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Category: Performance Tuning

Measuring the Performance Impact of TDE

Manvendra Singh does the math:

Transparent Data Encryption (TDE) encrypts database files to secure your data. It also encrypts the tempdb database to secure your data in a temporary space. The process of encryption and decryption adds additional overhead to the database system. Even non-encrypted databases hosted on the same SQL Server instance would have some performance degradation because of tempdb encryption. Today I will show you performance impact analysis using few simple T-SQL statements by comparing their stats gathered before and after enabling TDE.

I will execute T-SQL statements (INSERT, UPDATE, SELECT, BACKUP DATABASE) before and after enabling encryption (Transparent Data Encryption) and gather their performance statistics during each execution. Finally, once we will have performance stats taken before and after enabling encryption then we will compare them to understand the performance impact analysis. I have used DBCC DROPCLEANBUFFERS before executing each query to clean the buffer cache.

Click through for the results. My rule of thumb is about a 5% performance overhead, but Manvendra shows us some of the particulars of what that means.

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Star Schemas versus Header-Detail Tables in Power BI

Marco Russo and Alberto Ferrari lay out another proof that the star schema is the right schema for Power BI:

We already shown in a previous article (Power BI – Star schema or single table – SQLBI) how the star schema proves to be the best option when compared with a single table model. Single-table models are the evil: do not be tempted by them, choose a star schema.

In this article, I want to show you an example in the opposite direction. A single table model denormalizes everything in one table, and we already learned that it is bad. But what if we keep a more normalized structure, as it often happens in header/detail models (like orders and order lines)? Is a header/detail model better than a star schema? The quick answer is: “No. Nope. No way. Not at all. Are you kidding me? No.”. Nonetheless, this might be just our personal opinion. The goal of the article is to provide you with some numbers and considerations to prove the previous statement.

Read on and you make the call.

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Measuring File Latency in SQL Server

Anthony Nocentino has a script and some tips for us:

This post is a reference post for retrieving IO statistics for data and log files in SQL Server. We’ll look at where we can find IO statistics in SQL Server, query it to produce meaningful metrics, and discuss some key points when interpreting this data.

Click through for the script, and then a bulleted list of things to keep in mind as you’re reviewing the data.

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Against Reporting Tables

Erik Darling doesn’t like reporting tables:

I’ve seen a lot of crazy ways for people to run reports on live data, but one of the worst ideas is creating a reporting table based on the set of data a user wants, and then letting them query that data.

As usual, Erik says something I want to disagree with, and then I read the post and don’t really disagree with him—or if I do, he’s already laid out the “Yes, I understand X” exception. I’ve used reporting tables to good effect, but the important thing is that they’re general-purpose and designed into the application, not specific to a single user.

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Power BI Data Models with Multiple Aggregation Tables

Phil Seamark continues a series on Power BI aggregations:

Why might you consider having more than one aggregation table? The short answer is speed and overall resource efficiency.

Consider a heavily used Power BI report where page-load time is considered critical. A typical report may have half a dozen visuals on a page showing values computed over various grains. If the model used by the report has no aggregation tables, all calculations use the raw fact tables to produce values for each metric.

Adding an aggregation table to the model allows the same calculations as before to use smaller tables to produce the same result. Calculations using smaller aggregation tables will enable the server hosting the data model to use much less effort per query.

It was interesting to see just how easy the process is.

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Deciding on Forced Parameterization or Optimize for Ad Hoc Workloads

Erik Darling hosts a showdown:

I often speak with people who are confused about what these settings do, and which one they should be using to fix certain problems.

The first myth is that Optimize For Ad Hoc Workloads has some special effect on queries run outside of stored procedures: it does not. It’s very poorly named in that regard. There are no special optimizations applied because of that setting.

Read the whole thing.

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Improving Dataset Refresh with Query Folding and the Dataflows Connector

Chris Webb has a performance tip for us:

You may have noticed that a new dataflows connector was announced in the August 2021 release of Power BI Desktop, and that it now supports query folding between a dataset and a dataflow – which you may be surprised to learn was not possible before. In this post I thought I’d take a look at how much of an improvement in performance this can make to dataset refresh performance.

Click through for the demonstration.

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The Cost of Measures in Power BI Live Connection Reports

Chris Webb explains the cost side of the ledger when it comes to measure creation:

You probably know that it’s a best practice to build your Power BI datasets in a separate .pbix file from your reports – among other things it means that different people can develop the dataset and reports. You may also know that if you are building a report in Power BI Desktop with a Live connection to a published dataset or Azure Analysis Services you can define your own measures inside the report. While this is very convenient, if you create too many measures there’s a price to pay in terms of query performance.

Click through for a demonstration of this.

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