My Other Car is a Redis. Etan Grundstein & Sasha Popov DYNAMIC YIELD

Transcription

1 My Other Car is a Redis Etan Grundstein & Sasha Popov DYNAMIC YIELD

2 About Dynamic Yield Dynamic Yield helps marketers increase revenue by personalizing customer interactions across web, mobile web, mobile apps and MILLION unique users per month 10 BILLION page views per month 600 BILLION events captured per month Real-time Customer Segmentation Based on any first, second or third party data Machine Learning & Automation Predictive targeting & opportunity discovery Omnichannel Personalization Across web, mobile web, mobile apps, & ads Testing & Optimization A/B/n testing & automated optimization Product/Content Recommendations Individualized, AI powered recommendations Behavioral 1:1 Messaging Exit-intent, overlays, notifications & social proof Multi-Channel Triggering triggers, push notifications, onsite messages Personalized Search Behavior/interest based results, custom facets & filters

3 My Other Car is a Redis Dynamic yield is a large scale customer data personalization platform. However, we use Redis as a complement in numerous use cases. Agenda: Index of settings in MySQL Distributed Lock User Real-Time Activity Store Load Balancer Indexed Products Catalog Store Scaling with Redis

4 Demo

5 Use Case : DY s Open-Time Recommendations s Personalized Recommendations Based on onsite behaviors, product affinity, purchase history etc. Up to Date Offers Considering current stock, pricing, merchandising rules Consistent Experience Repeated open must render the same content Tracking User Interactions Clicking on an item redirects & tracks the choice accordingly Dynamic Templates Dynamically embed product attributes in item images Pure HTML No Javascript

6 Open Time Recommendations Flow Open Time Module open-time servers products catalog Recommendation Module recs server cluster RT on-site activity recs cache distributed lock settings User DB Module load balancer / request queue user store cluster

7 Zoom In : Campaign Settings Store Open Time Module open-time servers settings

8 DY Admin Campaign Settings Store Index instead of a Cache Why? Changes in Settings take effect immediately Settings are stored in optimized data structures No Thundering Herd Notifications Queue How? 1. Marketeer sets up an Campaign in DY Admin Console 2. Campaign settings are stored in MySQL 3. Admin Server notifies workers of changes (via Redis queue) 4. Campaign settings are indexed to the Serving-Layer Redis MySQL DB Serving Worker Settings Store

9 Zoom In : Open Time Flow (Distributed Lock) Open Time Module open-time servers recs cache distributed lock

10 Redis as a Distributed Lock Why? 1. Avoid duplicates and inconsistent results 2. Reduce load from recommendations server How? 1. Redis is mostly single threaded 2. SETNX with TTL 3. Full documentation at Redis.io

11 Zoom In : User Real-Time Activity Tracking Recommendation Module recs server cluster RT on-site activity

12 User s Real Time Activity Tracking Why? On-site activity affects served content immediately User s entire history is affecting served content User s entire history too big for Redis How? 1. User activity is sent to collection layer 2. Same mergeable structure for RT and History 3. Collection layer sends data to : a. Redis i. TTL of 2 days ii. Atomic Increments with LUA b. ETL Processor i. Aggregates history ii. Writes to User DB 4. Serving layer merges RT and History 5. Personalized content is served Personalized content On-site activity Serving Layer Collection Layer Real Time User Data Historical User DB ETL Processing

13 Zoom In : User DB Module (Redis as Load Balancer) User DB Module load balancer / request queue user store cluster

14 User DB Module (Redis as Load Balancer) Why? HTTP was underperforming Resilient Load Balancing Client 2 1&5 Request Queue How? 1. Client subscribes to a channel, one channel per client 3 2. Client pushes request (+ response channel) to queue 3. Server performs blocking pop from request queue 4. Server processes request and publishes response to channel 5. Client gets the pub-sub message with response Response Channel User Store Server 4

15 Zoom In : Products Catalog Store Open Time Module open-time servers products catalog

16 Products Catalog Store Why? Fetch product data fast Used across the board How? Render HTML template Using product attributes to fill in the blanks Products Catalog

17 Open Time Recommendations Flow Open Time Module open-time servers products catalog Recommendation Module recs server cluster RT on-site activity recs cache distributed lock settings User DB Module load balancer / request queue user store cluster

18 Scaling with Redis : Timeline 1.2 TB, 900 GB, RLEC, Multiple data centers, RT Activity, Product Catalog, Social Proof 400 GB, 100 GB, <50 GB, <10 GB, Local processes, Caching Dedicated server, Caching, RT Activity Dedicated server + replication, RT Activity, Product Catalog RLEC, Multiple data centers, RT Activity, Product Catalog ROF // RCP X Multiple data centers, Even More...

19 Scaling with Redis : Fancy Stuff Redis Cloud Private THE Redis experts maintaining your cluster Reduce own R&D effort Maintain control over security & costs Redis on Flash Leverage Longtail Data Pattern Cuts infrastructure costs

20 Scaling with Redis : Sharding How BIG can my data get, anyway?.. Sharding by Tenant Enables Multi-Keys operations Creates uneven shards Prevents scaling out vs. Distributing Tenant Reduces the ability to make Multi-Keys operations Allows to scale out better

21 Summing it all up...

22 Thank You We re Hiring...

23 Thank You!