Jiaheng Lu Renmin Universtiy of China 2009-08-25

1. 中科院软件所 中国人民大学 Cloud-based Data Management: Challenges & Opportunities Jiaheng Lu Renmin Universtiy of China 2009-08-25

2. Research experience and interesting • National University of Singapore PhD • XML query processing and XML keyword search • University of California, Irvine Postdoc • Approximate string processing • Data integration and data cleaning • Renmin University of China • Cloud data management • XML data management

3. Outline • Motivation: cloud data management • Database Future and Challenges： • Large-scale Data management & transaction processing • Cloud-based data indexing and query optimization • Recent research work： • An efficient multiple-dimensional indexes for cloud data management • CIKM Workshop CloudDB 2009

4. Motivation: Internet Chatter

5. BLOG Wisdom • “If you want vast, on-demand scalability, you need a non-relational database.” Since scalability requirements: • Can change very quickly and, • Can grow very rapidly. • Difficult to manage with a single in-house RDBMS server. • Although RDBMS scale well: • When limited to a single node. • Overwhelming complexity to scale on multiple sever nodes.

6. Current State • Most enterprise solutions are based on RDBMS technology. • Significant Operational Challenges: • Provisioning for Peak Demand • Resource under-utilization • Capacity planning: too many variables • Storage management: a massive challenge • System upgrades: extremely time-consuming

7. Internet Search Data Analytics: A Case Study • Data analytics: • Parsed WEB Logs ingested in a RDBMS store. • Hourly and Daily summarization for custom reporting. • Operational nightmare: • Maintaining live reporting system ON at all costs and at all times. • Timely completion of hourly summarization. • Constant tension between Ad-hoc workload versus reporting workload. • Data-driven feedback to live products. • Temporal depth of detailed data

8. Internet Search Data Analytics: A Case Study • Various solutions explored: • Data Warehousing appliance for fast summarization. • Parallel RDBMS technology for fast ad-hoc queries. • Business Intelligence Products (Data Cubes) for fast and intuitive reporting and analysis. • None of the solutions completely satisfactory: • Plans to migrate low-level data to file-based system to overcome Database scalability bottlenecks

9. Paradigm Shift in Computing

10. WEB is replacing the Desktop

11. What is Cloud Computing? • Old idea: Software as a service (SaaS) • Def: delivering applications over the internet • Recently: “[Hardware, infrastructure, Platform] as a service” • Poorly defined so we avoid all “X as a service” • Utility Computing: pay-as-you-go computing • Illusion of infinite resources • No up-front cost • Fine-grained billing (e.g. hourly)

12. Why Now? • Experience with very large datacenters • Unprecedented economies of scale • Other factors • Pervasive broadband internet • Pay-as-you-go billing model

13. Cloud Computing Spectrum • Instruction Set VM (Amazon EC2, 3Tera) • Framework VM • Google AppEngine, Force.com

14. Cloud Killer Apps • Mobile and web applications • Extensions of desktop software • Matlab, Mathematica • Batch processing/MapReduce

15. Economics of Cloud Users • Pay by use instead of provisioning for peak

16. Economics of Cloud Users • Risk of over-provisioning: underutilization

17. Economics of Cloud Users • Heavy penalty for under-provisioning

18. Economics of Cloud Providers • 5-7X economies of scale [Hamilton 2008] • Extra benefits • Amazon: utilize off-peak capacity • Microsoft: sell .NET tools • Google: reuse existing infrastructure

19. Engineering Definition • Providing services on virtual machines allocated on top of a large physical machine pool.

20. Business Definition • A method to address scalability and availability concerns for large scale applications.

21. Data Management in the Cloud?

22. Cloud Computing Implications on DBMSs • Where do Databases fit in this paradigm? • Generational reality: • Animoto.com • Started with 50 servers on Amazon EC2 • Growth of 25,000 users/hour • Need to scale to 3,500 servers in 2 days. • Many similar stories: • RightScale • Joyent • …

23. Clouded Data? • Reality Number Ⅰ： • Unlimited processing assumption • Interactive page views: • By targeting large number of SQL queries against MySQL • Still Expect sub-millisecond object retrieval • Reality Number Ⅱ: • Why can’t the database tier be replicated in the same way as the Web Server and App Server can? →These are the major challenges for Data Management in the cloud.

24. The Vision • R&D Challenges at the macro level: • Where and how does the DBMS fit into this model. • R&D Challenges at micro level: • Specific technology components that must be developed to enable the migration of enterprise data into the clouds.

