FULL PYTHON + MS SQL SCRIPT (BEST PRACTICE)

 

FULL PYTHON + MS SQL SCRIPT (BEST PRACTICE)

import pyodbc

# -----------------------------------------

# SQL CONNECTION (EDIT THIS PART)

# -----------------------------------------

def get_connection():

    try:

        conn = pyodbc.connect(

            "DRIVER={ODBC Driver 17 for SQL Server};"

            "SERVER=YOUR_SERVER_NAME;"      # e.g. DESKTOP-123\SQLEXPRESS

            "DATABASE=YOUR_DATABASE_NAME;"

            "UID=YOUR_USERNAME;"

            "PWD=YOUR_PASSWORD;",

            autocommit=False                # Manual commit = safer

        )

        return conn

    except Exception as ex:

        print("Database connection failed:", ex)

        return None

# -----------------------------------------

# INSERT RECORD

# -----------------------------------------

def insert_employee(emp_id, name, salary):

    conn = get_connection()

    if not conn:

        return

    try:

        cursor = conn.cursor()

        query = """

            INSERT INTO Employees (EmpID, Name, Salary)

            VALUES (?, ?, ?)

        """

        cursor.execute(query, (emp_id, name, salary))

        conn.commit()

        print("Record inserted successfully!")

    except Exception as ex:

        conn.rollback()

        print("Insert failed:", ex)

    finally:

        conn.close()

# -----------------------------------------

# UPDATE RECORD

# -----------------------------------------

def update_employee(emp_id, new_salary):

    conn = get_connection()

    if not conn:

        return

    try:

        cursor = conn.cursor()

        query = """

            UPDATE Employees

            SET Salary = ?

            WHERE EmpID = ?

        """

        cursor.execute(query, (new_salary, emp_id))

        conn.commit()

        print("Record updated successfully!")

    except Exception as ex:

        conn.rollback()

        print("Update failed:", ex)

    finally:

        conn.close()

# -----------------------------------------

# DELETE RECORD

# -----------------------------------------

def delete_employee(emp_id):

    conn = get_connection()

    if not conn:

        return

    try:

        cursor = conn.cursor()

        query = "DELETE FROM Employees WHERE EmpID = ?"

        cursor.execute(query, (emp_id,))

        conn.commit()

        print("Record deleted successfully!")

    except Exception as ex:

        conn.rollback()

        print("Delete failed:", ex)

    finally:

        conn.close()

# -----------------------------------------

# SELECT ALL RECORDS

# -----------------------------------------

def get_all_employees():

    conn = get_connection()

    if not conn:

        return

    try:

        cursor = conn.cursor()

        cursor.execute("SELECT EmpID, Name, Salary FROM Employees")

        print("\n--- Employee List ---")

        for row in cursor.fetchall():

            print(row.EmpID, row.Name, row.Salary)

    except Exception as ex:

        print("Select failed:", ex)

    finally:

        conn.close()

# -----------------------------------------

# CALL STORED PROCEDURE

# -----------------------------------------

def call_stored_procedure():

    """

    Example Stored Procedure:

    CREATE PROCEDURE GetEmployees

    AS

    BEGIN

        SELECT * FROM Employees

    END

    """

    conn = get_connection()

    if not conn:

        return

    try:

        cursor = conn.cursor()

        cursor.execute("{CALL GetEmployees}")   # or EXEC GetEmployees

        rows = cursor.fetchall()

        print("\n--- Stored Procedure Result ---")

        for row in rows:

            print(row.EmpID, row.Name, row.Salary)

    except Exception as ex:

        print("Stored procedure failed:", ex)

    finally:

        conn.close()

# -----------------------------------------

# MAIN TESTING

# -----------------------------------------

if __name__ == "__main__":

    insert_employee(1, "Vijay", 50000)

    update_employee(1, 60000)

    get_all_employees()

    delete_employee(1)

    get_all_employees()

    call_stored_procedure()

🚀 How to Use

1. Update:

SERVER=
DATABASE=
UID=
PWD=

2. Create sample table in SQL:

CREATE TABLE Employees (
    EmpID INT PRIMARY KEY,
    Name VARCHAR(100),
    Salary DECIMAL(18,2)
);

3. Run the Python file:

python mssql_python_demo.py

Beaver's functionality

 The errors you're encountering are due to differences in the SQL functions supported by Oracle and SQL Server. The LEFT and ISNULL functions are specific to SQL Server, and Oracle does not recognize them, which is why you're getting the ORA-00904: "LEFT": invalid identifier error.

