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Reflects many discussions with:

An Overview of Cloud Computing @ Yahoo! Raghu Ramakrishnan Chief Scientist, Audience and Cloud Computing Research Fellow, Yahoo! Research. Reflects many discussions with: Eric Baldeschwieler, Jay Kistler, Chuck Neerdaels, Shelton Shugar, and Raymie Stata

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Reflects many discussions with:

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  1. An Overview of Cloud Computing @ Yahoo!Raghu RamakrishnanChief Scientist, Audience and Cloud ComputingResearch Fellow, Yahoo! Research Reflects many discussions with: Eric Baldeschwieler, Jay Kistler, Chuck Neerdaels, Shelton Shugar, and Raymie Stata and joint work with the Sherpa team, in particular: Brian Cooper, Utkarsh Srivastava, Adam Silberstein, Rodrigo Fonseca and Nick Puz in Y! Research Chuck Neerdaels, P.P. Suryanarayanan and many others in CCDI

  2. Questions • What is cloud computing? • Horizontal and functional services • What’s it going to change? • Software business models, science, life • How many clouds will there be? • 1, 2, 3, infinity • What’s new in cloud computing? • HPC grids, ASPs, hosted services, Multics (!) • Emerging “cloud stack” to support a broad class of programs, including data intensive applications

  3. SCENARIOS Pie-in-the-sky

  4. Living in the Clouds • We want to start a new website, FredsList.com • Our site will provide listings of items for sale, jobs, etc. • As time goes on, we’ll add more features • And illustrate how more cloud capabilities (and corresponding infrastructure components) are used as needed • List of capabilities/components is illustrative, not exhaustive • Our cloud provides a “dataset” abstraction • FredsList doesn’t worry about the underlying components

  5. Step 1: Listings Scenario FredsList wants to store listings as (key, category, description) FredsList.com application DECLARE DATASET Listings AS ( ID String PRIMARY KEY, Category String, Description Text ) 5523442, childcare, Nanny available in San Jose 1234323, transportation, For sale: one bicycle, barely used 215534, wanted, Looking for issue 1 of Superman comic book Simple Web Service API’s Database PNUTS

  6. Step 2: System Evolution Fred belatedly realizes prices are useful information! FredsList.com application ALTER DATASET Listings ADD (Price Float) 5523442, childcare, Nanny available in San Jose 215534, wanted, Looking for issue 1 of Superman comic book 32138, camera, Nikon D40, USD 300 1234323, transportation, For sale: one bicycle, barely used Simple Web Service API’s Schemas are flexible, and evolve vs. Database PNUTS Not every record in a dataset has values defined for all fields declared for the dataset

  7. Federation of systems offering different capabilities Step 3: Search FredsList’s customers quickly ask for keyword search FredsList.com application ALTER Listings SET Description SEARCHABLE “dvd’s” “bicycle” “nanny” Simple Web Service API’s Database Search PNUTS Vespa Messaging Tribble

  8. Federation of systems offering different performance points Step 4: Photos FredsList decides to add photos/videos to listings FredsList.com application ALTER Listings ADD Photo BLOB Simple Web Service API’s Storage Database Search Foreign key photo → listing MObStor PNUTS Vespa Messaging Tribble

  9. Step 5: Data Analysis FredsList wants to analyze its listings to get statistics about category, do geocoding, etc. FredsList.com application ALTER Listings MAKE ANALYZABLE Hadoop program to generate fancy pages for listings Hadoop program to geocode data Pig query to analyze categories Simple Web Service API’s Storage Compute Database Search Foreign key photo → listing MObStor Grid PNUTS Vespa Messaging Tribble Batch export

  10. And by now, Fred is global, and wants geo-replication! Step 6: Performance FredsList wants to reduce its data access latency FredsList.com application ALTER Listings MAKE CACHEABLE Simple Web Service API’s Storage Compute Database Caching Search Foreign key photo → listing MObStor Grid PNUTS memcached Vespa Messaging Tribble Batch export

