How to Efficiently Query the Last n Rows in SQL Using PostgreSQL

Learn how to correctly retrieve the last `n` number of rows in SQL without syntax errors. Explore effective methods, using subqueries, to optimize your database queries in PostgreSQL.
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How to Efficiently Query the Last n Rows in SQL Using PostgreSQL

If you're working with large datasets in SQL, you may find yourself needing to query only a subset of your table. For instance, you might want to analyze the last n records—let’s say the last 100,000 rows—of your data. However, it’s not uncommon to encounter syntax errors when attempting to do this. In this post, we’ll examine why this happens and how to implement the desired solution properly.

The Problem: Querying the Last n Rows

Imagine you have a table with an overwhelming amount of data, growing by around 60 million rows each day. You may want to run a query similar to the following, intending to find the count of trades for a specific symbol and timestamp only from the latest entries:

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

Unfortunately, this results in a syntax error since SQL tables are essentially unordered sets by nature. SQL doesn’t recognize the concept of "last" rows without specifying how you define "last"—by date, by ID, etc.

The Solution: Using a Subquery

Understanding the Ordered Nature of Queries

When dealing with tables, you need to define how rows should be ordered. To achieve your goal of querying the last n records, you must specify a method to order your data. Here’s how you can do it using a subquery.

Step-by-Step Implementation

Create a Subquery that Retrieves the Last n Rows:
The first step is to write a subquery that selects the last n rows using an ORDER BY clause. This clause will define your "last" records based on a timestamp or another unique column.

Use the Subquery to Aggregate Data:
After getting the last n rows, you can perform your aggregation, such as counting the number of trades.

Here’s the Correct Query

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

Explanation of the Code

Subquery:

SELECT d.* FROM datas d ORDER BY tstamp DESC LIMIT 100000 fetches the last 100,000 rows based on the tstamp column.

Outer Query:

SELECT sym, tstamp, COUNT(*) AS trades then groups these entries by sym and tstamp, ensuring you can analyze the counts correctly.

HAVING Clause:

HAVING COUNT(*) > 500 filters results to show only entries with more than 500 trades.

Bonus Tip: Improving Performance with Indexing

If performance is crucial (especially with large tables like yours), consider creating an index on the column by which you are ordering. In this case, an index on datas(tstamp desc) would optimize the query speed for fetching recent records.

Conclusion

Querying large datasets effectively in SQL, especially when needing to focus on the "last" records, requires a clear understanding of how SQL organizes data and how to appropriately use subqueries. By applying the method outlined in this guide, you can efficiently retrieve insights from your table while avoiding syntax errors.

With the right approach, your SQL queries will be both effective and efficient—allowing you to tackle large datasets with confidence.