PPT-Query Plan Analysis:

5 Culprits That Cause 95 of Your Performance Headaches Adam Machanic AdamMachanic Data Education DBIB489 Adam Machanic SQL Architect Boston MA Financial industry Speaker PASS TechEd. Dr. Bjarne Berg . 3. What We’ll Cover …. . Introduction. Performance Issues & Tips. MultiProviders and Partitioning. Aggregates. Query Design & Caching. Hardware & Servers. Designing for Performance. Outlier Detection. Ayushi Dalmia. *. , Manish Gupta. *+. , Vasudeva Varma. *. 1. IIIT Hyderabad, India* Microsoft, India. +. Introduction. A. B. B. B. B. A. B. B. B. A. C. C. C. X. 1. Group 4: . Nipun . Garg, Surabhi . Mithal. http://www-users.cs.umn.edu/~smithal/. Chapter Organization. 2. OLD Organization. 5.1 . Evaluation of Spatial Operations . 5.2 . Query Optimization . 5.3 . Gu Xu. Microsoft Research Asia. Mismatching Problem. Mismatching is Fundamental Problem in Search. Examples:. NY ↔ New York, game cheats ↔ game . cheatcodes. Search Engine Challenges. Head or frequent queries. A . Deep Dive into Spatial Indexing. Michael Rys. Principal Program Manager. Microsoft Corporation. @. SQLServerMike. DBI405. Q: Why is my Query . so. Slow?. A: . Usually . because the index isn’t being used.. Matt Masson. Tessa Palmer. DBI-B410. Agenda. Overview of Power . Query. Why you would use the Power Query Formula Language (“M. ”). Introduction to the . Language. Types, values, and . functions. Abstract. With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed.. Gail Shaw. XpertEase. DAT 305 . Topics. Background Information. Query Hints. Plan Cache Metadata. Plan Guides. Plan Freezing. Monitoring Plan Guide Use. Background information. Optimal index usage. Minimal sorting. Query Formulation. Ling573. NLP Systems and Applications. April 14, 2011. Roadmap. Motivation:. Retrieval gaps. Query F. ormulation:. Question Series. Query reformulation:. AskMSR. patterns. MULDER parse-based formulation. A query is stated using SQL or other languages, ideally natural languages. After query parsing, each query is essentially treated as a . relational algebra expression. Query optimizer . Enumerates . the possible plans to evaluate expression,. 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. on OpenCL-based FPGAs. Zeke Wang. , Johns Paul, Hui Yan Cheah . (. NTU,. . Singapore), . Bingsheng He (. NUS,. Singapore), . Wei Zhang (HKUST, Hong Kong). 1. Outline. Background and Problem. Challenges. Kristina . Lerman. What can we learn from web search queries?. Characteristics. Length has steadily grown over the years. 1990’s: < 2 terms. 2001: 2.4 terms. 2014: long search queries, e.g., “where is the nearest coffee shop”. nrrnrrAbout the SpeakerJoe Seneganik Registered private researcherFirst experience with Oracle Version 4 in 198825years of experience with Oracle RDBMSProud member of the OakTable Network wwwoaktablen