The Ultimate TSQL Guide - Mastering Date, Conversion, Math & Window Functions in SQL Server

 

Welcome, data enthusiasts! This definitive guide is designed to take you from a TSQL novice to a confident user capable of wielding some of the most powerful functions in SQL Server. We won't just learn syntax; we'll immerse ourselves in real-world scenarios, understand the "why" behind each function, and explore best practices to write efficient, robust, and maintainable code.

Let's set up a sample dataset we'll use throughout our examples.

Sample Data Setup: CompanyData

sql

CREATE DATABASE CompanyDB;
GO

USE CompanyDB;
GO

-- Table for Employees
CREATE TABLE Employees (
    EmployeeID INT IDENTITY(1,1) PRIMARY KEY,
    FirstName NVARCHAR(50),
    LastName NVARCHAR(50),
    Email NVARCHAR(100),
    PhoneNumber NVARCHAR(15),
    HireDate DATE,
    JobTitle NVARCHAR(50),
    Salary DECIMAL(10, 2),
    DepartmentID INT,
    ManagerID INT NULL
);

-- Table for Sales
CREATE TABLE Sales (
    SaleID INT IDENTITY(1,1) PRIMARY KEY,
    EmployeeID INT FOREIGN KEY REFERENCES Employees(EmployeeID),
    SaleAmount DECIMAL(10, 2),
    SaleDate DATETIME2,
    Region NVARCHAR(50)
);

-- Table for Product Inventory
CREATE TABLE ProductInventory (
    ProductID INT IDENTITY(1,1) PRIMARY KEY,
    ProductName NVARCHAR(100),
    Price DECIMAL(10, 2),
    Cost DECIMAL(10, 2),
    LastStockedDate DATETIME,
    UnitsInStock INT
);

-- Insert sample data into Employees
INSERT INTO Employees (FirstName, LastName, Email, HireDate, JobTitle, Salary, DepartmentID, ManagerID)
VALUES
('John', 'Doe', 'john.doe@company.com', '2020-05-15', 'Sales Manager', 75000.00, 1, NULL),
('Jane', 'Smith', 'jane.smith@company.com', '2021-02-10', 'Sales Representative', 55000.00, 1, 1),
('Mike', 'Johnson', 'mike.johnson@company.com', '2018-11-23', 'Senior Developer', 90000.00, 2, NULL),
('Sarah', 'Williams', 'sarah.williams@company.com', '2022-08-01', 'Marketing Specialist', 60000.00, 3, NULL),
('David', 'Brown', 'david.brown@company.com', '2019-04-03', 'Sales Representative', 58000.00, 1, 1);

-- Insert sample data into Sales
INSERT INTO Sales (EmployeeID, SaleAmount, SaleDate, Region)
VALUES
(2, 1500.00, '2023-10-25 09:30:15', 'North'),
(2, 2500.50, '2023-11-12 14:22:05', 'North'),
(5, 999.99, '2023-11-05 10:11:45', 'South'),
(5, 3200.00, '2023-11-18 16:45:00', 'South'),
(2, 800.00, '2023-12-01 11:01:33', 'North'),
(5, 4500.75, '2023-12-02 12:55:27', 'South');

-- Insert sample data into ProductInventory
INSERT INTO ProductInventory (ProductName, Price, Cost, LastStockedDate, UnitsInStock)
VALUES
('Laptop Pro', 1299.99, 900.00, '2023-11-20 08:00:00', 15),
('Wireless Mouse', 49.99, 15.50, '2023-10-15 10:30:00', 100),
('Mechanical Keyboard', 119.50, 70.25, '2023-11-28 09:45:00', 30),
('4K Monitor', 499.95, 320.00, '2023-09-05 16:20:00', 8);

---

Module 1: Mastering Date & Time Functions

Handling dates and times is a cornerstone of database operations. SQL Server provides a robust set of functions to make this easier.

1.1 Getting the Current Date and Time

GETDATE() / CURRENT_TIMESTAMP

• Purpose: Returns the current system date and time as a DATETIME data type.

