Snippet Name: Executing_SQL_from_Notebooks
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Created Date: 2025-06-16 13:12:45
Last Modified Date: 2025-05-31 10:31:39
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Title: Executing SQL from Notebooks
Author: user
can i create a notebook in azure data studio with links to *.sql files to be executed, or a folder of files, or do the scripts ahve to be embedded int eh notebook
Author: ChatGPT
In Azure Data Studio, you can create a notebook that references `.sql` files, but the SQL code has to be embedded directly into the notebook cells if you want to execute the scripts from within the notebook.
However, if you want to keep the SQL scripts in separate `.sql` files, you have a couple of options:
1. **Manually Embed SQL Code:**
- You can copy the contents of your `.sql` files and paste them into code cells in the notebook.
- Each cell can then be executed independently.
2. **Use External Links (For Reference):**
- You can include markdown cells with links to `.sql` files stored locally or on a cloud storage service (like Azure Blob Storage or GitHub).
- These links will only open the files; they won???t execute them.
3. **Automate Execution Using Python or PowerShell:**
- You can embed a Python or PowerShell script within a notebook cell that reads the contents of your `.sql` files and executes them against your database.
- This approach allows you to maintain separate `.sql` files while still running them from within the notebook.
If your goal is to automate running multiple scripts or to keep your SQL scripts modular and maintainable, the third option might be the best approach.
Author: user
can you give me an example of the third option, assuming the folder full of files are located at /home/lowell/documents/SandBox
Author: ChatGPT
User has a folder full of files located at `/home/lowell/documents/SandBox`.
Author: tool
Model set context updated.
Author: ChatGPT
Here's an example of how you can automate the execution of multiple SQL files in a folder using Python within an Azure Data Studio notebook. The folder path is `/home/lowell/documents/SandBox`, and we'll assume you're using a connection string to your SQL Server database.
### Step 1: Set Up the Python Environment
Ensure you have the `pyodbc` package installed in your environment. You can install it by running:
```python
!pip install pyodbc
```
### Step 2: Write the Python Code to Execute SQL Files
```python
import os
import pyodbc
# Define the folder containing your SQL files
folder_path = '/home/lowell/documents/SandBox'
# Set up the database connection string
connection_string = (
"DRIVER={ODBC Driver 17 for SQL Server};"
"SERVER=your_server_name;"
"DATABASE=your_database_name;"
"UID=your_username;"
"PWD=your_password;"
)
# Create a connection to the SQL Server
connection = pyodbc.connect(connection_string)
cursor = connection.cursor()
# Loop through each SQL file in the folder and execute them
for filename in os.listdir(folder_path):
if filename.endswith('.sql'):
file_path = os.path.join(folder_path, filename)
# Read the content of the SQL file
with open(file_path, 'r') as file:
sql_script = file.read()
# Execute the SQL script
try:
cursor.execute(sql_script)
connection.commit()
print(f"Executed {filename} successfully.")
except Exception as e:
print(f"Error executing {filename}: {e}")
# Close the cursor and connection
cursor.close()
connection.close()
```
### Explanation:
1. **Folder Path**: The folder containing your SQL files is specified by `folder_path`.
2. **Connection String**: Replace `your_server_name`, `your_database_name`, `your_username`, and `your_password` with your actual SQL Server credentials.
3. **Execution Loop**: The script loops through each `.sql` file in the folder, reads its content, and executes it against the connected SQL Server database.
4. **Error Handling**: If an error occurs while executing a script, it will print the error message.
### Step 3: Run the Python Cell in Azure Data Studio
Once you've added this Python code to a cell in your Azure Data Studio notebook, you can run the cell to execute all the SQL files in the specified folder.
This approach allows you to keep your SQL scripts organized in separate files while still automating their execution from within an Azure Data Studio notebook.
Author: user
in Azure Data Studio, i am trying to add a Code block for Python, but it installs python, thenc hanges context back to SQL
Author: ChatGPT
It sounds like Azure Data Studio might be defaulting back to the SQL kernel after installing the Python extension. Here???s how you can ensure that the Python code block remains in Python context:
### Step-by-Step Guide
1. **Install Python Extension**:
- When you first attempt to use Python, Azure Data Studio may prompt you to install the necessary Python dependencies. Go ahead and allow it to install.
2. **Change Kernel to Python**:
- After installation, ensure that the notebook???s kernel is set to Python.
- You can do this by clicking on the kernel selection dropdown at the top right of the notebook, next to where it says "SQL" (if it???s currently set to SQL).
- Change it to Python from the list of available kernels.
3. **Add a Python Code Cell**:
- Once the kernel is set to Python, add a new code cell in your notebook.
- You can do this by clicking the "+ Code" button or by using the shortcut `Shift + Enter` after a markdown or code cell.
4. **Execute Python Code**:
- Enter your Python code into the new cell and execute it.
- The kernel should remain as Python, allowing you to run your Python script as intended.
If Azure Data Studio continues to revert to SQL, you may need to manually switch the kernel back to Python after each SQL code block or ensure the correct kernel is set before adding your Python code block.
