Definition
The process of converting a high-level query, such SQL, into a low-level query that the database system can execute is known as query processing. This involves creating a query execution plan in addition to parsing, verifying, and optimizing the query. The series of actions that the database system takes to obtain the data that the query has requested is known as the query execution plan.
In other words, An executable form of a user query is created through a process called query processing. Retrieving the results from a database is beneficial. In query processing, the high-level query is transformed into a database-specific low-level query. In DBMS, query processing is an important element. The performance of applications that depend on database activities is greatly affected by it.
Types of Query Processing in DBMS
1. Parallel Query Processing
We have assumed that our database is operating on a single computer up until now. One machine could not possibly handle the millions of requests and gigabytes of data that modern programs handle in a timely manner. We must determine the best way to run our database across several computers. Because a query will be executed concurrently on several machines, we shall refer to this as parallel query processing.
2. Cost-Based Query Optimization
Selecting the most cost-effective method of implementing relational algebra operations is the primary goal of query optimization. In order to determine which method has the lowest estimated cost, the query optimizer should not only use heuristic rules but also calculate the cost of implementing the various strategies. Cost-based query optimization is the process of optimizing the query by selecting a technique that yields the lowest cost.
Using formulas that assess the prices of several possibilities, the cost-based query optimization chooses the option that is both the least expensive and the most effective to implement. Estimates are the cost functions that are employed in query optimization.
3. Indexing :
An index is a structure that contains a pointer from each record to the matching record in the original table where the data is actually kept, as well as the field the index is sorting. Indexes are used in things like contact lists, where information may be physically stored in the order that you add people's contact details, but listing them alphabetically makes it easier to discover them.
4. Join Strategies :
This method of joining tables affects query speed. A nested loop is Derby's most popular join technique. Derby locates the corresponding rows in the inner table for each eligible row in the outer table using the relevant access path (index or table).