• Difference: CURRENT_TIMESTAMP is ANSI SQL standard, while GETDATE() is TSQL specific. They are functionally identical.

• Real-life Scenario: Logging the timestamp when a new record is inserted into an AuditLog table.

sql

-- Log an action
DECLARE @ActionDescription NVARCHAR(200) = 'User logged in';
INSERT INTO AuditLog (ActionDescription, ActionTime)
VALUES (@ActionDescription, GETDATE());

SELECT GETDATE() AS CurrentDateTime, CURRENT_TIMESTAMP AS CurrentTimestampANSIStyle;

SYSDATETIME() / SYSUTCDATETIME()

• Purpose: SYSDATETIME() returns the current date and time as a DATETIME2 (higher precision). SYSUTCDATETIME() returns the current UTC time as DATETIME2.

• Real-life Scenario: An international application needs to record event times in both local server time and coordinated universal time (UTC).

sql

-- Record a transaction with both local and UTC time
SELECT
    SYSDATETIME() AS LocalServerTime, -- e.g., 2023-12-03 10:05:23.1234567
    SYSUTCDATETIME() AS UTCTime;      -- e.g., 2023-12-03 15:05:23.1234567 (if server is EST -5)

SYSDATETIMEOFFSET()

• Purpose: Returns the current date and time along with the time zone offset.

• Real-life Scenario: Critical for systems operating across multiple time zones, as it preserves the original time context.

sql

SELECT SYSDATETIMEOFFSET() AS TimeWithOffset;
-- Result: 2023-12-03 10:07:01.1234567 -05:00

1.2 Deconstructing Dates (Extracting Parts)

DATEPART() / DATENAME()

• Purpose: Extract a specific part (year, month, day, hour, weekday, etc.) from a date.

• Difference: DATEPART returns an integer (e.g., 12 for December). DATENAME returns a string (e.g., 'December').

• Real-life Scenario: Generating a sales report grouped by year and quarter.

sql

-- Analyze sales by year and quarter
SELECT
    YEAR(SaleDate) AS SaleYear,
    DATEPART(QUARTER, SaleDate) AS SaleQuarter, -- Returns 1, 2, 3, or 4
    DATENAME(MONTH, SaleDate) AS SaleMonthName, -- Returns 'October', 'November', etc.
    SUM(SaleAmount) AS TotalSales
FROM Sales
GROUP BY YEAR(SaleDate), DATEPART(QUARTER, SaleDate), DATENAME(MONTH, SaleDate)
ORDER BY SaleYear, SaleQuarter;

DAY(), MONTH(), YEAR()

• Purpose: Shorthand functions to extract the day, month, or year as an integer. More readable than DATEPART for these specific components.

• Real-life Scenario: Finding all employees hired in a specific year.

sql

-- Find all employees hired in 2021
SELECT FirstName, LastName, HireDate
FROM Employees
WHERE YEAR(HireDate) = 2021;

1.3 Date Arithmetic and Manipulation

DATEADD()

• Purpose: Adds a specified number of intervals (days, months, hours, etc.) to a date.

• Real-life Scenario 1: Calculating a 90-day warranty expiration date.

• Real-life Scenario 2: Scheduling a follow-up task for 2 business days from now (simplified).

sql

-- Calculate warranty expiration
SELECT
    ProductName,
    LastStockedDate AS PurchaseDate,
    DATEADD(DAY, 90, LastStockedDate) AS WarrantyExpires
FROM ProductInventory;

-- Add 2 days to the current date
SELECT DATEADD(DAY, 2, GETDATE()) AS DateTwoDaysLater;

DATEDIFF()

• Purpose: Calculates the difference between two dates in a specified interval.

• Real-life Scenario 1: Calculating employee tenure in years.

