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.
This tutorial will assume you know how to create an SQLAlchemy session. We'll also assume you have some data models you'd like to work with to perform your queries. If you'd like to catch up, check out the previous posts in this series.
Basic Query Syntax
Let's quickly become familiar with the basic structure of SQLAlchemy's query API. We always query on an SQLAlchemy session and pass the name of the model we'd like to query:
The last part of our query determines how many rows to return, and the nature of how those rows are determined:
all()will return all records which match our query.
first()returns the first record in order of appearance.
one()returns a single value (not necessarily the first).
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 will 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:
Viewing Returned Rows
If multiple records are returned by a query, you'll have to loop through them to see the results. This isn't necessary when a single record is returned with
The SQLAlchemy ORM will return an instance of a class by default, which means the above will result in the following output:
If you're looking to get dictionaries instead, use the built-in
This instead returns dictionary objects for each row:
Of course, you could also create your own object instead to receive only the columns you want/need:
This outputs something a bit cleaner:
Probably the most common method you'll use on a query is the
filter() is the equivalent of a SQL WHERE clause to return only rows which match the criteria we want:
We could actually write the above query using the
filter_by() method instead like so:
filter_by() accepts keyword arguments (note the difference in syntax here:
filter() checks a conditional against a column object whereas
filter_by() finds columns which match the arguments we pass).
filter_by() can only search for exact values and serves as a kind of shorthand for simple filtering queries.
We can do more than filter on simple conditionals. SQLAlchemy has a
like() method which works in an equivalent manner to SQL's LIKE:
As expected, this will give us all rows where the customer's first name starts with a J:
High-level Query Methods
In addition to
filter(), there are a few basic methods we should absolutely 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.
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
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 individual order to the appropriate customer. Check out an example record:
Our friend Jerry here has two orders: one for some Coronas, and another for creamers. Get at it, Jerry.
In addition to simple JOINs, we can perform outer JOINs using the same syntax:
We can perform UNIONs and UNION ALLs as well:
To perform a union all, simply replace
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 which counts the values in a column:
This query can easily be modified to only count distinct values:
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:
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 taking a look at how to add, remove, and change records using the SQLAlchemy ORM.
The first way we can add data is by using the
add() expects an instance of a class (data model specifically) to be passed, and will create a new database row as a result:
An alternative way to add data is by using the
insert() method. Unlike
insert() is actually called on an SQLAlchemy Table object and doesn't rely on receiving a data model.
insert() is not part of the ORM:
Building on the syntax of
insert(), we can drop in the
update() method to change the values of an existing record. We chain in the
where() method to specify which rows should be updated:
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":
delete() accepts the synchronize_session parameter which determines how deletions should be handled:
Falsewon'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 and find the cool things we didn't speak to in detail here.