Geek Sync | Understand Indexes to Write Better Queries
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The document discusses the importance of understanding indexes in SQL for writing efficient queries, comparing the querying process to finding recipes in cookbooks. It explains different table structures, such as heaps and clustered/nonclustered indexes, and emphasizes the need for using indexes to improve query performance. The presentation covers how indexes work, the significance of sargable predicates, and tips for creating indexable queries.
Geek Sync | Understand Indexes to Write Better Queries
- 1. Understanding Indexes for Writing Better Queries Janice Gerbrandt
- 2. Agenda How Data is stored Types of indexes Writing queries that use indexes
- 3. About Me I design and implement database solutions for an ERP software solution (supply chain management). Twitter: @gerbyj Email: janice.gerbrandt@gmail.com
- 4. About Me
- 5. About Me
- 7. tSQL is a Declarative Language When I write a query in SQL Server, I only tell the database engine WHAT I want. Eg: “I want the names of all the people with the last name ‘Simpson’”
- 8. tSQL is a Declarative Language “I want the names of all the people with the last name ‘Simpson’” select FirstName, LastName from Person where LastName = 'Simpson'
- 9. Why are some queries slow? select FirstName, LastName from Person.Person where LastName = 'Simpson' vs select FirstName, LastName from Person.Person where FirstName = 'Kim'
- 10. Both Queries returned 10 rows
- 11. We’re spoiled in 2020 Index analogies used to be easy!
- 12. We’re spoiled in 2020
- 13. But we still eat, right? Finding a good recipe on the internet can take longer than cooking it. Luckily they still make cookbooks!
- 14. A Tale of 3 Cookbooks The Search for the Perfect Pot Roast Recipe
- 15. Book One – Aunt Gertrude’s Collection
- 16. Book Two – Basic Cookbook
- 17. Book Three – The Canadian Living Cookbook
- 18. Book Three – The Canadian Living Cookbook
- 19. How long did it take to find that Pot Roast recipe? Book 1: Scanned every page in the book Book 2: Looked in an index for the first page of section, turned to that page, then scanned each page of the section Book 3: Located list of recipes in an index, scanned that list, then turned to that page
- 20. We came here to talk about databases, right?
- 21. How do we find records in tables? Similar to how we find recipes in cookbooks: Scan Seek, then Scan Seek
- 22. How are tables structured? As Pages (8 KB) in the data files, organized into Extents (8 page chunks)
- 23. Page Types Data Index Text/Image Various types of Maps
- 24. Table Data Structure: Heap When a table doesn’t have a defined order its data rows are stored in an unordered structure. Data order cannot be predicted.
- 25. Table Data Structure: Clustered Index (CI) When a table does have a defined order its data rows are stored in an ordered structure sorted by its key values. Key values are the columns included in the index definition (eg: MealType) There can be only one clustered index per table, because the data rows themselves can be stored in only one order.
- 26. Optional Index: Nonclustered Index (NCI) An ordered structure sorted by its key values that is separate from the data rows. Entries contain the key values (eg: Keyword) and a pointer to the data row (ie: the row locator).
- 27. Heap id index_id = 0 first_iam_page Index Allocation Map (IAM) Page Header Data Rows Page Header Data Rows Page Header Data Rows
- 28. Clustered Index id index_id = 1 root_page Previous Page Next Page Index Rows Previous Page Next Page Index Rows Previous Page Next Page Index Rows Previous Page Next Page Index Rows Previous Page Next Page Data Rows Previous Page Next Page Data Rows Previous Page Next Page Data Rows Root Node Intermediate Node Leaf Node
- 29. Traversing the Tree Lets find ‘Simpson, Kim’ in the Employee table Key values are LastName, FirstName Root node: Aaronson, Alan Fairchild, Meagan Morgan, Beverly Thompson, Sam
- 30. Traversing the Tree Lets find ‘Simpson, Kim’ in the Employee table Key values are LastName, FirstName Intermediate node: Morgan, Beverly Potter, Henry Ramsay, Delores Smith, Sam
- 31. Traversing the Tree Lets find ‘Simpson, Kim’ in the Employee table Key values are LastName, FirstName Leaf node: Ramsay, Delores, (1992-04-23, Sr Engineer) Rumsfeld, Dan, (2016-03-22, Engineer) Sanderson, Betty, (2019-08-19, Jr Engineer) Simpson, Kim, (2014-01-04, Engineer)
- 32. Why are some queries slow? select FirstName, LastName from Person.Person where LastName = 'Simpson' vs select FirstName, LastName from Person.Person where FirstName = 'Kim'
- 33. Nonclustered Index id index_id > 1 root_page Previous Page Next Page Index Rows Previous Page Next Page Index Rows Previous Page Next Page Index Rows Previous Page Next Page Index Rows Previous Page Next Page Key Values Previous Page Next Page Key Values Previous Page Next Page Key Values Root Node Intermediate Node Leaf Node
- 34. Nonclustered Index on Heap Previous Page Next Page Key Values Nonclustered Index Leaf Node IAM Page Header Data Rows Page Header Data Rows Page Header Data Rows
- 35. Nonclustered Index on Clustered Index Previous Page Next Page Key Values Nonclustered Index Leaf Node Previous Page Next Page Index Rows Previous Page Next Page Index Rows Previous Page Next Page Index Rows Previous Page Next Page Index Rows Previous Page Next Page Data Rows Previous Page Next Page Data Rows Previous Page Next Page Data Rows
- 36. Finding Data Means Reading Pages Indexes = Good
- 37. Indexable Queries: Finding Data Check that the columns tested by your WHERE clause are in an index Ditto for: JOIN conditions, HAVING clause Don’t hide the column values to make them unusable by the index
- 38. Implicit Indexes Primary Key Constraints (PK) Unique Key Constraints (AK) Both require uniqueness Your only option on table variables
- 39. SARGable = Search ARGument ABLE A SARGable predicate is one where SQL server can isolate the single value or range of index key values to process Functions and operators can act like a black box around the column value, so the database engine can’t use it in an index and must scan -> Non-SARGable
- 40. Are any functions SARGable? Not many: LIKE (without a leading %) =, >, >=, <, <=, IN, BETWEEN IS NULL Some Date functions User Defined Functions that are basically parameterized views
- 41. Indexable Queries: Clustering Data Check that all the columns you’re asking for are covered by the index Pay attention to the order of key values in the index. Even non-SARGable conditions aren’t that bad if they’re grouped together Key values of the Clustered Index will be on the leaf pages of your Nonclustered Index
- 42. Indexable Queries: Sorting Data Check that your sort order is supported by the index. Ditto for: GROUP BY It works in reverse, too
- 43. Thank you!