• Real-life Scenario 2: Measuring the number of days between sale and current date for aging reports.

sql

-- Calculate employee tenure
SELECT
    FirstName,
    LastName,
    HireDate,
    DATEDIFF(YEAR, HireDate, GETDATE()) AS TenureYears,
    DATEDIFF(DAY, HireDate, GETDATE()) AS TenureDays
FROM Employees;

-- Days since last sale per region
SELECT
    Region,
    MAX(SaleDate) AS LastSaleDate,
    DATEDIFF(DAY, MAX(SaleDate), GETDATE()) AS DaysSinceLastSale
FROM Sales
GROUP BY Region;

EOMONTH()

• Purpose: Returns the last day of the month for a given date. Incredibly useful for financial period calculations.

• Real-life Scenario: Generating an end-of-month financial summary report.

sql

-- Get the last day of the current month, and the last day of the previous month
SELECT
    GETDATE() AS Today,
    EOMONTH(GETDATE()) AS EndOfThisMonth,
    EOMONTH(GETDATE(), -1) AS EndOfLastMonth; -- The second parameter allows offset

-- Find all sales that occurred in the last month
SELECT *
FROM Sales
WHERE SaleDate > EOMONTH(GETDATE(), -2) -- Start of last month (2 months back, then end of that month)
AND SaleDate <= EOMONTH(GETDATE(), -1); -- End of last month

1.4 Handling Time Zones

SWITCHOFFSET()

• Purpose: Changes the time zone offset of a DATETIMEOFFSET value.

• Real-life Scenario: A user in California (UTC-8) wants to see event times in their local time, but the database stores them in UTC.

sql

-- Assuming we store UTC time in a DATETIMEOFFSET column
DECLARE @StoredTime DATETIMEOFFSET = '2023-12-03 15:30:00 +00:00'; -- UTC

-- Convert UTC to Pacific Standard Time (UTC-8)
SELECT SWITCHOFFSET(@StoredTime, '-08:00') AS PacificTime;
-- Result: 2023-12-03 07:30:00 -08:00

TODATETIMEOFFSET()

• Purpose: Adds a time zone offset to a DATETIME2 value, effectively converting it to a DATETIMEOFFSET type. It does not convert the time; it just assigns the offset.

• Real-life Scenario: You are migrating a legacy system that stored local DATETIME values. You know the time was recorded in EST and you need to assign the correct offset to it.

sql

-- Legacy data: '2023-11-25 14:00:00' was recorded in EST
DECLARE @LegacyDateTime DATETIME2 = '2023-11-25 14:00:00';

-- Assign the EST offset (-05:00) to it
SELECT TODATETIMEOFFSET(@LegacyDateTime, '-05:00') AS TimeInEST;
-- Result: 2023-11-25 14:00:00 -05:00

-- Compare with SWITCHOFFSET which would assume the input is already in a different offset.

1.5 Validation

ISDATE()

• Purpose: Determines if an expression is a valid date. Returns 1 for valid, 0 for invalid.

• Real-life Scenario: Cleaning imported data from a CSV file where the date column might contain text or invalid values.

sql

-- Validate data before attempting conversion
SELECT
    '2023-13-45' AS InvalidDateExample,
    ISDATE('2023-13-45') AS IsThisValid; -- Returns 0

-- Use in a WHERE clause to filter bad data
SELECT * FROM StagingTable
WHERE ISDATE(PotentiallyDirtyDateColumn) = 1;

---

Module 2: Conversion & Casting Functions

Data is often stored in one type but needs to be presented or used in another. This is where conversion functions come in.

2.1 Implicit vs. Explicit Conversion

• Implicit Conversion: SQL Server automatically converts data types behind the scenes. Relies on data type precedence. It's convenient but can be a performance killer and sometimes unpredictable.

  sql

  -- SQL Server implicitly converts the VARCHAR '123' to an INT for the comparison
  SELECT * FROM Employees WHERE EmployeeID = '123';

• Explicit Conversion: You, the developer, dictate the conversion using CAST or CONVERT. This is always the best practice as it makes your intentions clear and avoids performance issues.

2.2 CAST vs. CONVERT

CAST ( expression AS data_type [ ( length ) ] )

• Purpose: ANSI-standard function for explicit type conversion.

