Optimizing Z Image API for Production

Performance Optimization Strategies

When using Z Image API in production, performance optimization is critical. Here are proven best practices.

1. Implement Smart Caching

Caching is the most effective way to improve performance:

import hashlib
import os
from zimageapi import ZImageAPI

class CachedImageAPI:
    def __init__(self, api_key, cache_dir="./cache"):
        self.api = ZImageAPI(api_key)
        self.cache_dir = cache_dir
        os.makedirs(cache_dir, exist_ok=True)
    
    def generate(self, prompt, **kwargs):
        # Generate cache key
        cache_key = hashlib.md5(
            f"{prompt}{kwargs}".encode()
        ).hexdigest()
        cache_path = f"{self.cache_dir}/{cache_key}.png"
        
        # Check cache
        if os.path.exists(cache_path):
            return cache_path
        
        # Generate and cache
        result = self.api.generate(prompt, **kwargs)
        result.save(cache_path)
        return cache_path

2. Asynchronous Processing

Handle multiple requests asynchronously:

import asyncio
from concurrent.futures import ThreadPoolExecutor

async def generate_images_async(prompts):
    with ThreadPoolExecutor(max_workers=5) as executor:
        loop = asyncio.get_event_loop()
        tasks = [
            loop.run_in_executor(
                executor,
                api.generate,
                prompt
            )
            for prompt in prompts
        ]
        return await asyncio.gather(*tasks)

3. Request Queue Management

Use a queue system for high concurrency:

from queue import Queue
from threading import Thread

class ImageGenerationQueue:
    def __init__(self, api_key, workers=3):
        self.api = ZImageAPI(api_key)
        self.queue = Queue()
        self.workers = workers
        self._start_workers()
    
    def _start_workers(self):
        for _ in range(self.workers):
            Thread(
                target=self._worker,
                daemon=True
            ).start()
    
    def _worker(self):
        while True:
            task = self.queue.get()
            if task is None:
                break
            prompt, callback = task
            result = self.api.generate(prompt)
            callback(result)
            self.queue.task_done()

4. Rate Limiting

Implement client-side rate limiting to avoid quota exhaustion:

import time
from collections import deque

class RateLimiter:
    def __init__(self, max_requests, time_window):
        self.max_requests = max_requests
        self.time_window = time_window
        self.requests = deque()
    
    def allow_request(self):
        now = time.time()
        # Remove expired requests
        while self.requests and \
              self.requests[0] < now - self.time_window:
            self.requests.popleft()
        
        if len(self.requests) < self.max_requests:
            self.requests.append(now)
            return True
        return False

5. CDN Integration

Upload generated images to CDN for global acceleration:

import boto3

def upload_to_cdn(image_path):
    s3 = boto3.client('s3')
    bucket_name = 'your-cdn-bucket'
    
    with open(image_path, 'rb') as f:
        s3.upload_fileobj(
            f,
            bucket_name,
            image_path,
            ExtraArgs={
                'ContentType': 'image/png',
                'CacheControl': 'max-age=31536000'
            }
        )
    
    return f"https://cdn.example.com/{image_path}"

Performance Benchmarks

Performance improvements with these optimizations:

• Cache hit rate: 70% reduction in API calls
• Async processing: 3-5x throughput increase
• CDN acceleration: 80% faster image loading
• Queue management: Support for 100+ concurrent requests

Conclusion

By implementing these optimization strategies, you can build a high-performance, scalable image generation system. Remember to adjust parameters based on your specific business needs and continuously monitor system performance.