Architecture of Cache Investment Strategies

Transcription

1 Architecture of Cache Investment Strategies Sanju Gupta The Research Scholar, The IIS University, Jaipur Abstract - Distributed database is an important field in database research and development. This field has experienced an increase of interest in the recent years, mainly due to new demands on the database to handle larger data volumes and more users. This sets new requirement of efficient query processing, an important challenge which must be properly addressed so that distributed database can become more efficient. To improve the performance of distributed database we describing the concept of cache investment in this paper. The purpose of this paper is to present architecture of cache investment strategies into distributed database system. Cache investment is implemented as a module that sits outside the query optimizer without changing basic components of it, such as the query optimizers, search strategy, the query engine and the buffer manager. Keywords Cache investment, Distributed query processing, Distributed query optimization, Search space, Dynamic data placement, I. INTRODUCTION When an organization is geographically dispersed, it may choose to store its database on a central database server or to a distributed them to local servers. A distributed database is a single logical database that is spread physically across computers in multiple locations that are connected by a data communication link. The performance of a distributed database depends on how fast and efficiently data is retrieved from multiple sites. Faster retrieval of data in a distributed database system is a complex problem. Since multiple sites are involved. Several factors impact the performance of distributed query processing. These factors are selection of appropriate site (when data is replicated at multiple sites), order of operation (like select, project and join) and selection of join method (like semi join, natural join, equi join etc). Due to large number. of factors involved, there could be multiple execution plans for a single query. Each plan is associated with a cost and the objective of a distributed query optimizer to find a plan with lowest possible cost. The execution cost is expressed as a sum of I/O, CPU and communication cost. In the research field of DBMS, there has been a great focus on minimizing data access, often at the cost of using more CPU resources. We expect caching between sites to be necessary in order for distributed database system to reach their full potential. The question on what to cache in distributed DBMS is however not as simple as for its centralized system [3]. Cache investment policies determine that when and for which fragment the investment required initiating caching. These policies are invoked for each query that is submitted at a client and can influence the way that operator site selection is done for that query. The rest of the paper is organized as follows. In section 2, we describe the process of distributed query processing and query optimization process and in section 3, we will study the query optimization process with cache investment. In section 4, we describe the architecture of cache investment policies and section 5 shows the conclusion of the study. II.QUERY PROCESSING AND OPTIMIZATION Query processing is the process of translating a query expressed in a high-level language such as SQL into lowlevel data manipulation operations. Query Optimization refers to the process by which the best execution strategy for a given query is found from setof alternatives. Typically query processing involves many steps. The first step is query decomposition in which an SQL query is first scanned, parsed and validate. The scanner identifies the language tokens such as SQL keywords, attribute names, and relation names in the text of the query, whereas the parser checks the query syntax to determine whether it is formulated according to the syntax rules of the query language. The query must also be validated, by checking that all attribute and relation names are valid and semantically meaningful names in the schema of the particular database being queried. An internal

2 representation of the query is then created. A query expressed in relational algebra is usually called initial algebraic query and can be represented as a tree data structure called query tree. It represents the input relations of the query as leaf nodes of the tree, and represents the relational algebra operations as internal nodes. For a given SQL query, there is more than one possible algebraic query. Some of these algebraic queries are better than others. The quality of an algebraic query is defined in terms of expected performance. Therefore, the second step is query optimization step that transforms the initial algebraic query using relational algebra transformations into other algebraic queries until the best one is found. A Query Execution Plan (QEP) is then founded which represented as a query tree includes information about the access method available for each relation as well as the algorithms used in computing the relational operations in the tree. The next step is to generate the code for the selected QEP; this code is then executed in either compiled or interpreted mode to produce the query result [11, 13]. Figure 1 shows the different steps of Query Processing- optimization. The input to data localization is the initial algebraic query generated by the query decomposition step. The initial algebraic query is specified on global relations irrespective of their fragmentation or distribution. The main role of data localization is to localize the query using data distributed information. In this step, the fragments that are involved in the query are determined and the query is transformed into one that operates on fragments rather than global relations. Thus during the data localization step, each global relation is first replaced by its localization program, which is union of the fragment of a horizontally or vertically fragment query, and then the resulting fragment query is simplified and restructured to produce another good query. Simplification and restructuring may be done according to the same rules used in the decomposition step. The final fragment query is generally far from optimal; this process only eliminates bad queries. The input to the third step is a fragment query that is an algebraic query on fragments. By permuting the ordering of operations within one fragment query, many equivalent query execution plans may be found. The goal of query optimization is to find an execution strategy for the query that is close optimal. An execution strategy for a distributed query can be described with relational algebra operations and communication primitives (send/ receive operations) for transferring data between sites [2]. The query optimizer that follows this approach is seen as three components: A search space, a search strategy and a cost model. The search space is the set of alternative execution to represent the input query. These strategies are equivalent, in the sense that they yield the same result but they differ on the execution order of operations and the way these operations are implemented. The search strategy explores the search space and selects the best plan. It defines which plans are examined and in which order. The cost model predicts the cost of a given execution plan which may consist of the following components [13]., 1. Distributed Query Optimization In distributed query optimization two more steps are involved between query decomposition and query optimization: Data localization and global query 1. Secondary storage cost: This is the cost of searching for reading and writing data blocks on secondary storage. 2. Memory storage cost: This is the cost pertaining to the number of memory buffers needed during query execution. 3. Computation cost: This the cost of performing in memory operations on the data buffers during query optimization.