• Pros: Portable across different SQL databases (more standard).

• Cons: Less formatting control.

CONVERT ( data_type [ ( length ) ], expression [ , style ] )

• Purpose: TSQL-specific function for explicit type conversion.

• Pros: The style parameter allows for powerful formatting, especially for dates and numeric types.

• Cons: Not ANSI standard, less portable.

Real-life Scenario 1: Basic Numeric to String Conversion

sql

-- Calculate a percentage and present it as a string
SELECT
    SaleAmount,
    SaleAmount * 0.1 AS CommissionRaw, -- Decimal
    CAST(SaleAmount * 0.1 AS DECIMAL(10,2)) AS CommissionAsNumber, -- Formatted as number
    '$' + CAST(CAST(SaleAmount * 0.1 AS DECIMAL(10,2)) AS NVARCHAR(20)) AS CommissionString -- Formatted as currency string
FROM Sales;

Real-life Scenario 2: Formatting Dates (Where CONVERT Shines)

sql

-- Different date formatting styles using CONVERT
SELECT
    GETDATE() AS DefaultFormat,
    CONVERT(NVARCHAR, GETDATE(), 1) AS Style1_US_mm_dd_yy, -- 12/03/23
    CONVERT(NVARCHAR, GETDATE(), 101) AS Style101_US_mm_dd_yyyy, -- 12/03/2023
    CONVERT(NVARCHAR, GETDATE(), 103) AS Style103_UK_dd_mm_yyyy, -- 03/12/2023
    CONVERT(NVARCHAR, GETDATE(), 112) AS Style112_ISO_yyyymmdd; -- 20231203 (great for sorting)

2.3 Safe Conversion: TRY_CAST and TRY_CONVERT

A major advancement in robust coding. These functions return NULL instead of causing an error if the conversion fails.

• Purpose: Safely attempt a conversion. Essential for handling dirty or unpredictable data.

• Real-life Scenario: Importing data from an external source where a "numeric" column might contain text like 'N/A' or ''.

sql

-- This query would fail with a conversion error
SELECT CAST('NotANumber' AS INT); -- Error: Conversion failed

-- These queries will handle the error gracefully by returning NULL
SELECT
    TRY_CAST('NotANumber' AS INT) AS SafeAttempt1, -- Returns NULL
    TRY_CONVERT(INT, '123') AS SafeAttempt2, -- Returns 123
    TRY_CONVERT(INT, '') AS SafeAttempt3; -- Returns NULL

-- Use with COALESCE or ISNULL to provide a default value
SELECT
    COALESCE(TRY_CONVERT(INT, DirtyDataColumn), 0) AS CleanData
FROM StagingTable;

Best Practice: Always prefer TRY_CAST/TRY_CONVERT over CAST/CONVERT when processing data from external files, user input, or any non-guaranteed-clean source.

2.4 Parsing String Data: PARSE and TRY_PARSE

These functions go a step further. They don't just convert types; they interpret culture-specific string formats (like dates).

• Purpose: Convert a string to a date/time or number type by recognizing a cultural format (e.g., '3 décembre 2023').

• How they work: They use the .NET Framework Common Language Runtime (CLR), which makes them significantly slower than CONVERT. Use them sparingly, only for truly ambiguous string formats.

• Real-life Scenario: Processing a multi-regional file where dates are written in their local language format.

sql

-- Using English US culture
SELECT PARSE('December 3, 2023' AS DATE USING 'en-US') AS ParsedDateUS;

-- Using French culture
SELECT TRY_PARSE('3 décembre 2023' AS DATE USING 'fr-FR') AS ParsedDateFR;

-- This will fail as the string doesn't match the expected French format
SELECT TRY_PARSE('12/03/2023' AS DATE USING 'fr-FR') AS FailedParse; -- Returns NULL

Best Practice: Avoid PARSE/TRY_PARSE in performance-critical queries. Use CONVERT with the correct style parameter whenever the string format is known and consistent. Reserve TRY_PARSE for the initial data cleansing and staging phase.

