Facebook style notifications using hbase and event streams
Download as PPTX, PDF12 likes3,511 views
This document describes Facebook-style notifications using HBase and event streams. It discusses capturing user intent from actions like browsing, buying, and registering on ecommerce sites. Notifications are pre-created in real-time by intersecting user intent data with product change events in HBase. The solution uses technologies like Trooper for event processing, Phantom as a reverse proxy, and Flipcast for multi-cast notifications. It provides low-latency access to notifications while scaling to handle large volumes of user, product, and event data.
Facebook style notifications using hbase and event streams
- 1. Facebook Style Notifications Using HBase and Event Streams github.com/regunathb RegunathB
- 2. Serving User Intent (eCommerce) • Mass targeted (Low relevance) – User Intent Captured from: Browse, Buy, Register • Quantified,Time-bound (Improved relevance) – User Intent Derived from: Category Affinity, Recommendations
- 3. Serving User Intent (social) Image Source : http://allfacebook.com/ • Near real-time – Quick updates about friends’ actions that most affect you • Relevant Actions – Likes, Comments etc • Personalized – Content only from social circle • Non-invasive – Users therefore tolerate less relevant content as compared to email
- 4. Notifications on Flipkart Search, Browse Add to Wish List Add to Cart Checkout/Buy User Intent derived from
- 5. Price Drop Notification iPhone 5C Price =42K Price =44K Price =39K . . Time. t2 t0 t1 t2
- 6. Solution 1 : Generate Notifications on Demand
- 7. Gather User Intent Retrieve Current, Past Data Intents Data store • Pros • Perceived optimal resource utilization • Cons • Gathering, Processing and Serving coupled • Read path is computationally expensive • High latency • Need versioning support on Product data • Repeated computations Product Data store Create Notifications on Visits
- 8. Solution 2 : Pre-create in Real-time, Serve on Demand
- 9. What Leads to a Notification? Intent (interest expressed by the user) ⋂ Event (price changes ) => Notification (Intersection of millions of User Intent and Product Changes) Intent Event Stream Change Event Stream Notifications
- 10. Intent Capturing System Event Processing System Notification Serving System Intents, Notifications Product changes append create update expire Event based Pre-processing Near real-time Serving read Pre-create, Serve on Demand
- 11. SEDA, Filtering using CEP Filtered event processing Intents Product changes Facts, Notifications CEP Engine intermediate stages intermediate stages Extract unique interests
- 12. The Data Store • Store large sets of data – Products(P) 10s M – Users(U) 10s M – Activity(I = U X P) 100s M – Events/day (E = P + U) 10s M – Notifications (N = E ⋂ I) >100 M (in total) • High write throughput • High read throughput for sets of data – Intents: user pivoted, Facts: product pivoted • Low latency reads – Notifications – user pivoted, ordered by recency
- 13. The Data Store - HBase U:USERID_A:TIMESTAMP:PRICE_DROP:MOBDSGU2ZMDYENQ U:USERID_B:TIMESTAMP:PRICE_DROP:MOBDSGU2ZMDYENQ U:USERID_B:TIMESTAMP:PRICE_DROP:MOBDQ9VXXXX6NF8V U:USERID_B:TIMESTAMP:PRICE_DROP:MOBDP6W6MCUWCF U:USERID_C:TIMESTAMP:PRICE_DROP:MOBDQ9VXXXX6NF8V LSM Tree Row key design for Notifications table Image Sources : http://blog.sematext.com/, http://dailyjs.com/ • Benefits of keeping related data together – Minimize disk seek for rows read – Rows may be returned from Block cache, MemStore
- 14. Intent Capturing System Event Processing System Notification Serving System HBase (Intents, Notifications) Product changes append create update expire Event based Pre-processing Near real-time Serving read Tech Stack Trooper Batch W3 via Phanto m Trooper SEDA (RabbitMQ, Mule), CEP (Esper) Phantom Flipcast CeryxTomcat CDNMemcached
- 15. Tech Stack • Phantom – Reverse proxy for latency sensitive user actions • Trooper Batch – Cron jobs • Trooper SEDA – Distributed, Event processing • FlipCast – Platform agnostic multi-cast notifications • RabbitMQ – Integration, Work distribution • Esper – Complex Event Processing (Filtering/Matching) • HBase – Data store • Tomcat – REST services container for Notifications • Ceryx – Target Group generation, User preferences Flipkart OSS Public domain OSS Closed source
- 16. Operating Notifications A/B framework Phantom: Intent Capture Phantom: Serve Notifications Trooper Batch : Jobs • Monitoring consoles – RabbitMQ queues – FQ service – Graphite – Nagios – Omniture tracking – Trooper SEDA & Batch consoles
- 18. Recap • Pros – Low latency read-path, resilience to failure (ok to not show notifications for some users) – Scales well (LSM trees, KV store, SEDA, CDN for images) – Immutable Facts, Change Events stored in append-only data store provides ability to re-compute notifications • Cons – Consistency challenges •HBase has strong consistency (single write master) but Notification source data can change – leading to Eventual Consistency – Pre-creating Notifications that may never be seen (cost of storage)
- 19. References • HBase : The Definitive Guide (http://www.flipkart.com/hbase-definitive- guide/p/itmd36cuhzdfq4za?pid=DGBDTYAYB3PNSGYN ) • Block cache 101(http://hortonworks.com/blog/hbase-blockcache-101/) • Trooper (https://github.com/regunathb/Trooper) • Flipkart Phantom (https://github.com/Flipkart/phantom) • Facebook messages & Hbase (http://www.slideshare.net/brizzzdotcom/facebook-messages-hbase)