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Correct understanding of this aspect of the language ties directly to the foundations of T-SQL�particularly mathematical set theory.

If you understand this from the very early stages of writing T-SQL code, you will have a much easier time than many who simply have incorrect assumptions and expectations from the language. Remember that a table in T-SQL is supposed to represent a relation; a relation is a set, and a set has no order to its elements. With this in mind, unless you explicitly instruct the query otherwise, the result of a query has no guaranteed order.

When the database engine SQL Server in this case processes this query, it knows that it can return the data in any order because there is no explicit instruction to return the data in a specific order. It could be that, due to optimization and other reasons, the SQL Server database engine chose to process the data in a particular way this time. The database engine can�and sometimes does�change choices that can affect the order in which rows are returned, knowing that it is free to do so.

Examples for such changes in choices include changes in data distribution, availability of physical structures such as indexes, and availability of resources like CPU and memory. Also, with changes in the engine after an upgrade to a newer version of the product, or even after application of a service pack, optimization aspects can change. In turn, such changes could affect, among other things, the order of the rows in the result.

You can be explicit and specify city ASC, but it means the same thing as not indicating the direction. One use case of this capability is to apply a tiebreaker for ordering. However, this practice is considered a bad one for a number of reasons.

For one, T-SQL does keep track of ordinal positions of columns in a table, in addition to in a query result, but this is nonrelational. Recall that the heading of a relation is a set of attributes, and 30 Chapter 1 Manage data with Transact-SQL a set has no order.

Also, when you are using ordinal positions, it is very easy after making changes to the SELECT list to miss changing the ordinals accordingly. For example, suppose that you decide to apply changes to your previous query, returning city right after empid in the SELECT list. The rule is that you can order the result rows by elements that are not part of the SELECT list, as long as those elements would have normally been allowed there.

So given one city say, Seattle with multiple employees, which of the employee birth dates should apply as the ordering value? As an example, the following query uses the MONTH function to return the birth month, assigning the expression with the column alias birthmonth. Should they all sort together? If so, should they sort before or after non-NULL values? As an interesting challenge, see if you can figure out how to sort the orders by shipped date ascending, but have NULLs sort last.

Without good indexes, SQL Server needs to sort the data, and sorting can be expensive, especially when a large set is involved. The former is used in a lot of common filtering tasks, and the latter is typically used in more specialized paging-related tasks. Filtering data with TOP With the TOP option, you can filter a requested number or percent of rows from the query result based on indicated ordering.

So you get the three rows with the most recent order dates. The correct syntax is with parentheses. You can also specify a percent of rows to filter instead of a number. In this example, without the TOP option, the number of rows in the result is Filtering 1 percent gives you 8.

The query filters three rows, but you have no guarantee which three rows will be returned. When I ran this query on my system, I received the following output. The other option to guarantee determinism is to break the ties by adding a tiebreaker that makes the ordering unique.

For example, in case of ties in the order date, suppose you wanted to use the order ID, descending, as the tiebreaker. But unlike TOP, it is standard, and also has a skipping capability, making it useful for ad-hoc paging purposes.

Another role is determining presentation ordering in the query. For example, the following query requests to skip 50 rows, returning all the rest. But what if you need to filter a certain number of rows based on arbitrary order? This is very handy when you need to compute the input values dynamically. The user passes as input parameters to your procedure or function the page number they are after pagenum parameter and page size pagesize parameter. This means that you need to skip as many rows as pagenum minus one times pagesize, and fetch the next pagesize rows.

Such indexing serves a similar purpose to indexing filtered columns and can help avoid scanning unnecessary data as well as sorting. Combining sets with set operators Set operators operate on two result sets of queries, comparing complete rows between the results. Depending on the result of the comparison and the operator used, the operator determines whether to return the row or not. These operators use distinctness-based comparison and not equality based.

The column names of result columns are determined by the first query. You can only unify two relations that share the same attributes. Also, chances are that the same terminology will be used in the exam. Therefore, I am using this terminology in this book. Customers; This query generates the following output, shown here in abbreviated form: country Argentina Austria Austria Employees table has nine rows, and the Sales.

Customers table has 91 rows, but there are 71 distinct locations in the unified results; hence, the UNION operator returns 71 rows. Customers; Skill 1.

