Mention the four layers of distributed query processing. Explain all the steps in query decomposition with the help of SQL query and necessary figures.

by

Four Layers of Distributed Query Processing

Distributed Query Processing in a Distributed Database System (DDBMS) involves the process of executing a query across multiple databases or sites in a distributed environment. The process of query execution is broken down into four layers:

  1. Global Query Optimization Layer: This layer focuses on optimizing the execution plan for the entire query across the distributed system. It decides how to break down the query into subqueries that can be processed at different sites, aiming to minimize communication costs, maximize parallelism, and ensure efficient data retrieval.
  2. Local Query Optimization Layer: Once the query is broken down into subqueries, this layer handles the optimization of individual subqueries at each site. The goal is to optimize the processing of each query by choosing the best access methods, join algorithms, etc., for the data stored at that particular site.
  3. Query Decomposition Layer: This layer is responsible for transforming a global query into a set of local queries or subqueries that can be processed independently at various sites. It involves parsing the global query, understanding its structure, and breaking it into simpler components that can be executed locally.
  4. Query Execution Layer: This layer performs the actual execution of the subqueries that were generated by the decomposition process. It involves accessing the data from the respective databases, performing computations, and combining the results to return the final result.

Query Decomposition Process

Query decomposition is a critical step in distributed query processing. It involves breaking down a global query into subqueries that can be executed at different sites. The process consists of three major steps:

  1. Parsing: The global query is parsed to understand its syntax and semantics. The SQL query is analyzed to identify the different components such as tables, columns, operators, conditions, and join clauses.
  2. Query Analysis: After parsing, the system analyzes the query to understand the relationships between tables, the filtering conditions, and how the data should be retrieved. This step involves identifying the data locations (which sites hold the required data) and how to break the query into smaller pieces for each site.
  3. Query Decomposition: This step involves transforming the global query into subqueries that can be executed on individual sites. The system determines how the query will be decomposed based on data distribution, the join method, and other optimization factors.
  4. Global and Local Execution Plans: Finally, the system prepares both global and local execution plans. The global execution plan determines how to orchestrate the subqueries across multiple sites, while the local execution plan optimizes how each individual subquery will be executed at each site.

Steps in Query Decomposition with an Example SQL Query

Consider the following SQL query:

SELECT employee.name, department.name
FROM employee, department
WHERE employee.department_id = department.id
  AND employee.salary > 50000;

Let’s go through the steps of query decomposition for this example.

1. Parsing

The system first parses the SQL query to break it down into its components. This step identifies:

  • The tables: employee and department.
  • The columns to be selected: employee.name, department.name.
  • The join condition: employee.department_id = department.id.
  • The filter condition: employee.salary > 50000.

2. Query Analysis

Next, the system analyzes the query to understand the relationships between the tables and the locations of the data. If the employee and department tables are distributed across different sites, the system will identify where each table is located and how to access it. For instance, suppose:

  • The employee table is located on Site 1.
  • The department table is located on Site 2.

The system will recognize the join condition (employee.department_id = department.id) and the need to transfer the relevant rows between sites for processing the join.

3. Query Decomposition

Now, the system breaks the query down into subqueries. Based on the analysis, the query can be decomposed into two parts:

  • Local query at Site 1 (for the employee table):
    • Select employees with salaries greater than 50,000.
    • Subquery at Site 1: SELECT name, department_id FROM employee WHERE salary > 50000;
    • This query retrieves the necessary employee data locally from Site 1.
  • Local query at Site 2 (for the department table):
    • Select department names based on the department_id values.
    • Subquery at Site 2: SELECT name, id FROM department;
  • Join operation: After retrieving data from both sites, the join operation is performed based on the department_id from the employee data and id from the department data. The join can be either performed at one of the sites (using a semi-join) or through a distributed join depending on the query optimization and system design.

4. Global and Local Execution Plans

The system then prepares the global execution plan and the local execution plans:

  • Local execution plan for Site 1: Execute the query to select employees with a salary greater than 50,000.
  • Local execution plan for Site 2: Execute the query to retrieve department data.
  • Global execution plan: Combine the results of the two local subqueries, apply the join condition, and filter the final results.

The final result is a set of employee names and department names for employees with salaries above 50,000.

Figures (Illustrative Diagrams)

  1. Global Query Decomposition:
    • Global Query: SELECT employee.name, department.name FROM employee, department WHERE employee.department_id = department.id AND employee.salary > 50000;
    • Break the query into subqueries:
      • Site 1: SELECT name, department_id FROM employee WHERE salary > 50000;
      • Site 2: SELECT name, id FROM department;
  2. Join Operation:
    • After retrieving data from the two sites, the join condition is applied.
    • A distributed join is performed, or a semi-join may be applied where only the relevant department_id values from Site 1 are sent to Site 2 to minimize data transfer.

Hence, in distributed query processing, query decomposition is a critical process that breaks down a global query into subqueries that can be executed independently at different sites. By parsing, analyzing, and decomposing the query, the system ensures that data retrieval is efficient, and resources are optimally used across the distributed system. Query optimization plays a vital role in minimizing communication costs and ensuring quick and accurate results.

Leave a Comment