  11. Data Serving vs. Analysis • Very different workloads, requirements • Data from serving system is one of many kinds of data (click streams are another common kind, as are syndicated feeds) to be analyzed and integrated • The result of analysis often goes right back into serving system

  12. EYES TO THE SKIES Motherhood-and-Apple-Pie

  13. Why Clouds? • On-demand infrastructure to create a fundamental shift in the OE curve: • Do things we can’t do • Build more robustly, more efficiently, more globally, more completely, more quickly, for a given budget • Cloud services should do heavy lifting of heavy-lifting of scaling & high-availability • Today, this is done at the app-level, which is not productive

  14. Requirements for Cloud Services • Multitenant. A cloud service must support multiple, organizationally distant customers. • Elasticity. Tenants should be able to negotiate and receive resources/QoS on-demand. • Resource Sharing. Ideally, spare cloud resources should be transparently applied when a tenant’s negotiated QoS is insufficient, e.g., due to spikes. • Horizontal scaling. It should be possible to add cloud capacity in small increments; this should be transparent to the tenants of the service. • Metering. A cloud service must support accounting that reasonably ascribes operational and capital expenditures to each of the tenants of the service. • Security. A cloud service should be secure in that tenants are not made vulnerable because of loopholes in the cloud. • Availability. A cloud service should be highly available. • Operability. A cloud service should be easy to operate, with few operators. Operating costs should scale linearly or better with the capacity of the service.

  15. Types of Cloud Services • Two kinds of cloud services: • Horizontal (“Platform”) Cloud Services • Functionality enabling tenants to build applications or new services on top of the cloud • Functional Cloud Services • Functionality that is useful in and of itself to tenants. E.g., various SaaS instances, such as Saleforce.com; Google Analytics and Yahoo!’s IndexTools; Yahoo! properties aimed at end-users and small businesses, e.g., flickr, Groups, Mail, News, Shopping • Could be built on top of horizontal cloud services or from scratch • Yahoo! has been offering these for a long while (e.g., Mail for SMB, Groups, Flickr, BOSS, Ad exchanges)

  16. Opening Up Yahoo! Search Phase 1 Phase 2 BOSS takes Yahoo!’s open strategy to the next level by providing Yahoo! Search infrastructure and technology to developers and companies to help them build their own search experiences. Giving site owners and developers control over the appearance of Yahoo! Search results.

  17. BOSS Offerings BOSS offers two options for companies and developers and has partnered with top technology universities to drive search experimentation, innovation and research into next generation search. • ACADEMIC • Working with the following universities to allow for wide-scale research in the search field: API A self-service, web services model for developers and start-ups to quickly build and deploy new search experiences. CUSTOM Working with 3rd parties to build a more relevant, brand/site specific web search experience. This option is jointly built by Yahoo! and select partners. • University of Illinois Urbana Champaign • Carnegie Mellon University • Stanford University • Purdue University • • MIT • Indian Institute of • Technology Bombay • University of • Massachusetts (Slide courtesy Prabhakar Raghavan)

  18. Partner Examples

  19. Horizontal Cloud Services • Horizontal cloudservices are foundations on which tenants build applications or new services. They should be: • Semantics-free. Must be "generic infrastructure,” and not tied to specific app-logic. • May provide the ability to inject application logic through well-defined APIs • Broadly applicable. Must be broadly applicable (i.e., it can't be intended for just one or two properties). • Fault-tolerant over commodity hardware. Must be built using inexpensive commodity hardware, and should mask component failures. • While each cloud service provides value, the power of the cloud paradigm will depend on a collection of well-chosen, loosely coupled services that collectively make it easy to quickly develop and operate innovative web applications.

