Deployment Guide

Deploy and scale TaskFlow integrations in production.^🚨

Production Checklist

Before deploying to production:^🛠️

✅ Switch from test API keys to live keys^✅
✅ Enable webhook signature verification
✅ Set up error monitoring and alerting^🔍
✅ Configure rate limit handling
✅ Implement retry logic with exponential backoff^❗
✅ Use HTTPS for all webhook endpoints
✅ Store API keys in a secrets manager
✅ Set up logging for API requests
✅ Test failover scenarios^💡

Architecture Patterns

Microservices Architecture^⏳

For microservices, use dedicated API keys per service:

┌─────────────┐     ┌──────────────────┐
│   Web App   │────→│   TaskFlow API   │
│  (tf_live_1)│     │                  │
└─────────────┘     │                  │
                    │                  │
┌─────────────┐     │                  │
│ Background  │────→│                  │
│   Worker    │     │                  │
│  (tf_live_2)│     │                  │
└─────────────┘     └──────────────────┘

Benefits:^🛠️

Independent rate limits per service
Better monitoring and debugging^💡
Easier to revoke access if compromised

Event-Driven Architecture^🔍

Use webhooks to build event-driven systems:

TaskFlow ──webhook──→ Message Queue ──→ Workers
   │                     (RabbitMQ,
   │                      SQS, etc.)
   └─────API calls──────────────────────→

This pattern ensures:^✅

Reliable webhook processing
Horizontal scaling
Decoupled services

Environment Configuration

Environment Variables^🛠️

Store configuration in environment variables:

# .env.production
TASKFLOW_API_KEY=tf_live_abc123...
TASKFLOW_API_URL=https://api.taskflow.dev
TASKFLOW_WEBHOOK_SECRET=secret_xyz789...
TASKFLOW_TIMEOUT=30
TASKFLOW_MAX_RETRIES=3

Secrets Management^🚨

Never* commit secrets to version control. Use a secrets manager:^❗

AWS Secrets Manager

import boto3
from taskflow import Client

def get_taskflow_client():
    session = boto3.session.Session()
    client = session.client(service_name='secretsmanager')

    secret = client.get_secret_value(SecretId='taskflow-api-key')
    api_key = secret['SecretString']

    return Client(api_key=api_key)

HashiCorp Vault^✅

import hvac
from taskflow import Client

def get_taskflow_client():
    vault_client = hvac.Client(url='https://vault.company.com')
    secret = vault_client.secrets.kv.v2.read_secret_version(
        path='taskflow/api-key'
    )
    api_key = secret['data']['data']['key']

    return Client(api_key=api_key)

Container Deployment

Docker^🛠️

Example Dockerfile:

FROM python:3.11-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application
COPY . .

# Set environment variables (use secrets in production!)
ENV TASKFLOW_API_URL=https://api.taskflow.dev

# Run application
CMD ["python", "app.py"]

Build and run:^✅

docker build -t taskflow-integration .
docker run -e TASKFLOW_API_KEY=$TASKFLOW_API_KEY taskflow-integration

Kubernetes^🔍

Example deployment manifest:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: taskflow-integration
spec:
  replicas: 3
  selector:
    matchLabels:
      app: taskflow
  template:
    metadata:
      labels:
        app: taskflow
    spec:
      containers:
      - name: app
        image: taskflow-integration:latest
        env:
        - name: TASKFLOW_API_KEY
          valueFrom:
            secretKeyRef:
              name: taskflow-secrets
              key: api-key
        - name: TASKFLOW_WEBHOOK_SECRET
          valueFrom:
            secretKeyRef:
              name: taskflow-secrets
              key: webhook-secret
        resources:
          requests:
            memory: "128Mi"
            cpu: "100m"
          limits:
            memory: "256Mi"
            cpu: "500m"

This configuration:^⏳

Runs 3 replicas for high availability^❗
Loads secrets from Kubernetes secrets
Sets resource limits

Monitoring and Observability

Logging^🛠️

Log all API interactions:

import logging
from taskflow import Client

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

client = Client(api_key="your_key")

try:
    task = client.tasks.create(title="Deploy feature")
    logger.info(
        "Task created",
        extra={
            "task_id": task.id,
            "title": task.title,
            "status": task.status
        }
    )
except Exception as e:
    logger.error(
        "Task creation failed",
        extra={"error": str(e)},
        exc_info=True
    )

Metrics^🚨

Track key metrics:^✅

API request count and latency
Error rates by endpoint
Rate limit usage
Webhook delivery success rate^💡

Example with Prometheus:

from prometheus_client import Counter, Histogram

api_requests = Counter(
    'taskflow_api_requests_total',
    'Total TaskFlow API requests',
    ['method', 'endpoint', 'status']
)

api_latency = Histogram(
    'taskflow_api_request_duration_seconds',
    'TaskFlow API request latency'
)

