A new model and architecture for data stream management. Aurora. Data Stream Management. Why on earth would one need it?. The Problem: Tokyo Traffic Control . Stream Processing for Traffic Control. 24-hour real-time control 1.000 traffic intersections 15.154 traffic signals Input CamerasBy irish
A new model and architecture for data stream management. Aurora. Sample Problem. Inspiration & Domain. Stream Processing Engines. HADP vs DAHP Events & Triggers Continuous Queries Real-time processing Transient data Lossy information. Application Domains. Online AuctionsBy daire
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Dynamic Query Optimization. Problems with static optimization. Cost function instability: cardinality error of n-way join grows exponentially with n Unknown run-time bindings for host variables Changing environment parameters: amount of available space, concurrency rate, etc.
sname. rating > 5. bid=100 . (Simple Nested Loops). sid=sid. Sailors. Reserves. Query Optimization. Goal:. Declarative SQL query. Imperative query execution plan:. SELECT S.sname FROM Reserves R, Sailors S WHERE R.sid=S.sid AND R.bid=100 AND S.rating>5.
Query Optimization. May 31st, 2002. Today. A few last transformations Size estimation Join ordering Summary of optimization. Rewrites: Group By and Join. Schema: Product ( pid , unitprice,…) Sales(tid, date, store, pid , units) Trees:. Join. groupBy(pid) Sum(units). groupBy(pid)
Query Optimization. Query Optimization Process (simplified a bit). Parse the SQL query into a logical tree: identify distinct blocks (corresponding to nested sub-queries or views). Query rewrite phase: apply algebraic transformations to yield a cheaper plan.
Query Optimization. Query Processing. Activities involved in retrieving data from the database. Aims of QP: transform query written in high-level language (e.g. SQL), into correct and efficient execution strategy expressed in low-level language (implementing RA);
Query Optimization. Allison Griffin. Importance of Optimization. Time is money Queries are faster Helps everyone who uses the server Solution to speed lies in the algorithm Different performance improvements with different database engines and schemas. Brief History.
Query Optimization. Ex.: SELECT DISTINCT Orders.Customer FROM Orders, Contains WHERE Orders.O_No = Contains.O_No AND Contains.Product = 'Brie' Assumptions: 100,000 tuples in Orders, 1000 bytes each
Query Optimization. Vishy Poosala Bell Labs. Outline. Introduction Necessary Details Cost Estimation Result Size Estimation Standard approach for query optimization Other ways Related Concepts.
Query Optimization. Very Big Picture. A query execution plan is a program. There are many of them. The optimizer is trying to chose a good one. Hence, the optimizer is reasoning about programs. Key: cost model, search space. Compilers don’t have cost models. Why?.