I spoke on SQL Server User Group Sweden

SQL Server User Group (SQLUG) Sweden had a meeting on October 24th. This one felt a bit special to me, as it was kind of a replay of my first time public speaking, in two ways. My first time speaking in public about SQL Server was on SQLUG Sweden, a few years ago. SQLUG Sweden arranged a Local Community Edition, with local speakers. That first time, it was me and Daniel Hutmacher (Twitter , Blog) speaking. And on October 24th, it was, again, me and Daniel Hutmacher speaking.

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OPTION(USE HINT) – New SQL Server 2016 SP1 feature

Many SQL Server developers and admins found, after upgrading to SQL Server 2014, that some queries started taking much longer time than before. The reason is the new cardinality estimation formula which was introduced in SQL Server 2014. Cardinality Estimation is done all the time by the SQL Server optimizer. To produce a Query plan, the optimizer makes some assumptions about how many rows exist for each condition in the table. In most cases, the new cardinality estimation formula in SQL Server 2014 and onwards gives slightly better estimates and the optimizer therefore produces slightly better plans. In some cases however, mostly when there are predicates on more than one column in a WHERE clause or JOIN clause, the 2014 cardinality estimation is a lot worse than in previous versions of SQL Server.

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Statistics on ascending columns

I have had an annoying problem for a while. In a database used for a statistical survey system reporting is painfully slow in the beginning of each reporting period.

The system
The tables contain a few million rows. Ola Hallengren’s index maintenance (which includes UPDATE STATISTICS) is running weekly. Each month is a new reporting period. When a new reporting period opens, there are no rows for the current period. From the first day of the month, we receive input, each input being less than 2000 new rows in the table.

The problem
Reporting of any previous period is always consistent in execution time – around 3 seconds to produce a full report. That’s an OK performance. But when reporting is done for current period early in a reporting period, execution takes up to 10 minutes.

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DELETE and Non Clustered Indexes

I’ll start off with a disclaimer: I’m going to tell you about something that happened in a specific system Environment. There’s no such thing as  a general advice you can build on this specific scenario. I’m just posting it because I was myself surprised by what order of magnitude I was able to speed up a specific query by slightly removing some of the work in the execution plan.

The other day I helped troubleshooting a database system. In a table with some 400 million records, a subset (50-60 million records) were to be deleted. The application kept timing out on this delete operation so I adviced the developer to split the delete operation into smaller chunks. I even helped writing a T-SQL script to perform the delete in one million row chunks. The script was pretty basic – a WHILE-loop which checked if any rows fulfilling the WHERE-condition of the delete was left in the table, and inside the loop a DELETE TOP(one million) followed by an explicit checkpoint.

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