Using Extended Events to Capture Implicit Conversions

SQL Server, SQL Server 2016, SQL Server 2017, T-SQL
Using the appropriate data type to avoid conversions or implicit conversions is a fundamental approach to good T-SQL coding practices. Implicit conversions can lead to poor performance. However, other than looking at every execution plan in your system, is there a way to see if you are experiencing implicit conversions? Actually, yeah, it's right there in Extended Events. plan_affecting_convert Built right into the Extended Events is an event that captures conversions that would affect execution plans, plan_affecting_convert. This event will show both CONVERT and CONVERT_IMPLICIT warnings that you would normally only see within an execution plan. You can capture this event with others. Capturing events together along with causality tracking makes it very easy to track queries that have the issue. Here's one example of how you might capture implicit…
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Query Store and Log Backups

T-SQL
A question that came up recently around Query Store is what happens when there are log backups in use on the database. Let's talk about it. Query Store and Log Backups The core of the answer is very simple. Query Store, like any other data written to a database, whether a system table or a user table, is a logged operation. So, when you backup the database, you're backing up Query Store data. When you backup the logs, you're also backing up Query Store data. A point in time will include all the data written to the Query Store at that point. However, that's the kicker. At what point was the Query Store information written to disk? By default, there's a fifteen minute cycle before the Query Store moves the…
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Estimated Plans and Forced Plans from Query Store

SQL Server 2016, SQL Server 2017, T-SQL
While all plans are estimated plans, there is still a difference between capturing an estimated plan and looking at a plan from the cache or from query store. Or is there? A question came up during a recent presentation; what happens to capturing an estimated plan when you're forcing plans? Let's find out. The answer is interesting. Estimated Plans Here's my stored procedure that I'll be using with AdventureWorks2017: [crayon-5bc6400a8c866290624382/] For reasons I'll explain in a bit, I'm going to free the procedure cache: [crayon-5bc6400a8c871553418114/] Then, if I capture an estimated plan for two different values: [crayon-5bc6400a8c877608791360/] I end up with two different execution plans: Click to embiggen This is because the different values have different data distribution within my statistics and parameter sniffing leads to difference in the plans.…
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Actual Execution Plan Costs

T-SQL
Why don't "actual execution plans" have "actual execution plan costs"? This is a question and a myth I have to fight against all the time. It's so hard to convince people that all execution plans are estimated plans in the first place (by the way, all execution plans are estimated plans). If we execute a query at the same time we capture a plan, we have enabled SQL Server to also capture run-time metrics with that plan. So we end up with what is known as an actual plan, but it's still just an estimated plan plus those run-time metrics. Execution Plan Costs When you look at a given operator within an estimated plan, it's going to show you four numbers related to cost: Estimated CPU Cost Estimated I/O Cost…
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Measuring Query Execution Time: What Is Most Accurate

SQL Server, SQL Server 2016, SQL Server 2017, T-SQL, Tools
Probably the single most important factor when deciding which query to tune, or actively tuning a query, is how you go about measuring query execution time. SQL Server provides a number of different mechanisms (really, maybe too many) to get this done. However, all measures are not created equally. In fact, they frequently disagree with one another. Let's take a look at this odd phenomenon. Measuring Query Execution Time Before we get into all the choices and compare them, let's baseline on methodology and a query to use. Not sure why, but many people give me blow back when I say "on average, this query runs in X amount of time." The feedback goes "You can't say that. What if it was just blocking or resources or..." I get it.…
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What Is The Preferred Join Operator in SQL Server?

SQL Server, SQL Server 2016, SQL Server 2017, T-SQL
I schedule many of my blog posts at least 2-3 weeks out, so I have time to adjust them, change, them, insert new things into the schedule, what have you. So, as you read this, I'll have written it at least two weeks ago. As I write this, I'm recovering from a very mild, but irritating, cold. I needed to write a blog post, but my stuffed head wasn't coming up with ideas. So, I half-jokingly asked Twitter for any execution plan questions so I could write something. First response, also a joke, was the question at the title of this post: What is the preferred operator when joining tables: Hash Match, Nested Loops or Merge? While my immediate response to this question is, yes. Meaning, they're all preferred, situationally.…
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Plan Metrics Without the Plan: Trace Flag 7412

SQL Server, SQL Server 2016, SQL Server 2017, T-SQL
I place a lot of emphasis on capturing actual execution plans because of the runtime metrics, but with Trace Flag 7412, we don't need the plan. This is great news, because capturing execution plans, even using extended events, is an expensive proposition. However, using either the query_thread_profile event, or, Trace Flag 7412, we can get the runtime metrics without the plan. Trace Flag 7412 Here's how it works.You can either be running the extended event, query_thread_profile (a debug event, but one documented and supported by Microsoft) or, enable the Trace Flag 7412. I like to use the extended event in a targeted fashion to easily see behaviors on a query without having to capture the plan. You can even capture the information and then combine it with an estimated plan…
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Extended Events, the system_health Session, and Waits

SQL Server, SQL Server 2016, SQL Server 2017, T-SQL
I advocate for, use, document, teach, and just downright love, Extended Events. They are so much better than the old Trace Events (aka, Profiler) that it's sometimes difficult to keep from just gushing. Let's talk about a common situation that you're going to run into on your servers all the time and how you can put Extended Events to work to help you, without actually doing any work at all. What's that? Be lazy and get rewards? Yes. The Extended Events system_health Session On your servers, any of them that are SQL Server 2008 or newer, right now, unless you've performed actions to prevent this, you're running the Extended Events system_health session. It's just happening, currently, on all your servers. Nothing you need to do about it at all. I'll…
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Query To Retrieve Statistics Data: dm_db_stats_histogram

SQL Server 2017, T-SQL
Starting with SQL Server 2016 Sp1 CU2, a new way of directly querying statistics, specifically the histogram, has been introduced: dm_db_stats_histogram. We've always been able to query the statistics using DBCC SHOW_STATISTICS. However, the output, three different result sets with three different structures, made automating access to statistics information a pain. Now, we have more capability through dm_db_stats_histogram. dm_db_stats_histogram To access the information in dm_db_stats_histogram, you just have to pass in the object_id and the statistics_id values for the statistics you're interested in like this: [crayon-5bc6400a973b7232690213/] It's very straight forward to use. The results look like this: Handy right? Now you can query the histogram directly. Yeah, I hear a few of you saying... and this helps me... how? Here's an example. This query will quickly find the rang_hi_key value…
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Adaptive Joins

T-SQL
I was surprised to find out that a lot people hadn't heard about the new join type, Adaptive join. So, I figured I could do a quick overview. Adaptive Join Behavior Currently the adaptive join only works with columnstore indexes, but according to Microsoft, at some point, they will also work with rowstore. The concept is simple. For larger data sets, frequently (but not always, let's not try to cover every possible caveat, it depends, right), a hash join is much faster than a loops join. For smaller data sets, frequently, a loops join is faster. Wouldn't it be nice if we could change the join type, on the fly, so that the most effective join was used depending on the data in the query. Ta-da, enter the adaptive join.…
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