Solutions:

Use Oracle-Compatible Functions:

For LEFT, use Oracle's SUBSTR function:

SELECT COLA, SUBSTR(COLB, 1, 10) FROM TABLEA;

For ISNULL, use Oracle's NVL function:

SELECT COLA, NVL(COLB, 'default_value') FROM TABLEA;

Transform Data in SQL Server:

If you can migrate the data without transformations, you can use the SQL Server-specific functions like LEFT and ISNULL after the data has been moved.

DBeaver Script Configuration:

If you want to write cross-platform scripts, you can use DBeaver's functionality to define SQL scripts for each database type, ensuring the correct functions are used for each.

If you need to run these functions as part of the migration process, you'll have to use Oracle-compatible syntax until the data is in SQL Server.

Removing duplicate rows from a large table with millions of records

 Removing duplicate rows from a large table with millions of records can be challenging, but it can be done efficiently using SQL. Here's a step-by-step guide to remove duplicates from a table in SQL Server:

1. Identify Duplicates:

First, you'll need to identify what constitutes a "duplicate." Typically, this means all columns (except for the primary key or a unique identifier) are the same.

2. Create a Backup:

Before making any changes, it's good practice to create a backup of your table.

 
SELECT * INTO YourTable_Backup
FROM YourTable;

3. Remove Duplicates Using a CTE (Common Table Expression):

The most common and efficient way to remove duplicates is to use a CTE combined with the ROW_NUMBER() function. Here's how you can do it:

 
WITH CTE AS (
    SELECT 
        *,
        ROW_NUMBER() OVER (PARTITION BY Column1, Column2, Column3 ORDER BY (SELECT NULL)) AS RN
    FROM 
        YourTable
)
DELETE FROM CTE
WHERE RN > 1;

• Explanation:
  • The ROW_NUMBER() function assigns a unique sequential integer to rows within a partition of a result set, starting at 1 for the first row in each partition.
  • The PARTITION BY clause divides the result set into partitions to which the ROW_NUMBER() function is applied.
  • The ORDER BY (SELECT NULL) clause simply ensures that the rows are arbitrarily ordered within each partition.
  • Rows with RN > 1 are considered duplicates and are deleted.

4. Verify the Results:

After running the deletion, it's essential to verify that duplicates have been removed and that the table integrity is maintained.

 
SELECT Column1, Column2, Column3, COUNT(*)
FROM YourTable
GROUP BY Column1, Column2, Column3
HAVING COUNT(*) > 1;

This query should return no results if all duplicates have been successfully removed.

5. Rebuild Indexes and Update Statistics:

After removing duplicates, it's a good idea to rebuild any indexes on the table and update the statistics.

 
ALTER INDEX ALL ON YourTable REBUILD;
UPDATE STATISTICS YourTable;

6. Considerations for Large Tables:

For very large tables (like your 260 million rows), you might need to remove duplicates in smaller batches to avoid long transaction times or transaction log growth. This can be done using a loop or by deleting rows in chunks.

Example:

 
WHILE EXISTS (SELECT 1 FROM CTE WHERE RN > 1)
BEGIN
    DELETE TOP (10000) FROM CTE WHERE RN > 1;
END

7. Monitor and Optimize:

Monitor the process and optimize as needed, particularly in a production environment where long-running queries could impact performance.

Conclusion:

By following these steps, you should be able to efficiently remove duplicate rows from your large table in SQL Server. The key is to use a method that minimizes the impact on your server and ensures the integrity of your data.

Partitioning a table in SQL Server that has a primary key referenced by foreign keys in other tables

 Partitioning a table in SQL Server that has a primary key referenced by foreign keys in other tables can indeed be challenging due to the constraints involved. Your approach of dropping and recreating constraints is a common solution, but here are a few considerations that might simplify the process or make it more efficient:

1. Use SWITCH Instead of Dropping and Recreating Constraints:

If you are partitioning for performance reasons, consider using the SWITCH statement. You can create a new partitioned table with the desired schema, and then use ALTER TABLE SWITCH to move data between tables. This avoids dropping foreign key constraints but requires more setup and might need the table to be empty when switching.

