If you have thousands of rows, that could be expensive to compute for the database.
You would normally want to filter the rows to receive only the ones you want. But we'll learn about that later in the next chapter.
### A SQL Shortcut
If we want to get all the columns like in this case above, in SQL there's a shortcut, instead of specifying each of the column names wew could write a `*`:
```SQL
SELECT *
FROM hero
```
That would end up in the same result. Although we won't use that for **SQLModel**.
Here we are only selecting the `id` and `name` columns.
And it would result in a table like this:
<table>
<tr>
<th>id</th><th>name</th>
</tr>
<tr>
<td>1</td><td>Deadpond</td>
</tr>
<tr>
<td>2</td><td>Spider-Boy</td>
</tr>
<tr>
<td>3</td><td>Rusty-Man</td>
</tr>
</table>
And here is something interesting to notice. SQL databases store their data in tables. And they also always communicate their results in **tables**.
### `SELECT` Variants
The SQL language allows several **variations** in several places.
One of those variations is that in `SELECT` statements you can use the names of the columns directly, or you can prefix them with the name of the table and a dot.
For example, the same SQL code above could be written as:
For example, in our database, we only have one table that has all the columns, `id`, `name`, `secret_name`, `age`. And here we are getting a result table with fewer columns.
One of the main points of SQL is to be able to keep the data structured in different tables, without repeating data, etc, and then query the database in many ways and get many different tables as a result.
## Read Data with **SQLModel**
Now let's do the same query to read all the heroes, but with **SQLModel**.
## Create a **Session**
The first step is to create a **Session**, the same way we did when creating the rows.
We will start with that in a new function `select_heroes()`:
It's a very simple line of code that conveys a lot of information:
```Python
statement = select(Hero)
```
This is equivalent to the first SQL `SELECT` statement above:
```SQL
SELECT id, name, secret_name, age
FROM hero
```
We pass the class model `Hero` to the `select()` function. And that tells it that we want to select all the columns necessary for the `Hero` class.
And notice that in the `select()` function we don't explicitly specify the `FROM` part. It is already obvious to **SQLModel** (actually to SQLAlchemy) that we want to select `FROM` the table `hero`, because that's the one associated with the `Hero` class model.
!!! tip
The value of the `statement` returned by `select()` is a special object that allows us to do other things.
I'll tell you about that in the next chapters.
## Execute the Statement
Now that we have the `select` statement, we can execute it with the **session**:
Check out the number bubbles to see what is done by each line of code.
Here it starts to become more evident why we should have a single **engine** for the whole application, but different **sessions** for each group of operations.
This new session we created uses the *same***engine**, but it's a new and independent **session**.
The code above creating the models could, for example, live in a function handling web API requests and creating models.
And the second section reading data from the database could be in another function for other requests.
So, both sections could be in **different places** and would need their own sessions.
!!! info
To be fair, in this example all that code could actually share the same **session**, there's actually no need to have two here.
But it allows me to show you how they could be separated and to reinforce the idea that you should have **one engine** per application, and **multiple sessions**, one per each group of operations.
## Get a List of `Hero` Objects
Up to now we are using the `results` to iterate over them.
But for different reasons you might want to have the full **list of `Hero`** objects right away instead of just an *iterable*. For example, if you want to return them in a web API.
The special `results` object also has a method `results.all()` that returns a list with all the objects:
Here we are putting it all on a single line, you will probably put the select statements in a single line like this more often.
## SQLModel or SQLAlchemy - Technical Details
**SQLModel** is actually, more or less, just **SQLAlchemy** and **Pydantic** underneath, combined together.
It uses and returns the same types of objects and is compatible with both libraries.
Nevertheless, **SQLModel** defines a few of its own internal parts to improve the developer experience.
In this chapter we are touching some of them.
### SQLModel's `select`
When importing from `sqlmodel` the `select()` function, you are using **SQLModel**'s version of `select`.
SQLAchemy also has it's own `select`, and SQLModel's `select` uses SQLAlchemy's `select` internally.
But SQLModel's version does a lot of **tricks** with type annotations to make sure you get the best **editor support** possible, no matter if you use **VS Code**, **PyCharm**, or something else. ✨
!!! info
There was a lot of work and research, with different versions of the internal code, to improve this as much as possible. 🤓
### SQLModel's `session.exec`
📢 This is one to pay special attention to.
SQLAlchemy's own `Session` has a method `session.execute()`. It doesn't have a `session.exec()` method.
If you see SQLAlchemy tutorials, they will always use `session.execute()`.
**SQLModel**'s own `Session` inherits directly from SQLAlchemy's `Session`, and adds this additonal method `session.exec()`. Underneath, it uses the same `session.execute()`.
But `session.exec()` does several **tricks** combined with the tricks in `session()` to give you the **best editor support**, with **autocompletion** and **inline errors** everywhere, even after getting data from a select. ✨
For example, in SQLAlchemy you would need to add a `.scalars()` here:
But you would have to remove it when selecting multiple things (we'll see that later).
SQLModel's `session.exec()` takes care of that for you, so you don't have to add the `.scalars()`.
This is something that SQLAlchemy currently can't provide, because the regular `session.execute()` supports several other use cases, including legacy ones, so it can't have all the internal type annotations and tricks to support this.
On top of that, **SQLModel**'s `session.exec()` also does some tricks to reduce the amount of code you have to write and to make it as intuitive as possible.
But SQLModel's `Session` still has access to `session.execute()` too.
!!! tip
Your editor will give you autocompletion for both `session.exec()` and `session.execute()`.
📢 Remember to **always use `session.exec()`** to get the best editor support and developer experience.
### Caveats of **SQLModel** Flavor
SQLModel is designed to have the best **developer experience** in a narrow set of **very common use cases**. ✨
You can still combine it with SQLAlchemy directly and use **all the features** of SQLAlchemy when you need to, including lower level more "pure" SQL constructs, exotic patterns, and even legacy ones. 🤓
But **SQLModel**'s design (e.g. type annotations) assume you are using it in the ways I explain here in the documentation.
Thanks to this, you will get as much **autocompletion** and **inline errors** as possible. 🚀
But this also means that if you use SQLModel with some more **exotic patterns** from SQLAlchemy, your editor might tell you that *there's an error*, while in fact, the code would still work.
That's the trade-off. 🤷
But for the situations where you need those exotic patterns, you can always use SQLAlchemy directly combined with SQLModel (using the same models, etc).