3 4. Communication cost: This is the cost of shipping the query and its results from the database site to the site or terminal where the query originated. Input Query Search Space Generation Transformation Rules Equivalent QEP Search Strategy Cost Model Figure3. Integrating. Cache Investment [7]. Best QEP Fig 2. Query Optimization Process III. QUERY OPTIMIZATION WITH CACHE INVESTMENT Caching has emerged as a fundamental technique for ensuring high performance in distributed system. It is an opportunistic form of data replication in which copies of data that are brought to a site by one query are retained at that site for possible use by subsequent queries. Caching is particularly important in large systems with many clients and servers because it reduces communication costs and off-loads shared sever machines [5]. Cache Investment, is a novel technique for combining data placement and query optimization. Rather than requiring the creation of a new optimizer from scratch, Cache Investment is implemented as a module that sits outside the query optimizer. This module influence the optimizer to sometimes make suboptimal operator site selection for individual queries in order to effect a data placement that will be beneficial for subsequent queries. In other words, it causes the optimizer to invest resource during the execution of one query in order to benefit later queries [7]. The cache investment based on some kind of policies decided that some part of a query would be a good idea to cache. The cache investment module influences the query optimizer by telling it that such a cached result in fact exists on a given site. This might be true or not. The cache investment module is in fact allowed to provide the query optimizer with false information about a cache. It is important to note that this is not going to be some malicious lie, but rather a friendly nudge telling the query optimizer that keeping such data in the cache would be a good idea. The cache investment knows this because of the policy it is using to keep track of data usage. It is ultimately up to the query optimizer to decide if it should believe the cache investment module, based on its own calculations on execution cost. If the query optimizer decides to go through with this fictional cache and produce the result, the data can be cached and the cache will become reality. Here is an example on how cache investment works. Consider three sites each with one table A, B, and C. The cache investment identifies the result of join of A and B as a profitable cache at site 2, since A and B are frequently used together. When a subsequent query consisting of the join of all three tables is submitted to the database, the cache investment module informs the optimizer that the join of A and B exists on site 2. The optimizer evaluates a plan consisting of the false cache on site 2 and the retrieval of table C from site 3. The optimizer determines that this is the best plan and sends it along to be executed. While the cache does not exist, the cache will have to be created during execution this first time. This might hurt performance during the first run, but any subsequent queries will now profit. Since the cache is based on statistics provided by the cache investment module, we have better assurance that this cache should prove to be useful in the future [3].

