Queries

Queries are used to retrieve data from a database.

A query is a request for information from a database table or combination of tables. A query can be used to retrieve data from a single table or multiple tables. A query can also be used to insert, update, or delete data from a table.

Session

To execute a query you must first create a rx.session. You can use the session to query the database using SQLModel or SQLAlchemy syntax.

The rx.session statement will automatically close the session when the code block is finished. If session.commit() is not called, the changes will be rolled back and not persisted to the database. The code can also explicitly rollback without closing the session via session.rollback().

The following example shows how to create a session and query the database. First we create a table called User.

python

class User(rx.Model, table=True):
    username: str
    email: str

Select

Then we create a session and query the User table.

python

class QueryUser(rx.State):
    name: str
    users: list[User]

    @rx.event
    def get_users(self):
        with rx.session() as session:
            self.users = session.exec(
                select(User).where(User.username.contains(self.name))
            ).all()

The get_users method will query the database for all users that contain the value of the state var name.

Insert

Similarly, the session.add() method to add a new record to the database or persist an existing object.

python

class AddUser(rx.State):
    username: str
    email: str

    @rx.event
    def add_user(self):
        with rx.session() as session:
            session.add(User(username=self.username, email=self.email))
            session.commit()

Update

To update the user, first query the database for the object, make the desired modifications, .add the object to the session and finally call .commit().

python

class ChangeEmail(rx.State):
    username: str
    email: str

    @rx.event
    def modify_user(self):
        with rx.session() as session:
            user = session.exec(
                select(User).where((User.username == self.username))
            ).first()
            user.email = self.email
            session.add(user)
            session.commit()

Delete

To delete a user, first query the database for the object, then call .delete() on the session and finally call .commit().

python

class RemoveUser(rx.State):
    username: str

    @rx.event
    def delete_user(self):
        with rx.session() as session:
            user = session.exec(
                select(User).where(User.username == self.username)
            ).first()
            session.delete(user)
            session.commit()

ORM Object Lifecycle

The objects returned by queries are bound to the session that created them, and cannot generally be used outside that session. After adding or updating an object, not all fields are automatically updated, so accessing certain attributes may trigger additional queries to refresh the object.

To avoid this, the session.refresh() method can be used to update the object explicitly and ensure all fields are up to date before exiting the session.

python

class AddUserForm(rx.State):
    user: User | None = None

    @rx.event
    def add_user(self, form_data: dict[str, Any]):
        with rx.session() as session:
            self.user = User(**form_data)
            session.add(self.user)
            session.commit()
            session.refresh(self.user)

Now the self.user object will have a correct reference to the autogenerated primary key, id, even though this was not provided when the object was created from the form data.

If self.user needs to be modified or used in another query in a new session, it must be added to the session. Adding an object to a session does not necessarily create the object, but rather associates it with a session where it may either be created or updated accordingly.

python

class AddUserForm(rx.State):
    ...

    @rx.event
    def update_user(self, form_data: dict[str, Any]):
        if self.user is None:
            return
        with rx.session() as session:
            self.user.set(**form_data)
            session.add(self.user)
            session.commit()
            session.refresh(self.user)

If an ORM object will be referenced and accessed outside of a session, you should call .refresh() on it to avoid stale object exceptions.

Using SQL Directly

Avoiding SQL is one of the main benefits of using an ORM, but sometimes it is necessary for particularly complex queries, or when using database-specific features.

SQLModel exposes the session.execute() method that can be used to execute raw SQL strings. If parameter binding is needed, the query may be wrapped in sqlalchemy.text, which allows colon-prefix names to be used as placeholders.

# Never use string formatting to construct SQL queries, as this may lead to SQL injection vulnerabilities in the app.

python

import sqlalchemy

import reflex as rx


class State(rx.State):
    @rx.event
    def insert_user_raw(self, username, email):
        with rx.session() as session:
            session.execute(
                sqlalchemy.text(
                    "INSERT INTO user (username, email) VALUES (:username, :email)"
                ),
                {"username": username, "email": email},
            )
            session.commit()

    @rx.var
    def raw_user_tuples(self) -> list[list]:
        with rx.session() as session:
            return [list(row) for row in session.execute("SELECT * FROM user").all()]

Writing Efficient Queries

Databases are generally much better at filtering, joining, and aggregating than Python. Every row returned by a query is sent over the network and held in memory as part of the state — for every user with an open session. Do the work in the query itself and return only display-ready results.