The plans appear in the Execution Plan tab. Observe that both plans start the same by scanning the two input tables and then concatenating unifying the results. It returns distinct rows that appear in the result of the first query but not the second. You can always force precedence by using parentheses.

Orders2; Skill 1. The tables are usually related through keys, such as a foreign key in one side and a primary key in the other. Then you can use joins to query the data from the different tables and match the rows that need to be related.

This section covers the different types of joins that T-SQL supports: cross, inner, and outer. In other words, it performs a multiplication between the tables, yielding a row for each combination of rows from both sides.

Figure provides an illustration of a cross join. The right table has four rows with the key values B1, C1, C2, and D1. The result is a table with 12 rows containing all possible combinations of rows from the two input tables. This database contains a table called dbo. Nums that has a column called n with a sequence of integers from 1 and on. Your task is to use the Nums table to generate a result with a row for each weekday 1 through 7 and shift number 1 through 3 , assuming there are three shifts a day.

The result can later be used as the basis for building information about activities in the different shifts in the different days. With seven days in the week, and three shifts every day, the result should have 21 rows. Scalar-valued functions return a single value and table-valued functions return a table result. Use of built-in functions can improve developer productivity, but you also need to understand cases where their use in certain context can end up negatively affecting query performance.

Note that this skill is not meant to be an exhaustive coverage of all functions that T-SQL supports�this would require a whole book in its own right. Instead, this chapter explains key aspects of working with functions, usually in the context of certain types of data, like date and time data, or character data. In my examples I use constants as the source values to demonstrate the use of the functions, but typically you apply such functions to columns or expressions based on columns as part of a query.

The former is standard whereas the latter is proprietary in T-SQL. For instance, when converting a character string to a date and time type or the other way around, you can specify the style number to avoid ambiguity in case the form you use is considered language dependent. NET culture name. NET format string and culture, if relevant. You can use any format string supported by the. NET Framework.

Note that like PARSE, the FORMAT function is also quite slow, so when you need to format a large number of values in a query, you typically get much better performance with alternative built-in functions. Date and time functions T-SQL supports a number of date and time functions that allow you to manipulate your date and time data. This section covers some of the important functions supported by T-SQL and provides some examples. Current date and time One important category of functions is the category that returns the current date and time.

Note that there are no built-in functions to return the current date and the current time. Using the DATEPART function, you can extract from an input date and time value a desired part, such as a year, minute, or nanosecond, and return the extracted part as an integer. Note that the function is language dependent. For example, suppose that today was February 12, This function supports a second optional input indicating how many months to add to the result or subtract if negative.

With it, you can add a requested number of units of a specified part to a specified date and time value. Note that this function looks only at the parts from the requested one and above in the date and time hierarchy�not below.

You can also use this function when migrating from data that is not offset-aware, where you keep the local date and time value in one attribute, and the offset in another, to offsetaware data. Say you have the local date and time in an attribute called mydatetime, and the offset in an attribute called theoffset.

Based on the point in the year, the function will know whether to apply daylight savings time. You want to present it in the time zone Pacific Standard Time. This section describes the character string functions that T-SQL does support, arranged in categories. Concatenation Character string concatenation is a very common need. If you want to substitute a NULL with an empty string, there are a number of ways for you to do this programmatically.

With the SUBSTRING function, you can extract a substring from a string given as the first argument, starting with the position given as the second argument, and a length given as the third argument. Note that you can provide a third argument indicating to the function the position where to start looking. You can combine, or nest, functions in the same expression.

The LEN function returns the length of an input string in terms of the number of characters. Note that it returns the number of characters, not bytes, whether the input is a regular character or Unicode character string.

If there are any trailing spaces, LEN removes them. This means, for example, that if the input is a Unicode character string, it will count 2 bytes per character. String alteration T-SQL supports a number of functions that you can use to apply alterations to an input string. With the REPLACE function, you can replace in an input string provided as the first argument all occurrences of the string provided as the second argument, with the string provided as the third argument.

The STUFF function operates on an input string provided as the first argument; then, from the character position indicated as the second argument, deletes the number of characters indicated by the third argument.

Then it inserts in that position the string specified as the fourth argument. Formatting This section covers functions that you can use to apply formatting options to an input string. The first four functions are self-explanatory uppercase form of the input, lowercase form of the input, input after removal of leading spaces, and input after removal of trailing spaces.

