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DISTRIBUTED KEY-VALUE STORE FOR LARGE-SCALE SYSTEMS Thanh Trung Nguyen, The thesis is submitted to Le Quy Don Technical University Tai Lieu Chat Luong for the degree of Ph.D at the Faculty of Information Technology Research Supervisor Assoc Prof Dr Hieu Minh Nguyen HaNoi 2016 Declaration I declare that this thesis contains no material that has been accepted for the award or any other degree or diploma in any university or other institution To the best of my knowledge and belief, this thesis contains no material that is previously published or written by another person, except where due reference is made in the text of the thesis PHD Candidate Nguyen Trung Thanh i Abstract In recent decades, network application systems have been growing rapidly Not only Business data processing or Online Transaction Processing (OLTP) applications in the market, but also many new types of application such as Text Management (eg Search Engine), Data Warehouses, Stream Processing, Scientific and Intelligent Databases have been developed and being researched Particularly, the size of many applications such as social network, online commerce system, personal cloud storage has exponentially increased In such huge applications, building high performance, scalable data storage system is one of the biggest challenges In order to efficiently address the data storage problem, new mechanisms in building data storage have been developed to fill the gap that the traditional relational database management system cannot The new data storage mechanism is called NoSQL Key-value store is one of NoSQL data store schemes and it plays an important role in many large systems Distributed key-value store is an extension of key-value store that supports data distribution across multiple servers The quality and capability of a distributed key-value store depend on many factors, including performance of key-value stores in each node, the efficiency of data distribution algorithms, the structure of storing system for a specific data type, consistency model, the ability of storing big-values and big data structures, etc There are three important questions in building high performance distributed key-value stores: How to minimize latency and maximize throughput of the Key - Value store with ii minimum memory overhead in persistent layer? How to store big values into key-value store or how to manage large number of big-files in a cloud storage with the advantages of distributed key-value store? How to efficiently store and distribute big data-structures in key-value store? This thesis attempts to address these questions by studying the movement from RDBMS to NoSQL databases and exploring techniques in designing Key-Value stores After analyzing some common approaches for transforming from RDBMS to NoSQL, we conducted several experiments in order to reveal mechanisms of each approach For one of the most popular key types (auto increasing integer key), a high performance key-value store was proposed to minimize the latency of both read and write operations This method ensures that there is a maximum of one disk seek per operation and memory overhead per key is fixed After that, we analyzed some popular existing personal cloud storage systems that showed that the space complexity of metadata of files in these systems is linear to file size or O(n) In other words, most existing key-value stores and database systems often lack the ability to efficiently store big values such as big files in cloud storage Consequently, we proposed a new architecture and algorithms for big-file cloud using the advantages of key-value store that reduce the space complexity of metadata of files from O(n) to O(1) Finally, we proposed the Forest of Distributed B+Tree based on key-value store for storing big data structure such as Sets and Maps The novelty of our method is that this structure supports for distributing partial value This ability is not supported in some existing popular systems such as BigTable, Cassandra where each row of these storage systems must be fitted in one server Our method allows us to build huge-row storage in which rows are large than in Google BigTable and Cassandra In summary, this thesis studies and proposes the methods for efficient storing data in large-scale systems iii Acknowledgements First of all, I would like to express my gratitude to my supervisor Minh Hieu Nguyen for his guidance, experiences and encouragement throughout my