Скачать или смотреть How to Filter SQL Results by Dynamic Date Ranges with Ease

Discover an effective method to filter SQL queries by dynamic date ranges, ensuring accurate results while handling varying snooze periods.
---
This video is based on the question https://stackoverflow.com/q/64228749/ asked by the user 'gar' ( https://stackoverflow.com/u/9754160/ ) and on the answer https://stackoverflow.com/a/64228797/ provided by the user 'Gordon Linoff' ( https://stackoverflow.com/u/1144035/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How do you filter in SQL by a dynamic number of date ranges?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding the Problem: Filtering Posts by Dynamic Date Ranges

In the realm of SQL databases, especially when dealing with dynamic datasets, one common challenge developers face is filtering records based on date ranges. For instance, consider you have a table of posts created by users, and you want to filter these posts to see only those made when the respective user was not on "snooze mode." The term "snooze mode" refers to periods where users have opted out of participating or interacting within the system.

The Scenario

Let’s set up our example based on the given tables from a PostgreSQL database:

Users Table: Contains user information.

Posts Table: Contains posts made by each user along with their creation timestamps.

Snoozes Table: Keeps track of time periods (from start_at to end_at) during which users are on snooze.

Here's a quick snapshot of the tables involved:

Users Table

[[See Video to Reveal this Text or Code Snippet]]

Posts Table

[[See Video to Reveal this Text or Code Snippet]]

Snoozes Table

[[See Video to Reveal this Text or Code Snippet]]

Goal

Your goal is to find out which posts were created by users during periods when they were not in snooze mode. For the data provided, the post that should be returned is post id 3, created on 2020-07-15, since it falls outside of all snooze intervals.

The Solution: Using SQL NOT EXISTS Clause

To tackle this problem efficiently, you can make use of the NOT EXISTS clause in your SQL query. This clause allows you to filter records based on the absence of certain records in a subquery. Here’s how it works:

SQL Query Breakdown

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Query

Selecting Posts: The main query selects all columns (p.*) from the Posts table where the condition holds true.

Subquery with NOT EXISTS: The subquery is designed to check if any snooze records exist for the user associated with the post. Specifically:

p.user_id = s.user_id links the post to the snooze record based on the user.

p.created_at BETWEEN s.start_at AND s.end_at checks whether the creation date of the post is within any of the snooze periods for that user.

Condition Check: If there are no matching snooze records during the post's creation time, the post will be included in the final result set.

Final Notes

By using the NOT EXISTS approach, you ensure that your SQL query is not only effective but also scalable to varying numbers of snooze records for each user. This method guarantees that you capture only the relevant posts accurately, even as the dataset grows.

In summary, SQL provides powerful tools to manage dynamic data scenarios efficiently. By employing strategies like NOT EXISTS, you can maintain clarity and performance within your queries, providing precise and actionable insights from your data.

If you have any further questions or need additional clarification, feel free to ask!