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Query Processing in DBMS: A Detailed Guide

SQL query processing is a fundamental aspect of database management systems (DBMS). When you run a query, the DBMS processes it to fetch or manipulate data from the database. This process involves several stages, ensuring that the query is executed efficiently and correctly. Understanding how this works is essential for optimising database performance.

In this blog, we will explore the definitions and aspects of SQL queries and query processing, and we will be providing an example to explain it. By the end of the article, you should be able to understand how queries are viewed and processed in a DBMS.

What is a Query in DBMS?

The query in a database management system (DBMS) is nothing but steps seeking a response or action on data that is executed in the database. Such a request is expressed in a particular language, which is typically SQL and instructs the DBMS to carry out operations which may include data selection, modification, removal or addition. These Queries help database users to interact with the database, which helps to manage complex databases. Queries can range from requesting a record from a table to undertaking several joins/tables and cleaning up the records.

What is Query Processing in DBMS?

In simple terms, query processing in a DBMS can be described as the entire chain that the system goes through to process a request and output data that has been requested. For instance, in response to a query, a DBMS does not only provide data but also seeks to carry out all processes that would help reduce the workload when performing the query. This involves disassembling the query, restructuring it, and executing it in the most effective manner possible.

The principal objective of query processing is to reduce the time and effort needed to obtain the results. The DBMS interprets the query and selects the most effective way to execute it, taking into account the structure of the database, existing indices, and the marked resources. Query processing has the utmost importance for the performance and timely response of a database system, especially for large and complex data sets and queries.

Steps in Query Processing

Query processing in DBMS involves several key steps to ensure that a query is executed efficiently and accurately. The main steps include parsing and translation, optimization, and evaluation. Each of these steps plays a crucial role in transforming a high-level query into a form that can be executed by the database engine.

Parsing and Translation

Parsing and translation are the initial steps in query processing. When a query is submitted, the DBMS first parses the SQL statement to check for syntax errors and to understand the query structure.

• Parsing:
  • The query is broken down into smaller components (keywords, operators, operands).
  • The DBMS checks for syntax errors and validates the query structure.
• Translation:
  • The query is converted into an internal representation, often an abstract syntax tree (AST).
  • Semantic checks ensure the query references valid database objects (tables, columns) and consistent data types.

Key Points:

• The parsing step prevents syntax errors from causing execution failures.
• Translation transforms the query into a form the optimizer can work with.
• Ensures that the query is logically correct and ready for optimization.

Optimization

The optimization phase determines the most efficient way to execute the parsed query. This is crucial for performance, especially with large databases or complex queries.

• Query Optimizer:
  • Generates multiple execution plans.
  • Evaluate each plan based on cost factors like CPU time, memory, and I/O operations.
  • Selects the execution plan with the lowest estimated cost.

• Optimization Considerations:
  • Indexes: Decide whether to use indexes or perform full table scans.
  • Data Size: Considers the volume of data involved.
  • Joins and Conditions: Analyses the complexity of joins and the selectivity of query conditions.

Key Points:

• The optimizer explores different strategies to execute the query efficiently.
• The final execution plan is designed to minimise resource usage.
• The optimization phase can include query rewriting or applying transformations.

Evaluation

The evaluation phase is where the DBMS executes the optimised query plan, interacting with the storage engine to retrieve or modify data.

• Execution:
  • Follow the steps outlined in the execution plan.
  • Performs operations like reading data, applying filters, joining tables, sorting, and aggregating results.

• Data Interaction:
  • Uses indexes or performs full table scans as per the execution plan.
  • Handles intermediate results in memory or on disk, depending on the query’s complexity and available resources.

Key Points:

• The DBMS retrieves the required data based on the optimised plan.
• Intermediate results are managed efficiently to complete the query.
• The final results are formatted and sent back to the user or application.

Also read: DBMS Architecture

To better understand how query processing works, let’s walk through a practical example. Suppose we have a simple database with a table called Employees, which contains the following columns: EmployeeID, FirstName, LastName, Department, and Salary. Now, imagine you want to retrieve the names and departments of employees who earn more than $50,000.

SQL Query:

Step 1: Parsing and Translation

• Parsing:
  • The DBMS checks the syntax of the SQL query.
  • The query is broken down into components: SELECT, FROM, WHERE, etc.
  • The system verifies that the table Employees and the columns FirstName, LastName, Department, and Salary exist.
• Translation:
  • The query is converted into an abstract syntax tree (AST).
  • Semantic checks are performed to ensure logical correctness.
  • The query is now in a form that the optimizer can understand.

Step 2: Optimization

• Generating Execution Plans:
  • The optimizer considers different ways to execute the query, such as:
    • Index Scan: If an index exists on the Salary column, the optimizer may choose to use it to quickly find rows where Salary > 50000.
    • Full Table Scan: If no suitable index is found, the optimizer may opt for a full table scan.

• Choosing the Best Plan:
  • The optimizer evaluates each plan’s cost, considering factors like the number of rows to be scanned and the resources required.
  • The plan with the lowest estimated cost, perhaps using an index scan, is selected.

Step 3: Evaluation

• Executing the Plan:
  • The DBMS executes the chosen execution plan.
  • If an index scan was selected, the DBMS quickly retrieves the rows where Salary > 50000.
  • The selected rows are then filtered, and only the FirstName, LastName, and Department columns are retrieved.

• Returning the Results:
  • The DBMS assembles the final result set containing the names and departments of employees earning more than $50,000.
  • The results are sent back to the user or application that issued the query.

Through these steps, the DBMS ensures that the query is executed efficiently, even for large datasets or complex conditions. This process helps maintain the performance and reliability of the database system.

Also read: Views of data in Dbms

Conclusion

Query processing in DBMS is a vital mechanism that ensures efficient and accurate execution of queries. By breaking down a query into smaller steps like parsing, optimization, and evaluation the DBMS can identify the best execution plan, resulting in faster and more efficient data retrieval. Understanding these stages helps optimise query performance and improves overall system efficiency.

Whether you’re dealing with simple queries or complex ones involving multiple tables, the query processing steps remain crucial. A good understanding of how this works allows database administrators and developers to improve query performance and handle large datasets more effectively, ensuring smooth and reliable database operations. Want to explore Database Management Systems (DBMS)? Consider pursuing the Accelerator Program in Business Analytics and Data Science by Hero Vired.