PhD journey I would like to thank Uy Quang Nguyen for his support and many helpful insights for my research I thank Associate Professor Lam Thu Bui for his meaningful questions in the early stage of my research, they helped me a lot to improve my academic point of view as a science researcher I thank my cool office mates, Loi Van Cao, Thien Duc Nguyen, etc for fun times and for a lot of interesting discussions I want to give my appreciation to Dung Hong Luu from Network Security group for his supports, advice and collaboration I also take this opportunity to thank all my colleagues in the Department of Network Security, for their effort to make the department such an excellent environment to work I would like to specially thank Thanh Ta Minh, Ly Vu Thi for always being helpful and responsive I thank to the Research Fund RF @ K12 that supported me a lot to publish my research result I thank to Research and Development Department of VNG Corporation for supporting big infrastructure and large real data sets for this research I thank friends in Research and Development Department of VNG Corporation: Anh Nguyen Hoai,Tin Khac Vu, my little brother Trung Thanh Nguyen and Tung Chi Vu You all have made the team such a great place to work and helped me to apply our research results in real products Most importantly, I wish to thank my family for their endless and unconditional love, for their sustained support and encouragement I am so grateful that they have always been there for me iv Contents Declaration i Abstract ii List of Figures ix List of Tables xi Abbreviations xii Introduction 1.1 Key-Value Store Overview 1.2 Big Data Challenges and Motivation 1.3 MCS: Data Storage Framework 1.3.1 Memory Cache 1.3.2 Key-Value Store Abstraction 10 1.3.3 Service Model 11 1.3.4 Commit-Log 11 1.4 Problem Statements 11 1.5 Contributions 13 1.6 Thesis structure 14 v Backgrounds 2.1 2.2 2.3 15 Overview 15 2.1.1 The development of NoSQL 15 2.1.2 Scalable Data Management for Cloud Computing and Big Data 19 Basic concepts 19 2.2.1 ACID Properties 20 2.2.2 Consistency, Availability and Partition tolerance 21 2.2.3 Eventual Consistency 22 2.2.4 The BASE Consistency Model 27 2.2.5 Partitioning 28 2.2.6 Data structures for persistent layer in key-value store 30 Cloud Storage Benchmarks and Workloads 32 High performance key-value store for large-scale storage service 35 3.1 Introduction 36 3.2 Related works 38 3.3 Sequential Log Storage Model 41 3.4 Proposed Key-Value Store 42 3.4.1 Data structure for the Index 43 3.4.2 Data Layout in Persistent File 46 3.4.3 Key-Value Data Table and Main Algorithms 46 3.4.4 Implementation 54 3.5 Analysis and Comparison with other key-value stores 55 3.6 Performance Evaluation 57 3.6.1 Standard Benchmark 57 3.6.2 Engine Evaluation 60 vi 3.6.3 3.7 Discussion 63 Summary 64 High-Performance Distributed Big-File Storage Based On Key-Value Store 66 4.1 Introduction 67 4.2 Related Works 69 4.3 Proposed Method for Big File Storage System 71 4.3.1 General Big File Model 72 4.3.2 Architecture Overview 72 4.3.3 Logical Data layout 74 4.3.4 Chunk Storage 75 4.3.5 Metadata 76 4.3.6 Data distribution and replication 79 4.3.7 Uploading and deduplication algorithm 79 4.3.8 Downloading algorithm 81 4.3.9 Secure Data Transfer Protocol 82 Evaluation 83 4.4.1 Evaluate Key-Value store for BFC 84 4.4.2 Locally performance comparison 85 4.4.3 Metadata comparison 85 4.4.4 Deduplication 86 Summary 87 4.4 4.5 Forest of distributed B+Tree for storing large number of big and growing sets based on key-value store 89 5.1 Introduction 90 5.2 Big Set Problem Statement 92 vii 5.2.1 Problem 92 5.2.2 Complexity 92 5.3 Related works 93 5.4 Forest of Distributed B+ Tree for solving Big-Set Problem 95 5.4.1 Method Overview 96 5.4.2 Forest of Distributed B+Tree Definition 98 5.4.3 Leaf Nodes of Distributed B+Tree 98 5.4.4 Non-leaf Nodes of Distributed B+Trees 101 5.4.5 Forest of Distributed B+Tree 106 5.4.6 General key-value store using Forest of Distributed B+Tree 110 5.5 Evaluation 111 5.6 Discussion 114 5.7 Applications of ZDB and Forest of Distributed B+Tree 116 5.8 5.7.1 Computing Architecture for Anomaly Detection System 116 5.7.2 Specific Storage Solution for Specific Structured Data 118 Summary 120 Conclusion and Future works 121 Publications 124 Bibliography 125 viii List of Figures 1.1 Storage Backend Framework overview 1.2 Contributions Overview 14 2.1 Consistent Hashing 30 2.2 B-Tree Example 31 3.1 Proposed key-value store architecture 43 3.2 Data file layout