Queries as Python Code with SQLAlchemy's Expression Language
Tags: sqlalchemy, python, sql, software
Published: 2022-05-15 17:44:37

---
title: Queries as Python Code with SQLAlchemy's Expression Language
published: true
date: 2019-02-01 17:03:00 UTC
tags: SQLAlchemy,Python,SQL,Software
series: Mastering SQLAlchemy
canonical_url: https://hackersandslackers.com/database-queries-sqlalchemy-orm/
---

![Queries as Python Code with SQLAlchemy's Expression Language](https://hackersandslackers-cdn.storage.googleapis.com/2019/07/sqlalchemy-queries.jpg)

Our SQLAlchemy journey thus far has covered [managing database connections](https://hackersandslackers.com/python-database-management-sqlalchemy/) and [model creation](https://hackersandslackers.com/sqlalchemy-data-models/). Yet, how do we extract the data we want from our database?

SQLAlchemy's ORM query API simplifies the way we write database queries. Instead of writing raw SQL queries, we can construct queries on our SQLAlchemy session by chaining together methods to retrieve data. We're going to dive into SQLAlchemy's extensive query API to get an idea of all the ways we can query our data.

### Create a Session

We covered SQLAlchemy session creation in the [previous post](https://hackersandslackers.com/implement-sqlalchemy-orm/) and explained the concept of engines in the [post before that](https://hackersandslackers.com/python-database-management-sqlalchemy/). If you skipped those posts, don't. The below is copy+pasta courtesy:

```python
"""Database engine & session creation."""
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

engine = create_engine(
    'mysql+pymysql://user:password@host:3600/database',
    echo=True
)
Session = sessionmaker(bind=engine)
session = Session()
```
<figcaption>database.py</figcaption>

## Basic Query Syntax

Let's quickly become familiar with the basic structure of SQLAlchemy's query API. SQLAlchemy **session** objects have a `query()` method which accepts the _raw_ class of a data model we've previously defined. Below are the humble beginnings of a query to run on `Customer` model; or in other words, a query on the **customers** SQL table:

```python
"""Construct database queries from SQLAlchemy sessions."""
from .database import session
from .models import Customer

# Example structure of an ORM query
records = session
    .query(Customer)
    .FUNCTION()
```
<figcaption>Select records from a SQLAlchemy session</figcaption>

Calling `.query(Customer)` on our **session** isn't a valid query until we add one more method to the chain. All session queries end with a final method to shape/anticipate the result(s) of our query:

- `all()` will return _all_ records which match our query as a list of objects. If we were to use **all** on the query above, we would receive all customer records with the Python data type `List[Customer]`. 
- `first()` returns the _first_ record matching our query, despite how many records match the query (what constitutes "first" depends on how your table is sorted). This is the equivalent of adding `LIMIT 1` to a SQL query. As a result, the Python type to be returned would be `Customer`.
- `one()` is extremely useful for cases where a maximum of one record should exist for the query we're executing (think of querying by primary key). This syntax is notably useful when verifying whether or not a record exists prior to creating one.
- `scalar()` returns a single value if one exists, **None** if no values exist, or raises an exception if multiple records are returned.
- `get([VALUE(S)])` searches against a model's primary key to return rows where the primary key is equal to the value provided. `get()` also accepts tuples in the event that multiple foreign keys should be searched. Lastly, `get()` can _also_ accept a dictionary and return rows where the columns (dictionary keys) match the values provided.

To create more complex queries, we'd add to our query by chaining methods on our original query:

```python
"""Construct database queries from SQLAlchemy sessions."""
from .database import session
from .models import Customer

# Example structure of an ORM query
records = session
    .query(Customer)
    .METHOD_1()
    .METHOD_2()
    .FUNCTION()
```
<figcaption>Complex SELECT query</figcaption>

### Query Results

If we execute a query that returns multiple records, we'll need to loop through them to see the results:

```python
"""Construct database queries from SQLAlchemy sessions."""
from .database import session
from .models import Customer

# Fetch all customer records 
records = session
    .query(Customer)
    .all()

# Loop over records
for record in records:
    print(record)
```
<figcaption>Fetch all customer records and print the output</figcaption>