NET format string. I demonstrated an example with date and time values. The function supports all character string types for both inputs�regular and Skill 1. Orders table to return information about the input orders. Many people incorrectly refer to CASE as a statement. The CASE expression has two forms�the simple form and the searched form. Instead of comparing an input expression to multiple possible expressions, it uses predicates in the WHEN clauses, and the first predicate that evaluates to true determines which when expression is returned.

If none is true, the CASE expression returns the else expression. One is which input determines the type of the output. This function accepts two input expressions, returns NULL if they are equal, and returns the first input if they are not.

If col1 is equal to col2, the function returns a NULL; otherwise, it returns the col1 value. However, when you are migrating from Access to SQL Server, these functions can help with smoother migration, and then gradually you can refactor your code to use the available standard functions. With the IIF function, you can return one value if an input predicate is true and another value otherwise. The CHOOSE function allows you to provide a position and a list of expressions, and returns the expression in the indicated position.

System functions System functions return information about various aspects of the system. Here I highlight a few of the functions. Note that you need to explicitly invoke the COMPRESS function to compress the input string before you store the result compressed binary string in a table. Your code might look like this. MyNotes; Context info and session context When you need to pass information from one level in the call stack to another, you usually use parameters.

For instance, if you want to pass something to a procedure, you use an input parameter, and if you want to return something back, you use an output parameter.

One technique to pass information between an outer level and a niladic module is to use either context info or session context. Context info is a binary string of up to bytes that is associated with your session. If you need to use it to store multiple values from different places in the code, you need to designate different parts of it for the different values.

Every time you need to store a value, you need to read the current contents, and reconstruct it with the new value planted in the right section, being careful not to overwrite existing used parts. The potential to corrupt meaningful information is high. T-SQL provides a tool called session context as a more convenient and robust alternative to context info. You can also mark the pair as read only, and then until the session resets, no one will be able to overwrite the value associated with that key.

T-SQL also provides system functions to generate and query the newly generated keys. Note that you cannot invoke this function independently, rather only as an expression in a default constraint that is associated with a column.

If you need a numeric key generator, you use either a sequence object or the identity column property. The latter is a property of a column in a table. In the same scope I mean that if a trigger was fired and also added a row to a table with an identity property, this will not affect the value that the function will return.

You can find examples for using both the identity property and the sequence object later in this chapter in Skill 1. The last computes the remainder of an integer division. T-SQL also supports aggregate functions, which you apply to a set of rows, and get a single value back. Arithmetic operators For the most part, work with these arithmetic operators is intuitive. They follow classic arithmetic operator precedence rules, which say that multiplication, division and modulo precede addition and subtraction.

To change precedence of operations, use parentheses because they precede arithmetic operators. The data types of the operands in an arithmetic computation determine the data type of the result. If the operands are integers, the result of arithmetic operations is an integer.

Obviously, when using constants, you can simply specify numeric values instead of integer values to get numeric division; however, when the operands are integer columns or parameters, but you need numeric division, you have two options.

The result of this expression is 4. The operation here is division. Aggregate functions ignore NULL inputs when applied to an expression. OrderValues GROUP BY empid; In a grouped query the aggregate is applied per group, and returns a single value per group, as part of the single result row that represents the group. This query generates the following output: empid 3 6 7 1 4 5 2 8 totalqty An aggregate function can also be applied as a scalar aggregate in an implied grouped query.

OrderValues; This query returns the grand total quantity 51, Like with arithmetic operators, also with aggregate functions like AVG, the data type of the input determines the data type of the result.

You can use the two aforementioned options that I described for arithmetic operations to get a numeric average. OrderValues; This time you get the result The scale of the input expression 1. As an alternative to grouping, aggregate functions can be applied in windowed queries, as window aggregates. This, as well as further aspects of grouping and aggregation are covered in Chapter 2, Skill 2. Suppose that you were tasked with computing the median quantity qty column from the Sales. OrderValues view using a continuous distribution model.

This means that if there are an odd number of rows you need to return the middle quantity, and if there are an even number of rows, you need to return the average of the two middle quantities. Using the COUNT aggregate, you can first count how many rows there are and store in a variable called cnt. When dividing that odd value by 2, the fraction part of the result.

The idea is that when the count is odd the result of the modulo operation is 1, and you need to fetch 1 row.

