Intermediate – Page 4 – sqlsunday.com

Availability Groups: How to sync logins between replicas

AlwaysOn Availability Groups are a reasonably simple way to set up disaster recovery (DR) for your SQL Server environment, and with fairly little effort, you can get a bit of high availability (HA) from it as well. But there are a few gotchas, the most obvious of them being that Availability Groups only synchronize specific user-databases, not the entire server setup.

Things that are not included in AGs include logins, SQL Server Agent jobs, SSIS packages stored in SQL Server, linked servers and server settings. You could sync these manually (as is often the case), but wouldn’t you just love to have an automated process do all this for you?

In this post, we’ll look at logins. For the sake of simplicity, I’ll assume that you have a primary replica with a single AG and any number of secondary replicas. The logic holds true if you have multiple AGs, it just gets trickier.

Human-readable ranges of integers or dates

This is a real-world problem that I came across the other day. In a reporting scenario, I wanted to output a number of values in an easy, human-readable way for a report. But just making a long, comma-separated string of numbers doesn’t really make it very readable. This is particularly true when there are hundreds of values.

So here’s a powerful pattern to solve that task.

Video: three SQL Server join operators in three minutes

In an attempt to try a different approach, here’s a three-minute video explanation of how the different physical join operators in SQL Server work and why you would choose one over the other.

Comparing nullable columns

Do you ever compare the values of a lot of columns in two tables? Sure you do. Like, for instance, in a cross update, when you need to figure out which rows you should actually update. But it gets worse if the columns are nullable. The fact that any value could potentially be NULL vastly complicates the comparison and might wreak havoc not only on your code but also on your query performance.

But there’s hope.

Calculating the number of weekdays between two dates

I wish the DATEDIFF() function would count the number of working days (mondays through fridays) between two dates for me, but until that happens, I’ve had to roll my own scalar function. I tried to think of a smart way involving perhaps a modulus calculation, but I quickly succumbed to a more down-to-earth approach.

Windowed DISTINCT aggregates

You may have discovered that the use of DISTINCT is not supported in windowed functions. A query that uses a distinct aggregate in a windowed function,

SELECT COUNT(DISTINCT something) OVER (PARTITION BY other)
FROM somewhere;

will generate the following error message:

Msg 10759, Level 15, State 1, Line 1
Use of DISTINCT is not allowed with the OVER clause.

There are, however, a few relatively simple workarounds that are suprisingly efficient.

Last row per group

A very common challenge in T-SQL development is filtering a result so it only shows the last row in each group (partition, in this context). Typically, you’ll see these types of queries for SCD 2 dimension tables, where you only want the most recent version for each dimension member. With the introduction of windowed functions in SQL Server, there are a number of ways to do this, and you’ll see that performance can vary considerably.

Different query plans for “OR” type queries

The SQL Server query optimizer can find interesting ways to tackle seemingly simple operations that can be hard to optimize. Consider the following query on a table with two indexes, one on (a), the other on (b):

SELECT a, b
FROM #data
WHERE a<=10 OR b<=10000;

The basic problem is that we would really want to use both indexes in a single query.

In this post, we’re going to take a look at a few examples of how this type of query would be optimized, as well as how statistics can affect the query plan, and finally, we’ll take a look at a slightly rare plan operator called “Merge Join (Concatenation)”.

Using UPDATE STATISTICS to fake row counts

Here’s a quick tip: When you’re evaluating query strategies, you may want to consider how your query will scale when the volume in your database goes up. This does not neccessarily mean that you have to start filling your tables with gigabytes on gigabytes of data.

Intermittent conversion issues

Although it’s rotten practice, values like dates, times, numbers, etc are often stored in columns with the wrong data type. Sometimes, it’s because the data model needs to allow for multiple types of data in the same column (sometimes even on the same row in the form of a formula or text syntax), but sometimes, it’s just plain and simple lazy.

With this type of setup, it’s only a matter of time before you run into conversion errors like this one:

Msg 245, Level 16, State 1, Line 29
Conversion failed when converting the varchar value 'xyz' to data type int.

To make things worse, these errors can be tricky to pinpoint, because they can appear to come and go without any real recognizable pattern. In this post, I’ll take a look at how this happens, how to look for these errors, and ultimately how to fix them if you can’t change the database schema.

Category: Intermediate