如何优化我代码的数据读取部分？

# 批量读取文件
def read_files(file_paths):
    data = []
    for file_path in file_paths:
        with open(file_path, 'r') as file:
            data.append(file.read())
    return data

import redis

# 初始化Redis连接
redis_client = redis.Redis(host='localhost', port=6379, db=0)

def get_data(key):
    data = redis_client.get(key)
    if data is None:
        data = fetch_data_from_source(key)  # 假设这是从源获取数据的函数
        redis_client.setex(key, 3600, data)  # 缓存1小时
    return data

import multiprocessing

def read_file(file_path):
    with open(file_path, 'r') as file:
        return file.read()

def parallel_read_files(file_paths):
    with multiprocessing.Pool() as pool:
        results = pool.map(read_file, file_paths)
    return results

from sqlalchemy import create_engine, Table, MetaData

engine = create_engine('sqlite:///example.db')
metadata = MetaData(bind=engine)
connection = engine.connect()

def fetch_data(query):
    result = connection.execute(query)
    return result.fetchall()

import asyncio

async def read_file_async(file_path):
    with open(file_path, 'r') as file:
        return file.read()

async def main():
    tasks = [read_file_async(path) for path in file_paths]
    results = await asyncio.gather(*tasks)
    return results

# 运行异步主函数
asyncio.run(main())

import gzip

def read_compressed_file(file_path):
    with gzip.open(file_path, 'rt') as file:
        return file.read()