This program is like a friendly robot that talks to a big box of flower information (a database). The robot asks the box for information about a specific type of flower called "Iris-setosa". Then, it looks at each flower's details and tells us about them, like how long and wide different parts of the flower are.

Now, let's go through the thought process of writing this code:

1. We want to connect to a PostgreSQL database that has information about flowers.
2. We need to ask the database for information about a specific type of flower.
3. We'll look at each flower the database tells us about and print its measurements.
4. If anything goes wrong at any step, we want to know about it.

Here's a breakdown of the code:

1. We import the packages we need, including the one for working with databases.

2. In our main program:

   • We get the database address (PGURL) from the environment variables.
   • If we can't find it, we stop the program and say the PGURL is empty.

3. We open a connection to the database:

   • If there's a problem, we stop the program and report the error.
   • We tell the program to close the database connection when we're done.

4. We ask the database a question (query) about Iris-setosa flowers:

   • We want to know the length and width of their sepals and petals.
   • If there's a problem with our question, we stop the program and report the error.

5. We look at each flower the database told us about:

   • For each flower, we get its measurements.
   • If there's a problem understanding the measurements, we stop and report the error.
   • We print out the measurements for each flower.

6. After we've looked at all the flowers, we check if there were any problems we missed.

This code demonstrates how to connect to a PostgreSQL database, run a query, and process the results in Go. The thought process involves setting up the database connection, formulating a SQL query to get the data we want, and then processing that data row by row. This kind of program is useful for analyzing data stored in databases, like scientific data about flowers in this case.