AUTONOMIC COMPUTING IN SQL SERVER

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1 Seventh IEEE/ACIS International Conference on Computer and Information Science AUTONOMIC COMPUTING IN SQL SERVER Abdul Mateen, Basit Raza, Tauqeer Hussain University of Central Punjab, Lahore, Pakistan M. M. Awais Lahore University of Management Sciences Lahore, Pakistan Abstract With the growing size of enterprise data, the task of managing a database is becoming more and more complex as well as time-consuming. A database administrator spends most of his time in activities that can be made automatic. Also, scarcity of skilled database administrators have motivated the database industry to develop Autonomic Database Management Systems (ADBMS) which can alleviate many of the database administrators problems. These ADBMS may have one or more autonomic properties. In this paper, autonomic characteristics of Microsoft SQL Server are presented and analyzed to illustrate how self-managing behaviour of an ADBMS can reduce the workload of a DBA significantly. It has also been discussed which components of SQL Server are not fully autonomic and require Human Intervention. This provides the motivation for devising algorithms to further reduce the degree of human intervention still required in ADBMS. Keywords Autonomic computing, human intervention, optimization, SQL Server. 1. Introduction The growing complexity of computer systems emphasizes the need for developing self-managing autonomic systems. Fundamentally, a complete autonomic computing system evolves through five levels of autonomicity i.e., Basic, Managed, Predictive, Adaptive and Autonomic [1-3]. The basic level is a starting point in which individuals or IT professionals manage different tasks such as the setting up of the IT environment, monitoring and making updation. At the managed level, different system management technologies are used to collect information from respective systems. This facilitates the system administrator to collect and analyze information more quickly. In predictive level, new technologies are used which correlate different components of a system to initiate pattern recognition, prediction and suggestions for optimal solutions. In adaptive level, components use available information and knowledge of the system to take different actions automatically. Finally, in autonomic level, business policies and objectives are monitored and can change business policies, objective or both. Through autonomic computing, we achieve minimal human intervention with software or system; maximum use of system resources such as processing power, memory, storage; labour and cost reduction for performing different activities; real-time responsiveness by the autonomic computing environment; and focus by the IT professionals on high value tasks rather than operational tasks. These promising benefits of autonomic computing are equally required in database management systems (DBMSs). During the database design stage, database designers are primarily responsible for logical and physical design of the database. This includes defining relation schema, normalization, indexes, materialized views, database recovery, data distribution and user management [4]. Further, when system becomes operational, continuous involvement of DBA is needed to monitor system operational health, perform query tuning, manage storage needs and to perform many other tasks [5]. These time-consuming responsibilities of DBAs, increases in data volumes, larger systems, requirement of maximum functionality, and scarcity of skilled DBAs have motivated the database industry to develop ADBMS that have one or more autonomic properties. In this context, early research projects focused on the selection and design of table indexes and memory optimization. However, current research focuses on intelligent tools such as expert design /08 $ IEEE DOI /ICIS

2 systems, performance tuning, configuration technology and ease-of-use administration interfaces. Within the context of DBMSs, six basic autonomic characteristics (self-*) [3, 5, 6] have been defined as follows: i. Self-Optimization Autonomic system improves performance and executes any task/ utility in an efficient manner, in the presence of workload parameters, available resources and environment settings. ii. Self-Configuration It is the capability to configure itself according to the set goals or desires. This involves dynamically recognizing any change in the environment and reconfiguring without any human interference. iii. Self-Healing It is the ability of a system to remain in a consistent state at all the times. Whenever there is a danger of failure, system recovers itself using logs and backups. iv. Self-Protection This includes security, privacy auditing mechanisms and data encryption capabilities. material that is available in research papers, white papers and technical reports. The organization of this paper is as follows: Section 2 discusses characteristic-wise autonomic features of SQL Server, Section 3 discusses the extent of human intervention required by the autonomic components of SQL Server and finally Section 4 concludes the paper. 2. Autonomic Capabilities in SQL Server Various components of Microsoft SQL Server are studied in this research and the following self-* capabilities are observed in the context of autonomic computing: 2.1 Self-Optimization SQL Server has a number of components as well as tools that have self-optimization characteristics. These components are discussed below: Query Optimizer SQL tuning is performed in four steps that are identification of high load SQL statements, generation of Optimal Query Execution Plan (QEP), QEP improvement and selection of best QEP, as shown in Figure 1. v. Self-Inspection/ Reflexivity The capability of making intelligent decisions based on self-awareness. This includes knowing system components, available resources, environment, status, capabilities, limitations, boundaries and interdependencies with other systems. vi. Self-Organization/ Adoption The capability to dynamically reorganize and restructure the layout of stored data in the databases, indexes to improve system performance. In this paper, we present components of SQL Sever that support autonomic behaviour. SQL Server is a product of Microsoft and whenever mentioned in this paper, it refers to Microsoft SQL Server. This research has explored features available in SQL Server However, wherever appropriate, references to the respective SQL Server versions are also given. The objective is to explore and present the current state of autonomic computing in DBMSs by taking the example of a well-known relational database management system through review of technical Figure 1 SQL Tuning Life Cycle In SQL Server, optimization starts from the query analysis in which there is identification of search arguments through what-if analysis, OR and join clauses where different join processing strategies are considered. Then optimizer performs the index selection where it evaluates join selectivity, estimate index cost, use of multiple indexes and index views. The matching plan of the query is searched in cache. If matched plan exists, optimizer executes the same, otherwise searches the PLAN GUIDES [9]. If an active plan guide exists, the original matching statement is replaced with one of the PLAN GUIDES 114

