Filter Data Based on Aggregated Results

Databases Postgres
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In data analysis and database management, filtering data based on aggregated results is a common and crucial operation. It helps in extracting meaningful insights from large datasets by applying specific conditions on aggregated metrics such as sums, counts, or averages. In this article, we delve into SQL queries that illustrate this process effectively, showcasing their structure and execution logic.

Query 1: Filtering Orders Based on Minimum Order Count

Consider a scenario where we need to extract records from an orders table, focusing only on those days when the number of orders was particularly high. Specifically, we want to find all dates where the order count was 120 or more and the orders were either completed or closed. Here’s the query that accomplishes this task:

SELECT order_date, count(*) AS order_count
FROM orders
WHERE order_status IN ('COMPLETE', 'CLOSED')
GROUP BY order_date
HAVING count(*) >= 120
ORDER BY order_count DESC;

Understanding the Query Execution Flow

The execution of this query follows a specific order that is important for SQL beginners to understand:

  • FROM: The data retrieval begins from the orders table.
  • WHERE: Filters are applied to consider only the rows where order_status is either ‘COMPLETE’ or ‘CLOSED’.
  • GROUP BY: The data is then grouped by order_date, aggregating the results per date.
  • SELECT: After grouping, the query selects order_date and the count of orders per date (order_count).
  • ORDER BY: Finally, the results are sorted in descending order based on order_count.

Query 2: Filtering Orders Based on Revenue Threshold

In another common analysis scenario, we might want to identify all orders that generated a total revenue exceeding a certain threshold. Here’s how we can extract records with an order revenue of $2000 or more:

SELECT order_item_order_id,
    round(sum(order_item_subtotal)::numeric, 2) AS order_revenue
FROM order_items
GROUP BY order_item_order_id
HAVING round(sum(order_item_subtotal)::numeric, 2) >= 2000
ORDER BY order_revenue DESC;

Logic Behind the Query

This query follows a similar structure but focuses on financial metrics:

  • FROM: Data is selected from the order_items table.
  • GROUP BY: Aggregation is done per order_item_order_id to calculate total revenue per order.
  • HAVING: Filters out those groups where the aggregated revenue is less than $2000.
  • SELECT: The final output includes the order ID and its respective total revenue.
  • ORDER BY: Results are sorted in descending order of order_revenue.

Both queries exemplify how SQL allows for sophisticated data filtering based on aggregated results, providing vital insights into business operations or customer behavior. Understanding these patterns is essential for anyone looking to analyze large datasets efficiently.

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