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Category: Dates and Numbers

Currency Conversion in Power BI

Published 2020-03-09 by Kevin Feasel

Marco Russo takes us through some tips when performing currency conversions using Power BI:

The rule of thumb is to apply the currency exchange conversion upfront. Therefore, it is a good idea to solve all the scenarios requiring a single currency in the report by converting all amounts at the time of data import into the data model. When you need to create a report in multiple currencies, computing data in advance could be challenging and expensive. Therefore, a dynamic solution based on DAX measures and a properly designed data model makes more sense.

In this article, we only consider the third scenario, “Data in a single currency, report with multiple currencies”. The second scenario could be implemented by transforming data so that it is imported in the model in a single currency, moving the challenge over to the very scenario we describe in this article. An extended description of the three scenarios with other dynamic solutions is included in a chapter of the Analyzing Data with Microsoft Power BI and Power Pivot for Excel book.

This is quite a useful article if you work with multiple currencies.

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Fun with Filtering Between Start and End Dates

Published 2020-02-19 by Kevin Feasel

Brent Ozar shows why the StartDate + EndDate pattern is not great for filtering:

If all you need to do is look up the memberships for a specific UserId, and you know the UserId, then it’s a piece of cake. You put a nonclustered index on UserId, and call it a day.

But what if you frequently need to pull all of the memberships that were active on a specific date? That’s where performance tuning gets hard: when you don’t know the UserId, and even worse, you can’t predict the date/time you’re looking up, or if it’s always Right Now.

This is where I advocate pivoting to a series of event records, so instead of a start date and end date, you have an event type (started, expired, cancelled, etc.) and a date. There are other alternatives as well, but it’s a good thought exercise.

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SQL Server Truncating Numbers to Asterisks

Published 2019-12-20 by Kevin Feasel

Bert Wagner points out that some numeric types handle overflow in a weird way:

Why does SQL Server sometimes error when converting a number into a string, but other times succeeds and returns an asterisk?

I don’t know.

The best (and logical) answer I could find online is from Robert Sheldon, who attributes it to poor error handling practices, “…before error handling got a more reputable foothold.”

This makes it important to check your results. I imagine that there’s somebody who relies upon this exact functionality, but it’s pretty weird.

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TRY_PARSE and NaN

Published 2019-12-20 by Kevin Feasel

Slava Murygin finds a nasty bug in SQL Server:

Database in trouble has a table with FLOAT column. It’s Front-End application verifies user’s input and inserts the data into that column using TRY_PARSE function.
The developer’s intention was that any “Not-a-Numeric” or “Out-of-Range” values will be automatically converted to NULL and it will be for user’s discretion to verify and fix these values.

However, one of the application users was very educated and instead of empty space, NULL or any other bad not numeric value the user supplied data with value of “NaN” for empty cells, which simply stands for “Not a Numeric”.
That action caused a database corruption!

Click through for a demo which you should not repeat on a work server.

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Comparing Dates in a WHERE Clause

Published 2019-12-02 by Kevin Feasel

Erik Darling has been diving into issues with date comparison lately, including comparing date columns in the WHERE clause:

A common dilemma is when you have two date columns, and you need to judge the gap between them for something.

For instance, say you have a table of orders and you want to figure out how long on average it takes an ordered item to ship, or a shipped item to be delivered.

You’re not left with many good ways to write the query to take advantage of indexes.

Read on to see what Erik means, and one way you can speed it up a bit.

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Fun with Date Math and Performance

Published 2019-11-26 by Kevin Feasel

Erik Darling has started a new series on how hard it can be to get a date:

I’ll often see people need to “flatten” dates to certain intervals.

By flatten, I mean the start of a day, week, month, or year, and likewise for the end values to all those intervals.

I’ve seen some really bad implementations of this. Most notable was probably a scalar valued function that converted a datetime to a 10 character string to remove the time portion.

In every where clause.

Click through for additional introductory notes and some links to good resources.

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Dealing with Time Zones in SQL Server

Published 2019-11-01 by Kevin Feasel

Nate Johnson hates time zones:

To start with, the transactional data is in PST/PDT — i.e. Pacific Time with DST fluctuation. Yes, it’s horrible. No, I don’t know what happens to events or jobs at 2am on the “Fall Back” date, or between 2am and 3am on the “Spring Forward” date. No, I can’t change it right now. Stop whining.

Now, I have offices in Paris France, Hong Kong, and Beijing China. These are 3 different “time zones”, but only 2 different offsets — China and Hong Kong are in the same bucket, namely, UTC +08:00. More on that later.

Dates and currencies are seemingly two of the simplest things we deal with, but two of the most complex sets of data types.

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Date Buckets in SQL Server

Published 2019-10-30 by Kevin Feasel

Andy Mallon needs to create groups of date ranges in T-SQL:

Here’s where my math nerd-iness comes out… There’s a little trick for “bucketizing” numbers (in this case, turning “Months” into “Month Buckets”):

– Take a number
– Divide it by your bucket size
– Round that number down to a whole number–We’ll call this the “divided number”
– Multiply the “divided number” by the bucket size–This is your bucket floor
– Add one to the “divided number” and then multiply that by your bucket size–This is your bucket ceiling

I was promised that there would be no math.

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Date and Time Aggregation in T-SQL

Published 2019-10-11 by Kevin Feasel

Daniel Hutmacher shows how you can aggregate date and time data types in SQL Server:

Because the “Duration” column is a “time(3)” datatype, it doesn’t really aggregate into an average that easily.

Msg 8117, Level 16, State 1, Line 20 Operand data type time is invalid for avg operator.

This may seem a little odd, as time is really a continuum just like any integer or floating-point value, right?

Read on to see how to do this and a warning from Daniel about overflowing. This sort of aggregation is a lot easier to do in R, but you can still do it in T-SQL.

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