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Transcript of 1 2009-08-25 Cloud-based Data Management: Challenges & Opportunities.
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陆嘉恒2009-08-25
Cloud-based Data Management: Challenges & Opportunities
云数据管理:挑战和机遇
中科院软件所 中国人民大学
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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
Research experience and interesting
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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
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Motivation: Internet Chatter
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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.
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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
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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
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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
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Paradigm Shift in Computing
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WEB is replacing the Desktop
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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)
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Why Now?
Experience with very large datacenters• Unprecedented economies of scale
Other factors• Pervasive broadband internet• Pay-as-you-go billing model
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Cloud Computing Spectrum
Instruction Set VM (Amazon EC2, 3Tera)
Framework VM• Google AppEngine, Force.com
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Cloud Killer Apps
Mobile and web applications
Extensions of desktop software• Matlab, Mathematica
Batch processing/MapReduce
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Economics of Cloud Users
Pay by use instead of provisioning for peak
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Economics of Cloud Users
Risk of over-provisioning: underutilization
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Economics of Cloud Users
Heavy penalty for under-provisioning
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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
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Engineering Definition
Providing services on virtual machines allocated on top of a large physical machine pool.
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Business Definition
A method to address scalability and availability concerns for large scale applications.
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Data Management in the Cloud?
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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• …
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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.
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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.
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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
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Data and Networks: Attempt Ⅱ
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Data and Networks: Pragmatics
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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
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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
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Analytical Processing
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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.
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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
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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
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Self management and self tuning in cloud-based data management
Self management and self tuning
Query optimization on thousands of nodes
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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
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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
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CIKM workshop CloudDB09
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INTRODUCTION
MULTI-DIMENSIONAL INDEX WITH KDTREE AND RTREE
Extended Nodes partition• Node partition• Cost Estimation Strategy
EVALUATION
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Google File System
Yahoo PNUTS
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• BigTable
• HBase
How to query on other attributes besides primary key?
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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.
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INTRODUCTION
MULTI-DIMENSIONAL INDEX WITH KDTREE AND RTREE
Extended Nodes partition• Node partition• Cost Estimation Strategy
EVALUATION
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R-trees is a tree data structure that is similar to a B-tree, but is used for spatial access methods
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kd-tree (short for k-dimensional tree) is a space-partitioning data structure for organizing points in a k-dimensional space.
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Master
Slave Slave Slave Slave Slave
range :0 ~ 2000,500~1200
range :800 ~3500,300~1300
range :6300 ~7000,599~1400
range :2000 ~40000,3400~8900
range :6800 ~9000,3400~8900
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INTRODUCTION
MULTI-DIMENSIONAL INDEX WITH KDTREE AND RTREE
Extended Nodes partition
• Node partition
• Cost Estimation Strategy
EVALUATION
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Random cutting: Pick several random values on the attribute and cut by the points. with the random method you may receive great 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 will happen.
Clustering-based cutting: Cut the attribute by clustering values on the attribute and cut between clusters. This method may receive foreseeable better performance, but the time cost is also apparently higher. The time complexity of a clustering algorithm is typically O(nlogn) or even higher.
Nodes partition for data summary
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Random cutting Equal cutting Clustering-based cutting
Nodes partition
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Update of node cube:• Why? If the data distribution in the node cube
have “greatly” changed and caused the cube to be sparse or greatly uneven
• How? Reorganize the nodes partition again• When? A two-phase approach
• After each update, compute the minimal ΔT for next update
• When the ΔT expires, check if needs update
Dynamic maintenance of Indexes
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Basic idea: benefit > cost
Volume of a node cube is defined as the number of combination of records can be made out of the cube. The volume can be calculated as the product of lengths of all the intervals. We note volume of a cube by v.
For the cube \{[1, 11], [2, 5]\}, the volume is (11-1)*(5-2) = 30.
Dynamic maintenance of Indexes
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Assumption:• The amount of queries forwarded to each slave
node is proportional to the total volume of all the node cubes of the slave node.
Dynamic maintenance of Indexes
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benefit = (Δv/v) * nq * ΔT• Δ v: decrement of volume after update• nq: number of queries this node must process
before update.
cost = mt/qt• mt: time cost of last update• qt: time needed for processing one query
benefit > cost => T > (mt * v)/(qt * Δ v * nq)
Dynamic maintenance of Indexes
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After Δ T expires, check if an update is needed. This check involves following:• Record update frequency• Expected benefit ratio• Performance requirement
We leave this as a future work.
Dynamic maintenance of Indexes
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6 machines• 1 master• 5 slaves : 100~1000 nodes
Each machine had a 2.33GHz Intel Core2 Quad CPU, 4GB of main memory, and a 320G disk.
Machines ran Ubuntu 9.04 Server OS.
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Result Cover Rate: one ten thousandth
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In this paper we presented a series of approaches on building efficient multi-dimensional index in cloud platform.
We used the combination of R-tree and KD-tree to support the index structure.
We developed the node partition technique to reduce query processing cost on the cloud platform.
In order to maintain efficiency of the index, we proposed a cost estimation-based approach for index update.
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Better node partition algorithms
Improve the estimation-based approach
Consider multiple replicas of data
Future works
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谢谢,敬请提问交流!
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Result Cover Rate: one thousandth1 ‰ ~ 2 ‰
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Result Cover Rate: one thousandth4 ‰ ~ 5 ‰