SQL Server 2017 (compatibility 140) brought us many Intelligent Query Processing (IQP), formally known as Adaptive Query Processing, features that improve performance on workloads straight out of the box with no code changes to implement. One of those features introduced was Adaptive Joins. In this feature the join operator is dynamically determined at runtime by the optimizer, which defines a threshold number of rows and then chooses between a Nested Loop or Hash Match join operator. This operator switch can produce a better join and improve performance of your queries without you having to lift a finger. Not all queries will qualify for this new feature. The feature only applies…
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Under compatibility level 150, in both SQL Server 2019 and Azure SQL Database, you now can use batch mode for CPU-bound analytic type workloads without requiring columnstore indexes. There is no action needed to turn on batch mode aside from being on the proper compatibility mode. You also have the ability to enable it as a database scoped configuration option (as shown below), and you can hint individual queries to either use or not use batch mode (also shown below). If you recall in my earlier blogs on columnstore, it is batch mode in conjunction with page compression that drastically increases query performance. This feature, Batch Mode on Rowstore, allows…
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My last two blogs were on understanding columnstore and creating columnstore indexes. In this post, I will touch on one last topic, when to use columnstore indexes. As noted in my prior posts these indexes are designed for large data warehouse workloads, not normal OLTP workload tables. As with any index design it is important to know your data and what you will be using the data for. Be sure to look to what types of queries you will be running against it, as well as how the data is loaded and maintained. Let’s look at a few questions you should ask yourself before deciding to implement a columnstore index.…
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In my last blog I explained what a columnstore index is, in this blog, we will dive into creating a clustered columnstore index and look at the performance differences the index can make. Let’s get started. Using AdventureworksDW2016CTP3 we will work with the FactResellerSalesXL table which has 11.6 million rows in it. The simple query we will use as a demo just selects the ProductKey and returns some aggregations grouping them by the different product keys. First, we will run the query with no existing columnstore index and only using the current clustered rowstore (normal) index. Note that I turned on SET STATISTICS IO and TIME on. These two SET…
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Recently I reviewed filtered indexes, this time let’s look at columnstore indexes. These indexes are very useful for data warehouse workloads and large tables. They can improve query performance by a factor of 10 in some cases, so knowing and understanding how they work is important if you work in an environment with larger scaled data. Now, I admit when these first were introduced in SQL Server 2012 I found them very intimidating (additionally, you couldn’t update them directly). For me, anytime you say columnstore, my mind tends to set off alarms saying wait stay away, this is too complicated. So, in this post I am going to try and…