The examples below use a simple Order model.

python

class Order(rx.Model, table=True):
    user_id: int
    status: str
    amount: float | None = None

Aggregate in SQL, not Python

Avoid fetching a whole table just to compute a summary in Python. Use the database's aggregate functions (COUNT, SUM, AVG, MIN, MAX) with GROUP BY so only the summary rows come back.

python

from sqlmodel import select, func


class OrderStats(rx.State):
    order_count: int = 0
    orders_by_status: list[tuple[str, int]] = []

    @rx.event
    def load_stats(self):
        with rx.session() as session:
            # Count rows in the database instead of len() on a full fetch.
            self.order_count = session.exec(select(func.count(Order.id))).one()

            # One row per group, ready for a chart or summary table.
            self.orders_by_status = [
                tuple(row)
                for row in session.exec(
                    select(Order.status, func.count(Order.id)).group_by(Order.status)
                ).all()
            ]

Related metrics can be computed in a single query with conditional aggregation, instead of issuing one query per metric:

python

import sqlalchemy
from sqlmodel import select, func


class OrderKPIs(rx.State):
    total_orders: int = 0
    completed_orders: int = 0
    total_amount: float = 0.0

    @rx.event
    def load_kpis(self):
        with rx.session() as session:
            total, completed, amount = session.exec(
                select(
                    func.count(Order.id),
                    func.sum(
                        sqlalchemy.case((Order.status == "completed", 1), else_=0)
                    ),
                    func.coalesce(func.sum(Order.amount), 0.0),
                )
            ).one()
        self.total_orders = total
        self.completed_orders = completed or 0
        self.total_amount = float(amount)

Avoid Queries Inside Loops

Issuing a query per item — the "N+1" pattern — multiplies network round trips. Fetch everything in one statement with a JOIN, an IN (...) list, or GROUP BY.

python

# Efficient: one query for all users at once.
with rx.session() as session:
    orders = session.exec(
        select(Order).where(Order.user_id.in_([user.id for user in users]))
    ).all()

# Inefficient: one query per user (N+1).
with rx.session() as session:
    for user in users:
        orders = session.exec(select(Order).where(Order.user_id == user.id)).all()

The .in_() method binds each value as a separate query parameter, so it is safe to use with user-provided values.

# Raw SQL: pass lists with an expanding bind parameter
In raw SQL, use an *expanding* bind parameter for an `IN` list — never join the values into the SQL string, which exposes the query to SQL injection.

The example below binds the list with an expanding parameter:

python

import sqlalchemy

with rx.session() as session:
    rows = session.execute(
        sqlalchemy.text("SELECT * FROM users WHERE id IN :user_ids").bindparams(
            sqlalchemy.bindparam("user_ids", expanding=True)
        ),
        {"user_ids": [1, 2, 3]},
    ).all()

If the related objects are linked with foreign keys, the relationship loading techniques can also fetch linked objects without extra queries.

Return Only What the UI Needs

Select only the columns the UI displays when tables are wide, instead of whole rows.
Give every query that returns detail rows a .limit(), and use offset-based pagination when the user needs more rows.
Load static data (dropdown options, date bounds) once in an on_load event handler. When a filter changes, re-run only the filtered queries — not the option lookups.
Avoid caching large raw query results in state vars to work around a slow query: per-user state duplicates that data in server memory for every session. Fix the query instead — aggregate in SQL, add a limit, combine round trips. Keeping small, display-ready results in state (chart rows, KPI values, one page of a table) is the intended pattern.

Handling NULL Values

Database columns may contain NULL, which arrives in Python as None. Aggregates like SUM and AVG also return NULL when they run over zero rows. Casting such results directly will crash:

python

float(row[0])  # TypeError: float() argument must be ... not 'NoneType'

There are two ways to handle this.

Coalesce in the Query (Preferred)

COALESCE returns its first non-NULL argument, so the query itself guarantees a usable value. This is especially important for nullable numeric columns and for aggregates that may run over empty groups.

python

from sqlmodel import select, func

with rx.session() as session:
    total = session.exec(select(func.coalesce(func.sum(Order.amount), 0.0))).one()

The same works in raw SQL: SELECT COALESCE(SUM(amount), 0) FROM ....