with data files 47 3.3 Put, Get, Remove algorithms of Flat Table 48 3.4 Data partitioning 51 3.5 Write only 1KB records using YCSB 59 3.6 Write only 4KB records using YCSB 59 3.7 High read / low write 1KB records using YCSB 60 3.8 High read / low write 4KB records using YCSB 61 3.9 High Write / low read 1KB records using YCSB 61 3.10 High Write / low read 4KB records using YCSB 62 4.1 BFC Architecture 73 4.2 BFC Main Backend Components 75 4.3 Data layout of Big File in the system 75 4.4 Chunk storage system 76 ix one disk seeking All writes can be configured to be sequential go achieve best writing performance of both SSD and HDD Proposed key-value store using shared memory Flat Index to fast lookup key-value pair position in data file without unnecessary disk accessing The proposed key-value store is optimized for auto increasing integer keys - one of the most popular key types A high performance key-value store called Zing Database (ZDB) is implemented The results are presented in the first two publications of this thesis Secondly, this thesis propose architecture for building big file cloud storage based on key-value store It takes advantages of proposed key-value store to minimize the size of metadata when a system managing large number of big files for serving millions of users For storing big-files (big values) into key-value store, every file has a same size metadata Each big-file is split into multiple fixed-size chunks and stored in ZDB The chunks of a file have a contiguous ID range, thus it is easy to distribute data and scale-out storage system, especially when using ZDB This thesis proposes method to store big-files efficiently with advantages of key-value store Finally, this thesis proposed Forest of Distributed B+Tree based on key-value store This result convert efficiently binary key-space into auto increasing integer key-space It is useful for building scalable Nosql data storage for large data structure such as big set, wide-column data Data is distributed in key-value store automatically and make it easy to scale the systems Every Big Set as a value in a key-value store is split into multiple small sets and store them in distributed ZDB key-value store It supports building scalable data storage system for big data structure The experiment results show that Forest of Distributed B+Tree has a good performance in both read and write operations Moreover, it is a general key-value store that support binary-key type efficiently and order-preserving In future, we will continue to extend and research storage architecture for big data We will firstly focus to data storage system that support computing in data mining system more efficiently such as large time series storage In the ”Internet of things” trend, many data 122 sources from millions sensors with multiple long time series need to be stored for querying and mining efficiently Second, we will research to make our storage systems more secure in network environment, make them not only high performance but also secure 123 Publications [1] Thanh Trung Nguyen, Minh Hieu Nguyen “ZDB-High performance key-value store.” In Proceedings of the 2013 Third World Congress on Information and Communication Technologies (WICT 2013) [2] Thanh Trung Nguyen, Minh Hieu Nguyen “Zing Database: high-performance keyvalue store for large-scale storage service.” Vietnam Journal of Computer Science, February 2015, Volume 2, Issue 1, pp 13-23 [3] Thanh Trung Nguyen, Tin Khac Vu, Minh Hieu Nguyen “BFC: High-Performance Distributed Big-File Cloud Storage Based On Key-Value Store” in Proceeding of 16th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2015) [4] Thanh Trung Nguyen, Anh Tuan Nguyen, Tuan Anh Ha Nguyen, Ly Thi Vu, Quang Uy Nguyen, and Long Dao Hai 2015 “Unsupervised Anomaly Detection in Online Game.” In Proceedings of the Sixth International Symposium on Information and Communication Technology (SoICT 2015) ACM, New York, NY, USA, 4-10 DOI=http://dx.doi.org/ 10.1145/2833258.2833305 [5] Thanh Trung Nguyen, Minh Hieu Nguyen “Forest of Distributed B+Tree Based On Key-Value Store for Big-Set Problem.” In Database Systems for Advanced Applications Volume 9645 of the series Lecture Notes in Computer Science pp 268-282, 2016 [6] Thanh Trung Nguyen, Minh Hieu Nguyen “Distributed and High Performance BigFile Cloud Storage Based On Key-Value Store” International Journal of Networked and Distributed Computing, Volume 4, Issue 3, pp 159 - 172, July 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