STEPS IN QUERY PROCESSING IN DBMS UPDATE

We can optimize the update plans by checking if the Halloween problem can occur, and if it cannot occur, perform the update during the processing of the query. Thus, following these steps increases the execution cost of the query evaluation plan. At last, updating the tuples and indices.Executing the queries that define the update first.But, we can avoid this problem by breaking up the execution plan by executing the following steps: The problem of an update that affects execution of a query associated with the update, known as the Halloween problem. The problem was named so because it was first identified on Halloween Day at IBM. Also, several problems can arise in the updation of the subqueries, whose result is affected by the update. If the update is done during the selection performed by an index scan, then we need to re-insert an updated tuple in the index ahead of the scan. However, if the updates involve a selection on the updated column, we need to handle such updations carefully. Update student set score = 97 where roll_no = 102 For example, if a user wants to update the score as 97 of a student in a student table whose roll_no is 102. So, while optimizing the update, both these subqueries must also get included.

An update query often involves subqueries in the set as well as where clauses. Optimization of UpdatesДЄn update query is used to make changes in the already persisted data. There are also more numbers of similar cases, and we can apply the join minimization there also. We have discussed only one of such cases. Such type of solution or method is known as Join Minimization. To resolve such cases, we need to drop such relations from a join. In cases, when queries are generated through views, then for computing the query, it is required to join more number of relations than the actual requirement. There are different join operations used for processing the given user query. Such tuples are discarded, and if too few tuples are generated that do not reach the top K results, then we need to execute the query again, and also, there is a need to change the selection condition. Estimating the highest value on the sorted attributes that will appear in the top K result and introducing the selection predicates used for eliminating the larger values.ДЄny how, some extra tuples are generated beyond the top-K results.Using pipelined query evaluation plans for producing the results in sorted order.Therefore, we use several methods to optimize such top-k queries. Such a step is not at all effective as well as inefficient as it may likely to discard most of the computed intermediate results. In cases, the value of K is small, and then also, if the query optimization plan produces the entire set of results, sorts them and generates the top K results. It does not matter if the query generates more number of results, including the top k results. For such queries, the optimizer specifies a hint that indicates the results of the query retrieved should be the top k results only. But, some queries do not support the bound K. Also, some queries support bound K, or limit K clause, which accesses the top K results. But there are some queries that the user has given that access results sorted on some attributes and require only top K results for some K. Top-K OptimizationДЄ database system is used for fetching data from it. In this section, we will discuss a few of such topics. There are various topics which lead the query optimization to its advanced level. Next в†’ в†ђ prev Advanced Query Optimization

Steps in query processing in dbms