  20. Yahoo! Cloud Stack EDGE Horizontal Cloud Services YCS YCPI Brooklyn … WEB Horizontal Cloud Services VM/OS yApache PHP App Engine APP Provisioning (Self-serve) Monitoring/Metering/Security Horizontal Cloud Services VM/OS Serving Grid … Data Highway STORAGE Horizontal Cloud Services Sherpa MOBStor … BATCH Horizontal Cloud Services Hadoop …

  21. Yahoo! CCDI Thrust Areas • Fast Provisioning and Machine Virtualization: On demand, deliver a set of hosts imaged with desired software and configured against standard services • Multiple hosts may be multiplexed onto the same physical machine. • Batch Storage and Processing: Scalable data storage optimized for batch processing, together with computational capabilities • Operational Storage: Persistent storage that supports low-latency updates and flexible retrieval • Edge Content Services: Support for dealing with network topology, communication protocols, caching, and BCP Rest of today’s talk

  22. Web Data Management • CRUD • Point lookups and short scans • Index organized table and random I/Os • $ per latency • Scan oriented workloads • Focus on sequential disk I/O • $ per cpu cycle Structured record storage (PNUTS/Sherpa) Large data analysis (Hadoop) • Object retrieval and streaming • Scalable file storage • $ per GB Blob storage (SAN/NAS)

  23. Hadoop: Batch Storage/Analysis Why is batch processing important? • Whether it’s • response-prediction for advertising • machine-learned relevance for Search, or • content optimization for audience, • data-intensive computing is increasingly central to everything Yahoo! does • Hadoop is central to addressing this need • Hadoop is a case-study in our cloud vision • Processes enormous amounts of data • Provides horizontal scaling and fault-tolerance for our users • Allows those users to focus on their app logic [Workflow] High-level query layer (Pig) Map-Reduce HDFS

  24. The World Has Changed • Web serving applications need: • Scalability! • Preferably elastic • Flexible schemas • Geographic distribution • High availability • Reliable storage • Web serving applications can do without: • Complicated queries • Strong transactions

  25. MObStor Yahoo!’s next-generation globally replicated, virtualized media object storage service Better provisioning, easy migration, replication, better BCP, and performance New features (Evergreen URLs, CDN integration, REST API, …) The object metadata problem addressed using Sherpa, though MObStor is focused on blob storage. 27

  26. Storage & Delivery Stack

  27. PNUTS / SHERPA To Help You Scale Your Mountains of Data

  28. Yahoo! Research Raghu Ramakrishnan Brian Cooper Utkarsh Srivastava Adam Silberstein Rodrigo Fonseca CCDI Chuck Neerdaels P.P.S. Narayan Kevin Athey Toby Negrin Plus Dev/QA teams CCDI—Research Collaboration

  29. Yahoo! Serving Storage Problem • Small records – 100KB or less • Structured records – lots of fields, evolving • Extreme data scale - Tens of TB • Extreme request scale - Tens of thousands of requests/sec • Low latency globally - 20+ datacenters worldwide • High Availability - outages cost $millions • Variable usage patterns - as applications and users change 31

  30. What is PNUTS/Sherpa? A 42342 E A 42342 E B 42521 W B 42521 W C 66354 W D 12352 E F 15677 E A 42342 E E 75656 C B 42521 W C 66354 W C 66354 W D 12352 E D 12352 E E 75656 C E 75656 C F 15677 E F 15677 E CREATE TABLE Parts ( ID VARCHAR, StockNumber INT, Status VARCHAR … ) Structured, flexible schema Geographic replication Parallel database Hosted, managed infrastructure 33

  31. A 42342 E A 42342 E A 42342 E B 42521 W B 42521 W B 42521 W C 66354 W C 66354 W C 66354 W D 12352 E D 12352 E D 12352 E E 75656 C E 75656 C E 75656 C F 15677 E F 15677 E F 15677 E What Will It Become? Indexes and views

  32. Design Goals Consistency Per-record guarantees Timeline model Option to relax if needed Multiple access paths Hash table, ordered table Primary, secondary access Hosted service Applications plug and play Share operational cost Scalability Thousands of machines Easy to add capacity Restrict query language to avoid costly queries Geographic replication Asynchronous replication around the globe Low-latency local access High availability and fault tolerance Automatically recover from failures Serve reads and writes despite failures 36

