In my practice, I encountered several variants. It gets more interesting when we need to count the number of records for all tables at once. With optimizer, this case can be simplified to the plan we got in EXISTS. Here is one more case I encountered: IF (SELECT COUNT(*) FROM Sales.SalesOrderHeader) > 0 In practical terms, it will be a bit slower, since SQL Server generates a more complicated execution plan for selection from metadata. The best way to get the record count is to use the sys.dm_db_partition_stats or sys.partitions system views (there is also sysindexes, but it has been left for the backward compatibility with SQL Server 2000). Anyways, these operations are far from being fast.
Postgresql count rows in table full#
If we use the aforesaid queries, we should use Full Index Scan (or Full Table Scan if it is a heap table) for counting SQL Server records. SELECT COUNT_BIG(1) FROM Sales.SalesOrderDetail
If you indicate a constant value in COUNT, the query won’t become faster, since the optimizer create identical execution plan for these queries: SELECT COUNT_BIG(*) FROM Sales.SalesOrderDetail
This variant is approximately identical to COUNT – we will also get the excessive Compute Scalar in execution plan: = Scalar Operator(CASE WHEN =(0) THEN NULL ELSE END) I also know people who like using SUM instead of COUNT: SELECT SUM(1) FROM Sales.SalesOrderDetail Nevertheless, the aforesaid example does not require worrying about performance – the truncation of Int64 to Int32 does not require much resources. However, there is a thing worth mentioning – SQL Server tends to underestimate the Compute Scalar operators. Many of you may say that this operator is not a big deal in terms of execution. Remember that data type conversion increases the processor load. It happens because COUNT_BIG is used implicitly when calling COUNT, and then the result is converted into INT. If we take a look at the operator properties, we will see the following: = Scalar Operator(CONVERT_IMPLICIT(int,0)) When using COUNT, the plan will show the Compute Scalar operation.
If we analyze the execution plan, we will notice the differences, that are often overlooked. The given queries will return the identical result, but COUNT will return the value of the INT type, while COUNT_BIG will return the value of the BIGINT type. Most of you will say there is no difference. SELECT COUNT_BIG(*) FROM Sales.SalesOrderDetail Do the following queries differ in terms of the end result? SELECT COUNT(*) FROM Sales.SalesOrderDetail Here is the one: how do you count the total number of records in a table? At first sight, it’s a snap, but if you dig a little deeper, you can reveal lots of peculiar nuances. I have always liked simple questions with lots of pitfalls.
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