We’ll use the following query as an example (which returns a list of customers and their sale order identifiers): SELECT The “Merge” algorithm is the most efficient way to join between two very large sets of data which are both sorted on the join key. (spoiler alert: this particular problem is partly solved using "Adaptive Joins" in SQL Server 2017) MERGE Join For example: Performing 10 times index seek is nothing like performing 100,000 times index seek, especially if the table size of the inner input is around 120,000 and you’d be better off doing one table scan instead. Having indexes and up-to-date statistics is crucial for this join type, because you don’t want SQL Server to accidentally think there’s a small number of rows in one of the inputs when in fact there are a whole lot. The bigger the difference in number of rows between the outer and inner inputs, the more benefit this operator will provide over the other operator types. The SQL Server Optimizer will prefer to choose this operator type when the outer input is small and the inner input has an index on the column(s) by which the two data sets are joined. In terms of complexity (assume N is the number of rows from the outer output and M is the total number of rows in the SalesOrderDetail table): The complexity of this query is: O(NlogM) where “logM” is the logarithmic complexity of each seek in the inner input. This time we can see that the estimated number of executions is 179 which is the approximate number of rows returned from the outer input. The resulting execution plan looks like this: OH.OrderDate, OD.OrderQty, OD.ProductID, OD.UnitPrice We’ll take the following query as an example, which gets some order detail columns for orders placed during July 2001 (assuming the OrderDate column only includes dates without time): SELECT “Nested Loops” is the simplest operator of the bunch. It’s possible to find the operator which was used by looking at the execution plan that SQL Server has prepared for your query.įor more information on execution plans and how to read them, I recommend checking out the first chapter out of Grant Fritchey’s excellent book: The SQL Server Optimizer may choose a different algorithm for different scenarios based on the requested query, available indexes, statistics and number of estimated rows in each data set. Note: This article was re-published on my official blog website: įor the samples in this post, we’ll use the free AdventureWorks database sample available here: Introduction: What are Join Operators?Ī join operator is a type of an algorithm which the SQL Server Optimizer chooses in order to implement logical joins between two sets of data. Or Ingredient is specified as 3 ounces of starch.Today I’ll talk about the available JOIN operator types in SQL Server (Nested Loops, Hash and Merge Joins), their differences, best practices and complexity. So to display 500 ounces of sugar in tsp, you would use the formula units x nversion x sugar.vol-to-weightĪnother example with 2 weights Ingredient is specified as 3 ounces of starch. Tsp | 5 // assuming that 1 tsp = 5 grams of waterĮxample ingredient volume to weight conversion type | vol-to-weight This gets quite tricky when you want to show ounces as TSP, because the conversion depends on the ingredient itself, so you need a 3rd table - ingredient: id, name, volume-to-weight ratio.Įxample typeofweight table, where the standard unit is grams type | conversion Store a reference table somewhere showing what the conversion ratio is for each (to a certain standard). You have the right idea with the typeofweight column. If you are storing weights (a non-discrete value) I would strongly suggest using numeric or decimal for this data.
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