---

Module 3: Mathematical Functions

SQL Server provides a full suite of mathematical functions for calculations directly within your queries.

3.1 Basic Arithmetic and Rounding

ABS(), CEILING(), FLOOR(), ROUND()

• Real-life Scenario: Calculating absolute profit/loss, applying different rounding rules for financial reporting.

sql

-- Profit/Loss calculation and rounding
SELECT
    ProductName,
    Price,
    Cost,
    (Price - Cost) AS ProfitRaw,
    ABS(Price - Cost) AS AbsoluteValue,
    CEILING(Price - Cost) AS ProfitRoundedUp, -- Always rounds up
    FLOOR(Price - Cost) AS ProfitRoundedDown, -- Always rounds down
    ROUND(Price - Cost, 1) AS ProfitRoundedToDime -- Rounds to 1 decimal place
FROM ProductInventory;

SIGN()

• Purpose: Returns -1 for negative numbers, 0 for zero, and 1 for positive numbers.

• Real-life Scenario: Categorizing sales performance vs. target.

sql

SELECT
    SaleAmount,
    2000.00 AS Target,
    (SaleAmount - 2000.00) AS VsTarget,
    SIGN(SaleAmount - 2000.00) AS PerformanceIndicator -- 1=Above, 0=Met, -1=Below
FROM Sales;

3.2 Exponents, Roots, and Logarithms

POWER(), SQUARE(), SQRT()

• Real-life Scenario: Calculating area or volume based on side lengths stored in the DB.

sql

-- Assume a table of circles with a Radius column
DECLARE @Radius DECIMAL(10,2) = 10.0;
SELECT
    @Radius AS Radius,
    PI() * SQUARE(@Radius) AS Area, -- SQUARE(Radius) = Radius^2
    SQRT(100) AS SquareRootOf100; -- = 10

LOG(), LOG10(), EXP()

• Purpose: For logarithmic and exponential calculations.

• Real-life Scenario: Scientific data analysis, calculating decay rates, or financial compound interest models (though financial functions might be better).

sql

-- Calculate compound interest (A = P * (1 + r/n)^(nt))
DECLARE @Principal DECIMAL(10,2) = 1000.00;
DECLARE @Rate DECIMAL(5,4) = 0.05; -- 5%
DECLARE @Years INT = 10;
DECLARE @CompoundsPerYear INT = 1;

SELECT
    @Principal * POWER((1 + @Rate/@CompoundsPerYear), @CompoundsPerYear * @Years) AS FutureValue;

3.3 Trigonometry

SIN(), COS(), TAN(), COT(), ASIN(), ACOS(), ATAN(), ATN2()

• Purpose: Standard trigonometric functions. Angles are in radians.

• Real-life Scenario: GIS or geometric calculations, e.g., calculating the distance between two points on the earth (simplified).

sql

-- Convert 90 degrees to radians
SELECT RADIANS(90) AS NinetyDegreesInRadians; -- ~1.57079

-- Calculate the Sine of 90 degrees
SELECT SIN(RADIANS(90)) AS SineOf90Degrees; -- = 1

-- A more advanced example: Calculate distance between two points (using Haversine formula would be more accurate for Earth)
DECLARE @x1 FLOAT = 0, @y1 FLOAT = 0;
DECLARE @x2 FLOAT = 3, @y2 FLOAT = 4;
SELECT SQRT( SQUARE(@x2 - @x1) + SQUARE(@y2 - @y1) ) AS Distance; -- Pythagorean theorem = 5

RAND()

• Purpose: Returns a random float value between 0 and 1.

