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Category: Syntax

Time Series Features in SQL Server 2022

Published 2023-01-18 by Kevin Feasel

Kendal Van Dyke walks us through a few new bits of T-SQL in SQL Server 2022:

Time series data is often used for historical comparisons, anomaly detection and alerting, predictive analysis, and reporting, where time is a meaningful axis for viewing or analyzing data.

Time series capabilities in SQL Server were introduced in Azure SQL Edge, Microsoft’s version of SQL Server for the Internet of Things (IoT) which combines capabilities such as data streaming and time series with built-in machine learning and graph features.

I am happy to see that these operators and functions made the leap from Azure SQL Edge and am hopeful that we’ll see a bit more of what makes databases like influxdb so useful for time series make their way in as well.

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Using the Native Pipe in R 4.1+

Published 2023-01-13 by Kevin Feasel

Michael Mayer shows off the native R pipe:

What does the pipe do? It puts the object on its left as the first argument into the function on its right: iris %>% head() is a funny way of writing head(iris). It helps to avoid long function chains like f(g(h(x))), or repeated assignments.

In 2021 and version 4.1, R has received its native forward pipe operator |> so that we can write nice code like this:

Tying pipe syntax all back together, the magrittr pipe %>% was (as I recall) built with the F# pipe |> in mind. In R 4.1 and later, the built-in pipe is |>, as is right and natural in this world. Regardless, do check the comment before trying out this code, as it appear to work for R 4.2 and later, though not 4.1.

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Finding Skipped Identity Values in a Table

Published 2023-01-09 by Kevin Feasel

Brent Ozar minds the gap:

When someone says, “Find all the rows that have been deleted,” it’s a lot easier when the table has an Id/Identity column. Let’s take the Stack Overflow Users table:

It has Ids -1, 1, 2, 3, 4, 5 … but no 6 or 7. (Or 0.) If someone asks you to find all the Ids that got deleted or skipped, how do we do it?

Click through for two methods, one specific to SQL Server 2022 and one which works for all versions of SQL Server.

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Common Table Expressions in MySQL

Published 2023-01-06 by Kevin Feasel

Robert Sheldon looks at the syntax for common table expressions in MySQL:

As with many relational database management systems, MySQL provides a variety of methods for combining data in a data manipulation language (DML) statement. You can join multiple tables in a single query or add subqueries that pull data in from other tables. You can also access views and temporary tables from within a statement, often along with permanent tables.

MySQL also offers another valuable tool for working with data—the common table expression (CTE). A CTE is a named result set that you define in a WITH clause. The WITH clause is associated with a single DML statement but is created outside the statement. However, only that statement can access the result set.

The syntax is very similar to that of SQL Server save for an explicit RECURSIVE clause rather implicit recursion as in T-SQL.

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Join Types in Spark SQL

Published 2022-12-29 by Kevin Feasel

Rituraj Khare makes some connections:

In Apache Spark, we can use the following types of joins in SQL:

Inner join: An inner join in Apache Spark is a type of join that returns only the rows that match a given predicate in both tables. To perform an inner join in Spark using Scala, we can use the join method on a DataFrame.

The set of options is the same as you’d see in a relational database: inner, left outer, right outer, full outer, and cross. The examples here are in Scala, though would apply just as easily to PySpark and, of course, writing classic SQL statements.

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Comparing Table Records with T-SQL

Published 2022-12-22 by Kevin Feasel

Chad Callihan compares and contrasts:

We recently looked at looked at comparing schemas using Azure Data Studio. What if we need to compare tables by using a query? For this post we’ll compare using EXCEPT, NOT IN, and NOT EXISTS to find differences between two tables.

Our two tables to compare will be Comic and Comic_Copy. Based on counts, we have 48 more records in Comic than we do in Comic_Copy. Let’s find the differences.

In Chad’s specific query, NOT EXISTS works great. Where I like EXCEPT is when you need to see if any of the non-key columns differ. For example, if you also needed to compare titles for rows with the same ID and ensure those titles matched.

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The Value (and Cost) of DATETRUNC

Published 2022-12-21 by Kevin Feasel

Brent Ozar points out the ups and downs of DATETRUNC():

The first one, passing in a specific start & end date, gets the best plan, runs the most quickly, and does the least logical reads (4,299.) It’s a winner by every possible measure except ease of writing the query. When SQL Server is handed a specific start date, it can seek to that specific part of the index, and read only the rows that matched.

DATETRUNC and YEAR both produce much less efficient plans. They scan the entire index (19,918 pages), reading every single row in the table, and run the function against every row, burning more CPU.

SQL Server’s thought process is, and has always been, “I have no idea what’s the first date that would produce YEAR(2017). There’s just no way I could possibly guess that. I might as well read every date since the dawn of time.”

Read on for the upshot.

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Window Functions in DAX

Published 2022-12-15 by Kevin Feasel

Jeffrey Wang is speaking my language:

The December 2022 release of Power BI Desktop includes three new DAX functions: OFFSET, INDEX, and WINDOW. They are collectively called window functions because they are closely related to SQL window functions, a powerful feature of the SQL language that allows users to perform calculations on a set of rows that are related to the current row. Because these functions are often used for data analysis, they are sometimes called analytical functions. In contrast, DAX, a language invented specifically for data analysis, had been missing similar functionalities. As a result, users found it hard to write cross-row calculations, such as calculating the difference of the values of a column between two rows or the moving average of the values of a column over a set of rows.

Read on to learn more about how these functions work and how they differ from their SQL Server counterparts.

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