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Data Podcast
Rajib Bahar, Shabnam Khan
7 episodes
4 months ago
Joe Sack is a Principal Program Manager in the Azure SQL Database and SQL Server product team at Microsoft, with a focus on the Query Processor. Joe is an author and speaker with over 20 years of experience in the industry, specializing in performance tuning, high availability and disaster recovery. Interviewer: Rajib Bahar, Shabnam Khan Agenda: RB - Your team created Adaptive Query Processing or QP. It is new in SQL Server 2017 and SQL Azure. As we know, SQL Server uses query plan internally to run tsql statements. Sometimes the plan chosen by the query optimizer is not optimal for reasons such as incorrect cardinal estimate and various other issues. What are some other pain points Adaptive QP is meant to cure? SB - Adapative QP's strength lies behind Batch mode memory grant feedback, Batch mode adaptive joins, Interleaved execution... How do they work internally? RB - What are steps to enabling QP and some best practices? Can you tell us what's in the pipe line for upcoming enhancement? SB - How do we connect with you on Social Media? Music: www.freesfx.co.uk
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Technology
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Joe Sack is a Principal Program Manager in the Azure SQL Database and SQL Server product team at Microsoft, with a focus on the Query Processor. Joe is an author and speaker with over 20 years of experience in the industry, specializing in performance tuning, high availability and disaster recovery. Interviewer: Rajib Bahar, Shabnam Khan Agenda: RB - Your team created Adaptive Query Processing or QP. It is new in SQL Server 2017 and SQL Azure. As we know, SQL Server uses query plan internally to run tsql statements. Sometimes the plan chosen by the query optimizer is not optimal for reasons such as incorrect cardinal estimate and various other issues. What are some other pain points Adaptive QP is meant to cure? SB - Adapative QP's strength lies behind Batch mode memory grant feedback, Batch mode adaptive joins, Interleaved execution... How do they work internally? RB - What are steps to enabling QP and some best practices? Can you tell us what's in the pipe line for upcoming enhancement? SB - How do we connect with you on Social Media? Music: www.freesfx.co.uk
Show more...
Technology
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Joe Sack (@JoeSackMSFT) - Adaptive Query Processing in SQL Server 2017 Engine
Data Podcast
21 minutes 23 seconds
7 years ago
Joe Sack (@JoeSackMSFT) - Adaptive Query Processing in SQL Server 2017 Engine
Joe Sack is a Principal Program Manager in the Azure SQL Database and SQL Server product team at Microsoft, with a focus on the Query Processor. Joe is an author and speaker with over 20 years of experience in the industry, specializing in performance tuning, high availability and disaster recovery. Interviewer: Rajib Bahar, Shabnam Khan Agenda: RB - Your team created Adaptive Query Processing or QP. It is new in SQL Server 2017 and SQL Azure. As we know, SQL Server uses query plan internally to run tsql statements. Sometimes the plan chosen by the query optimizer is not optimal for reasons such as incorrect cardinal estimate and various other issues. What are some other pain points Adaptive QP is meant to cure? SB - Adapative QP's strength lies behind Batch mode memory grant feedback, Batch mode adaptive joins, Interleaved execution... How do they work internally? RB - What are steps to enabling QP and some best practices? Can you tell us what's in the pipe line for upcoming enhancement? SB - How do we connect with you on Social Media? Music: www.freesfx.co.uk
Data Podcast
Joe Sack is a Principal Program Manager in the Azure SQL Database and SQL Server product team at Microsoft, with a focus on the Query Processor. Joe is an author and speaker with over 20 years of experience in the industry, specializing in performance tuning, high availability and disaster recovery. Interviewer: Rajib Bahar, Shabnam Khan Agenda: RB - Your team created Adaptive Query Processing or QP. It is new in SQL Server 2017 and SQL Azure. As we know, SQL Server uses query plan internally to run tsql statements. Sometimes the plan chosen by the query optimizer is not optimal for reasons such as incorrect cardinal estimate and various other issues. What are some other pain points Adaptive QP is meant to cure? SB - Adapative QP's strength lies behind Batch mode memory grant feedback, Batch mode adaptive joins, Interleaved execution... How do they work internally? RB - What are steps to enabling QP and some best practices? Can you tell us what's in the pipe line for upcoming enhancement? SB - How do we connect with you on Social Media? Music: www.freesfx.co.uk