I recently ran into a curious deadlock issue. I have a process that performs a lot of updates in a “state” table using multiple, concurrent connections. The business logic in the application guarantees that two connections won’t try to update the same item, so we shouldn’t ever run into any locking issues. And yet, we keep getting deadlocks.
What’s going on here? Hint: it has to do with isolation levels and range locks.
The MERGE statement is a very powerful way to combine INSERT, UPDATE and/or DELETE in a single statement. But there’s more than meets the eye. There are situations where you could use a MERGE statement to perform just one of those tasks.
A cross-UPDATE is an UPDATE operation that includes one or more other tables, using a JOIN. This is very useful in order to update one table with a value derived from another table.
This installment in the series on efficient data is on versioning changes in a table. The article is a re-post of a post I wrote in september on compressing slowly changing dimensions, although the concept does not only apply to dimensions – it can be used pretty much on any data that changes over time.
The idea is to “compress” a versioned table, so instead of just adding a date column for each version, you can compress multiple, sequential versions into a single row with a “from” date and a “to” date. This can significantly compress the size of the table.
In this second installment of the Slowly Changing Dimensions series (see part one here), we’ll take a look at how to practically create a slowly changing dimension table using T-SQL.
The MERGE statement is a really powerful way to create what’s called “upserts”. In this article, I’ll take a more detailed look at how you can make the best use of MERGE and I’ll also show you some cool tricks I picked up along the way.
As of SQL Server 2008, there’s a new powerful consolidation statement in the DML toolbox: MERGE. Using MERGE, you can perform so-called “upserts”, i.e. one statement that performs an insert, delete and/or update in a single statement. And, more importantly, with just a single join.
The OUTPUT clause allows you to combine DML statements with a kind of SELECT statement on the rows affected by the DML operation. This is a powerful way to visualize what records were touched by your statement, or an easy way to build an auditing mechanism.
A lot of times, T-SQL queries aren’t just for compiling huge sets of aggregate calculations or advanced joins and indexing strategies. Sometimes, you need the skills and tools to do some basic string parsing. Here, you’ll learn the most common basic string parsing and functions found in T-SQL.