Apache Samza

This presentation gives an overview of the Apache Samza project. It explains Samza's stream processing capabilities as well as its architecture, users, use cases etc.

Links for further information and connecting

http://www.amazon.com/Michael-Frampton/e/B00NIQDOOM/

https://nz.linkedin.com/pub/mike-frampton/20/630/385

https://open-source-systems.blogspot.com/

1. What Is Apache Samza ?
●An asynchronous computational framework
●For distributed sub second stream processing
●Fault tolerance, isolation and stateful processing
●Open source / Apache 2.0 license
●Developed in Java and Scala
●Runs stand-alone or on YARN

2. Samza Use Cases
●Applications that require millisecond - second response
–Streaming analytics
–DDOS attack detection
–Fraud detection
–Metric anomaly detection
–System notifications
–Performance monitoring

3. Samza Users

4. Samza Partitioned Stream
●Samza uses streams to process data
●Collections of ordered immutable objects
●Each object uses a key-value pair
●Each stream is sharded into partitions
●This allows the architecture to scale

5. Samza API's
●High Level Streams API (Java)
–Stream based processing API
●Low Level Task API (Java)
–Message based processing API
●Table API
–Random access by key data sources
●Testing Samza
–Samza's testing Integration framework
●Samza SQL
–Stream processing via SQL and UDF's
●Apache BEAM
–Samza provides a Beam runner for application execution

6. Samza Architecture

7. Samza Architecture
●Application are broken down into tasks
●Each task consumes data from a stream partition
●Tasks are executed with containers
●A coordinator assigns tasks to containers
●Tasks checkpoint their last processed task offset
●Each task has its own state store for state management
●Samza replicates changes to local store in separate stream
●This allows later recovery of local stores

8. Samza Architecture
●Task container coordination

9. Samza Architecture
●Fault tolerance of state

10. Samza Architecture
●Incremental checkpointing

11. Samza Architecture
●State management

12. Available Books
● See “Big Data Made Easy”
–Apress Jan 2015
●
See “Mastering Apache Spark”
–Packt Oct 2015
●
See “Complete Guide to Open Source Big Data Stack
–“Apress Jan 2018”
● Find the author on Amazon
–www.amazon.com/Michael-Frampton/e/B00NIQDOOM/
●
Connect on LinkedIn
–www.linkedin.com/in/mike-frampton-38563020

13. Connect
● Feel free to connect on LinkedIn
– www.linkedin.com/in/mike-frampton-38563020
● See my open source blog at
–open-source-systems.blogspot.com/
● I am always interested in
–New technology
–Opportunities
–Technology based issues
–Big data integration