Database Essentials - Partitioning Tables and Indexes - Usage Scenarios

In this article, we will explore the concept of partitioning in databases to manage large tables efficiently. Through this guide, you will learn the key concepts of partitioning and hands-on tasks to implement partitions for your database tables.

Explanation for the video

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Key Concepts Explanation

In this section, we will delve into the key concepts of database partitioning and provide explanations with code examples.

Key Concept 1

Database partitioning is used to manage large tables effectively by dividing them into smaller, more manageable partitions. Here is an example of creating a partitioned table:

-- Sample code for creating a partitioned table
CREATE TABLE users_mthly (
    user_id SERIAL,
    user_first_name VARCHAR(30) NOT NULL,
) PARTITION BY RANGE(created_dt)

Key Concept 2

Another key concept is range partitioning, which involves dividing table data into partitions based on a specific range. Here is an example of creating a range partitioned table:

-- Sample code for creating a range partitioned table
CREATE TABLE users_mthly_201601
PARTITION OF users_mthly
FOR VALUES FROM ('2016-01-01') TO ('2016-01-31')

Hands-On Tasks

Now, let’s jump into some hands-on tasks to help you practice implementing database partitioning.

  1. Create a monthly partitioned table with a specified range.
  2. Add partitions to the monthly partitioned table based on year and month.


In summary, partitioning is a powerful technique for managing large database tables efficiently. By dividing tables into smaller partitions, you can improve performance and maintenance tasks. We encourage you to try out the hands-on tasks provided and explore further use cases for database partitioning.

Usage Scenarios

Let us go through some of the usage scenarios with respect to partitioning:

  • It is typically used to manage large tables so that the tables do not grow abnormally over time.
  • Partitioning is often used for log tables, reporting tables, etc.
  • Dropping partitions to clean up large data chunks is faster than deleting from a non-partitioned table.
  • Using list partitioning for tables with a limited set of statuses can improve performance.
  • For log tables with long retention periods, range partitioning based on a date column is preferred to avoid unnecessary data duplication.

Watch the video tutorial here