When the count is even the result of the modulo operation is 0, and you need to fetch 2 rows. By subtracting the 1 or 0 result of the modulo operation from 2, you get the desired 1 or 2, respectively. Then the reference to the detail qty column in the ORDER BY clause, which is processed in the sixth logical query processing step, would have been invalid. Therefore, the solution defines a derived table a table subquery in the FROM clause called D that represents the one or two quantities that need to participate in the median calculation, and then the outer query handles the average calculation.

This query returns the median quantity Search arguments One of the most important aspects of query tuning to know is what a search argument is. A search argument, or SARG in short, is a filter predicate that enables the optimizer to rely on index order.

The filter predicate uses the following form or a variant with two delimiters of a range, or with the operand positions flipped : WHERE Such a filter is sargable if: 1. The operator identifies a consecutive range of qualifying rows in the index. The cache size defines how frequently to write a recoverable value to disk.

Our code defines a default constraint with the function call for the orderid column to automate the creation of keys when new rows are inserted. Suppose that you need to define a stored procedure that accepts as input parameters attributes of an order. If an order with the input order ID already exists in the Sales. MyOrders table, you need to update the row, setting the values of the nonkey columns to the new ones.

MyOrders table. To turn the inputs into a table expression, you can define a derived table based on the VALUES clause, which is also known as a table value constructor.

Remember that you cleared the Sales. MyOrders table at the beginning of this section. An update costs you resources and time, and furthermore, if there are any triggers or auditing activity taking place, they consider the target row as updated. You can add a predicate that says that at least one of the nonkey column values in the source and the target must be different in order to apply the UPDATE action.

For instance, suppose that the custid column used NULLs. The predicates for this column would be: TGT. You can refer to real tables, temporary tables, or table variables as the source. With this clause, you can define an action to take against the target row when the target row exists but is not matched by a source row. For example, suppose that you want to add such a clause to the last example to indicate that if a target row exists and it is not matched by a source row, you want to delete the target row.

So the statement inserted the three rows with order IDs 2, 3, and 4, and deleted the row that had order ID 1. You can use the output for purposes like auditing, archiving and others.

This section covers the OUTPUT clause with the different types of modification statements and demonstrates using the clause through examples.

I use the same Sales. MyOrders table and Sales. SeqOrderIDs sequence from the Merging data section in my examples, so make sure you still have them around. Use the prefix inserted when the rows are inserted rows and the prefix deleted when they are deleted rows.

In an UPDATE statement, inserted represents the state of the rows after the update and deleted represents the state before the update.

Or you can add an INTO clause to direct the output rows into a target table. If you do use the INTO clause, the target table cannot participate in either side of a foreign key relationship and cannot have triggers defined on it. An example for a practical use case is when you have a multi-row INSERT statement that generates new keys by using the identity property or a sequence, and you need to know which new keys were generated. For example, suppose that you need to query the Sales.

Orders table and insert orders shipped to Norway to the Sales. You are not going to use the original order IDs in the target rows; instead, let the sequence object generate those for you. You need to prefix the columns that you refer to with the keyword deleted.

The following example deletes the rows from the Sales. MyOrders table where the employee ID is equal to 1. With updated rows, you have access to both the old and the new images of the modified rows. To refer to columns from the original state of the row before the update, prefix the column names with the keyword deleted.

To refer to columns from the new state of the row after the update, prefix the column names with the keyword inserted. As explained before, you can refer to columns from the deleted rows with the deleted prefix and to columns from the inserted rows with the inserted prefix. Suppose that you need to capture only the rows affected by an INSERT action in a table variable for further processing.

When you run the previous code for the first time, you get the following output: orderid 2 3 4 5 6 custid 70 70 70 70 70 empid 7 7 3 1 2 orderdate Run it for the second time.

It should return an empty set this time. In the examples in this section I use the Sales. SeqOrderIDs sequence from the Merging data section. Column 'requireddate' cannot be added to non-empty table 'MyOrders' because it does not satisfy these conditions. Read the error message carefully. Observe that in order to add a column to a nonempty table, the column either needs to allow NULLs, or somehow get its values automatically.

For instance, you can associate a default constraint with the column when you add it. Query the table after adding this column and notice that the requireddate is January 1, in all rows. In order to drop the column, you need to drop the constraint first.