The SQLAlchemy ORM will return an instance of a class by default, which means the above will result in the following output:

```shell
<Customer model 1>
<Customer model 2>
<Customer model 3>
<Customer model 4>
<Customer model 5>
<Customer model 6>
<Customer model 7>
<Customer model 8>
<Customer model 9>
<Customer model 10>
```
<figcaption>Output of a session query</figcaption>

If you're looking to get dictionaries instead, use the built-in ` __dict__ ` method:

```python
...

records = session
    .query(Customer)
    .all()

for record in records:
    pp.pprint(record. __dict__ )
```
<figcaption>View query results as dictionaries</figcaption>

This instead returns dictionary objects for each row:

```shell
{ '_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x7f98c8221748>,
    'email': 'kpaladini5i@senate.gov',
    'first_name': 'Kenna',
    'id': 199,
    'join_date': datetime.datetime(2019, 4, 19, 0, 0),
    'last_name': 'Paladini',
    'preferred_language': 'Bulgarian'}
{ '_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x7f9918192d68>,
    'email': 'rlebrun5j@narod.ru',
    'first_name': 'Rriocard',
    'id': 200,
    'join_date': datetime.datetime(2015, 6, 8, 0, 0),
    'last_name': 'Le Brun',
    'preferred_language': 'Khmer'},
...
```
<figcaption>Output of a session query as dictionaries</figcaption>

Of course, you could also create your own object instead to receive only the columns you want/need:

```python
...

# Fetch all customers
records = session.query(Customer).all()

# Loop through records
for record in records:
    recordObject = {
        'name': record.name,
        'position': record.position,
        'team_name': record.team.name,
        'team_city': record.team.city
    }
    print(recordObject)
```
<figcaption>Deserialize results of a query</figcaption>

This outputs something a bit cleaner:

```shell
{ 'email': 'kpaladini5i@senate.gov',
    'first_name': 'Kenna',
    'join_date': datetime.datetime(2019, 4, 19, 0, 0),
    'last_name': 'Paladini',
    'preferred_language': 'Bulgarian'}
{ 'email': 'rlebrun5j@narod.ru',
    'first_name': 'Rriocard',
    'join_date': datetime.datetime(2015, 6, 8, 0, 0),
    'last_name': 'Le Brun',
    'preferred_language': 'Khmer'},
...
```
<figcaption>Output of query deserialization</figcaption>
## Filtering Results

Probably the most common method you'll use on a query is the `filter()` method. `filter()` is the equivalent of a SQL **WHERE** clause to return only rows that match the criteria we want:

```python
...

# Fetch records where `first_name` is `Carl`
records = session
    .query(Customer)
    .filter(Customer)
    .first_name == 'Carl')
    .all()
```
<figcaption>Select all customers named Carl</figcaption>
### filter\_by()

We could write the above query using the `filter_by()` method instead like so:

```python
...

# Fetch records where `first_name` is `Carl`
records = session
    .query(Customer)
    .filter_by(first_name="Carl")
    .all()
```
<figcaption>Filter with <code>filter_by</code></figcaption>

Unlike `filter()`, `filter_by()` accepts keyword arguments (note the difference in syntax here: `filter()` checks a conditional against a column object whereas `filter_by()` finds columns that match the arguments we pass). `filter_by()` can only search for exact values and serves as a kind of shorthand for simple filtering queries.