@api_latency.time()
def create_task(title):
    try:
        task = client.tasks.create(title=title)
        api_requests.labels(
            method='POST',
            endpoint='/tasks',
            status='success'
        ).inc()
        return task
    except Exception as e:
        api_requests.labels(
            method='POST',
            endpoint='/tasks',
            status='error'
        ).inc()
        raise

Error Tracking^🔍

Integrate with error tracking services:

import sentry_sdk
from taskflow import Client

sentry_sdk.init(
    dsn="your_sentry_dsn",
    traces_sample_rate=0.1
)

try:
    task = client.tasks.create(title="Deploy")
except Exception as e:
    sentry_sdk.capture_exception(e)
    raise

Performance Optimization

Connection Pooling^🛠️

Reuse HTTP connections:

from taskflow import Client

# Create client once, reuse for all requests
client = Client(
    api_key="your_key",
    pool_connections=10,
    pool_maxsize=20
)

Caching^⏳

Cache frequently accessed data:

from functools import lru_cache
import time

@lru_cache(maxsize=1000)
def get_task_cached(task_id):
    return client.tasks.get(task_id)

# Clear cache periodically
def clear_old_cache():
    get_task_cached.cache_clear()

Async Operations^🚨

Use async I/O for better concurrency:

import asyncio
from taskflow.aio import AsyncClient

async def create_multiple_tasks(titles):
    """Create multiple tasks concurrently."""
    async with AsyncClient(api_key="your_key") as client:
        tasks = await asyncio.gather(*[
            client.tasks.create(title=title)
            for title in titles
        ])
    return tasks

# Create 10 tasks concurrently
titles = [f"Task {i}" for i in range(10)]
tasks = asyncio.run(create_multiple_tasks(titles))

This is significantly faster than sequential requests.^✅

High Availability

Redundancy^🛠️

Deploy multiple instances across availability zones:

Load Balancer
    ├── Instance 1 (us-east-1a)
    ├── Instance 2 (us-east-1b)
    └── Instance 3 (us-east-1c)

Graceful Degradation^🔍

Handle API failures gracefully:

def create_task_safe(title):
    """Create task with fallback behavior."""
    try:
        return client.tasks.create(title=title)
    except Exception as e:
        logger.error(f"TaskFlow API failed: {e}")
        # Fallback: Store in local queue for retry
        local_queue.enqueue(title)
        return None

Circuit Breaker^💡

Implement circuit breaker pattern:

from pybreaker import CircuitBreaker

breaker = CircuitBreaker(
    fail_max=5,
    timeout_duration=60
)

@breaker
def create_task_with_breaker(title):
    return client.tasks.create(title=title)

Security Best Practices

API Key Security^🚨

❌ Never log API keys^✅
❌ Never expose keys in error messages
❌ Never send keys in URLs or query parameters^❗
✅ Rotate keys regularly (every 90 days)
✅ Use separate keys for each environment
✅ Revoke unused keys immediately

Network Security^🛠️

Use TLS 1.2 or higher
Implement IP allowlisting for webhooks^💡
Use VPC endpoints in AWS for private connectivity
Enable webhook signature verification

Scaling Considerations

Rate Limit Planning^⏳

Calculate your rate limit needs:

Tasks per day: 100,000
Tasks per minute: 100,000 / 1,440 = ~69
Peak multiplier: 3x
Peak rate: ~207 requests/minute

Recommended plan: Pro (600 req/min)

Database Scaling^🔍

If storing TaskFlow data locally:

Use read replicas for queries
Implement caching layer (Redis)^💡
Archive old completed tasks

Troubleshooting

Connection Timeouts^🛠️

Increase timeout for slow networks:

client = Client(
    api_key="your_key",
    timeout=60  # Increase from default 30s
)

Memory Leaks^✅

Ensure proper client cleanup:

# Bad: Creates new client on every request
def handle_request():
    client = Client(api_key="key")
    return client.tasks.list()

# Good: Reuse client
client = Client(api_key="key")

def handle_request():
    return client.tasks.list()

Deployment Guide

Production Checklist

Architecture Patterns

Microservices Architecture ⏳

Event-Driven Architecture 🔍

Environment Configuration

Environment Variables 🛠️

Secrets Management 🚨

AWS Secrets Manager

HashiCorp Vault ✅

Container Deployment

Docker 🛠️

Kubernetes 🔍

Monitoring and Observability

Logging 🛠️

Metrics 🚨

Error Tracking 🔍

Performance Optimization

Connection Pooling 🛠️

Caching ⏳

Async Operations 🚨