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Sergio Govoni

A place where share my experience on SQL Server and related technologies

SQL Server 2016 Query Store: How to fix problems you face for slow running queries

This article allows us to focus on a new feature of SQL Server 2016 known as Query Store. We will talk about performance issues related to the query plan choice change and how the Query Store can help us to identify queries that have become slower.



Have you ever experienced to have your system slowed down or completely down? What happens? Everyone is waiting for you to fix the problem as soon as possible! The Boss is over your desk! Have you ever experienced to upgrade an application to the latest version of SQL Server and facing an issue with plan change that slows your application down? Query plan choice change can cause these problems. A performance problem related to the system database tempdb is not as hard to fix as a problem related to a query plan changes. You know, query plan changes give you more and more problems. As far as the tempdb is concerned, you can move it into a faster hard-drive or you can increase the number of the tempdb files, but when you have to find the slow running queries you have to figure out why they are slow!

Detecting and fixing problems you face for slow running queries takes you long because you have to look into the plan change as well as the lock occur and a question grow up in your mind: What was the former plan like? So you try to find out an answer for this question, but, has the Data Collector been activated on the server? If the Data Collector hasn’t been activated the only tool you have to investigate slow running queries is the plan cache, but it may not be suitable for troubleshooting. When memory pressure occurs on the server, the queries you are finding could be already gone away from the cache. Finally, when you have the issue on your hands, can you modify the query text? If you cannot, do you know the system stored procedures to create and manage the Plan Giude?

Supposing you are given a query with two predicates on it, one predicate has the highest selectivity, which plan is the best? Supposing you are given a query with two joint tables, for example you have the table A Joined to the table B. Which is the best way to implement the Join? A Joined to B or the opposite? Imagine now a query that has 80 joint tables. Each color in the following picture represents an execution plan generated and evaluated by the Query Optimizer for the same query. In practice we have hundreds or thousands possible plans for a query with medium complexity.

Picture 1 – Execution Plans (Projects PICASSO:

SQL Server Query Optimizer considers many plans, but when your data changes, it might select a different plan. Usually when it crosses a boundary, performance is approximately the same, but sometimes, the actual performance is visibly different.


What does the query store do for you?

The Query Store stores all the plan choices and related performance metrics for each query, it identifies queries that have become slower recently and it allows DBAs to force an execution plan easily! If you force an execution plan for a particular query, it makes sure your changes work across server restart, upgrades, failover and query recompiles.


How the Query Store captures data?

Every time SQL Server compiles a query, a compile message comes into the Query Store and the execution plan of the query is stored. In the same way, when a query is executed, an execute message comes into the Query Store, the runtime statistics are stored into the Query Store after the query has been executed. Query Store aggregates the information according to the time granularity you have chosen, the aggregation will be done in memory (because we don't want to kill the server) and then, based on the database option DATA_FLUSH_INTERNAL_SECONDS, aggregated data will be stored on disk in a background asynchronous way like the checkpoint mechanism.



Picture 2 – How the Query Store captures data

I have just told you that the aggregation is done in memory and not on the disk, so suppose an unexpected shutdown occurs, how many in memory data would be lost? If you keep a smaller number of data in memory, you will have bigger IO cost and a smaller number of information will be lost in case of an unexpected shutdown, otherwise, if you keep a bigger number of data in memory, you will lose a larger number of data in case of an unexpected shutdown, but you will have smaller IO cost. This setting is your choice! The important thing is that you have the possibility to choice! If you want to see data captured by the Query Store, you need a tool that combines both In-Memory and On-Disk statistics. Each DMV related to the Query Store, joined In-Memory and On-Disk data. For example, the sys.query_store_runtime_stats table valued function groups In-Memory and On-Disk data in a unified place, so you can use it your scripts or in your application.

Bear in mind, when memory pressure occurs on the server, some data In-Memory will be flushed to the disk in order to release the memory for others.. read the complete article here..


Additional resources about SQL Server Query Store are available here on

Enjoy the Query Store!

Published Wednesday, March 30, 2016 11:45 PM by Sergio Govoni

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