![]() ![]() ![]() ![]() ON e.ProductGroup1Cd = g2.ProductGroup1CdĪ LEFT JOIN is absolutely not faster than an INNER JOIN. INNER JOIN coProductGroup2 g2 - PK = Compan圜d, ProductCategor圜d, UsedDepartment, ProductGroup2Cd ON e.ProductGroup1Cd = g1.ProductGroup1Cd INNER JOIN coProductGroup1 g1 - PK = Compan圜d, ProductCategor圜d, UsedDepartment, ProductGroup1Cd INNER JOIN coProductOldInformation j - PK = Compan圜d, BFStatus, SpecCd INNER JOIN coProduct e - PK = Compan圜d, ProductSalesCd INNER JOIN coMappingExpParty d - NO PK AND FK INNER JOIN exFSlipHdr c - PK = Compan圜d, FSlipNo, FSlipSuffix INNER JOIN exFSlipDet h - PK = Compan圜d, FSlipNo, FSlipSuffix, FSlipLine INNER JOIN sidisalesdetmly b - THIS TABLE ALSO HAVE NO PK AND FK FROM sidisaleshdrmly a - NOT HAVE PK AND FK ![]() I would like to know why LEFT JOIN is faster than INNER JOIN? After I changed it, the speed of query got significantly improved. So my folk suggested me to change INNER JOIN to LEFT JOIN because the performance of LEFT JOIN is better, despite what I know. This changed in PostgreSQL 12, where CTEs are no longer materialized and are treated like sub-queries.I've created SQL command that uses INNER JOIN on 9 tables, anyway this command takes a very long time (more than five minutes). Keep in mind that the database has to evaluate all the predicates, so adding too many might make a query slower.īefore PostgreSQL 12, Common Table Expressions (aka CTE) were materialized. It should only be used to provide more information to the optimizer that can improve the query performance. You might be able to improve the performance of a query by providing additional information to the optimizer, using what I like to call, a "Faux Predicate".ĭb=# ( SELECT * FROM sale WHERE modified < ' asia/tel_aviv' AND created < ' asia/tel_aviv' ) QUERY PLAN - Index Scan using sale_created_ix on sale (cost=0.44.4.52 rows=1 width=276) Index Cond: (created < ' 00:00:00+02'::timestamp with time zone) Filter: (modified < ' 00:00:00+02'::timestamp with time zone)Īfter we added the "Faux Predicate" the optimizer decided to use the index on the created field, and the query got much faster! Note that the previous predicate on the modified field is still being evaluated, but it's now being applied on much fewer rows.Ī "Faux Predicate" should not change the result of the query. In some circumstances, you have knowledge of your data that the optimizer does not have, or cannot have. In this case, it might conclude that it makes more sense to use an index rather than scan the entire table. For example, if you apply a filter on a column with a unique constraint, the optimizer knows it can expect exactly one row for each value. To generate a good execution plan, the optimizer utilizes metadata and statistics it has on your data. For example, the optimizer decides whether to use a specific index or not, in what order to execute a join, which table to filter first, and so on. The execution plan describes how the database in going to access the data necessary to satisfy the query. The query optimizer looks at your SQL and generates an execution plan. One of the most important components in database implementations, and usually the one that makes one database better than the other, is the query optimizer. Using BETWEEN incorrectly might produce overlapping results, for example, counting sales twice in two different periods. SELECT * FROM sales WHERE created >= '' AND created < '' ![]()
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