### like()

We can do more than filter on simple conditionals. SQLAlchemy has a `like()` method which works in an equivalent manner to SQL's `LIKE`:

```python
...

# Fetch records where `first_name` begins with the letter `J`
records = session
    .query(Customer)
    .filter(Customer.first_name.like('J%'))
    .all()
```
<figcaption>Select customer records where first names begin with "J"</figcaption>

As expected, this will give us all rows where the customer's first name starts with a **J** :

```shell
{ 'email': 'jpugsley9@netvibes.com',
    'first_name': 'Jarid',
    'join_date': datetime.datetime(2017, 10, 11, 0, 0),
    'last_name': 'Pugsley',
    'preferred_language': 'Burmese'}
{ 'email': 'jdymockek@is.gd',
    'first_name': 'Jeanna',
    'join_date': datetime.datetime(2017, 11, 13, 0, 0),
    'last_name': 'Dymocke',
    'preferred_language': 'Malayalam'}
...
```
<figcaption>Output</figcaption>

### High-level Query Methods

In addition to `filter()`, there are a few basic methods we should be familiar with. Each of these corresponds to SQL keywords you're probably familiar with:

- `limit([INTEGER])`: Limits the number of rows to a maximum of the number provided.
- `order_by([COLUMN])`: Sorts results by the provided column.  
- `offset([INTEGER])`: Begins the query at row _n_.

<!--kg-card-begin: html-->

This next part involves executing JOIN queries between models, which requires us to define relationships on our models first. Things are a bit out of order at the moment, as I actually don't cover this until the [next post](https://hackersandslackers.com/sqlalchemy-data-models). Sorry for the mess, I'm working on it!

<!--kg-card-end: html-->
## Performing Joins & Unions

We've touched on JOINs a bit previously, but we're about to kick it up a notch. We have two data models we're working with: one for **customers**, and one for **orders**. Each customer

```python
...
import pprint
from .models import Order, Customer

pp = pprint.PrettyPrinter(indent=4)

# Execute a SELECT query on JOINed tables
records = session
    .query(Customer)
    .join(Order, Order.customer_id == Customer.id)
    .all()
    
# Loop through results
for record in records:
    record_object = {
        'first_name': record.first_name,
        'last_name': record.last_name,
        'email': record.email,
        'preferred_language': record.preferred_language,
        'join_date': record.join_date,
        'orders': []
    }
    for order in record.order:
        order = {
            'order_price': order.price,
            'currency': order.currency,
            'purchase_date': order.purchase_date,
            'product': order.product
        }
        record_object['orders'].append(order)
        pp.pprint(record_object)
```
<figcaption>Join records from different tables and deserialize records</figcaption>

We perform our JOIN using the `join()` method. The first parameter we pass is the data model we'll be joining with on the "right." We then specify what we'll be joining "on": the **customer\_id** column of our **order** model, and the **id** column of our **customer** model.

Our outer loop gives us each customer, and our inner loop adds each order to the appropriate customer. Check out an example record:

```shell
{ 'email': 'jtinline16@arizona.edu',
    'first_name': 'Jerry',
    'join_date': datetime.datetime(2016, 10, 27, 0, 0),
    'last_name': 'Tinline',
    'preferred_language': 'Icelandic',
    'orders': [{'currency': 'IDR',
                'order_price': 34.24,
                'product': 'Beer - Corona',
                'purchase_date': datetime.datetime(2019, 5, 5, 0, 0)},
               {'currency': 'GEL',
                'order_price': 25.75,
                'product': 'Creamers - 10%',
                'purchase_date': datetime.datetime(2019, 1, 27, 0, 0)}]}
```
<figcaption>Output</figcaption>

Our friend Jerry here has two orders: one for some Coronas, and another for creamers. Get at it, Jerry.

### Outer JOINs

In addition to simple JOINs, we can perform outer JOINs using the same syntax:

```python
...
from .models import ExampleModel1, ExampleModel2

# Execute an outer JOIN
records = session
    .query(ExampleModel1)
    .outerjoin(ExampleModel2)
    .all()
```
<figcaption>Perform an outer join</figcaption>

### Unions

We can perform UNIONs and UNION ALLs as well:

```python
...
from .models import ExampleModel1, ExampleModel2

# Execute a UNION
records = ExampleModel1.union(ExampleModel2)
```
<figcaption>Perform a union</figcaption>

To perform a union all, simply replace `union()` with `union_all()`!

## Aggregate Functions and Stats

As with all SQL-like query languages, we can perform some aggregate stats as well. The following are available to us:

- `count([COLUMN])`: Counts the number of records in a column.
- `count(distinct([COLUMN]))`: Counts the _distinct_ number of records in a column.
- `sum([COLUMN])`: Adds the numerical values in a column.

