In this post, we will continue to look at the cardinality estimation changes in SQL Server 2016. This time we will talk about scalar UDF estimation. Scalar UDFs (sUDF) in SQL Server have quite bad performance and I encourage you try to avoid them in general, however, a lot of systems still use them.
In this post we continue looking at the Cardinality Estimator (CE). The article explores some join estimation algorithms in the details, however this is not a comprehensive join estimation analysis, the goal of this article is to give a reader a flavor of join estimation in SQL Server.
Yesterday I came across a question on one of SQL forums, that I may rephrase like:
“Does a query plan compilation depend on how busy is SQL Server”.
Before we go further, I should explicitly mention that we talk about a Compiled plan, not an Executable plan. Plan execution will of course depend on how busy is server, for example, the query may wait for the memory grant to start execution, or execution may be slow because there are no cached pages in the Buffer Pool etc.
However, the question was about a Compiled plan: does the shape of a plan depend on the server load.
From the first glance it should not. But…
Sometimes, when I saw expressions like ‘Expr1002’ or ‘WindowCount1007’ or something similar in the columns Output List of a query plan, I asked myself, is there a way to project those columns in to the final result to look at the values. That question first came to me out of curiosity when I was playing with window aggregate functions and a Window Spool plan operator in SQL Server 2012, I wanted to look into the Window Spool to understand, how it performs an aggregation.
Interestingly, that SQL Server 2016 CTP3.0 allows us to look deep inside into the iterator and observe the data flowing through it. Let’s turn on an “xRay machine” and take a look.
Most of the people knows about the so-called “Parameter Sniffing”. This topic was discussed in many aspects in a number of great articles. It is interesting that not only parameters might be “sniffed” during the first execution, but also a runtime constant functions. Let’s look at the example.
In this blog post, we will look at one more Nested Loops (NL) Join Post Optimization Rewrite. This time we will talk about parallel NL and Few Outer Rows Optimization.
Continuing my blog post series after 24HOP Russia “Query Processor Internals – Joins”.
In this (and the next one) blog post, we will talk about the Nested Loops Post Optimization Rewrite optimizations.
Two days ago, on the 24HOP Russia I was talking about the Query Processor internals and joins. Despite I had three hours, I felt the lack of time, and something left behind, because it is a huge topic, if you try to cover it in different aspects in details. With the few next articles, I’ll try to describe some interesting parts of my talk in more details. I will start with Hash Join execution internals.
This post is for the absolutely geeky people. For those that will not afraid when they hear: “query transformation rules”.
I would like to share one curios case that I recently came across.
Long story short:
This bug may lead to incorrect results if you use a partitioned table and the FORCESCAN hint.