Patterns and Best Practices¶
This guide covers common patterns, best practices, and real-world examples for using reaktiv effectively.
Effect Patterns¶
Dependency Tracking¶
Important: Signal dependencies are established when signals are called within the effect function. Call dependent signals at the beginning of your effect to ensure proper dependency tracking.
from reaktiv import Signal, Effect
name = Signal("Alice")
age = Signal(30)
enabled = Signal(True)
def my_effect():
# ✅ GOOD: Call all dependent signals first
current_name = name()
current_age = age()
is_enabled = enabled()
# Then use the values
if is_enabled:
print(f"{current_name} is {current_age} years old")
# Keep reference to prevent GC
effect = Effect(my_effect)
name.set("Grace")
effect.dispose()Why this matters:
- Dependencies are tracked during effect execution
- Conditional signal reads can lead to inconsistent dependencies
- Reading signals early ensures they're always tracked
from reaktiv import Effect, Signal
name = Signal("Alice")
enabled = Signal(True)
# ❌ AVOID: Conditional dependency tracking
def problematic_effect():
is_enabled = enabled()
if is_enabled:
# This creates dependency only when enabled is True
print(name())
# Effect won't re-run when name changes if enabled is False
# ✅ BETTER: Always establish dependencies
def better_effect():
# Read all dependencies first
current_name = name()
is_enabled = enabled()
# Then use conditionally
if is_enabled:
print(current_name)
effect = Effect(better_effect)
enabled.set(False)
name.set("Grace")
effect.dispose()Memory Management¶
⚠️ CRITICAL: Effects must be retained in a variable to prevent garbage collection. An effect created without storing the reference will be immediately garbage collected and won't work.
from reaktiv import Signal, Effect
counter = Signal(0)
# ❌ WRONG: Effect will be garbage collected immediately
Effect(lambda: print(counter()))
# ✅ CORRECT: Store reference to prevent GC
effect = Effect(lambda: print(counter()))
To prevent memory leaks:
- Always keep a reference to your effect as long as you need it
- Call
dispose()when you're done with the effect - Avoid creating effects inside loops or frequently-called functions without disposing of them
from reaktiv import Signal, Effect
def create_temporary_effect(s):
# Store reference while needed
temp_effect = Effect(lambda: print(f"Value: {s()}"))
# ... do something ...
temp_effect.dispose() # Clean up properly
# Better pattern for component lifecycle
class MyComponent:
def __init__(self, s):
self.s = s
# Store as instance variable
self.effect_instance = Effect(self._render)
def _render(self):
print(f"Rendering: {self.s()}")
def destroy(self):
self.effect_instance.dispose()
signal = Signal("ready")
component = MyComponent(signal)
signal.set("updated")
component.destroy()Notification Batching¶
When multiple signals change, their effects are batched to avoid unnecessary executions:
from reaktiv import Signal, Effect, batch
x = Signal(1)
y = Signal(2)
def log_values():
print(f"x: {x()}, y: {y()}")
# Keep reference to prevent GC
logger = Effect(log_values) # Prints: "x: 1, y: 2"
# Without batching, the effect would run twice:
# x.set(10) # Effect runs
# y.set(20) # Effect runs again
# With batching, the effect runs only once:
with batch():
x.set(10) # No effect execution yet
y.set(20) # No effect execution yet
# After batch completes: Effect runs once
# Prints: "x: 10, y: 20"
logger.dispose()