  33. Technology Elements Applications Tabular API PNUTS API • PNUTS • Query planning and execution • Index maintenance • Distributed infrastructure for tabular data • Data partitioning • Update consistency • Replication YCA: Authorization • YDOT FS • Ordered tables • YDHT FS • Hash tables • Tribble • Pub/sub messaging • Zookeeper • Consistency service 37

  34. Data Manipulation Per-record operations Get Set Delete Multi-record operations Multiget Scan Getrange Web service (RESTful) API 38

  35. Tablets—Hash Table Name Description Price 0x0000 $12 Grape Grapes are good to eat $9 Limes are green Lime $1 Apple Apple is wisdom $900 Strawberry Strawberry shortcake 0x2AF3 $2 Orange Arrgh! Don’t get scurvy! $3 Avocado But at what price? Lemon How much did you pay for this lemon? $1 $14 Is this a vegetable? Tomato 0x911F $2 The perfect fruit Banana $8 Kiwi New Zealand 0xFFFF 39

  36. Tablets—Ordered Table Name Description Price A $1 Apple Apple is wisdom $3 Avocado But at what price? $2 Banana The perfect fruit $12 Grape Grapes are good to eat H $8 Kiwi New Zealand Lemon $1 How much did you pay for this lemon? Limes are green Lime $9 $2 Orange Arrgh! Don’t get scurvy! Q $900 Strawberry Strawberry shortcake $14 Is this a vegetable? Tomato Z 40

  37. Flexible Schema

  38. Detailed Architecture Remote regions Local region Clients REST API Routers Tribble Tablet Controller Storage units 42

  39. Tablet Splitting and Balancing Storage unit Tablet Each storage unit has many tablets (horizontal partitions of the table) Storage unit may become a hotspot Tablets may grow over time Overfull tablets split Shed load by moving tablets to other servers 43

  40. QUERY PROCESSING 44

  41. Accessing Data Record for key k Get key k Record for key k 1 2 3 4 Get key k SU SU SU 45

  42. Bulk Read {k1, k2, … kn} Get k1 Get k2 Get k3 Scatter/ gather server 1 2 SU SU SU 46

  43. Storage unit 1 Canteloupe Storage unit 3 Lime Storage unit 2 Strawberry Storage unit 1 Grapefruit…Pear? Grapefruit…Lime? Storage unit 1 Canteloupe Storage unit 3 Lime Storage unit 2 Strawberry Storage unit 1 Lime…Pear? Router Storage unit 1 Storage unit 2 Storage unit 3 Range Queries in YDOT • Clustered, ordered retrieval of records Apple Avocado Banana Blueberry Canteloupe Grape Kiwi Lemon Lime Mango Orange Strawberry Tomato Watermelon Apple Avocado Banana Blueberry Strawberry Tomato Watermelon Lime Mango Orange Canteloupe Grape Kiwi Lemon

  44. Updates Write key k SU SU SU 6 5 2 4 1 8 7 3 Write key k Sequence # for key k Routers Message brokers Write key k Sequence # for key k SUCCESS Write key k 48

  45. ASYNCHRONOUS REPLICATION AND CONSISTENCY 49

  46. Asynchronous Replication 50

  47. Goal: Make it easier for applications to reason about updates and cope with asynchrony What happens to a record with primary key “Alice”? Consistency Model Record inserted Delete Update Update Update Update Update Update Update v. 2 v. 5 v. 1 v. 3 v. 4 v. 6 v. 7 v. 8 Time Time Generation 1 As the record is updated, copies may get out of sync. 51

  48. Example: Social Alice East Record Timeline West ___ Busy Free Free

  49. Consistency Model Read Stale version Current version Stale version v. 2 v. 5 v. 1 v. 3 v. 4 v. 6 v. 7 v. 8 Time Generation 1 In general, reads are served using a local copy 53

  50. Consistency Model Read up-to-date Stale version Current version Stale version v. 2 v. 5 v. 1 v. 3 v. 4 v. 6 v. 7 v. 8 Time Generation 1 But application can request and get current version 54

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