### Kernel Management Tips
- **Mixed Kernels**: If you need to switch between SQL and Python frequently, make sure you manually set the kernel before adding a new cell.
- **Notebook Settings**: Ensure your notebook is set up with Python as the primary kernel if most of your work is in Python.
Let me know if you continue to encounter issues!
Author: user
install packages due to an OSError: [Errno 20] Not a directory: '/home/lowell/.local/bin/pygmentize'
Author: ChatGPT
The error `OSError: [Errno 20] Not a directory: '/home/lowell/.local/bin/pygmentize'` usually indicates that there's an issue with the installation or configuration of the Python environment in Azure Data Studio. Specifically, it suggests that the installation process tried to access something it expected to be a directory, but it wasn't.
### Steps to Resolve the Issue:
1. **Check the Pygmentize Path**:
- Verify if `/home/lowell/.local/bin/pygmentize` is a file rather than a directory. You can do this by running:
```bash
ls -l /home/lowell/.local/bin/pygmentize
```
- If it's a file and not a directory, that???s expected, as `pygmentize` is a script for syntax highlighting used by the `Pygments` library.
2. **Reinstall Pygments**:
- It might be useful to reinstall `Pygments` to ensure that all dependencies and scripts are correctly installed.
```bash
pip uninstall pygments
pip install pygments
```
- You might need to prepend the commands with `!` if you???re running them within a notebook cell in Azure Data Studio.
3. **Check File Permissions**:
- Ensure that the file has the correct permissions to be executed:
```bash
chmod +x /home/lowell/.local/bin/pygmentize
```
4. **Use a Virtual Environment**:
- Sometimes issues arise from conflicts with local installations. You could try creating and using a virtual environment:
```bash
python3 -m venv ~/.ads_venv
source ~/.ads_venv/bin/activate
pip install pygments
```
- Once the virtual environment is set up, you can point Azure Data Studio to use this environment for Python notebooks.
5. **Clear and Reinstall Python Environment in Azure Data Studio**:
- If the problem persists, you might consider resetting or reinstalling the Python environment within Azure Data Studio.
- Remove the existing Python installation and let Azure Data Studio reinstall it, ensuring that the installation paths are correct and all dependencies are installed properly.
### Additional Considerations:
- **Configuration Files**: If any `.bashrc`, `.zshrc`, or other shell configuration files point to an incorrect Python environment, adjust them accordingly.
- **Logs and Details**: Check any logs or additional error details provided by Azure Data Studio during the installation process for more clues.
If these steps don't resolve the issue, it may help to fully uninstall and then reinstall Azure Data Studio itself to reset the environment.
Author: user
[Errno 20] Not a directory: '/home/lowell/.local/bin/jsonpointer'
Author: ChatGPT
The `[Errno 20] Not a directory: '/home/lowell/.local/bin/jsonpointer'` error indicates a similar issue where the installation process is trying to treat a file as if it were a directory. This issue could be due to a corrupt or incomplete installation of the `jsonpointer` package or a misconfiguration in your environment.
### Steps to Resolve the Issue:
1. **Check the File Type**:
- Verify whether `/home/lowell/.local/bin/jsonpointer` is a file or directory:
```bash
ls -l /home/lowell/.local/bin/jsonpointer
```
- If it???s a file, this is expected, as `jsonpointer` is a script, not a directory.
2. **Reinstall the `jsonpointer` Package**:
- To ensure that the `jsonpointer` package and its scripts are correctly installed, you can try uninstalling and then reinstalling the package:
```bash
pip uninstall jsonpointer
pip install jsonpointer
```
- Again, prepend the commands with `!` if running from a notebook cell in Azure Data Studio.
3. **Check and Set Correct Permissions**:
- Ensure that the `jsonpointer` file has the correct permissions:
```bash
chmod +x /home/lowell/.local/bin/jsonpointer
```
4. **Virtual Environment**:
- Consider using a virtual environment to isolate dependencies and avoid conflicts with global Python packages:
```bash
python3 -m venv ~/.ads_venv
source ~/.ads_venv/bin/activate
pip install jsonpointer
```
- Set your Azure Data Studio notebook to use this virtual environment.
5. **Cleanup and Reinstall**:
- If issues persist, you might want to manually clean up the `.local/bin/` directory and reinstall the problematic packages:
```bash
rm /home/lowell/.local/bin/jsonpointer
pip install --force-reinstall jsonpointer
```
6. **Check Path Conflicts**:
- Ensure there are no conflicts or incorrect paths in your environment. For example, check your `$PATH` variable to make sure it doesn???t contain paths that might cause conflicts.
### Further Actions:
If the issue is persistent after these steps, consider looking into the following:
- **Logs and Error Messages**: Look for any additional logs or error messages that might provide more details about what's causing the issue.
- **Python Environment Reset**: Reset the Python environment within Azure Data Studio, which might help resolve any lingering configuration issues.
These steps should help resolve the `Errno 20` issue with `jsonpointer`. Let me know if you need further assistance!