Feature Flags Explained

# Package: io.thecodeforge.python.devops

# Simplest possible feature flag — environment variable
import os

def get_recommendations(user_id: int):
    if os.getenv('ENABLE_ML_RECOMMENDATIONS', 'false') == 'true':
        return ml_recommendations(user_id)   # new ML-based system
    else:
        return rule_based_recommendations(user_id)  # old system

# Better: percentage rollout — test on a fraction of users
import hashlib

def is_flag_enabled(flag_name: str, user_id: int, percentage: float) -> bool:
    """Consistently assign users to buckets using hash — same user always gets same result."""
    hash_input = f'{flag_name}:{user_id}'.encode()
    hash_val   = int(hashlib.md5(hash_input).hexdigest(), 16)
    bucket     = (hash_val % 100) + 1  # 1-100
    return bucket <= percentage

# Roll out to 5% of users
def get_checkout_flow(user_id: int):
    if is_flag_enabled('new_checkout', user_id, 5.0):
        return new_checkout_flow(user_id)
    return old_checkout_flow(user_id)

# Using a feature flag service (LaunchDarkly, Unleash, Flagsmith)
import ldclient
from ldclient.config import Config

ldclient.set_config(Config(sdk_key='your-sdk-key'))
client = ldclient.get()

# Evaluate a flag for a specific user
def get_dashboard(user):
    context = {
        'key': str(user.id),
        'name': user.name,
        'email': user.email,
        'plan': user.subscription_plan,   # target premium users
        'country': user.country           # GDPR rollout by country
    }

    # Flag evaluated with user context — targeting rules in dashboard
    if client.variation('new-dashboard-v2', context, default=False):
        return render_new_dashboard(user)
    return render_old_dashboard(user)

# Targeting rules set in LaunchDarkly UI:
# - 100% of users with plan='premium'
# - 10% of users in country='US'
# - 0% of all others (gradually increase)