Guard in Python

When consuming rows that may contain NULLs, fall back explicitly while building the values:

python

orders = [
    {
        "status": str(row[0] or ""),
        "amount": float(row[1]) if row[1] is not None else 0.0,
    }
    for row in rows
]

Async Database Operations

Reflex provides an async version of the session function called rx.asession for asynchronous database operations. This is useful when you need to perform database operations in an async context, such as within async event handlers.

Configuring the Async Database URL

rx.asession needs its own async_db_url in rxconfig.py. It must point at the same database as db_url but use an async driver. Calling rx.asession() when it is unset raises an error.

python

config = rx.Config(
    app_name="my_app",
    db_url="sqlite:///reflex.db",
    async_db_url="sqlite+aiosqlite:///reflex.db",
)

The matching async DBAPI driver is not part of the reflex[db] extra, so install it separately. For the SQLite URL above, install aiosqlite:

bash

pip install aiosqlite

For PostgreSQL, install psycopg (psycopg3). It works as both the sync and async driver, so db_url and async_db_url can share one postgresql+psycopg://... scheme:

bash

pip install "psycopg[binary]"

The rx.asession function returns an async SQLAlchemy session that must be used with an async context manager. Most operations against the asession must be awaited.

python

import sqlalchemy.ext.asyncio
import sqlalchemy

import reflex as rx


class AsyncUserState(rx.State):
    users: list[User] = []

    @rx.event(background=True)
    async def get_users_async(self):
        async with rx.asession() as asession:
            result = await asession.execute(select(User))
            async with self:
                self.users = result.all()

Async Select

The following example shows how to query the database asynchronously:

python

class AsyncQueryUser(rx.State):
    name: str
    users: list[User] = []

    @rx.event(background=True)
    async def get_users(self):
        async with rx.asession() as asession:
            stmt = select(User).where(User.username.contains(self.name))
            result = await asession.execute(stmt)
            async with self:
                self.users = result.all()

Async Insert

To add a new record to the database asynchronously:

python

class AsyncAddUser(rx.State):
    username: str
    email: str

    @rx.event(background=True)
    async def add_user(self):
        async with rx.asession() as asession:
            asession.add(User(username=self.username, email=self.email))
            await asession.commit()

Async Update

To update a user asynchronously:

python

class AsyncChangeEmail(rx.State):
    username: str
    email: str

    @rx.event(background=True)
    async def modify_user(self):
        async with rx.asession() as asession:
            stmt = select(User).where(User.username == self.username)
            result = await asession.execute(stmt)
            user = result.first()
            if user:
                user.email = self.email
                asession.add(user)
                await asession.commit()

Async Delete

To delete a user asynchronously:

python

class AsyncRemoveUser(rx.State):
    username: str

    @rx.event(background=True)
    async def delete_user(self):
        async with rx.asession() as asession:
            stmt = select(User).where(User.username == self.username)
            result = await asession.execute(stmt)
            user = result.first()
            if user:
                await asession.delete(user)
                await asession.commit()

Async Refresh

Similar to the regular session, you can refresh an object to ensure all fields are up to date:

python

class AsyncAddUserForm(rx.State):
    user: User | None = None

    @rx.event(background=True)
    async def add_user(self, form_data: dict[str, str]):
        async with rx.asession() as asession:
            async with self:
                self.user = User(**form_data)
            asession.add(self.user)
            await asession.commit()
            await asession.refresh(self.user)

Async SQL Execution

You can also execute raw SQL asynchronously:

python

class AsyncRawSQL(rx.State):
    users: list[list] = []

    @rx.event(background=True)
    async def insert_user_raw(self, username, email):
        async with rx.asession() as asession:
            await asession.execute(
                sqlalchemy.text(
                    "INSERT INTO user (username, email) VALUES (:username, :email)"
                ),
                dict(username=username, email=email),
            )
            await asession.commit()

    @rx.event(background=True)
    async def get_raw_users(self):
        async with rx.asession() as asession:
            result = await asession.execute("SELECT * FROM user")
            async with self:
                self.users = [list(row) for row in result.all()]

# Important Notes for Async Database Operations

- Always use the `@rx.event(background=True)` decorator for async event handlers
- Most operations against the `asession` must be awaited, including `commit()`, `execute()`, `refresh()`, and `delete()`
- The `add()` method does not need to be awaited
- Result objects from queries have methods like `all()` and `first()` that are synchronous and return data directly
- Use `async with self:` when updating state variables in background tasks