From Raghu Ramakrishnan's presentation "Key Challenges in Cloud Computing and How Yahoo! is Approaching Them" at the 2009 Cloud Computing Expo in Santa Clara, CA, USA. Here's the talk description on the Expo's site: http://cloudcomputingexpo.com/event/session/510

1. Vendor Speech by Raghu Ramakrishnan Cloud Computing Conference & Expo November 3, 2009 Key Challenges in Cloud Computing … and the Yahoo! Approach

3. Inside Yahoo!’s Cloud KEY CHALLENGES

4. DATA MANAGEMENT IN THE CLOUD

6. What Are You Trying to Do? Data Workloads OLTP (Random access to a few records) OLAP (Scan access to a large number of records) Read-heavy Write-heavy By rows By columns Unstructured Combined (Some OLTP and OLAP tasks)

7. Yahoo! Solution Space OLTP (Random access to a few records) OLAP (Scan access to a large number of records) Read-heavy Write-heavy By rows By columns Unstructured Combined (Some OLTP and OLAP tasks) UDS UDB ??? STCache Sherpa Read, read/write Write-heavy Main-memory SQL on Grid Zebra Pig HDFS MapReduce

14. One Slide Hadoop Primer HDFS Data file Map tasks HDFS Reduce tasks Good for analyzing (scanning) huge files Not great for serving (reading or writing individual objects)

16. Ways of Using Hadoop Data workloads OLAP (Scan access to a large number of records) By rows By columns Unstructured HadoopDB SQL on Grid Zebra

19. SHERPA * Yahoo!’s Serving Store * The system also known as PNUTS

20. What is Sherpa? CREATE TABLE Parts ( ID VARCHAR, StockNumber INT, Status VARCHAR … ) Parallel database Geographic replication Structured, flexible schemas Hashed and ordered tables Hosted, managed infrastructure E 75656 C A 42342 E B 42521 W C 66354 W D 12352 E F 15677 E E 75656 C A 42342 E B 42521 W C 66354 W D 12352 E F 15677 E A 42342 E B 42521 W C 66354 W D 12352 E E 75656 C F 15677 E

22. Architecture Storage units Routers Tablet Controller REST API Clients Local region Remote regions Tribble

23. Updates Write key k Sequence # for key k Sequence # for key k Write key k SUCCESS Write key k Routers Message brokers 1 2 Write key k 7 8 SU SU SU 3 4 5 6

24. Accessing Data SU SU SU Get key k 1 2 Get key k 3 Record for key k 4 Record for key k

25. ASYNCHRONOUS REPLICATION AND CONSISTENCY

26. Asynchronous Replication

28. Example: Social Alice West East ___ Busy Free Free Record Timeline (Network fault, updt goes to East) (Alice logs on) User Status Alice Busy User Status Alice Free User Status Alice ??? User Status Alice ??? User Status Alice Busy User Status Alice ___

29. Sherpa Consistency Model Time v. 1 v. 2 v. 3 v. 4 v. 5 v. 7 Generation 1 v. 6 v. 8 Write Current version Stale version Stale version Achieved via per-record primary copy protocol (To maximize availability, record masterships automaticlly transferred if site fails) Can be selectively weakened to eventual consistency (local writes that are reconciled using version vectors)

30. Sherpa Consistency Model Time v. 1 v. 2 v. 3 v. 4 v. 5 v. 7 Generation 1 v. 6 v. 8 Current version Stale version Stale version Read In general, reads are served using a local copy

31. Sherpa Consistency Model Time v. 1 v. 2 v. 3 v. 4 v. 5 v. 7 Generation 1 v. 6 v. 8 Read up-to-date Current version Stale version Stale version But application can request and get current version

32. OPERABILITY

33. Distribution Distribution for parallelism Data shuffling for load balancing Server 1 Server 2 Server 3 Server 4 Bike $86 6/2/07 636353 Chair $10 6/5/07 662113 Couch $570 6/1/07 424252 Car $1123 6/1/07 256623 Lamp $19 6/7/07 121113 Bike $56 6/9/07 887734 Scooter $18 6/11/07 252111 Hammer $8000 6/11/07 116458

34. Tablet Splitting and Balancing Each storage unit has many tablets (horizontal partitions of the table) Tablets may grow over time Overfull tablets split Storage unit may become a hotspot Shed load by moving tablets to other servers Storage unit Tablet

35. Mastering A 42342 E B 42521 W C 66354 W D 12352 E E 75656 C F 15677 E A 42342 E B 42521 W C 66354 W D 12352 E E 75656 C F 15677 E A 42342 E B 42521 W C 66354 W D 12352 E E 75656 C F 15677 E A 42342 E B 42521 E C 66354 W D 12352 E E 75656 C F 15677 E C 66354 W B 42521 E A 42342 E D 12352 E E 75656 C F 15677 E

36. Record vs. Tablet Master A 42342 E B 42521 W C 66354 W D 12352 E E 75656 C F 15677 E Record master serializes updates Tablet master serializes inserts A 42342 E B 42521 W C 66354 W D 12352 E E 75656 C F 15677 E A 42342 E B 42521 W C 66354 W D 12352 E E 75656 C F 15677 E A 42342 E B 42521 E C 66354 W D 12352 E E 75656 C F 15677 E C 66354 W B 42521 E A 42342 E D 12352 E E 75656 C F 15677 E

37. Coping With Failures A 42342 E B 42521 W C 66354 W D 12352 E E 75656 C F 15677 E A 42342 E B 42521 W C 66354 W D 12352 E E 75656 C F 15677 E A 42342 E B 42521 W C 66354 W D 12352 E E 75656 C F 15677 E X X OVERRIDE W -> E

38. COMPARING SYSTEMS

44. Preliminary Results Sherpa

47. EDGE, CLOUD SERVING, AND MORE

48. Cloud Serving: The Integrated Cloud Big idea: Declarative language for specifying the full, end-to-end structure of a service Key insight: this “full, end-to-end structure” includes multiple environments Central mechanism: the Integrated Cloud , which (re)deploys these specifications ( Surendra Reddy, 2:20 pm, Session 7) Development, multiple testing environments, alpha and bucket-testing environments, production

52. YCS Scale YCS handles so much Yahoo traffic, this is noise!

56. TUESDAY, 11/3 9:10 am – 9:55 am Yahoo! Query Language - YQL: Select * from Internet [Including Live Demo!] Dr. Jonathan Trevor Senior Architect TUESDAY, 11/3 2:20 pm – 3:05 pm Walking through Cloud Serving at Yahoo! Surendra Reddy VP, Integrated Cloud and Visualization TUESDAY, 11/3 4:50pm – 5:35 pm Hadoop @ Yahoo! – Internet Scale Data Processing Eric Baldeschwieler VP, Hadoop Software Development WEDNESDAY, 11/4 9:10 am - 9:55 am Yahoo! Scalable Storage and Delivery Services Chuck Neerdaels VP, Storage and Edge Services VISIT BOOTH #103 TO TALK WITH YAHOO! ENGINEERS AND LEARN MORE ABOUT YAHOO!’S VISION FOR CLOUD COMPUTING.