SQL Window Functions Series: RANK() and DENSE_RANK()
Welcome to the fascinating world of SQL window functions! Today, we'll explore in detail: RANK() and DENSE_RANK().
2023-11-20 (first published: 2023-11-15)
13,984 reads
Welcome to the fascinating world of SQL window functions! Today, we'll explore in detail: RANK() and DENSE_RANK().
2023-11-20 (first published: 2023-11-15)
13,984 reads
In this Article , We will delve into the world of Query Store and explore how to use Optimized Plan Forcing to improve performance in SQL Server 2022. We will discuss what it is, how it works, and how it can impact your system's performance.
2023-09-04
5,317 reads
Learn how to conduct deep SQL Query optimization with SQL Grease with the Enterprise dashboard, historical data, troubleshooting SQL Server Wait Stats, capturing anomalies and intelligent notifications.
2022-02-02
As SQL developers, we tend to think of performance tuning in terms of crafting the best table indices, avoiding scalar and table valued functions, and analyzing query plans (among other things). But sometimes going back to the spec and applying some properties of elementary math can be the best way to begin to improve performance of SQL queries which implement mathematical formulas. This article is a case study of how I used this technique to optimize my SQL implementation of the Inverse Simpson Index.
2021-05-07 (first published: 2019-09-12)
5,394 reads
2016-01-14
1,814 reads
In his continuing quest to bring a deeper understanding of Query Optimizer to the world at large, Fabiano Amorim takes a moment to point out a potential pitfall you may encounter. A light read, but one worth persuing.
2010-01-01
3,379 reads
In SQL Server 2005, a feature was introduced that was hardly noticed, but which might make a great difference to anyone doing queries involving temporal data. For anyone doing Data Warehousing, timetabling, or time-based pricing, this could speed up your queries considerably. Who better to introduce this than Query Optimizer expert, Fabiano Amorim?
2009-10-26
3,485 reads
Microsoft SQL Server 2008 collects statistical information about indexes and column data stored in the database. These statistics are used by the SQL Server query optimizer to choose the most efficient plan for retrieving or updating data. This paper describes what data is collected, where it is stored, and which commands create, update, and delete statistics. By default, SQL Server 2008 also creates and updates statistics automatically, when such an operation is considered to be useful. This paper also outlines how these defaults can be changed on different levels (column, table, and database).
2009-07-24
2,506 reads
By Vinay Thakur
Continuing from Day 4 where we learned Encoder, Decoder, and Attention Mechanism, today we...
By Vinay Thakur
Continuing from Day 3 where we covered LLM models open/closed and their parameters, Today...
By Steve Jones
One of the nice things about Flyway Desktop is that it helps you manage...
I'm fairly certain I know the answer to this from digging into it yesterday,...
Hi Team, I am trying to refresh the Azure Synapse Dedicated pool from production...
hi everyone I am not sure how to write the query that will produce...
I have some data in a table:
CREATE TABLE #test_data
(
id INT PRIMARY KEY,
name VARCHAR(100),
birth_date DATE
);
-- Step 2: Insert rows
INSERT INTO #test_data
VALUES
(1, 'Olivia', '2025-01-05'),
(2, 'Emma', '2025-03-02'),
(3, 'Liam', '2025-11-15'),
(4, 'Noah', '2025-12-22');
If I run this query, how many rows are returned?
SELECT *
FROM OPENJSON(
(
SELECT t.* FROM #test_data AS t FOR JSON PATH
)
) t; See possible answers