In a similar way, attempting to add a primary key or unique constraint fails if duplicates exist in the data. With check and foreign key constraints you do have control over whether existing data is verified or not. With this option set to ON, the table is available while the alter operation is in progress. Examples for operations that can be done online include a change in the data type, nullability, precision, length and others.

For instance, the orderid column in the Sales. MyOrders table gets its values from the Sales. SeqOrderIDs sequence using a default constraint. This means that you need to memorize the syntax of the different T-SQL statements that are covered by the exam. Also, try to focus on what the question is asking exactly, and what seems to be the most correct answer to the question, as opposed to what is considered the best practice or how you would have done things.

Use the ORDER BY clause in the outer query to apply presentation ordering to the query result, and remember that a query without an order by clause does not guarantee presentation order, despite any observed behavior.

Joins allow you to combine rows from tables and return both matched attributes and additional attributes from both sides. T-SQL provides you with built-in functions of various categories such as string, date and time, conversion, system, and others. Scalar-valued functions return a single value; table-valued functions return a table result and are used in the FROM clause of a query.

Aggregate functions are applied to a set and return a single value, and can be used in grouped queries and windowed queries. When at all possible, try to avoid applying manipulation to filtered columns to enable filter sargability and efficient use of indexes. Function determinism determines whether the function is guaranteed to return the same output given the same set of inputs. Use the OUTPUT clause in a modification statement to return data from the modified rows for purposes like auditing, archiving, and others.

You can either return the result set to the caller, or write it to a table using the INTO clause. Make sure you understand the impact that structural changes to a table like adding, altering and dropping columns have on existing data.

Thought experiment In this thought experiment, demonstrate your skills and knowledge of the topics covered in this chapter. You can find the answer to this thought experiment in the next section. Answer the following questions to the best of your knowledge: 1. Where can you use such an alias?

What are the differences between joins and set operators? What could prevent SQL Server from treating a query filter optimally, meaning, from using an index efficiently to support the filter? What other query elements could also be affected in a similar manner and what can you do to get optimal treatment? Explain what function determinism means and what are the implications of using non- deterministic functions?

Thought experiment Chapter 1 7. You need to perform a multi-row insert into a target table that has a column with an identity property. You need to capture the newly generated identity values for further processing. How can you achieve this? Thought experiment answer This section contains the solution to the thought experiment. Also, a join uses equality or inequality based comparison as the join predicate, whereas a comparison between two NULLs or between a NULL and anything yields unknown.

A set operator implicitly compares all expressions in corresponding positions in the two input queries. This means that the optimizer cannot rely on index order, for instance, to perform a seek within the index. In a similar way, manipulation of a column can prevent the optimizer from relying on index order for purposes of joining, grouping, and ordering.

In an outer join the ON clause serves a matching purpose. It determines which rows from the preserved side get matched with which rows from the non-preserved side. It determines which rows from the result of the FROM clause to keep and which to discard. A function is said to be deterministic if given the same set of input values it is guaran- teed to return repeatable results, otherwise it is said to be nondeterministic. If you use a nondeterministic function in a computed column, you cannot create an index on that Chapter 1 Manage data with Transact-SQL column.

Similarly, if you use a nondeterministic function in a view, you cannot create a clustered index on the view. Use the OUTPUT clause and write the newly generated identity values along with any other data that you need from the inserted rows aside, for example into a table variable. You can then use the data from the table variable in the next step where you apply further processing. When the column is defined as a nullable one, and you want to apply the default expression that is associated with the column in the new rows, you need to specify the WITH VALUES clause explicitly.

In terms of the result of the subquery, it can be scalar, multi-valued table with a single column , or multicolumn table-valued table with multiple columns.

This section starts by covering the simpler self-contained subqueries, and then continues to correlated subqueries. Self-contained subqueries Self-contained subqueries are subqueries that have no dependency on the outer query. If you want, you can highlight the inner query in SSMS and run it independently.

This makes the troubleshooting of problems with self-contained subqueries easier compared to correlated subqueries. As mentioned, a subquery can return different forms of results. It can return a single value, table with multiple values in a single column, or even a multi-column table result.

Table-valued subqueries, or table expressions, are discussed in Skill 2. Subqueries that return a single value, or scalar subqueries, can be used where a single-valued expression is expected, like in one side of a comparison. Products table. The outer query then returns information about products with the minimum unit price.

