SQL Query
SELECT
    r.blocking_session_id AS BlockingSessionID,
    DB_NAME(r.database_id) AS DatabaseName,
    --OBJECT_NAME(object_id, r.database_id) AS BlockedObjectName,
    --OBJECT_SCHEMA_NAME(object_id, r.database_id) AS BlockedObjectSchema,
    r.database_id AS BlockedObjectName,
    r.database_id AS BlockedObjectSchema,
    st.text AS BlockedSQLText,
    r.blocking_session_id AS BlockedBySessionID,
    r.command AS BlockingCommand,
    CASE WHEN r.transaction_id IS NULL THEN 'Not In Transaction' ELSE 'In Transaction' END AS BlockingInTransaction,
    es.login_name AS BlockedUser,
    er.blocking_session_id AS BlockingSessionID,
    es.host_name AS BlockingHostName,
    es.program_name AS BlockingProgram,
    er.start_time AS BlockingStartTime
FROM
    sys.dm_exec_requests AS r
JOIN
    sys.dm_exec_sessions AS es ON r.session_id = es.session_id
JOIN
    sys.dm_exec_connections AS ec ON es.session_id = ec.session_id
JOIN
    sys.dm_exec_requests AS er ON ec.most_recent_session_id = er.session_id
CROSS APPLY
    sys.dm_exec_sql_text(r.sql_handle) AS st
WHERE
    r.session_id != @@SPID
    AND r.blocking_session_id != 0
ORDER BY
    r.blocking_session_id;

Let's talk about this table definition and how it will assist us make this query.

sys.dm_exec_requestspan
sys.dm_exec_requests is not a table in the traditional sense; rather, it's a Dynamic Management View (DMV) provided by Microsoft SQL Server.

• It is a useful tool for database administrators and developers to monitor and manage the current requests or sessions on the SQL Server instance.
• This dynamic management view is particularly useful for monitoring and debugging database performance, recognizing long-running queries, discovering blocking issues, and determining server resource use. It provides significant insights into the present activity on the SQL Server instance, helping database administrators and developers to manage and modify database processes efficiently.

Overall, sys.dm_exec_requests is an essential tool for real-time performance monitoring and troubleshooting in SQL Server. It enables database administrators and developers to monitor server activity and take proactive steps to improve performance and stability.

sys.dm_exec_sessions
sys.dm_exec_sessions is another Dynamic Management View (DMV) offered by Microsoft SQL Server.

It contains information about all active sessions on the SQL Server instance, including user sessions, system sessions, and internal background processes.
The goal of sys.dm_exec_sessions is to offer a full picture of the current sessions connected to the SQL Server instance, as well as the many attributes and features associated with each. Here's how the DMV can be useful.

Overall, sys.dm_exec_sessions is a useful tool for monitoring and managing SQL Server sessions. Querying this DMV provides admins with insights into session activity, resource use, blocking circumstances, and transactional behavior, allowing them to efficiently optimize server performance and handle issues.

sys.dm_exec_connections
Microsoft SQL Server has a Dynamic Management View (DMV) called sys.dm_exec_connections.

It holds information on the current connections to the SQL Server instance, such as client connections, protocols, and network address.

The purpose of sys. dm_exec_connections is to give administrators and developers with information about the active connections to SQL Server instances.
Overall, sys.dm_exec_connections is an effective tool for monitoring and controlling client connections to SQL Server instances. Querying this DMV provides administrators with insights on connection attributes, network protocols, resource use, and session associations, allowing them to effectively diagnose connectivity issues and enhance server performance.

sys.dm_exec_sql_text
sys.dm_exec_sql_text is a Dynamic Management Function (DMF) that Microsoft SQL Server provides. It retrieves the text of SQL statements that are presently being executed or have been executed recently. This feature is especially useful for monitoring and troubleshooting purposes.

Overall, sys.dm_exec_sql_text is an effective monitoring and troubleshooting tool for SQL Server instances. It gives information about the SQL statements that are being executed, allowing administrators to identify performance issues, optimize queries, and assure the database's security and reliability.

Attention
Dynamic management views (DMVs) and dynamic management functions (DMFs) provide server state information that can be used to monitor a server instance's health, identify problems, and optimize performance.

Deadlock Monitor 

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SQL window functions are strong instruments for complex data manipulation and analysis. The four primary window functions in T-SQL are ROW_NUMBER, RANK, DENSE_RANK, and NTILE. Within result sets, these functions aid in ranking, sorting, and organizing data. Let's examine each function in more detail, learning about its purpose and practical applications.

To comprehend the ideas, let's look at the table below.

CREATE TABLE StudentMarks (
    StudentID INT,
    Subject VARCHAR(50),
    Marks INT
);

Adding a few sample records in the above table.

