1 / 7

Status

Status. “Lifetime of a Query” Query Rewrite Query Optimization Query Execution Optimization Use cost-estimation to iterate over all possible plans, pick one of minimum cost Saw how to cost 1 relation ops Saw how to cost joins Saw that join ordering is complex

juancarlos
Télécharger la présentation

Status

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Status • “Lifetime of a Query” • Query Rewrite • Query Optimization • Query Execution • Optimization • Use cost-estimation to iterate over all possible plans, pick one of minimum cost • Saw how to cost 1 relation ops • Saw how to cost joins • Saw that join ordering is complex • Inner vs. outer (e.g., AB ≠ BA) • Join ordering (e.g., A(BC) ≠ (AB)C) • Join type (e.g., nested loops vs. sort-merge) • We will return to Shapiro at the end of class O(2n-1)! Plans; n = 7 -> > 6 Billion

  2. Selinger Pruning • How does Selinger reduce the search space? • Only considers left-deep plans • Pushes all cross products to the top • Uses a dynamic programming algorithm

  3. (Basic) Selinger Dynamic Prog Alg. findBestPlan(JoinList S) if (bestPlan[S].cost ≠ ∞) ; array lookup ; independent of ; ordering of S return bestPlan[S] if (|S| = 1) bestPlan[S].plan = scan S bestPlan[S].cost = cost(scan S) return bestPlan[S] for each size 1 non-empty subset S1 of S P1 = findBestPlan(S1) P2 = findBestPlan(S - S1) A = best algorithm for joining P1, P2 ;inner v outer? cost = P1.cost + P2.cost + cost(A) if (cost < bestPlan[S].cost) bestPlan[S].plan ={execute P1.plan, execute P2.plan, join P1 and P2 using A } bestPlan[S].cost = cost return bestPlan[S]

  4. Merge-Sort Phase 1: Repeat until S is done Read a run of S Sort Write out (Repeat with R) Phase 2: Read concurrently from each run of S and R Merge runs, then join overlapping regions

  5. Simple Hash i=0 Choose partition of hash range {vi, vi+1} Scan S, hash, if in partition, insert into hash table Otherwise, write back out Scan R, hash, probe into hash table, output matches Otherwise, write back out Repeat with reduced R and S, in round i+1

  6. GRACE Hash Choose sqrt(|R|) partitions, with one page memory per partition Hash R into partitions, flushing pages as they fill Hash S into partitions, flushing pages as they fill For each partition p Build a hash table H on R tuples in p Hash S tuples in p into H, output matches

  7. Comparison GRACE Choose sqrt(|R|) partitions, with one page memory per partition Hash R into partitions, flushing pages as they fill Hash S into partitions, flushing pages as they fill For each partition p Build a hash table H on R tuples in p Hash S tuples in p into H, output matches Sort-Merge Phase 1: Repeat until S is done Read a run of S Sort Write out (Repeat with R) Phase 2: Read concurrently from each run of S and R Merge runs, then join overlapping regions Simple i=0 Choose partition of hash range {vi, vi+1} Scan S, hash, if in partition, insert into hash table Otherwise, write back out Scan R, hash, probe into hash table, output matches Otherwise, write back out Repeat with reduced R and S, in round i+1

More Related