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Best Practices

Follow these best practices to ensure successful production deployment of Heimdall in your applications.

Security Best Practices​

API Key Management​

Store your API keys securely using environment variables:

import os

# Store API key in environment variable
api_key = os.getenv('HEIMDALL_API_KEY')
username = os.getenv('HEIMDALL_USERNAME')

# Use in your application
headers = {
    'X-api-key': api_key,
    'X-username': username
}

Secure Configuration​

# config.py
import os

class Config:
    HEIMDALL_API_KEY = os.getenv('HEIMDALL_API_KEY')
    HEIMDALL_USERNAME = os.getenv('HEIMDALL_USERNAME')
    
    # Security settings
    SSL_VERIFY = True
    TIMEOUT = 30

Performance Best Practices​

Connection Pooling​

import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

# Create session with connection pooling
session = requests.Session()

# Configure retry strategy
retry_strategy = Retry(
    total=3,
    backoff_factor=1,
    status_forcelist=[429, 500, 502, 503, 504],
)

adapter = HTTPAdapter(max_retries=retry_strategy)
session.mount("http://", adapter)
session.mount("https://", adapter)

Error Handling​

import requests
import time

def call_heimdall_api(data, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = requests.post(
                'https://predict.heimdallapp.org/predict/',
                headers=headers,
                json=data,
                timeout=30
            )
            response.raise_for_status()
            return response.json()
            
        except requests.exceptions.RequestException as e:
            if attempt == max_retries - 1:
                raise e
            time.sleep(2 ** attempt)  # Exponential backoff

Deployment Best Practices​

Environment Configuration​

# .env file
HEIMDALL_API_KEY=your-api-key-here
HEIMDALL_USERNAME=your-username
DEBUG=False

Docker Configuration​

# Dockerfile
FROM python:3.9-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

# Use environment variables
ENV HEIMDALL_API_KEY=""
ENV HEIMDALL_USERNAME=""

EXPOSE 8000
CMD ["python", "app.py"]

Data Management​

Database Connection Best Practices​

import psycopg2
from contextlib import contextmanager

@contextmanager
def get_db_connection():
    connection = None
    try:
        connection = psycopg2.connect(
            host=os.getenv('DB_HOST'),
            database=os.getenv('DB_NAME'),
            user=os.getenv('DB_USER'),
            password=os.getenv('DB_PASSWORD')
        )
        yield connection
    finally:
        if connection:
            connection.close()

Data Validation​

def validate_input_data(data):
    required_fields = ['text', 'model_id']
    
    for field in required_fields:
        if field not in data:
            raise ValueError(f"Missing required field: {field}")
    
    if not isinstance(data['text'], str):
        raise ValueError("Text must be a string")
    
    if len(data['text']) > 10000:
        raise ValueError("Text too long")

Next Steps​

Now that you understand best practices:

  1. Monitor Performance - Track your production systems
  2. Integrate APIs - Connect to your applications
  3. Connect Databases - Integrate with your data sources