Here's how we'd perform a query that counts the values in a column:

```python
...
from sqlalchemy import func

# Count number of records with a `first_name` value
records = session
    .query(func.count(Customer.first_name))
    .all()   

for record in records:
    print(record)
```
<figcaption>Aggregate queries</figcaption>

Which outputs:

```shell
(200,)
```
<figcaption>Output</figcaption>

This query can easily be modified to only count distinct values:

```python
...
from sqlalchemy import func
from sqlalchemy import distinct

# Count number of DISTINCT `first_name` values
records = session
    .query(func.count(distinct(Customer.first_name)))
    .all()

for record in records:
    print(record)
```
<figcaption>Query to aggregate distinct results</figcaption>
### Using Group\_by()

Of course, we can use the `group_by()` method on queries based around aggregates as well. `group_by()` works similarly to what we'd expect from SQL and Pandas:

```python
...

# Execute a `GROUP BY` aggregation query
records = session
    .query(func.count(Customer.first_name))
    .group_by(Customer.first_name)
    .all()
```
<figcaption>"Group by" aggregation</figcaption>

## Mutations

We've spent an awful lot of time going over how to extract data from our database, but haven't talked about modifying our data yet! The last item on our agenda today is looking at how to add, remove, and change records using the SQLAlchemy ORM.

### Inserting Rows

The first way we can add data is by using the `add()` method. `add()` expects an instance of a class (data model specifically) to be passed, and will create a new database row as a result:

```python
from .database import session
from .models import Customer

# Inserting records via data models
customer = Customer(
    first_name='Todd',
    last_name='Birchard',
    email='fake@example.com',
    preferred_language='English',
    join_date=datetime.now()
)
session.add(customer)
session.commit()
```
<figcaption>Insert records via ORM</figcaption>

An alternative way to add data is by using the `insert()` method. Unlike `add()`, `insert()` is called on an SQLAlchemy **Table** object and doesn't rely on receiving a data model. `insert()` is not part of the ORM:

```python
...

# Inserting records via SQLAlchemy `Table` objects
insert = [TABLE]
    .insert()
    .values(
        first_name='Todd',
        last_name='Jack Jones',
        email='fake@example.com',
        preferred_language='English',
        join_date=datetime.now()
    )
```
<figcaption>Insert records</figcaption>

### Updating

Building on the syntax of `insert()`, we can drop in the `update()` method to change an existing record's values. We chain in the `where()` method to specify which rows should be updated:

```python
...

# Updating records via SQLAlchemy `Table` objects
result = [TABLE]
    .update()
    .where([TABLE].c.name == 'Todd')
    .values(email='newemail@example.com')
```
<figcaption>Update records</figcaption>
### Deleting

On any query we execute, we can append the `delete()` method to delete _all rows_ which are contained in that query (be careful!). The below deletes all records where the **first\_name** column contains a value of "Carl":

```python
...

# Delete records where `first_name` is `Carl`
result = session
    .query(Customer)
    .filter(Customer.first_name == 'Carl')
    .delete()
```
<figcaption>Delete records</figcaption>

`delete()` accepts the **synchronize\_session** parameter, which determines how deletions should be handled:

- `False` won't perform the delete until the session is committed.
- `'fetch'` selects all rows to be deleted and removes matched rows.
- `'evaluate'` will evaluate the objects in the current session to determine which rows should be removed.

## Never Stop Exploring™

There's a lot we've left out for the sake of simplicity. There are plenty of cool methods left to explore, like the `correlate()` method, for instance. You're armed with enough to be dangerous in SQLAlchemy now, but I encourage anybody to look over the [query documentation](https://docs.sqlalchemy.org/en/13/orm/query.html) and find the cool things we didn't speak to in detail here.

I'm still working on throwing together the source code for this post in the Github repo below. Source for the previous chapters can be found there as well. In the meantime, I apologize for being a bit of a shit show:

{% github https://github.com/hackersandslackers/sqlalchemy-tutorial %}