Streaming Ingestion & Processing at Flipkart

Presented at the Bangalore Hadoop Meetup held on 15th May 2015.

Transcript

1. Streaming Ingestion & Processing at Flipkart Siddhartha Reddy @sids
2. Flipkart Data Platform (an oversimplified view)
3. Streaming Ingestion
4. Choices • push, not pull • schemas & validations
5. Streaming Ingestion v1.0
7. • Push 㱺 accountability (with source teams) • good call! • Schemas 㱺 contracts for consumers • can make assumptions that are assured to be true • Insufficient tooling 㱺 too many “ingestion frameworks” • adopt some frameworks & offer as tools! • Synchronous error handling 㱺 complexity • accept all data
8. Streaming Ingestion v2.0
9. Stream Processing
10. An Example
11. Streaming Joins: Example It works! But… how do we deal with lookup failures?
12. Streaming Joins: Handling Failures
15. Streaming Joins: Bootstrapping With a little help from MR friends
16. Streaming Joins: But… The example that doesn’t really work correctly
17. Streaming Joins
18. In summary • Streaming Ingestion: push, schemas & validation, HTTP service, local daemon, change data capture • Streaming Joins: indexing, lookup tables, map-joins, retry queue, batch re-driver sid@flipkart.com