1. ROW_NUMBER

A window function called ROW_NUMBER uses the given ordering to assign a distinct sequential number to each row inside a partition of a result set. It starts at 1 and generates a new number for every row, without any pauses.

Use Cases: ROW_NUMBER is frequently used to filter the top N rows in a partition, discover duplicates, and create pagination.

Example

SELECT *,
       ROW_NUMBER() OVER (ORDER BY Marks DESC) AS RowNumber
FROM StudentMarks;

It will return the output.

2. RANK

Another window method called RANK uses a given ordering to give each row inside a partition of a result set a distinct rank. When gaps occur in the ranking sequence, it leaves them there and gives the same rank to rows with equal values.

Use Cases: When ranking is permitted to have ties, like in the case of rating students based on exam results, RANK is frequently employed.

Another window method called RANK uses a given ordering to give each row inside a partition of a result set a distinct rank. When gaps occur in the ranking sequence, it leaves them there and gives the same rank to rows with equal values.

Use Cases: RANK is often used when ranking is allowed to contain ties, such as when grading students according to exam scores.

Example:

SELECT *,
       RANK() OVER (ORDER BY Marks DESC) AS Rank
FROM StudentMarks;

It will return the output.

3. DENSE_RANK
DENSE_RANK is similar to RANK but differs in that it assigns consecutive ranks to rows with equal values, without leaving gaps in the ranking sequence. It ensures that ranks are assigned in a continuous, sequential manner.

Use Cases: DENSE_RANK is preferred when consecutive ranking without gaps is desired, such as ranking products by sales performance.

Example
SELECT *,
       DENSE_RANK() OVER (ORDER BY Marks DESC) AS DenseRank
FROM StudentMarks;

It will return the output.

4. NTILE
NTILE is a window function that divides the result set into a specified number of roughly equal-sized buckets or partitions, assigning each row to one of these buckets. The function ensures that the size difference between buckets is minimized.

Use Cases: NTILE is commonly used for data segmentation and percentile calculations, such as dividing customers into groups based on their income.

Example
SELECT *,
       NTILE(4) OVER (PARTITION BY SUBJECT ORDER BY Marks DESC) AS Student_Group
FROM StudentMarks;

It will return the output.
studentID   Subject

It will return the output.

In summary
With SQL Server, window functions like ROW_NUMBER, RANK, DENSE_RANK, and NTILE give analysts and developers strong capabilities for data analysis, ranking, and partitioning. Through the appropriate utilization of these functionalities, users can accomplish intricate analytical tasks, acquire a deeper understanding of their data, and retrieve important information from their databases. By being aware of the subtleties and functionalities of each window function, SQL practitioners can fully utilize T-SQL for complex data manipulation and analysis activities.

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In the dynamic realm of database management, making sure that SQL performance is optimized is essential to the smooth and effective operation of applications. Delivering a responsive and scalable application depends heavily on performance optimization, regardless of the size of the database or the system in question. We'll look at important tactics and industry best practices for SQL database performance optimization in this post.

1. Recognizing Performance Optimization's Significance
Providing a seamless user experience requires good database performance. Application responsiveness and scalability may be impacted by bottlenecks caused by slow queries and wasteful database design. SQL database efficiency can be greatly increased by developers by devoting work to performance enhancement.

2. Determining Performance Bottlenecks via Profiling
Finding performance constraints is essential before implementing optimization approaches. To examine query execution times, resource consumption, and index statistics, use SQL profiling tools. This diagnostic process aids in identifying areas that require improvement.

3. Query Enhancement
Indexing Strategies: An essential component of query optimization is indexing. Examine various indexing techniques, such as non-clustered and clustered indexes. For quicker query execution, identify the columns that are commonly utilized in JOIN conditions or WHERE clauses and think about indexing them.

Indexing Strategies, for instance:

-- Creating a Non-Clustered Index
CREATE INDEX IX_Employee_LastName
ON Employee (LastName);

-- Query using the index
SELECT * FROM Employee WHERE LastName = 'Smith';

Rewriting Complex Queries: Assess and rework intricate queries to increase their effectiveness. JOIN optimizations, subquery removal, and appropriate index usage are a few strategies that can help speed up query processing.

4. Considerations for Database Design

• Normalization: Aim for the best possible level of normalization for your database. Optimized normalized databases frequently yield higher performance. Finding a balance is necessary though, since over-normalization can also cause problems with performance.
• Partitioning: Take into account dividing up data in large tables according to particular standards, like important values or date ranges. This can minimize the quantity of data that needs to be scanned, which can greatly improve query performance.
  Imagine you have a large Sales table with most queries including data from a given set of dates. Queries can target certain divisions in the table by partitioning it according to the transaction date, which facilitates quicker and more effective data retrieval.