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DBMS: Parallel Query in DBMS for Beginners


In this article, I will explore the concept of parallel query in DBMS for beginners, discussing what it is, and how it works, and coming up with easy-to-understand examples with the surprising benefits of parallel query processing.


Databases are one of the important parts of today’s modern software applications, and the efficient recovery and modification of data from databases is important for the implementation of these applications. In large-scale database systems, dealing with multiple queries in huge amounts of data can be a difficult task. To manage this challenge, database management systems (DBMS) are available with many techniques to improve query performance, and one of the most progressive in parallel query processing.

Table of Contents

What is a Parallel Query?


Parallel Query is the process of handling large queries in parallel mode. It defines breaking up the queries into multiple sub-queries to run them parallel together in different systems to provide efficiency and fast improved performance.

Key Points: Parallel Query Processing

To grab the attention of parallel query processing, let’s consider the key points one by one:

  1. Query Processing: A query is defined as an SQL query that involves query handling activities such as data retrieval, data manipulation, deleting, or updating data in the database server. With the help of DBMS, the SQL queries are converted to a series of operations that are accessible as needed to modify.

  2. Parallel Processing: Parallel Processing defines running multiple queries in parallel mode. The execution of queries is done throughout by sharing the resources parallelly, such resources include, disk, RAM, and multiple processors, which makes it easy to reduce the implementation time and increases the result-producing speed.

    3. Parallel Query Processing: Parallel query processing is a technique of breaking a single query into multiple sub-queries that are executed together on multiple processors. This process improves the query execution speed by using computational resources efficiently.

How Parallel Query Works?

Let’s take into consideration the process of how parallel query works in a DBMS:

  1. Query Decomposition: A query decomposition is known as breaking up the queries into multiple sub-queries that can be run together parallelly. Each sub-query is considered as the standalone main query that operates based on a subset of the data.

    Task Assignment: The DBMS assigned each sub-query to different resources such as processors and RAM to execute them parallelly. The number of processors in allocation depended on the system’s hardware and requirements.

  2. Simultaneous Execution: Once the sub-queries are allocated to resources, they are run together in parallel mode. Each processor performs as it is defined sub-query, independently retrieving or modifying the data.

  3. Result Aggregation: Once the sub-queries execution is done, their results are aggregated to form the final result of the original query. This involves combining rows, performing calculations, and applying any necessary modification.

Benefits of Parallel Query Processing

Let’s discuss some advantages of Parallel query processing database management systems:

  1. Improved Performance: One of the crucial benefits of parallel query processing is to improve query performance. By dividing the big query into su queries and running them parallelly, the overall query execution time is drastically reduced. This is important for complex and time-consuming queries.
  1. Resource Utilization: The process of Resource Utilization enables the Parallel processing to use the available resources efficiently with different sub-queries. Whether the resources are Disk, Processors, RAM, Cores, etc, is assigned correctly to each sub-queries upon its requirements. Instead of putting other queries on wait to complete the single query, these resources allow to run multiple queries together.
  1. Scalability: Parallel query processing is scalable, meaning it is capable of being applied to databases of various sizes and complexities. Whether the query is at a small or large-scale database, parallel query processing can adapt to the system’s requirements.
  1. Consistency: A parallel query system is designed in a way that ensures consistency and data integrity of multiple sub-queries. The process of consistency is achieved through synchronization techniques to prevent conflicts and maintain data consistency.

Illustration: Parallel Query Processing

Let’s make it easy to understand the concept of Parallel Query processing by explaining some examples.

Example 1: Sales Database


Let’s think of a retail company with a large-scale sales transactions database. They want to find the total sales for each product category for the past year. Without parallel processing, a query is not enough to retrieve this information without a long time due to the volume of data.

With parallel query processing:

  1. The DBMS decomposes the query into subqueries for each product category (e.g., electronics, clothing, food).
  1. These subqueries are allocated to different processors.
  1. Each processor retrieves the sales data for its assigned product category.
  1. The results are collected, and the total sales for each category are calculated.

This parallel method reduces the time required to find the desired results.

Example 2: Social Media Analytics

Consider a social media platform that wants to find user engagement data. They want to find the average number of comments, likes, and shares for each user’s posts. This involves querying a large dataset of user activity.

With parallel query processing:

  1. The DBMS breaks up the query into sub-queries for each user’s posts.

  2. These sub-queries are distributed to different processors.

  3. Each processor calculates the average engagement for the posts of its assigned users.

  4. The results are collected to determine the overall averages.

By running these sub-queries in parallel, the social media platform can easily find user engagement data, providing valuable information about user behavior.

Implementing Parallel Query Processing

Here are some common techniques for implementing the Parallel Query:

1. Query Optimizer: The query optimizer plays an important role in identifying which queries are beneficial to parallel processing. It finds query complexity, available resources, and data distribution to make decisions.

2. Data Distribution: The process of data distribution across storage devices and processors can impact parallel query performance. Data is important to be distributed to control resource bottlenecks.

3. Parallelism Level: The parallelism, or the number of processors assigned to a query, should be carefully determined. Utilizing many processors can lead to bad results, while less than required can lead to underutilized resources.

4. Synchronization: To maintain data consistency, the synchronization technique coordinates the execution of parallel subqueries. There are a few synchronizations such as Locks, semaphores, and others to prevent conflicts.

5. Hardware Configuration: The hardware resources like the number of CPU cores and memory capacity, should be optimized for parallel processing. High-performance hardware can effectively boost query performance.

Limitations and Considerations

Let’s take into consideration its limitations and other factors:

  1. Overhead: Overhead is the process of managing parallel tasks. The DBMS must coordinate task distribution, synchronization, and result aggregation, which can consume additional resources.

  2. Queries Not Benefit Equally: Parallel processing is most important for complex queries that involve activities like data retrieval and manipulation. Simple queries may not get the same level of performance improvement.

  3. Data Distribution Challenges: Distributing data across multiple storage devices can be a complex task, and poorly distributed data can lead to performance issues.

  4. Hardware Requirements: Implementing parallel query processing often requires a high and advanced level of a hardware system, which may sometimes cost highly.

  5. Concurrency Control: Ensuring data consistency in a parallel processing environment requires careful management of concurrency control techniques.


Parallel query processing is an important technique to improve the performance of database management systems. It allows the efficient execution of complex queries by dividing them into sub-queries that run simultaneously on multiple processors. This method reduces query response times and maximizes the utilization of available hardware resources.

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