The document outlines a meetup presentation on big data stream processing using Apache Storm, focusing on its architecture, features, and real-time data use cases such as fraud detection and network monitoring. It discusses the core concepts of Storm, including the role of spouts and bolts, as well as integrations with systems like Apache Kafka. Additionally, it emphasizes the importance of designing big data applications for scalability and continuous integration.

1. Big Data - Meetup
Big Data
Stream processing
using
Apache Storm
Athens - May 2016

2. Who we are?
● Adrianos Dadis (@qiozas )
● Patroclos Christou (@christoupat )
● Eleftheria Chavelia
● Sofia Nomikou

3. Agenda
● Apache Storm
● Apache Kafka
● Streaming application demo

4. Why stream processing?
● Increasement of available real-time data
● Extract actionable intelligence on real-time
● Act on real-time

5. Use Cases examples
● Fraud detection
● Network monitoring
● Smart order routing
● E-commerce
● Bandwith allocation optimization
● Algorithmic trading

6. End-to-End Deployment
Real-Time
Data Stream
Streamimg Processing
Solution
Dashboards
Data Store
Applications
Alerts
Batch
Processing

7. Apache Storm
● Creator: Nathan Marz (2011)
● Distributed real-time computation system for processing
large volumes of high-velocity data
● Characteristics:
– Fast
– Scalable
– Fault-tolerant
– Reliable
– Easy to operate
– Easy to develop

8. Storm core concepts
● Tuple : Storm uses tuples as its data model
● Stream : An unbounded sequence of tuples
● Spout : A source of streams in a topology
● Bolt : All processing in topologies is done in bolts
● Topology : DAG of Spout and Bolts

9. Storm topology

10. Storm Architecture
Nimbus
Zookeper
Supervisor
Worker
Worker
Zookeper
Zookeper
Supervisor
Worker
Worker
Supervisor
Worker
Worker
Supervisor
Worker
Worker
Master
Node
Cluster
Coordination
Node
Coordination
Processing
Worker
Nimbus
Nimbus

11. Storm topology parallelism

12. Worker Internal Messaging
Worker Receiver
Thread
Router
Inbound Queue
Disruptor
Outbound Queue
Disruptor
Task
Executor Thread
Send
Thread
List<Tuple>
Transfer Buffer
List<Tuple>
Receiver Buffer
Worker Transfer
Thread
Worker
Port Worker
Port

13. Stream Grouping
● Shuffle
● LocalOrShuffle
● All
● Global
● Field
● Partial Key
● Direct

14. Reliable Processing
{A} {B}
{D}
{F}
{C}
{E}
{H}
{X}
{G}
● Acking
● Anchoring
● Failures
ACK
FAIL

15. Streaming Windows
● Sliding Windows
● Tumbling Windows
{...}{...}{...}{...}{...}{...}{...}{...}{...}{...}
Time
{...}{...}{...}{...}{...}{...}{...}{...}{...}{...}
Time

16. Storm topology example

17. Storm Trident
● High level abstraction on top of Storm
● Micro Batching
● Stateful
● Built-in support:
– Functions
– Fliters
– Merges and Joins
– Aggregations
– Grouping

18. Trident Example

19. Partitioning

20. Trident execution analysis

21. Storm 1.x Features
● HA Nimbus
● Distributed Cache API
● Pacemaker - Heartbeat Server
● Automatic Backpressure
● Resource Aware Scheduler
● State Management
● Native Streaming Windows

22. Storm Integrations
● Kafka
● Redis
● Hive, HDFS
● HBase, Cassandra
● MongoDB
● Elasticsearch, Solr
● JDBC
● MQTT

23. Storm modes
● One-at-a-time processing (pure Storm)
– Very low latency
– Very simple development model
– At-Most-Once and At-Least-Once semantics
● Micro batch processing (Storm Trident)
– Increased latency on event
– Better throughput for large rates
– More complex development model
– Exactly-Once semantics

24. Messaging Systems
● Core needs:
– Decouple processing from data producers
– Buffer unprocessed messages
● Models:
– Queuing
– Publish-Subscribe
● Frameworks
– Kafka
– RabbitMQ
– ActiveMQ

25. Apache Kafka
● Distributed, partitioned, replicated commit log service
● Publish-Subscribe model
● Maintains feeds of messages in Topics
● Automatic Replication and Retention
● Brokers

26. Apache Kafka
● Offset uniquely identifies each message within the partition
● Consumers coordinate what to read
● Consumer & Consumer Group

27. Implementing Big Data Apps
● Design for scalability from day one
● Queries drive schema design
● Failure (HW or data) is a normal case
● Continuous Integration
● Metrics & Monitoring from day one
● Appropriate people

28. Sentiment Analysis Demo
Random
Sentence
Spout
Stemming
Bolt
Positive
Scoring
Bolt
Negative
Scoring
Bolt
Final
Scoring
Bolt
Persistence
Bolt
Kafka
Topic
Kafka
Spout
Kafka
Topic
NoSQL
src => https://github.com/qiozas/sentiment-analysis-storm

29. Athens Big Data - Meetup - 2016
THANK YOU :-)
[ Updates / Questions / Comments ]
@qiozas
@christoupat