• Step-by-Step:
  1. Create a new partitioned table with the same schema as the original table.
  2. Use INSERT INTO ... SELECT to move data to the new table.
  3. Use ALTER TABLE SWITCH to switch the tables.
  4. Drop the old table and rename the new one.

2. Temporarily Disable Constraints:

SQL Server allows you to disable foreign key constraints temporarily, which might help in avoiding the need to drop them.

• Step-by-Step:

  1. Disable the foreign key constraints on the dependent tables.
  2. Drop the primary key constraint and clustered index.
  3. Partition the table and recreate the primary key constraint on the partitioned table.
  4. Re-enable the foreign key constraints.

• Example:

   
  ALTER TABLE [DependentTable] NOCHECK CONSTRAINT [FK_Name];
  -- Drop and recreate the primary key and clustered index
  ALTER TABLE [DependentTable] CHECK CONSTRAINT [FK_Name];
  

3. Using Schema Modification with Minimal Downtime:

If downtime is a concern, consider using techniques like online index creation and schema modification that might reduce the impact on the application.

• Online Index Creation: SQL Server Enterprise Edition supports creating and rebuilding indexes online, which might reduce the impact during partitioning.
• Schema Modifications: You could stage the new partitioned table while keeping the original table intact, then switch over with minimal downtime.

4. Consideration for SQL Server Version:

If you're using SQL Server 2016 or later, take advantage of improvements in partitioning and index creation features, like support for more efficient operations on partitioned tables.

5. Using a Maintenance Window:

Since the process involves significant changes, performing this operation during a maintenance window might be the best option, even if it means temporarily disabling or dropping constraints.

6. Documentation and Backup:

Document each step carefully and ensure you have a full backup before proceeding. This will help in case anything goes wrong during the process.

Conclusion:

Unfortunately, there isn’t a way to completely avoid the process of dropping and recreating constraints when partitioning a table that is heavily referenced by foreign keys. However, depending on your specific environment and requirements, the alternatives like SWITCH, temporarily disabling constraints, or using online operations might make the process smoother and less disruptive.

 

Check all indexes in all table in Database in MS SQL Server

 USE YourDatabaseName; -- Replace with your actual database name

-- Query to retrieve index information
SELECT 
    TableName = t.name,
    IndexName = ind.name,
    IndexType = CASE ind.index_id 
                    WHEN 0 THEN 'Heap (No Clustered Index)'
                    WHEN 1 THEN 'Clustered'
                    ELSE 'Nonclustered'
                END,
    ColumnName = col.name,
    ColumnPosition = ic.key_ordinal
FROM 
    sys.indexes ind
INNER JOIN 
    sys.index_columns ic ON ind.object_id = ic.object_id AND ind.index_id = ic.index_id
INNER JOIN 
    sys.columns col ON ic.object_id = col.object_id AND ic.column_id = col.column_id
INNER JOIN 
    sys.tables t ON ind.object_id = t.object_id
WHERE 
    ind.type_desc <> 'Heap' -- Exclude heap tables (tables without clustered indexes)
ORDER BY 
    t.name, ind.name, ic.key_ordinal;

Explanation:

1. USE YourDatabaseName: Replace YourDatabaseName with the name of your SQL Server database to switch to that database context.

2. sys.indexes: This system view contains information about indexes in the database.

3. sys.index_columns: This view provides details about the columns that are part of each index.

4. sys.columns: This view gives information about columns in tables.

5. sys.tables: This view contains information about tables in the database.

6. SELECT: Retrieves the table name (t.name), index name (ind.name), index type (ind.index_id), column name (col.name), and column position (ic.key_ordinal).

7. CASE statement: Checks the ind.index_id to determine the type of index (clustered, nonclustered, or heap).

8. WHERE: Filters out heap tables (ind.type_desc <> 'Heap') because they don't have traditional indexes.

9. ORDER BY: Orders the results by table name (t.name), index name (ind.name), and column position (ic.key_ordinal).

Notes:

• Make sure to replace YourDatabaseName with the actual name of your database.
• This query provides comprehensive information about all indexes in the database except for heap tables.
• Running this query requires appropriate permissions to access the system views (sys.indexes, sys.index_columns, sys.columns, sys.tables).

By executing this query in SQL Server Management Studio (SSMS) or any other SQL query tool, you can obtain a detailed list of indexes and their configurations across all tables in your database.