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Contenido proporcionado por Machine Learning Archives - Software Engineering Daily. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Machine Learning Archives - Software Engineering Daily o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.
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Automatic Database Tuning with Andy Pavlo

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Manage episode 343707392 series 1433944
Contenido proporcionado por Machine Learning Archives - Software Engineering Daily. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Machine Learning Archives - Software Engineering Daily o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

The default configuration in most databases is meant for broad compatibility rather than performance. Database tuning is a process in which the configurations of a database are modified to achieve optimal performance. Databases have hundreds of configuration knobs that control various factors, such as the amount of memory to use for caches or how often the data is written to the storage.

The problem with these knobs is that

  • they are not standardized (i.e., two databases may have a different name for the same knob),
  • not independent (i.e., changing one knob can impact others),
  • and not universal (i.e., what works for one application may be suboptimal for another).

In reality, information about the effects of the knobs typically comes only from (expensive) experience.

OtterTune is automatic database tuning software that promises to overcome these problems. It uses machine learning to tune the configuration knobs of your database automatically to improve performance.

In this episode, we interview Andy Pavlo. Andy is a Database Professor at Carnegie Mellon and Co-Founder of OtterTune.

Sponsorship inquiries: sponsor@softwareengineeringdaily.com

The post Automatic Database Tuning with Andy Pavlo appeared first on Software Engineering Daily.

  continue reading

176 episodios

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iconCompartir
 
Manage episode 343707392 series 1433944
Contenido proporcionado por Machine Learning Archives - Software Engineering Daily. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Machine Learning Archives - Software Engineering Daily o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

The default configuration in most databases is meant for broad compatibility rather than performance. Database tuning is a process in which the configurations of a database are modified to achieve optimal performance. Databases have hundreds of configuration knobs that control various factors, such as the amount of memory to use for caches or how often the data is written to the storage.

The problem with these knobs is that

  • they are not standardized (i.e., two databases may have a different name for the same knob),
  • not independent (i.e., changing one knob can impact others),
  • and not universal (i.e., what works for one application may be suboptimal for another).

In reality, information about the effects of the knobs typically comes only from (expensive) experience.

OtterTune is automatic database tuning software that promises to overcome these problems. It uses machine learning to tune the configuration knobs of your database automatically to improve performance.

In this episode, we interview Andy Pavlo. Andy is a Database Professor at Carnegie Mellon and Co-Founder of OtterTune.

Sponsorship inquiries: sponsor@softwareengineeringdaily.com

The post Automatic Database Tuning with Andy Pavlo appeared first on Software Engineering Daily.

  continue reading

176 episodios

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