25. Data and Networks: Attempt Ⅰ • Distributed Database (1980s): • Idealized view: unified access to distributed data • Prohibitively expensive: global synchronization • Remained a laboratory prototype: • Associated technology widely in-use: 2PC

26. Data and Networks: Attempt Ⅱ

27. Data and Networks: Pragmatics

28. Database on S3: SIGMOD’08 • Amazon’s Simple Storage Service(S3): • Updates may not preserve initiation order • No “force” writes • Eventual guarantee • Proposed solution: • Pending Update Queue • Checkpoint protocol to ensure consistent ordering • ACID: only Atomicity + Durability

29. Unbundling Txns in the Cloud • Research results: • CIDR’09 proposal to unbundle Transactions Management for Cloud Infrastructures • Attempts to refit the DBMS engine in the cloud storage and computing

30. Analytical Processing

31. Architectural and System Impacts • Current state: • MapReduce Paradigm for data analysis • What is missing: • Auxiliary structures and indexes for associative access to data (i.e., attribute-based access) • Caveat: inherent inconsistency and approximation • Future projection: • Eventual merger of databases (ODSs) and data warehouses as we have learned to use and implement them.

32. Underlying Principles: CIDR’2009 • Business data may not always reflect the state of the world or the business: • Inherent lack of perfect information • Secondary data need not be updated with primary data: • Inherent latency • Transactions/Events may temporarily violate integrity constraints: • Referential integrity may need to be compromised

33. Data Security & Privacy • Data privacy remains a show-stopper in the context of database outsourcing. • Encryption-based solutions are too expensive and are projected to be so in the foreseeable future: • Private Information Retrieval (Sion’2008) • Other approaches: • Information-theoretic approaches that uses data-partitioning for security (Emekci’2007) • Hardware-based solution for information security

34. Self management and self tuning in cloud-based data management • Self management and self tuning • Query optimization on thousands of nodes

35. Remarks • Data Management for Cloud Computing poses a fundamental challenge to database researchers: • Scalability • Reliability • Data Consistency • Radically different approaches and solution are warranted to overcome this challenge: • Need to understand the nature of new applications

36. References • Life Beyond Distributed Transactions: An Apostate’s Opinion by P.Helland, CIDR’07 • Building a Database on S3 M.Brartner, D.Florescu, D.Graf, D.Kossman, T.Kraska, SIGMOD’08 • Unbundling Transaction Services in the Cloud D.Lo,et, A.Fekete, G.Weikum, M.Zwilling, CIDR’09 • Principles of Inconsistency S.Finkelstein, R.Brendle, D.Jacobs, CIDR’09 • VLDB Database School (China) 2009 http://www.sei.ecnu.edu.cn/~vldbschool2009/VLDBSchool2009English.htm

37. An Efficient Multi-Dimensional Index for Cloud Data Management CIKM workshop CloudDB09

38. Outline INTRODUCTION MULTI-DIMENSIONAL INDEX WITH KDTREE AND RTREE Extended Nodes partition Node partition Cost Estimation Strategy EVALUATION

39. Cloud Computing Google File System Yahoo PNUTS

40. Distributed Cloud base? • BigTable How to query on other attributes besides primary key? • HBase

41. Distributed Index: Single Dimension? S. Wu and K.-L. Wu, “An indexing framework for efficient retrieval on the cloud,” IEEE Data Eng. Bull., vol. 32, pp.75–82, 2009. H. chih Yang and D. S. Parker, “Traverse: Simplified indexing on large map-reduce-merge clusters,” in Proceedings of DASFAA 2009, Brisbane, Australia, April 2009, pp. 308–322. M. K. Aguilera, W. Golab, and M. A. Shah, “A practical scalable distributed b-tree,” in Proceedings of VLDB’08, Auckland, New Zealand, August 2008, pp. 598–609.

42. Outline INTRODUCTION MULTI-DIMENSIONAL INDEX WITH KDTREE AND RTREE Extended Nodes partition Node partition Cost Estimation Strategy EVALUATION

43. Framework of Request Processing in Cloud

44. R-Tree R-trees is a tree data structure that is similar to a B-tree, but is used for spatial access methods

45. KD-Tree kd-tree (short for k-dimensional tree) is a space-partitioning data structure for organizing points in a k-dimensional space.

46. R-Tree & KD-Tree: RKDTree Master range ： 6800～9000, 3400~8900 range ： 2000～40000, 3400~8900 range ： 6300～7000, 599~1400 range： 0～2000, 500~1200 range： 800～3500, 300~1300 Slave Slave Slave Slave Slave

47. Outline INTRODUCTION MULTI-DIMENSIONAL INDEX WITH KDTREE AND RTREE Extended Nodes partition Node partition Cost Estimation Strategy EVALUATION

48. Random cutting: Pick several random values on the attributeand cut by the points. with the random method you may receivegreat performance, but also possible to have poor performance. Equal cutting: Cut the attribute into several equal intervals.This method is relatively stable since no extreme case willhappen. Clustering-based cutting: Cut the attribute by clustering valueson the attribute and cut between clusters. This methodmay receive foreseeable better performance, but the time costis also apparently higher. The time complexity of a clusteringalgorithm is typically O(nlogn) or even higher. Nodes partition for data summary

49. Nodes partition Random cutting Equal cutting Clustering-based cutting