Apache Storm Concepts

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André Dias

Highlights
● Background
● Storm’s History
● Concepts
● Integrations
● Real Cases
● Q&A

Background
Big Data V’s
Volume
Petabytes / Terabytes
Velocity
Real-time / Near Real-time
Variety
Sensors, Blog Posts, Logs, Social Networks...

Background
Data Streaming
Now Please… I’
m in traffic…

● Discovery
● Ingest
● Process
● Persist
● Analyze
● Expose
Background
Data Value Chain

● Discovery
● Ingest
● Process
● Persist
● Analyze
● Expose
Background
Data Value Chain (Storm Focus)

Background
Data Value Chain
● Discovery
● Ingest
● Process
● Persist
● Analyze
● Expose

Background
Data Value Chain (Ingest)
Stream Processing
Data in Motion
Batch Processing

● Data processing architecture
○ Generic
○ Scalable
○ Fault-tolerant (human/hardware)
● Low latency
Background
Lambda Architecture (LA)

Storm’s History
Nathan Marz
● Backtype (2008), acquired by Twitter (2011)
● Lambda Architecture Creator
● September, 2011. Storm Creation
● September, 2013. Storm entered to ASF

Why Use It?
● Scalable, as Hadoop
● No data loss, reliable
● Fault tolerant
● Language agnostic
● Real-time, real fast

Concepts
● Tuple
● Streams
● Spouts
● Bolts
● Topologies / Trident API
● Stream Groupings

Concepts
Spouts
● Source of Streams
● Data Consumers (Ingestion)
● Emits Tuples

Concepts
Bolts
● Units of Work to tuples
● Data streaming logic
● Can emit tuples as well
● Data store integration

Concepts
Topology
● Data Streaming Flow Representation
● DAG (Direct Acyclic Graph) of Spouts and
Bolts
● Streaming computation
● Each node as individual task (parallel
execution)
● Stateless

Concepts
Trident API
● Abstraction Layer over low-level Storm API
● More Complex Topologies
● Stateful
● Micro-batch
● High-level API (similar to Pig / Cascading -
Hadoop)
● Message processed at least once
(guaranteed)

Concepts
Trident API - Micro Batch
● Trident Batches
○ are Ordered

Concepts
Trident API - Micro Batch
● Trident Batches
○ can be Partitioned

Concepts
Streaming Groups
● Data Flow Control over
Topologies

Architecture
Components - Nimbus
● Master node (similar to JobTracker)
● Monitor and distribute the processing
workload across worker nodes
● Stores all its data into Zookeeper

Architecture
Components - Supervisor
● Worker node (similar to TaskTracker)
● Monitor and distribute the processing
workload across worker nodes
● Stores all its data into Zookeeper

Architecture
Overview
● Master-slave approach
● Cluster coordination
(Zookeeper)
● Nimbus HA

Real Cases
Collector sensor
information to a
Data Lake
Micro-batch
user contents,
content feeds
and application
logs
Real-time user
music
recommendations

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Apache Storm Concepts

1. Apache Storm Concepts André Dias andre.dias@summa.com.br

2. Highlights ● Background ● Storm’s History ● Concepts ● Integrations ● Real Cases ● Q&A

3. Background

4. Background Big Data V’s

5. Background Big Data V’s Volume Petabytes / Terabytes Velocity Real-time / Near Real-time Variety Sensors, Blog Posts, Logs, Social Networks...

6. Background Data Streaming

7. Background Data Streaming

8. Background Data Streaming Now Please… I’ m in traffic…

9. ● Discovery ● Ingest ● Process ● Persist ● Analyze ● Expose Background Data Value Chain

10. ● Discovery ● Ingest ● Process ● Persist ● Analyze ● Expose Background Data Value Chain (Storm Focus)

11. Background Data Value Chain ● Discovery ● Ingest ● Process ● Persist ● Analyze ● Expose

12. Background Data Value Chain (Ingest) Stream Processing Data in Motion Batch Processing

13. ● Data processing architecture ○ Generic ○ Scalable ○ Fault-tolerant (human/hardware) ● Low latency Background Lambda Architecture (LA)

14. Background Lambda Architecture (LA)

15. Storm’s History Nathan Marz ● Backtype (2008), acquired by Twitter (2011) ● Lambda Architecture Creator ● September, 2011. Storm Creation ● September, 2013. Storm entered to ASF

16. Why Use It? ● Scalable, as Hadoop ● No data loss, reliable ● Fault tolerant ● Language agnostic ● Real-time, real fast

17. Why Use It? Storm is TLP !!

18. Concepts

19. Concepts ● Tuple ● Streams ● Spouts ● Bolts ● Topologies / Trident API ● Stream Groupings

20. Concepts Spouts ● Source of Streams ● Data Consumers (Ingestion) ● Emits Tuples

21. Concepts Bolts ● Units of Work to tuples ● Data streaming logic ● Can emit tuples as well ● Data store integration

22. Concepts Topology ● Data Streaming Flow Representation ● DAG (Direct Acyclic Graph) of Spouts and Bolts ● Streaming computation ● Each node as individual task (parallel execution) ● Stateless

23. Concepts Trident API ● Abstraction Layer over low-level Storm API ● More Complex Topologies ● Stateful ● Micro-batch ● High-level API (similar to Pig / Cascading - Hadoop) ● Message processed at least once (guaranteed)

24. Concepts Trident API - Micro Batch ● Trident Batches ○ are Ordered

25. Concepts Trident API - Micro Batch ● Trident Batches ○ can be Partitioned

26. Concepts Streaming Groups ● Data Flow Control over Topologies

27. Architecture

28. Architecture Components - Nimbus ● Master node (similar to JobTracker) ● Monitor and distribute the processing workload across worker nodes ● Stores all its data into Zookeeper

29. Architecture Components - Supervisor ● Worker node (similar to TaskTracker) ● Monitor and distribute the processing workload across worker nodes ● Stores all its data into Zookeeper

30. Architecture Overview ● Master-slave approach ● Cluster coordination (Zookeeper) ● Nimbus HA

31. Integrations

32. Real Cases Collector sensor information to a Data Lake Micro-batch user contents, content feeds and application logs Real-time user music recommendations

33. Q&A

34. THANK YOU! Use Storm

Apache Storm Concepts

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