Implement database connection functionality

[MrB-Can]

Develop the functionality to establish connections with different databases, allowing users to input connection details and validate the connection status.

[MrB-Can]

Supported Databases: Which types of databases should the software be able to connect to? You've mentioned PostgreSQL, MySQL, and SQL Server previously, but are there any other types that should be supported?

• Let's start with postgres but we will keep in mind that the solutionn needs to work with:

1. BigQuery
2. Databricks
3. MySQL
4. Oracle
5. Postgres
6. Redshift
7. Rockset
8. SAP HANA
9. Snowflake
10. SQL Server
11. Synapse
12. Trino
13. Vertica

Connection Details: What specific connection details do you want users to input? These usually include hostname, port, username, password, and database name, but there might be additional details required for certain types of databases.

• It will depend on how we connect. I am used to SQLAlchemy connections, but am open to others.

Validation: How do you want the software to validate the connection status? For instance, should it simply check if a connection can be established, or should it also validate user permissions, database existence, etc.?

1. Check connection status
2. List databases or schemas or another command that will verify

Error Handling: What should happen if a connection fails? How should the software handle this, and what feedback should be given to the user?

• Ideally we can find out why it failed and provide that feedback to the user

Security Considerations: As this task involves handling potentially sensitive information (like usernames and passwords), what security measures should be in place to protect this data?

• What's recommended here?

Interface: How should this functionality be presented to the user? Is this going to be a command-line interface, a graphical interface, or both?

• Can we start with connecting with python and python tests? We are working on the UI later.

[MrB-Can]

Supported Databases: We'll start with implementing the functionality for PostgreSQL. We'll use an architecture that's modular and scalable so that support for other databases can be added easily in the future.

Connection Details: The connection details will follow the structure required by SQLAlchemy or a similar ORM. For PostgreSQL, these are generally the hostname, port, database name, username, and password.

Validation: The software will check if a connection can be established using the provided details. After connection, it will execute a simple query (like listing the databases/schemas) to ensure the user has necessary permissions.

Error Handling: If a connection fails, the software will try to determine the cause of the failure (like incorrect credentials, unreachable host, insufficient permissions, etc.) and provide this information to the user.

Security Considerations: Handling sensitive data like usernames and passwords requires secure practices. Credentials should not be hardcoded into the program and should be stored securely if persisting them is necessary. Tools such as Python's getpass module, which can securely prompt the user for a password, or encryption modules such as cryptography could be useful here.

Interface: As the UI will be developed later, for now, we'll focus on creating a Python module to handle database connections. This will include a set of Python functions to establish a connection, validate it, and handle errors. We'll also create unit tests to verify the correctness of these functions.

Based on this, here is a proposed list of subtasks for task #25:

1. Research and decide on the Python library to use for database connections (such as SQLAlchemy or psycopg2 for PostgreSQL).
2. Develop a Python module with functions to establish a connection to a PostgreSQL database given the necessary details.
3. Implement validation function that performs simple query after connection.
4. Incorporate error handling to capture and interpret common connection errors.
5. Ensure secure handling of sensitive information like passwords.
6. Write Python unit tests to test the connection functionality with various scenarios (valid details, invalid details, insufficient permissions, etc.).

[MrB-Can]

We are going to have to account for major differences in dialect.

The differences often manifest in the following areas:

Data Types: Different databases support different data types. For example, the way PostgreSQL handles arrays or JSON data is different from MySQL or SQL Server.

Functions: SQL functions such as string manipulation, date and time, mathematical, aggregate, and conversion functions often have different syntax across databases.

Subqueries and Joins: Some databases support complex subqueries and different types of joins which might not be available in other databases.

Full-Text Search: The syntax and capabilities for full-text search can vary greatly between databases.

Indexing and Constraints: The syntax for creating indices and applying constraints like unique, foreign key, etc., varies across databases.

Stored Procedures and Triggers: If used, the creation and calling of stored procedures and triggers differ significantly across databases.

Paging and Limiting Result Sets: Different databases have different ways of limiting results returned by a query (LIMIT in MySQL, TOP in SQL Server).

Error Handling: Each database will return errors in a unique way which will need to be handled individually.

Need to update the design document to reflect these thoughts.