Introduction to Query OptimizationOverview of Query OptimizationHighlights of System R OptimizerSchema for ExamplesMotivating ExampleAlternative Plans 1 (No Indexes)Alternative Plans 2 With IndexesCost EstimationSummaryIntroduction to Query Optimization, R. Ramakrishnan and J. Gehrke1Introduction to Query OptimizationChapter 13Introduction to Query Optimization, R. Ramakrishnan and J. Gehrke2Overview of Query Optimization•Plan: Tree of R.A. ops, with choice of alg for each op.•Each operator typically implemented using a `pull’ interface: when an operator is `pulled’ for the next output tuples, it `pulls’ on its inputs and computes them.•Two main issues:•For a given query, what plans are considered?•Algorithm to search plan space for cheapest (estimated) plan.•How is the cost of a plan estimated?•Ideally: Want to find best plan. Practically: Avoid worst plans!•We will study the System R approach.Introduction to Query Optimization, R. Ramakrishnan and J. Gehrke3Highlights of System R Optimizer•Impact:•Most widely used currently; works well for < 10 joins.•Cost estimation: Approximate art at best.•Statistics, maintained in system catalogs, used to estimate cost of operations and result sizes.•Considers combination of CPU and I/O costs.•Plan Space: Too large, must be pruned.•Only the space of left-deep plans is considered.•Left-deep plans allow output of each operator to be pipelined into the next operator without storing it in a temporary relation.•Cartesian products avoided.Introduction to Query Optimization, R. Ramakrishnan and J. Gehrke4Schema for Examples•Similar to old schema; rname added for variations.•Reserves:•Each tuple is 40 bytes long, 100 tuples per page, 1000 pages.•Sailors:•Each tuple is 50 bytes long, 80 tuples per page, 500 pages. Sailors (sid: integer, sname: string, rating: integer, age: real)Reserves (sid: integer, bid: integer, day: dates, rname: string)Introduction to Query Optimization, R. Ramakrishnan and J. Gehrke5Motivating Example•Cost: 500+500*1000 I/Os•By no means the worst plan! •Misses several opportunities: selections could have been `pushed’ earlier, no use is made of any available indexes, etc.•Goal of optimization: To find more efficient plans that compute the same answer. SELECT S.snameFROM Reserves R, Sailors SWHERE R.sid=S.sid AND R.bid=100 AND S.rating>5ReservesSailorssid=sidbid=100 rating > 5snameReservesSailorssid=sidbid=100 rating > 5sname(Simple Nested Loops)(On-the-fly)(On-the-fly)RA Tree:Plan:Introduction to Query Optimization, R. Ramakrishnan and J. Gehrke6Alternative Plans 1 (No Indexes)•Main difference: push selects.•With 5 buffers, cost of plan:•Scan Reserves (1000) + write temp T1 (10 pages, if we have 100 boats, uniform distribution).•Scan Sailors (500) + write temp T2 (250 pages, if we have 10 ratings).•Sort T1 (2*2*10), sort T2 (2*3*250), merge (10+250)•Total: 3560 page I/Os.•If we used BNL join, join cost = 10+4*250, total cost = 2770.•If we `push’ projections, T1 has only sid, T2 only sid and sname:•T1 fits in 3 pages, cost of BNL drops to under 250 pages, total < 2000.ReservesSailorssid=sidbid=100 sname(On-the-fly)rating > 5(Scan;write to temp T1)(Scan;write totemp T2)(Sort-Merge Join)Introduction to Query Optimization, R. Ramakrishnan and J. Gehrke7Alternative Plans 2With Indexes•With clustered index on bid of Reserves, we get 100,000/100 = 1000 tuples on 1000/100 = 10 pages.•INL with pipelining (outer is not materialized). Decision not to push rating>5 before the join is based on availability of sid index on Sailors. Cost: Selection of Reserves tuples (10 I/Os); for each, must get matching Sailors tuple (1000*1.2); total 1210 I/Os. Join column sid is a key for Sailors.–At most one matching tuple, unclustered index on sid OK.–Projecting out unnecessary fields from outer doesn’t help.ReservesSailorssid=sidbid=100 sname(On-the-fly)rating > 5(Use hashindex; donot writeresult to temp)(Index Nested Loops,with pipelining )(On-the-fly)Introduction to Query Optimization, R. Ramakrishnan and J. Gehrke8Cost Estimation•For each plan considered, must estimate cost:•Must estimate cost of each operation in plan tree.•Depends on input cardinalities.•We’ve already discussed how to estimate the cost of operations (sequential scan, index scan, joins, etc.)•Must estimate size of result for each operation in tree!•Use information about the input relations.•For selections and joins, assume independence of predicates.•We’ll discuss the System R cost estimation approach.•Very inexact, but works ok in practice.•More sophisticated techniques known now.Introduction to Query Optimization, R. Ramakrishnan and J. Gehrke9Summary•Query optimization is an important task in a relational DBMS.•Must understand optimization in order to understand the performance impact of a given database design (relations, indexes) on a workload (set of queries).•Two parts to optimizing a query:•Consider a set of alternative plans.•Must prune search space; typically, left-deep plans only.•Must estimate cost of each plan that is considered.•Must estimate size of result and cost for each plan node.•Key issues: Statistics, indexes, operator