Spout: SentenceProducer.java
 Bolt: SentenceTokenizer.java
 Bolt: TokenCounter.java
 Putting it all together: TokenCountingTopology.java

9. Storm Use Cases & Patterns
 Consume data from Kafka, Kinesis or other queue
 Persist data to high write perf datastore like Cassandra
 Streaming map reduce, multi-stage map reduce
 Storm is stateless & fail-fast. Externalize state using Redis or other
cache for resiliency
 Online learning / realtime model updates (using frameworks like
WEKA or others)
 Real world use cases: Real time ad targeting, travel market
analytics, user behavior analytics, system monitoring & SLA
 Storm multi lang API (Python, Ruby, PERL, JavaScript, Scala, and
more)

10. Distributed Streaming Processing
with Storm
 Going Further
https://storm.apache.org/
http://storm.apache.org/documentation/Common-patterns.html
Frameworks: Trident, Summingbird
Stand up Storm cluster: http://www.michael-
noll.com/tutorials/running-multi-node-storm-cluster/
 Contact
Brandon O’Brien, Data Engineer @ Expedia
https://www.linkedin.com/in/brandonjobrien
 Q&A