4 Cache investment is not a technique for the actual caching process, but more like a helpful tool for bringing data together to produce a good candidate for caching [7]. Data replacement in the cache is left to policies native to the cache being used, such as the LRU-policy. IV. DESIGN OF CACHE INVESTMENT The design of cache investment consists of 5 steps, as seen in Figure5 [3]- 1. Query Logging:- The first step is query logging, which is responsible for publishing information about queries in execution to the index. This information is collected and stored in the index, and made available for the next step, History Analysis. 2. History Analysis: - During history analysis the raw information is post-processed to produce statistics of data usage in queries. 3. Evaluation:- The third step, Evaluation, determines the most profitable candidates from these statistics and suggests a site where this candidate can be created. 4. Publish candidate:- The fourth step, Publish Candidate, publishes the candidate to the index as a false cache entry. 5. Cache creation:- This cache entry, if used by the optimizer during planning, will ultimately be turned into a real cache entry as a part of the fifth step, Cache Creation. Query Logging History Analysis Evaluation Publish candidate Cache Creation Queries are logged in history Candidates are identified from history The optimizer evaluate the benefit of candidate to history Profitable candidate are added to cache index Fig.3 : The Process of cache investment [3] V. CONCLUSION A distributed database is a collection of independent cooperating centralized system. Management of query processing in distributed database becomes very complex and time taking process. So performance enhancement of distributed database queries is a key issue in distributed database system.to improves the performance of distributed database; here we review the caching and cache investment strategies. These strategies help to take decision that when and for which fragment the investment require initiating caching. These policies are invoked for each query that is submitted at a client and can influence the way that operator site selection is done for that query. Query optimization using cache based approach has proved to be a better option in distributed database environment.. REFERENCES [1] Mantu kumar,neera Batra and Hemant Aggarwal, Cache Based Query Optimization Approach in Distributed Database,IJCSI, [2] Alaa Aljanaby,Emad Abuelrub abd Mohammed Odeh, A Survey of Distributed Query Optimization,The International Journal of Information Technology,vol.2,No.1,January2005. [3]Konrad G.Beiske,Jan Bjorndalen Semantic Cache Invetment,Norwegain University of f Science, and Technology, [4I Ideh Azari, Efficient Execution of Query in Distributed Database System, 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE),2010. [5]sanju Gupta, Cache Investment Strategies in Distributed Database [6] Donald Kossmann, Michael J. Franklin. Cache Investment Strategies". Univ.of MD Technical CS-TR-3803 and UMIACS-TR ,May [7] Donald Kossmann, Michael J. Franklin,Gehard Drasch, "Cache Investment : Integrating Query Optimization and Distributed Data Placement," ACM Transaction on Database System (TODS), Dec [8]. Donald Kossmann, Michael J. Franklin. Cache Investment for indexes".vldb Conference,Feb,1998. [9] ] Donald Kossmann, The State of the Art in Distributed Query Processing,ACM Computing Surveys,Dec [10.] C.T. Yu and C.C. Chang, Distributed Query Processing ACM Computational Surveys, vol. 16, Dec [11] Ioannidis Y.E., Query Optimization, in Trucker A (Ed),The Science and Engineering Handbook,CRC Press, pp ,1996. [12] R.M Monjurul Alom, Frans Henskens and Michael Hannaford, Query processing and optimization in Distributed Database System,,International Journal of Computer Science and Network Security(IJCSNS),Sep2009. [13] Elmasri R. and Navathe S. B., Fundamentals of Database Systems, Reading, MA, Addison- Wesley, [14] Ozsu M.T. and Valdureiz P: Principles of Distributed Database System, 2nd Edition, Prentice Hall, [15] Shahabi C, Zarkesh A M, Adibi J, Introduction of distributed database. IEEE,2001. [16] Yan T,IacobesnM,Garcia-Mo Lina H, Introduction of Query optimization of distributed database, WAM Press, I 999. [17] Doshi P. and Raisinghani V., Review of Dynamic Optimization Strategies in Distributed Database, Electronics Computer Technology (ICECT), 3rd International Conference, April [18]. Konrad Stocker, Donald Kossmann, Reinhard Braumand and Alfons Kemper, Integrating Semi-Join-Reducers into State-of-the-Art Query Processors, Proceedings of the 17th International Conference on Data Engineering, HYPERLINK IEEE Computer Society Washington, DC, USA 2001.

5 [19] Fan Yuanyuan and Mi Xifeng, Distributed Database system Query Optimization Algorithm Research, IEEE,2010. [20] Swati Gupta, Kunal Saroba, Bhawna, Fundamental Research of Distributed Database,International Journal of Computer Science and Management Studies, vol 11,2011 [21] ] XUE Lin, Query Optimization Strategies and Implementation Based on Distributed Database,IEEE,2009.

6