Try highlighting only the inner query and executing it, and you will find that this is possible. If the scalar subquery returns an empty set, it is converted to a NULL. A subquery can also return multiple values in the form of a single column and multiple rows.

Such a subquery can be used where a multi-valued result is expected�for example, Chapter 2 Query data with advanced Transact-SQL components when using the IN predicate. As an example, the following query uses a multi-valued subquery to return products supplied by suppliers from Japan.

The outer query then returns information about products whose supplier ID is in the set returned by the subquery. T-SQL supports a few esoteric predicates that operate on subqueries. Correlated subqueries Correlated subqueries are subqueries where the inner query has a reference to a column from the table in the outer query. As an example, suppose that you need to return products with the minimum unit price per category.

You can use a correlated subquery to return the minimum unit price out of the products where the category ID is equal to the one in the outer row the correlation , as follows: SELECT categoryid, productid, productname, unitprice FROM Production. In order for the subquery to be able to distinguish between the two, you must assign different aliases to the different instances. The query assigns the alias P1 to the outer instance and P2 to the inner instance, and by using the table alias as a prefix, you can refer to columns in an unambiguous way.

The subquery uses a correlation in the predicate P2. So, when the outer row has category ID 1, the inner query returns the minimum unit price out of all products where the category ID is 1. And when the outer row has category ID 2, the inner query returns the minimum unit price out of all the products where the category ID is 2; and so on.

In this case, the subquery returns orders placed by the customer whose ID is equal to the customer ID in the outer row the correlation and where the order date is February 12, For this reason, the query optimizer ignores the SELECT list of the subquery, and therefore, whatever you specify there will not affect optimization choices like index selection. There are cases where you will get the same query execution plans for both, cases where subqueries perform better, and cases where joins perform better.

Ultimately, in performance critical cases you will want to test solutions based on both tools. If you have multiple subqueries that need to apply computations such as aggregates based on the same set of rows, SQL Server will perform a separate access to the data for each subquery. With a join, you can apply multiple aggregate calculations based on the same access to the data. Orders table and compute for each order the percent of the current freight value out of the customer total, as well as the difference from the customer average.

Figure shows the execution plans for both solutions. Query 1 represents the solution with the subqueries and Query 2 represents the solution with the join. In the second plan the index is accessed only twice; once for the detail reference instance O , and only one more time for the computation of both aggregates. Also notice the relative cost of each query plan out of the entire batch; the first plan costs twice as much as the second.

Orders; In the second example, consider a case where SQL Server optimizes subqueries better than joins. For this example, first run the following code to add a shipper row into the Sales. The important index for this task is a nonclustered index on the shipperid column in the Sales. Orders table, which already exists. Skill 2. Query 1 represents the solution based on the subquery and Query 2 represents the solution based on the join. In this algorithm the outer input of the loop scans shipper rows from the Sales.

Shippers table. For each shipper row, the inner input of the loop looks for matching orders in the nonclustered index on Sales. The key difference between the plans is that with the subquery-based solution the optimizer is capable of using a specialized optimization called Anti Semi Join.

With this optimization, as soon as a match is found, the execution short circuits notice the Top operator with the Top Expression property set to 1. Observe the relative cost of each query plan out of the entire batch. The plan for the subquery-based solution costs less than half of the plan for the join-based solution. At the time of writing, SQL Server does not use the Anti Semi Join optimization for queries based on an actual join, but does so for queries based on subqueries and set operators.

Also, make sure to keep an open mind, test different solutions, compare their run times and query plans, and eventually choose the optimal one. The operator evaluates the left input first, and for each of its rows, applies a derived table query or table function that you provide as the right input.

This means that if any of the join inputs is a query, you cannot refer in that query to elements from the other side. Conversely, the APPLY operator evaluates the left side first, and for each of the left rows, applies the table expression that you provide as the right input. As a result, the query in the right side can have references to elements from the left side. The references from the right side to elements from the left are correlations. For example, suppose that you have a query that performs some logic for a particular supplier.

Suppliers table. You could use a cursor to iterate through the suppliers, and in each iteration invoke the query for the current supplier. Suppliers table as the left input, and a table expression based on your query as the right input.

You can correlate the supplier ID in the inner query of the right table expression to the supplier ID from the left table. Suppliers Skill 2. The right table expression can have a correlation to elements from the left table. The right table expression is applied to each row from the left input. The reason that this operator is called CROSS APPLY is that per the left row, the operator behaves like a cross join between that row and the result set returned for it from the right input.