3 statement. Finally, QEP is compiled, cached and executed. In SQL Server, parallelism is performed for the execution of single and complex queries, workload and query plan. SQL Server dynamically decides the number of processors to execute the query, which is called the Degree of Parallelism (DOP). A parallel QEP cannot be generated if the execution cost is less than the threshold for parallelism or in case of computer having single processor. recommendations for horizontal partitioning, materialized views and indexes. It provides not only performance but also indicate manageability requirement to DBA (shown in Figure 2) during optimization. It also provides scalability using different methods such as workload compression, reducing statistics creation and workload, enhances script ability and customization using public XML Schema for input and output Automatic Statistics Management Component In the generation of optimal QEP, the role of correct and relevant statistics is very important. It improves the performance while unnecessary statistics cause performance degradation. Automatic statistics management [10] has been introduced in SQL Server 7.0. Statistic management component decides how to build statistics, which columns to build statistics on, and how to build statistics on any column efficiently Performance Monitor Performance Monitor [11] is used to monitor, save and tune the database server (locally or remotely). It observes and controls the CPU activity, memory, paging and disk I/O. Performance monitor can capture data to a file for further analysis. It can also monitor in real time environment that helps in analyzing data and making recommendations. 2.2 Self-Configuration Automatic configuration tools in SQL Server are: Configuration Manager Configuration Manager allows controlling services associated with SQL Server. This Manager replaces service manager, server and client networking tools [9]. The Configuration Manager controls SQL Server, SQL Agent, SQL Server Analysis Services, Data Transformation Service (DTS) Server, SQL Browser and full-text search [12]. In version 2000, configuration changes are made which require restarting of SQL Server while in version 2005 restart is not necessary Database Tuning Advisor (DTA) DTA [13] is an automated physical database design tool in SQL Server 2005, which is an enhanced version of index tuning wizard of version DTA provides Figure 2 Data Base Tuning Advisor [13] Dynamic Self-Tuning Memory Management (DSTMM) Component DSTMM is responsible for automatic memory management and was introduced in version 7.0. DSTMM tunes total amount of memory for the database instead of memory distribution for each database [14]. DSTMM sets aside sufficient memory, so that applications can run without any delay. SQL Server uses Non-uniform Memory Access (NUMA) architecture [15] that provides larger scalability. SQL Server has introduced the uniform memory management in the SQL Server Operating System layer. Common memory brokerage is also introduced between different SQL Server components that enhance performance and provide operational flexibility. The function of internal cache is improved due to uniform dynamic caching framework. 2.3 Self-Healing Automatic Backup and Recovery is provided through a feature called Maintenance Plans. Whenever Microsoft SQL Server 2005 starts, it performs automatic data recovery by checking each database in the system [16]. It first checks the master database and then initiates threads to recover all of the other databases in the system. For each database, the 115

