python

import random
import time
import asyncio

import reflex as rx

Computed Vars

Computed vars have values derived from other properties on the backend. They are defined as methods in your State class with the @rx.var decorator.

Try typing in the input box and clicking out.

python

class UppercaseState(rx.State):
    text: str = "hello"

    def set_text(self, value: str):
        self.text = value

    @rx.var
    def upper_text(self) -> str:
        # This will be recomputed whenever `text` changes.
        return self.text.upper()


def uppercase_example():
    return rx.vstack(
        rx.heading(UppercaseState.upper_text),
        rx.input(on_blur=UppercaseState.set_text, placeholder="Type here..."),
    )

Here, upper_text is a computed var that always holds the upper case version of text.

We recommend always using type annotations for computed vars.

Cached Vars

By default, all computed vars are cached (cache=True). A cached var is only recomputed when the other state vars it depends on change. This is useful for expensive computations, but in some cases it may not update when you expect it to.

To create a computed var that recomputes on every state update regardless of dependencies, use @rx.var(cache=False).

Previous versions of Reflex had a @rx.cached_var decorator, which is now replaced by the cache argument of @rx.var (which defaults to True).

python

class CachedVarState(rx.State):
    counter_a: int = 0
    counter_b: int = 0

    @rx.var(cache=False)
    def last_touch_time(self) -> str:
        # This is updated anytime the state is updated.
        return time.strftime("%H:%M:%S")

    @rx.event
    def increment_a(self):
        self.counter_a += 1

    @rx.var(cache=True)
    def last_counter_a_update(self) -> str:
        # This is updated only when `counter_a` changes.
        return f"{self.counter_a} at {time.strftime('%H:%M:%S')}"

    @rx.event
    def increment_b(self):
        self.counter_b += 1

    @rx.var(cache=True)
    def last_counter_b_update(self) -> str:
        # This is updated only when `counter_b` changes.
        return f"{self.counter_b} at {time.strftime('%H:%M:%S')}"


def cached_var_example():
    return rx.vstack(
        rx.text(f"State touched at: {CachedVarState.last_touch_time}"),
        rx.text(f"Counter A: {CachedVarState.last_counter_a_update}"),
        rx.text(f"Counter B: {CachedVarState.last_counter_b_update}"),
        rx.hstack(
            rx.button("Increment A", on_click=CachedVarState.increment_a),
            rx.button("Increment B", on_click=CachedVarState.increment_b),
        ),
    )

In this example last_touch_time uses cache=False to ensure it updates any time the state is modified. last_counter_a_update is a cached computed var (using the default cache=True) that only depends on counter_a, so it only gets recomputed when counter_a changes. Similarly last_counter_b_update only depends on counter_b, and thus is updated only when counter_b changes.

Async Computed Vars

Async computed vars allow you to use asynchronous operations in your computed vars. They are defined as async methods in your State class with the same @rx.var decorator. Async computed vars are useful for operations that require asynchronous processing, such as:

Fetching data from external APIs
Database operations
File I/O operations
Any other operations that benefit from async/await

python

class AsyncVarState(rx.State):
    count: int = 0

    @rx.var
    async def delayed_count(self) -> int:
        # Simulate an async operation like an API call
        await asyncio.sleep(0.5)
        return self.count * 2

    @rx.event
    def increment(self):
        self.count += 1


def async_var_example():
    return rx.vstack(
        rx.heading("Async Computed Var Example"),
        rx.text(f"Count: {AsyncVarState.count}"),
        rx.text(f"Delayed count (x2): {AsyncVarState.delayed_count}"),
        rx.button("Increment", on_click=AsyncVarState.increment),
        spacing="4",
    )

In this example, delayed_count is an async computed var that returns the count multiplied by 2 after a simulated delay. When the count changes, the async computed var is automatically recomputed.

Caching Async Computed Vars

Just like regular computed vars, async computed vars can also be cached. This is especially useful for expensive async operations like API calls or database queries.

python

class AsyncCachedVarState(rx.State):
    user_id: int = 1
    refresh_trigger: int = 0

    @rx.var(cache=True)
    async def user_data(self) -> dict:
        # In a real app, this would be an API call
        await asyncio.sleep(1)  # Simulate network delay

        # Simulate different user data based on user_id
        users = {
            1: {"name": "Alice", "email": "[email protected]"},
            2: {"name": "Bob", "email": "[email protected]"},
            3: {"name": "Charlie", "email": "[email protected]"},
        }

        return users.get(self.user_id, {"name": "Unknown", "email": "unknown"})

    @rx.event
    def change_user(self):
        # Cycle through users 1-3
        self.user_id = (self.user_id % 3) + 1

    @rx.event
    def force_refresh(self):
        # This will not affect user_data dependencies, but will trigger a state update
        self.refresh_trigger += 1


def async_cached_var_example():
    return rx.vstack(
        rx.heading("Cached Async Computed Var Example"),
        rx.text(f"User ID: {AsyncCachedVarState.user_id}"),
        rx.text(f"User Name: {AsyncCachedVarState.user_data['name']}"),
        rx.text(f"User Email: {AsyncCachedVarState.user_data['email']}"),
        rx.hstack(
            rx.button("Change User", on_click=AsyncCachedVarState.change_user),
            rx.button(
                "Force Refresh (No Effect)", on_click=AsyncCachedVarState.force_refresh
            ),
        ),
        rx.text(
            "Note: The cached async var only updates when user_id changes, not when refresh_trigger changes."
        ),
        spacing="4",
    )

In this example, user_data is a cached async computed var that simulates fetching user data. It is only recomputed when user_id changes, not when other state variables like refresh_trigger change. This demonstrates how caching works with async computed vars to optimize performance for expensive operations.