年度牛「码」实战案例分享：轻舟已过万重山的代码创新之路

import redis
from kafka import KafkaProducer

# 初始化Redis缓存
redis_cache = redis.StrictRedis(host='localhost', port=6379, db=0)

# 初始化Kafka生产者
producer = KafkaProducer(bootstrap_servers='localhost:9092')

def cache_data(key, data):
    """将数据存入Redis缓存"""
    redis_cache.set(key, data)

def send_to_kafka(topic, data):
    """将数据发送至Kafka队列"""
    producer.send(topic, value=data.encode())

def process_data(data):
    """处理数据并进行缓存与分流"""
    # 存入Redis缓存
    cache_data('data_key', data)
    # 发送至Kafka队列
    send_to_kafka('data_topic', data)

import asyncio

async def event_listener():
    """模拟事件监听，每次收到事件后进行处理"""
    while True:
        event_data = await get_event_data()
        await process_event(event_data)

async def get_event_data():
    """获取事件数据（模拟）"""
    await asyncio.sleep(0.01)  # 模拟延迟
    return "new_event_data"

async def process_event(data):
    """事件数据的处理逻辑"""
    print(f"Processing event data: {data}")
    # 进一步的处理逻辑...
    
# 启动事件监听
asyncio.run(event_listener())

import concurrent.futures

async def async_task_1():
    # 模拟异步任务1
    await asyncio.sleep(1)
    print("Task 1 completed")

async def async_task_2():
    # 模拟异步任务2
    await asyncio.sleep(1)
    print("Task 2 completed")

async def main_async_tasks():
    await asyncio.gather(
        async_task_1(),
        async_task_2()
    )

def parallel_task():
    # 多线程任务
    print("Parallel task completed")

# 启动主任务和并行任务
async def run_tasks():
    # 异步任务
    await main_async_tasks()
    
    # 并行任务
    with concurrent.futures.ThreadPoolExecutor() as executor:
        executor.submit(parallel_task)

asyncio.run(run_tasks())

def cache_data_with_ttl(key, data, ttl=3600):
    """缓存数据并设置TTL过期时间"""
    redis_cache.setex(key, ttl, data)

def prioritize_and_send_to_kafka(data, priority):
    """根据优先级分流数据至Kafka不同主题"""
    if priority == 'high':
        send_to_kafka('high_priority_topic', data)
    else:
        send_to_kafka('low_priority_topic', data)

async def async_process_pipeline(data):
    # 异步处理数据的流水线
    await clean_data(data)
    await enrich_data(data)
    await save_processed_data(data)

from concurrent.futures import ThreadPoolExecutor
import psutil

def get_system_load():
    """获取系统当前负载"""
    return psutil.cpu_percent(), psutil.virtual_memory().percent

def distribute_task(data):
    """根据系统负载动态分配任务"""
    cpu_load, memory_load = get_system_load()
    if cpu_load < 50 and memory_load < 70:
        process_high_priority(data)
    else:
        process_low_priority(data)

with ThreadPoolExecutor() as executor:
    data_queue = get_data_queue()
    for data in data_queue:
        executor.submit(distribute_task, data)

# Prometheus配置示例
scrape_configs:
  - job_name: 'app_metrics'
    static_configs:
      - targets: ['localhost:9090']
    relabel_configs:
      - source_labels: [__address__]
        target_label: instance
        replacement: 'app_server'