4 automatic recovery mechanism checks the transaction log. If the transaction log contains any uncommitted transactions, the transactions are rolled back. The recovery mechanism then checks the transaction log for committed transactions that have not yet been written into the database. If it finds any, it performs those transactions again, rolling forward. 2.4 Self-Protection Self-Protection ensures the confidentiality, integrity and availability of system, network and data from unauthorized assess. Database protection involves database security, privacy, analytical auditing mechanism and data encryption strategies. These strategies protect the DBMS from performance degradation and persist it in a consistent state [12]. SQL Server provides single sign on, encrypted network traffic and directory enabled security features. It provides authentication and security for databases. Privileges are assigned to users for security. It provides access granularity on per table level basis [17]. 2.5 Self-Inspection SQL Server provides the following features with respect to self-inspection: Monitoring Tools In version 2005, three monitoring tools are used for performance monitoring and tuning of the database. These tools are SQL Server Profiler, System Monitor and Activity Monitor [16]. SQL Server Profiler tracks different events and database activities. Events may be the start or end of a batch/ transaction whereas database activities are deadlocks, fatal errors, or login. SQL Server Profiler captures data to the table or file for later analysis. Sequence of captured events in the file can be replayed to watch what happened in the database. System Monitor tracks resource usage (such as memory utilization) to achieve performance. It also monitors the SQL Server sub system in a local or remote machine. SQL Server Profiler monitors database events, whereas the System Monitor observes resource usage associated with the server processes. Activity Monitor helps in viewing the currently running processes SQL Server environment, such as status of blocked process, current activity of users etc Database Consistency Check (DBCC) DBCC is used to check the database consistency (index consistency, table and index fragmentation, tuning of data and procedure cache, performance statistics) and database corruption automatically. However, DBCC performs shrinking manually. It also assists in T-SQL programming work [18]. 2.6 Self- Organization SQL Server provides tools for the self-organization of data, which are: Dynamic Online Index Reorganization To make efficient use of system resources, dynamic online index reorganization is used [17, 19]. This feature allows concurrent modifications such as Insert, Update and Delete for the table. Therefore data can be accessed or updated by the user during the index reorganization. SQL Server performs re-organization of data in two situations. The first case is when index is either fragmented or highly skewed due to the huge deletion. In second case, whenever the huge insertion is completed and dynamic response is required Automatic Storage Management (ASM) Component In SQL Server Storage is managed automatically [19] through ASM component. It divides large tables across multiple files so that I/O throughput can be improved. In case of large tables, it stores text and image column in different files from their respective tables and places database objects on specific disk spindles. It takes backup and restores table(s) within a filegroup Storage Design & Partition Wizards SQL Server provides the Storage Design Wizard [20, 21] and Partition Wizard [22] to manage the layout of data cubes on the disks. Partition wizard provides flexible options for table and index partitions. Storage wizard is used to specify the data storage as well as query performance for joining on a cube/partition. During later analysis, storage and partition wizards are used to separate one logical cube into separate physical partitions that increases flexibility of data sources and query performance. 116

5 3. Human Intervention in SQL Server Human intervention can be used to measure the autonomicity in DBMSs. Less human intervention reveals that DBMS is more autonomic and vice versa. Human intervention may be mandatory or optional, that is user can interact with the system if required. Statistics can be collected automatically and manually. However, when there are no statistics against any object only then human intervention is required to provide sufficient information. Memory management is also automatic however, DBA can set the amount of memory when multiple instances are running. Backup can be performed manual and automatic. Database Snapshot feature is automatic but if fails then lost changes will be recovered manually. DTA can be partially configured by the user if needed. SQL Server uses Use Plan and Plan Guide features to generate plan if statistics are missing. However, these features require human intervention. Shrinking of database or file is performed through Enterprise Manager and its scheduling is performed by DBA. Shrinking can be performed manually through a DBCC statement. Human intervention is necessary for security and authentication as user s login and roles are created and assigned manually. The optional and mandatory human intervention for SQL Server s autonomic components along with type and degree is shown in Table 1. Table 1 SQL Server s Components with HI SQL Server Type of HI HI Components Required Degree Query Optimizer Mandatory Low Automatic Statistics Management Mandatory Low Performance Monitor Optional - Configuration Manager Optional - DTA Optional - DSTMM Optional - Maintenance Plan Optional - Authentication Mandatory High Table Level Security None - Monitoring Tool Optional - DBCC Mandatory Low Dynamic Online Index Reorganization Optional - ASM Optional - Storage Design & Partition Wizard Optional - 4. Conclusion & Future Work This paper has explored and analyzed main autonomic components of Microsoft SQL Server while exhibiting various self-* characteristics. It has been presented how these components increase the efficiency and reduce the burden over DBA. It has also been observed that none of these components are fully automatic some features require necessarily human intervention whereas for some it is optional. The degree of human intervention required in each component has been reported in this paper. It is expected that this research is helpful in identifying the areas where future endeavors should be focused to improve the extent of autonomicity in Microsoft SQL Server. Table 2 provides a summarized list of various autonomic components of Microsoft SQL Server categorized into respective self-* autonomic characteristics. Table 2 Autonomic Components of SQL Server Autonomic Feature Self-Optimization Self-Configuration Self-Healing Self-Protection Self-Inspection Self-Organization SQL Server Components Query Optimizer Automatic Statistics Management Component Performance Monitor Configuration Manager DTA DSTMM Maintenance Plan Authentication Table Level Security Monitoring Tool Database Consistency Check (DBCC) Dynamic Online Index Reorganization Automatic Storage Management Component Storage Design & Partition Wizard In future, we intend to compare the autonomic features of Microsoft SQL Server with other leading relational database management systems. We also intend to work on providing some guidelines, simulations and benchmarks to evaluate and assess the 117

