Jared Poche
Database Engineer - ChannelAdvisor
Cary, North Carolina, United States
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Jared Poche began working with SQL Server as an instructor for certification classes and has a passion for teaching and performance troubleshooting. Jared spent 10 years providing customer support at Microsoft, most recently as a Sr. Support Escalation Engineer.
Jared is currently working as a database architect for Incomm Payments, blogs about his recent experiences, and speaks regularly at SQL Server events.
Area of Expertise
Topics
Parameter Sensitive Plan Optimization and Query Store
Parameter Sensitive Plan optimization is a new feature in SQL Server 2022. This allows up to three plans to be created for a query susceptible to parameter sniffing and makes some changes to Query Store catalog views.
In this session, we’ll review the feature and the changes it brings to Query Store. We’ll look at examples of dispatcher plans and query variant plans, and review the changes needed to make your queries run correctly in SQL Server 2022.
Every Millisecond Counts
Query optimization is relatively easy when you look at a plan and find table scans, hash match joins, and missing indexes. But how do you find opportunities when all the low hanging fruit has been picked? This session is a case study on improving a procedure that runs 350 million times per day, so any improvement is greatly magnified. We will discuss where we found opportunities to improve a procedure running in 3.1 milliseconds; how effective our attempts were, what didn't work, and the results achieved.
Maximizing Performance with Memory-Optimized Table Variables
Table variables have a bad reputation, but memory-optimized table variables can help us optimize the performance of our SQL Server procedures in several scenarios.
In this session, I will discuss and provide examples where memory-optimized table variables are a huge win for our database and application. We will discuss batched stored procedures, tempdb contention, the Halloween problem, and an improvement from intelligent query processing. We will also discuss issues that can arise with memory optimized table variables and how best to mitigate them. This session should be especially helpful to engineers and administrators with high throughput OLTP systems.
One Bite at a Time: Deleting Millions of Rows from Production Systems
Many maintenance tasks operate piecemeal. Whether we are garbage collecting, archiving data, or anonymizing data, we'll want to use the TOP operator to keep our operations bite-sized and our performance smooth. Backfilling a column or generating reports from an OLTP database can operate quite similarly. But there are some mechanics we'll need to understand to get the most out of these processes. In this session, we'll talk about the TOP operator, row mode execution, and blocking operators. We'll have examples from public databases based on real world cases, and we'll talk about anonymizing 15 million records containing PII in 5 minutes.
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