Machine Learning meets Databases. Ioannis Papapanagiotou Cloud Database Engineering

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1 Machine Learning meets Databases Ioannis Papapanagiotou Cloud Database Engineering

2 Create Personalized Recommendations for discoveries of engaging video content that maximizes member joy.

3 Personalize Everything

14 90 seconds

15 90 seconds...

16 What do caches touch? Signing up* Logging in Choosing a profile Picking liked videos Personalization* Loading home page* Scrolling home page* A/B tests Video image selection * multiple caches involved Searching* Viewing title details Playing a title* Subtitle / language prefs Rating a title My List Video history* UI strings Video production*

18 Ephemeral Volatile Cache Key-Value store optimized for AWS and tuned for Netflix

19 What is EVCache? Distributed, sharded, replicated key-value store Tunable in-region and global replication Based on Memcached Resilient to failure Topology aware Linearly scalable Seamless deployments

20 Why Optimize for AWS Instances disappear Zones fail Regions become unstable Network is lossy Customer requests bounce between regions Failures happen and we test all the time

21 EVCache Netflix Hundreds of terabytes of data Trillions of ops / day Tens of billions of items stored Tens of millions of ops / sec Millions of replications / sec Thousands of servers Hundreds of instances per cluster Hundreds of microservice clients Tens of distinct clusters 3 regions

22 Architecture Application Server Client Library Client Memcached EVCar Eureka (Service Discovery)

23 Architecture us-west-2a Client us-west-2b Client us-west-2c Client

24 Reading (get) us-west-2a us-west-2b Client Primary Secondary us-west-2c

25 Writing (set, delete, add, etc.) us-west-2a Client us-west-2b Client us-west-2c Client

26 Use Case: Lookaside Cache Application Client Library Client REST/gRPC Client S Data Flow C S S C C S C

27 Use Case: Transient Data Store Time Application Application Client Library Application Client Library Client Client Library Client Client

28 Use Case: Primary Store Online Application Client Library Client Online Services Offline Services Offline / Nearline Precomputes for Recommendations Data Flow

29 Use Case: Impression store Online Application Client Library Client Online Services Offline Services Hive Data Flow

30 Pipeline of Personalization Online 1 Online 2 Online Services Offline Services Compute A Compute B Data Flow Compute D Compute C Compute E

31 Additional Features Kafka Global data replication Consistency metrics Key Iteration Cache warming Lost instance recovery Backup (and restore)

32 Cross-Region Replication Region A APP Region B 7 read 1 mutate APP a t da t ge t 4 r se fo 6 mutate 2 send metadata Repl Proxy Repl Proxy sg s tp 5 Kafka 3 poll msg Repl Relay ht nd se m Repl Relay Kafka

33 Open Source (client and REST proxy)

34 Viewing History

36 Requirements for Viewing History Time series dataset Support high writes Cross region replication Large Dataset

37 Growth of Viewing History

38 1) Massively scalable architecture 2) Multi-datacenter, multi-directional replication 3) Linear scale performance 4) Transparent fault detection and recovery 5) Flexible, dynamic schema data

39 Viewing History 1) Apply Custom Filters (user, device, subtitle, episode, season) 2) Tunable consistency to tradeoff performance vs data consistency

40 Growth of Viewing History

41 New Data Model

42 Use Case: A/B Metadata Wanted to capture information about each test Owner Properties Start time/end Time Allocation

43 Dynomite A framework that makes non-distributed data stores, distributed. Can be used with many key-value storage engines Features: highly available, automatic failover, node warmup, tunable consistency, backups/restores

44 Pluggable Storage Engines Layer on top of a non-distributed key value data store Peer-peer, Shared Nothing Auto-Sharding Multi-datacenter Linear scale Replication Gossiping

45 Replication

46 Dyno - Java Client Connection Pooling Load Balancing Effective failover Pipelining Scatter/Gather Metrics

47 Moving Across Storage Engines

48 Data Explorer for Dynomite (UI)

49 Open Source /dynomite Proxy (C) /dyno Client (Jedis) /dynomite-manager Sidecar (Tomcat Container) /dyno-queues Distributed queue recipe (Java)

50 Other Datastores Source of truth: Hive backed by S3 Elastic Search MySQL, Postgres, AWS Aurora

51 We are Hiring! LinkedIn: Github:

52 Thank you.

EVCache: Lowering Costs for a Low Latency Cache with RocksDB. Scott Mansfield Vu Nguyen EVCache

EVCache: Lowering Costs for a Low Latency Cache with RocksDB Scott Mansfield Vu Nguyen EVCache 90 seconds What do caches touch? Signing up* Logging in Choosing a profile Picking liked videos