F represents the table expression provided as the right input, and in parentheses, you can see the key value from the left row passed as the correlated element. On the right side of the illustration, you can see the result returned from the right table expression for each left row. Such is the case with the row with the key value Z. Suppliers table, with only suppliers from Japan filtered. The right table expression is a correlated derived table returning the two products with the lowest prices for the left supplier.

Because the APPLY operator applies the right table expression to each supplier from the left, you get the two products with the lowest prices for each supplier from Japan. NULLs are used as placeholders for the result columns from the right side. It starts with a description of what table expressions are, compares them to temporary tables, and then provides the details about the different kinds of table expressions. You write an inner query that returns a relational result set, name it, and query it from an outer query.

T-SQL supports four forms of table expressions: derived tables, common table expressions CTEs , views and inline table-valued functions. The first two forms are visible only to the statement that defines them. Note that because a table expression is supposed to represent a relation, the inner query defining it needs to be relational. This means that all columns returned by the inner query must have names use aliases if the column is a result of an expression , and all column names must be unique.

Remember, a set has no order. Table expressions or temporary tables? If for optimization reasons you do need to persist the result of a query for further processing, you should be using a temporary table or table variable. There are cases where the use of table expressions is more optimal than temporary tables.

For instance, imagine that you need to query some table T1 only once, then interact with the result of that query from some outer query, and finally interact with the result of that outer query from yet another outer query. You do not want to pay the penalty of writing the intermediate results physically to some temporary table, rather, you want the physical processing to interact directly with the underlying table. To achieve this, define a table expression based on the query against T1, give it a name, say D1, and then write an outer query against D1.

Behind the scenes, SQL Server will unnest, or inline, the logic of the inner queries, like pealing the layers of an onion, and the query plan will interact directly with T1. You need to interact with that query result multiple times�whether with a single query that joins multiple instances of the result or with multiple separate queries. If you use a table expression, the physical treatment repeats the work for each reference.

In such cases you want to persist the result of the expensive work in a temporary table or table variable, and then interact with that temporary object a number of times. Between table variables and temporary tables, the main difference from an optimization perspective is that SQL Server maintains full blown statistics on temporary tables but very minimal statistics on table variables.

Therefore, cardinality estimates estimates for row counts during optimization tend to be more accurate with temporary tables. With larger table sizes, the recommendation is to use temporary tables, to allow better estimates, that will hopefully result in more optimal plans. The following sections describe the different forms of table expressions that T-SQL supports.

Derived tables A derived table is a named table subquery. Before demonstrating the use of derived tables, this section describes a query that returns a certain desired result.

Then it explains a need that cannot be addressed directly in the query, and shows how you can address that need by using a derived table or any other table expression type for that matter. Products; This query generates the following output, shown here in abbreviated form: rownum categoryid productid productname unitprice 1 24 Product QOGNU 4.

SWNJY For example, suppose you want to return only the rows where the row number is less than or equal to 2; namely, in each category you want to return the two products with the lowest unit prices, with the product ID used as a tiebreaker. You can circumvent the restriction by using a table expression.

You write a query such as the previous query that computes the window function in the SELECT clause, and assign a column alias to the result column. It is not recommended to query the underlying source table directly; the reason for this is explained shortly. You issue the outer query against the table expression and apply the PIVOT operator to that table expression.

You need to assign an alias to that table, for example, P. Then you specify the FOR clause followed by the spreading column, which in this example is shipperid. Then you specify the IN clause followed by the list of distinct values that appear in the spreading element, separated by commas.

What used to be values in the spreading column in this example, shipper IDs become column names in the result table. Therefore, the items in the list should be expressed as column identifiers. Remember that if a column identifier is irregular, it has to be delimited.

Because shipper IDs are integers, they have to be delimited: [1],[2],[3]. This is why it is recommended to prepare a table expression for the pivot operator returning only the three elements that should be involved in the pivoting task.

If you query the underlying table directly Sales. Orders in this case , all columns from the table besides the aggregation freight and spreading shipperid columns will implicitly become your grouping elements.

This includes even the primary key column orderid. So instead of getting a row per customer, you end up getting a row per order. Orders table. By defining a table expression as was shown in the recommended solution, you control which columns will be used as the grouping columns. If you return custid, shipperid, and freight in the table expression, and use the last two as the spreading and aggregation elements, respectively, the PIVOT operator implicitly assumes that custid is the grouping element.