• Real-life Scenario: Generating sample data, selecting a random row (though TABLESAMPLE or NEWID() ordering might be better for this).

sql

-- Generate a random number between 1 and 100
SELECT FLOOR(RAND() * 100) + 1 AS RandomNumber1To100;

-- Note: RAND() is evaluated once per query. For a different value per row, see below.
SELECT SaleID, RAND() AS StaticRandomValue FROM Sales; -- Same value for all rows

-- To get a different random value per row, use NEWID()
SELECT SaleID, ABS(CAST(CAST(NEWID() AS VARBINARY) AS INT)) % 100 + 1 AS DifferentRandomPerRow
FROM Sales;

---

Module 4: Powerful Analytic & Window Functions

This is where TSQL becomes incredibly powerful for data analysis. Window functions perform a calculation across a set of table rows that are somehow related to the current row, without collapsing them into a single output row like GROUP BY does.

4.1 Ranking Functions: ROW_NUMBER, RANK, DENSE_RANK, NTILE

ROW_NUMBER()

• Purpose: Assigns a unique sequential integer to rows within a partition, based on a specified order. Ties get different numbers (arbitrarily).

• Real-life Scenario: Pagination, or finding the top N records per group.

sql

-- Assign a unique row number to each sale within a region, ordered by sale amount (descending)
SELECT
    SaleID,
    Region,
    SaleAmount,
    SaleDate,
    ROW_NUMBER() OVER (PARTITION BY Region ORDER BY SaleAmount DESC) AS RankInRegion
FROM Sales;

Result:

SaleID	Region	SaleAmount	SaleDate	RankInRegion
6	South	4500.75	2023-12-02	1
4	South	3200.00	2023-11-18	2
3	South	999.99	2023-11-05	3
2	North	2500.50	2023-11-12	1
1	North	1500.00	2023-10-25	2
5	North	800.00	2023-12-01	3

RANK() and DENSE_RANK()

• RANK: Ranks rows within a partition. Ties get the same rank, and the next rank is skipped.

• DENSE_RANK: Similar to RANK, but the next rank after a tie is not skipped.

• Real-life Scenario: Ranking students by test scores. If two students are tied for 1st, the next one should be 2nd (DENSE_RANK) or 3rd (RANK).

sql

-- Compare the ranking functions
SELECT
    SaleID,
    SaleAmount,
    ROW_NUMBER() OVER (ORDER BY SaleAmount DESC) AS RowNum,
    RANK() OVER (ORDER BY SaleAmount DESC) AS Rank,
    DENSE_RANK() OVER (ORDER BY SaleAmount DESC) AS DenseRank
FROM Sales;

Result: (Notice the tie for 2nd place)

SaleID	SaleAmount	RowNum	Rank	DenseRank
6	4500.75	1	1	1
4	3200.00	2	2	2
2	2500.50	3	3	3	-- RANK skips nothing after tie for 2nd? Wait, there is no tie in this data. Let's insert one.
-- Let's force a tie for a clearer example
INSERT INTO Sales (EmployeeID, SaleAmount, SaleDate, Region) VALUES (5, 3200.00, '2023-11-19', 'South');

-- Now run the query again

text

**New Result:**
| SaleID | SaleAmount | RowNum | Rank | DenseRank |
| :--- | :--- | :--- | :--- | :--- |
| 6 | 4500.75 | 1 | 1 | 1 |
| 4 | 3200.00 | 2 | 2 | 2 |
| 7 | 3200.00 | 3 | 2 | 2 | -- Tied for 2nd place
| 2 | 2500.50 | 4 | 4 | 3 | -- RANK skips 3, DENSERANK uses 3
| ... | ... | ... | ... | ... |