6 autonomic features available in database management systems. 5. References [1] H. A. Müller, L. O Brien, M. Klein, and B. Wood, Autonomic Computing, Technical Note by Carnegie Mellon University, April pdf [2] Y. Diao, J. L. Hellerstein, S. Parekh, R. Griffith, G. Kaiser, and D. Phung, Self-managing systems: A control theory foundation, In Proceedings of the 12th IEEE Int. Conf. & Workshop on the Engg. of Computer-Based Systems,. IEEE Computer Society, 2005, pp [3] Practical Autonomic Computing: Roadmap to Self Managing Technology, An IBM White Paper, Jan cticalroadmapwhitepaper_ pdf [4] Oracle Database Performance Tuning Guide, 10g Release 1 (10.1), Part No. B , December /title.htm [5] S. S. Lightstone, G. Lohman, and D. Zilio, Toward Autonomic Computing with DB2 Universal Database, SIGMOD Record 2002, Vol. 31(3), [6] M. Parashar and S. Hariri, Autonomic Computing: An Overview, Springer-Verlag Berlin Heidelberg 2005, 2005, pp [7] S. R. White, J. E. Hanson, I. Whalley, D. M. Chess, and J. O. Kephart, An Architectural Approach to Autonomic Computing, Proceedings of the IEEE Int. Conf. on Autonomic Computing (ICAC 04), [8] S. Chaudhuri, and V. Narasayya, AutoAdmin Whatif Index Analysis Utility, Proceedings of the 1998 ACM SIGMOD Int. conf. on Management of data, United States, 1998, pp [9] J. S. Barrera, Self-tuning Systems Software, Proceeding of the Fourth Workshop on Workstation Operating Systems, 1993, pp [12] New Features in SQL Server 2005, Part I: SQL Server 2005: An Overview, / pdf [13] S. Agrawal, S. Chaudhuri, L. Kollar, A. Marathe, V. Narasayya, and M. Syamala, Database Tuning Advisor for Microsoft SQL Server 2005, Proceedings of the 30th Int. Conf. on VLDB, Tronto, Canada, 2004, pp [14] Memory Architecture, SQL Server 2005, Books Online, April, [15] B. A. Patel, Forcing Query Plans, SQL Server Technical Article, November Forcing_Query_Plans.doc [16] Microsoft SQL Server 2005 for the Oracle Professional, Scalability Experts, A White Paper, [17] D. Gornshtein, and B. Tamarkin, Features, Strengths and weaknesses comparison between MS SQL 2005 (Yukon) and Oracle 10g databases, _ORACLE10g_compare.pdf [18] K. Kline, L. Gould, and A. Zanevsky, Transact-SQL Programming, ISBN: ) ACTSQL_PROGRAMMING_by_Kline,_Kevin_ISBN_ [19] K. England, Microsoft SQL Server 2000 Performance Optimization and Tuning Handbook, ISBN , Jan [20] W. Pearson, The Storage Design Wizard, Apr biz/optimizin g_ssas_p1.aspx [21] Storage Design Wizard, SQL Server 2000, MSDN. [22] Partition Wizard, Analysis Services, SQL Server [10] S. Chaudhuri, and V. Narasayya, Automating Statistics Management for Query Optimizers, IEEE Transactions on Knowledge & Data Engg, 13 (1), Feb [11] S. Warren, SQL Server Performance Monitor, Database Journal, May 27,

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