Microsoft PhD Summit 2019: Bailu Ding [Short Talk]

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Microsoft Research
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Published on ● Video Link: https://www.youtube.com/watch?v=9Xdq-QB2grg


Duration: 4:07
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Selecting an appropriate set of indexes to build in a database for a given workload can result in significant reductions in query execution cost, e.g., CPU time. Being able to fully automate index recommendation and implementation is a significant value-add for a cloud database service provider. One key requirement of automated index implementation for production systems is that creating or dropping indexes does not cause significant query performance regressions. Such regressions, where a query’s execution cost increases after changing the indexes, is a major impediment to fully-automated indexing as users desire to enforce a no query regression constraint.

In this talk, Bailu Ding gives an overview of the architecture of auto-indexing. She then covers two research directions in this area from her previous work: Plan Forcing for regression recovery and AI Meets AI for regression prevention.



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Tags:
Automatically Indexing Databases
indexing databases
databases
indexes
query execution
query execution cost
CPU time
cloud database service
Bailu Ding
Microsoft Research
PhD Summit