**NTILE()**
*   **Purpose:** Distributes rows into a specified number of roughly equal groups (tiles).
*   **Real-life Scenario:** Dividing customers into 4 groups (quartiles) based on their lifetime value for targeted marketing.

```sql
-- Divide all sales into 4 tiers based on sale amount
SELECT
    SaleID,
    SaleAmount,
    NTILE(4) OVER (ORDER BY SaleAmount DESC) AS Quartile
FROM Sales;

4.2 Offset Functions: LAG and LEAD

• Purpose: Access data from a previous (LAG) or subsequent (LEAD) row in the same result set without a self-join.

• Real-life Scenario: Calculating month-over-month growth, finding the time between consecutive events.

sql

-- For each sale, show the previous sale amount by the same employee
SELECT
    EmployeeID,
    SaleDate,
    SaleAmount,
    LAG(SaleAmount) OVER (PARTITION BY EmployeeID ORDER BY SaleDate) AS PreviousSaleAmount,
    SaleAmount - LAG(SaleAmount) OVER (PARTITION BY EmployeeID ORDER BY SaleDate) AS AmountChange
FROM Sales
ORDER BY EmployeeID, SaleDate;

4.3 Analytic Functions: FIRST_VALUE, LAST_VALUE

• Purpose: FIRST_VALUE returns the first value in an ordered partition. LAST_VALUE can be tricky because its default window is "RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW", which might not be what you expect.

• Real-life Scenario: Comparing an employee's first sale to their most recent sale.

sql

-- Find the first and most recent sale amount for each employee
SELECT DISTINCT
    EmployeeID,
    FIRST_VALUE(SaleAmount) OVER (PARTITION BY EmployeeID ORDER BY SaleDate) AS FirstSaleAmount,
    LAST_VALUE(SaleAmount) OVER (PARTITION BY EmployeeID ORDER BY SaleDate RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS LastSaleAmount
FROM Sales;
-- Note the explicit window frame for LAST_VALUE to get the true last value in the partition.

4.4 Distribution Functions: PERCENT_RANK, CUME_DIST

• PERCENT_RANK: (Rank - 1) / (Total Rows - 1). The relative rank of a row within a group.

• CUME_DIST: Cumulative distribution. The percentage of rows with values less than or equal to the current row's value.

• Real-life Scenario: Advanced statistical analysis of sales performance. "This sale is in the top 10% of all sales."

sql

-- Analyze the distribution of sales
SELECT
    SaleID,
    SaleAmount,
    PERCENT_RANK() OVER (ORDER BY SaleAmount) AS PercentRank, -- % of sales with a value less than this one
    CUME_DIST() OVER (ORDER BY SaleAmount) AS CumeDist -- % of sales with a value <= this one
FROM Sales;

Window Functions Best Practices & Pros/Cons

• Pros:

  • Powerful: Solve complex problems with simple, readable syntax.

  • Performance: Often much faster than equivalent solutions using self-joins, correlated subqueries, or cursors.

• Cons:

  • Can be complex to understand at first.

  • Not all window functions are available in all versions of SQL Server (mostly available from 2005 onwards, with enhancements in 2012).

• Best Practices:

  1. Always use an ORDER BY in the OVER() clause for ranking and offset functions. It's mandatory for them.

  2. Use PARTITION BY to define your groups (like a non-collapsing GROUP BY).

  3. Be very careful with the window frame for functions like LAST_VALUE. Always specify RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING if you want the true last value in the partition.

  4. Window functions are evaluated after WHERE, GROUP BY, and HAVING. To filter their results, you must use a CTE or subquery.

sql

-- Correct way to filter based on a window function
WITH RankedSales AS (
    SELECT
        SaleID,
        SaleAmount,
        ROW_NUMBER() OVER (PARTITION BY Region ORDER BY SaleAmount DESC) AS RankInRegion
    FROM Sales
)
SELECT * FROM RankedSales
WHERE RankInRegion = 1; -- Get the top-selling sale for each region

---

Conclusion

This guide has taken you on a journey from the fundamental building blocks of TSQL (GETDATE, CAST) to the advanced analytical power of window functions. The key to mastery is understanding not just the syntax, but the real-world application of each tool.

Remember:

• Use Date Functions to accurately manage and analyze time-based data.

• Use Conversion Functions explicitly and safely, preferring the TRY_ variants.

• Use Mathematical Functions to keep calculation logic within the database layer.

• Use Window Functions to write elegant, performant queries for complex ranking, aggregation, and comparison tasks.

Experiment with the provided examples on the CompanyData database, modify them, and try to apply them to problems you encounter in your own work. Happy querying

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