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SkinnerDB uses reinforcement learning for reliable join ordering, exploiting an adaptive processing engine with specialized join algorithms and data structures. It maintains no data statistics and uses no cost or cardinality models. Also, it uses no training workloads nor does it try to link the current query to seemingly similar queries in the past. Instead, it uses reinforcement learning to learn optimal join orders from scratch during the execution of the current query. To that purpose, it divides the execution of a query into many small time slices. Different join orders are tried in different time slices. SkinnerDB merges result tuples generated according to different join orders until a complete query result is obtained. By measuring execution progress per time slice, it identifies promising join orders as execution proceeds. Along with SkinnerDB, we introduce a new quality criterion for query execution strategies. We upper-bound expected execution cost regret, i.e., the expected amount of execution cost wasted due to sub-optimal join order choices. SkinnerDB features multiple execution strategies that are optimized for that criterion. Some of them can be executed on top of existing database systems. For maximal performance, we introduce a customized execution engine, facilitating fast join order switching via specialized multi-way join algorithms and tuple representations. We experimentally compare SkinnerDB’s performance against various baselines, including MonetDB, Postgres, and adaptive processing methods. We consider various benchmarks, including the join order benchmark, TPC-H, and JCC-H, as well as benchmark variants with user-defined functions. Overall, the overheads of reliable join ordering are negligible compared to the performance impact of the occasional, catastrophic join order choice.
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Technical Perspective: Revisiting Runtime Dynamic Optimization for Join Queries in Big Data Management Systems
Query optimization is the process of finding an efficient query execution plan for a given SQL query. The runtime difference between a good and a bad plan can be tremendous. For example, in the case of TPC-H query 5, a query with 5 joins, the difference between the best and the worst plan is more than 10,000×. Therefore, it is vital to avoid bad plans. The dominating factor which differentiates a good from a bad plan is their join order and whether this join order avoids large intermediate results.
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- Award ID(s):
- 1901379
- PAR ID:
- 10430061
- Date Published:
- Journal Name:
- ACM SIGMOD Record
- Volume:
- 52
- Issue:
- 1
- ISSN:
- 0163-5808
- Page Range / eLocation ID:
- 103 to 103
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3604437.3604459