Therefore, it groups the data by custid, and as a result, returns a single row per customer. You can, however, apply expressions in the query defining the table expression, assign aliases to those expressions, and then use the aliases in the PIVOT operator. You need to know ahead what the distinct values are in the spreading column and specify those in the IN clause.

Unpivoting Data Unpivoting data can be considered the inverse of pivoting. The starting point is some pivoted data. When unpivoting data, you rotate the input data from a state of columns to a state of rows. The operator operates on the input table that is provided to its left, which could be the result of other table operators, like joins. To demonstrate unpivoting, use as an example a sample table called Sales.

FreightTotals; This code generates the following output, shown here in abbreviated form: custid 2 3 4 5 6 7 8 9 The intersection of each customer and shipper has the total freight values.

The unpivoting task at hand is to return a row for each customer and shipper holding the customer ID in one column, the shipper ID in a second column, and the freight value in a third column. Unpivoting always takes a set of source columns and rotates those to multiple rows, generating two target columns: one to hold the source column values and another to hold the source column names.

The source columns already exist, so their names should be known to you. But the two target columns are created by the unpivoting solution, so you need to choose names for those. In our example, the source columns are [1], [2], and [3]. As for names for the target columns, you need to decide on those.

In this case, it might be suitable to call the values column freight and the names column shipperid. So remember, in every unpivoting task, you need to identify the three elements involved: 1. The name you want to assign to the target values column in this case, freight. The name you want to assign to the target names column in this case, shipperid.

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VALUES statement, and if there is an error in the transaction it will be caught ant he transaction will be rolled back. In this case no records will have been inserted. If an INSERT statement violates a constraint or rule, or if it has a value incompatible with the data type of the column, the statement fails and an error message is returned. You create a table named Customer by running the following Transact-SQL statement: You must insert the following data into the Customer table: You need to ensure that both records are inserted or neither record is inserted.

Question: 5 Note: This question is part of a series of questions that present the same scenario. Question: 6 Note: This question is part of a series of questions that present the same scenario. This ensures that both records or neither is inserted. You have a database that tracks orders and deliveries for customers in North Americ a. The database contains the following tables: Sales. Customers Application. The application must list customers by the area code of their phone number.

The area code is defined as the first three characters of the phone number. The main page of the application will be based on an indexed view that contains the area and phone number for all customers. You need to return the area code from the PhoneNumber field. No Answer: A Explanation: As the result of the function will be used in an indexed view we should use schemabinding. Cities Sales. No Answer: B Explanation: As the result of the function will be used in an indexed view we should use schemabinding.

Question: 10 Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.

Their responsibilities also include setting up database systems, making sure those systems operate efficiently, and regularly storing, backing up, and securing data from unauthorized access. You should also have experience with setting up database systems, ensuring those systems operate efficiently, regularly storing and backing up data, and securing data from unauthorized access.

About the Exam Exam focuses on skills and knowledge required for database administration. Next, it walks you through core topics such as single-table queries, joins, subqueries, table expressions, and set operators. Then the book covers more-advanced data-query topics such as window functions, pivoting, and grouping sets. The book also explains how to modify data, work with temporal tables, and handle transactions, and provides an overview of programmable objects.

Microsoft Data Platform MVP Itzik Ben-Gan shows you how to: Review core SQL concepts and its mathematical roots Create tables and enforce data integrity Perform effective single-table queries by using the SELECT statement Query multiple tables by using joins, subqueries, table expressions, and set operators Use advanced query techniques such as window functions, pivoting, and grouping sets Insert, update, delete, and merge data Use transactions in a concurrent environment Get started with programmable objects�from variables and batches to user-defined functions, stored procedures, triggers, and dynamic SQL.

T-SQL insiders help you tackle your toughest queries and query-tuning problems Squeeze maximum performance and efficiency from every T-SQL query you write or tune.

Emphasizing a correct understanding of the language and its foundations, the authors present unique solutions they have spent years developing and refining. It focuses on the specific areas of expertise modern database professionals need to succeed with T-SQL database queries. Exam Ref Developing SQL Databases, , offers professional-level preparation that helps candidates maximize their exam performance and sharpen their skills on the job.

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