MYSQL冷备份数据上传到对象存储

以谁为师

发布于 2023-12-23 08:20:46

1660

发布于 2023-12-23 08:20:46

介绍

将MySQL数据库中的冷数据备份并上传至云平台对象存储的过程。冷数据是指数据库中的历史或不经常访问的数据。我们首先通过执行SQL查询语句从MySQL数据库中提取所需数据，然后将其保存为CSV文件格式，接着通过SDK将备份文件上传到对象存储。

查询

-- 查询最早的数据
SELECT *
FROM bos_balance_flow limit 5

-- 查询最后10条数据
SELECT *
FROM bos_balance_flow
ORDER BY created_at DESC
LIMIT 10;

添加索引

给时间字段加上索引提高查询速度

-- 给订单归档表加时间字段加索引
CREATE INDEX idx_created_at ON bos_order_archive(created_at);

历史数据上传s3

# 后台执行数据备份脚本
nohup python3 db-upload-mongo-s3.py &

# 一次性上传历史mysql数据到s3

import logging
from logging.handlers import RotatingFileHandler
import os
from datetime import datetime, timedelta
import pandas as pd
import pymysql
import boto3
import time

# 配置日志
logger = logging.getLogger('sql_logger')
logger.setLevel(logging.INFO)

# 配置RotatingFileHandler，设置日志文件路径、文件大小和文件数量
handler = RotatingFileHandler('/tmp/sql.log', maxBytes=50 * 1024 * 1024, backupCount=2)

# 设置日志格式
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)

# 添加handler到logger对象
logger.addHandler(handler)

# 添加日志输出到终端
stream_handler = logging.StreamHandler()
stream_handler.setFormatter(formatter)
logger.addHandler(stream_handler)

# AWS S3 配置
AWS_ACCESS_KEY = "AKIASEGBIssss"
AWS_SECRET_KEY = "xxxxxxxx"
S3_BUCKET_NAME = "pro-s3-db"
S3_DIRECTORY = "mongo_bos_server"

# 数据库连接配置
DB_HOST = "172.16.99.99"
DB_USER = "root"
DB_PASSWORD = "xxxxxxxx"
DB_NAME = "bos_server"

# 日期格式
DATE_FORMAT = "%Y-%m-%d"

# 获取每个表的最早和最后日期
table_dates = {}

with pymysql.connect(host=DB_HOST, user=DB_USER, password=DB_PASSWORD, database=DB_NAME) as connection:
    
    # 将需要处理的表添加到列表中
    for table in ["bos_order_archive", "bos_order_future"]:
        # 查询每个表中最早和最后的日期
        min_date_query = f"SELECT MIN(created_at) FROM {table}"
        max_date_query = f"SELECT MAX(created_at) FROM {table}"

        min_date_result = pd.read_sql_query(min_date_query, connection)
        max_date_result = pd.read_sql_query(max_date_query, connection)

        # 转换日期格式
        min_date = min_date_result.iloc[0, 0].strftime(DATE_FORMAT)
        max_date = max_date_result.iloc[0, 0].strftime(DATE_FORMAT)

        table_dates[table] = {"min_date": min_date, "max_date": max_date}

# 遍历每个表的日期范围
for table, dates in table_dates.items():
    current_date = datetime.strptime(dates["min_date"], DATE_FORMAT)
    end_date = datetime.strptime(dates["max_date"], DATE_FORMAT)

    while current_date <= end_date:
        current_date_str = current_date.strftime(DATE_FORMAT)

        # 构建 SQL 查询语句
        sql_query = f"SELECT * FROM {table} WHERE created_at >= '{current_date_str} 00:00:00.000' AND created_at < '{current_date_str} 23:59:59.999'"

        # 使用 with 语句连接数据库
        with pymysql.connect(host=DB_HOST, user=DB_USER, password=DB_PASSWORD, database=DB_NAME) as connection:
            # 使用 pandas 读取数据库数据
            df = pd.read_sql_query(sql_query, connection)

        # 如果数据不为空则上传到S3
        if not df.empty:
            # 生成 CSV 文件名
            csv_filename = f"{table}_{current_date_str}.csv"

            # 保存数据到 CSV 文件
            df.to_csv(csv_filename, index=False)

            # 获取文件大小
            file_size = os.path.getsize(csv_filename)
            file_size_mb = file_size / (1024 * 1024)  # Convert bytes to megabytes

            # 构建S3目标路径
            s3_object_key = f"{S3_DIRECTORY}/{csv_filename}"

            # 使用 boto3 上传文件至 S3
            s3_client = boto3.client('s3', aws_access_key_id=AWS_ACCESS_KEY, aws_secret_access_key=AWS_SECRET_KEY)
            s3_client.upload_file(csv_filename, S3_BUCKET_NAME, s3_object_key)

            # 记录日志
            logger.info(f"文件 {csv_filename} 已上传到 S3 存储桶 {S3_BUCKET_NAME} 目录 {S3_DIRECTORY}，文件大小: {file_size_mb:.2f} MB，上传成功")

            # 等待5秒
            time.sleep(5)

        # 增加一天
        current_date += timedelta(days=1)

每日数据上传s3

从一个数据库中获取前一天的数据。将数据存储到一个 CSV 文件中。检查本地是否已存在该 CSV 文件，如果存在则不执行数据库查询，直接将已有文件上传到 Amazon S3 存储桶中。

import os
import pandas as pd
import boto3
from datetime import datetime, timedelta
import requests
import pymysql



def send_notification(title, content):
    message = {
        "msg_type": "text",
        "content": {
            "text": f"{title}\n{content}"
        }
    }
    lark_headers = {"Content-Type": "application/json"}
    webhook_url = "https://open.larksuite.com/open-apis/bot/v2/hook/xxxxxxxxxxxxxxx"
    response = requests.post(webhook_url, json=message, headers=lark_headers)
    if response.status_code == 200:
        print("Notification sent successfully.")
    else:
        print(f"Failed to send notification. Status code: {response.status_code}, Response text: {response.text}")
    #print(response.text)



class S3Uploader:
    def __init__(self, aws_access_key, aws_secret_key, s3_bucket_name):
        self.s3_client = boto3.client('s3', aws_access_key_id=aws_access_key, aws_secret_access_key=aws_secret_key)
        self.s3_bucket_name = s3_bucket_name

    def file_exists_in_s3(self, s3_object_key):
        try:
            self.s3_client.head_object(Bucket=self.s3_bucket_name, Key=s3_object_key)
            return True
        except:
            return False

    def upload_to_s3(self, local_file_path, s3_object_key):
        self.s3_client.upload_file(local_file_path, self.s3_bucket_name, s3_object_key)
        print(f"File {local_file_path} uploaded to S3 bucket {self.s3_bucket_name} with key {s3_object_key}")

def main():
    # 日期格式
    DATE_FORMAT = "%Y-%m-%d"

    # AWS S3 配置
    AWS_ACCESS_KEY = "AKIASEGBxxxxx"
    AWS_SECRET_KEY = "Myk97UInMQXXXX"
    S3_BUCKET_NAME = "pro-s3-db"

    # 数据库连接配置
    DB_HOST = "pro-rds-xxxx-slave.xxxx.com"
    DB_USER = "dwh_XXXX"
    DB_PASSWORD = "XXXXXXX"
    DB_NAME = "bos_x"


    # 凌晨5点触发脚本，计算前一天的日期
    yesterday = datetime.now() - timedelta(days=1)
    yesterday_str = yesterday.strftime(DATE_FORMAT)

    # 构建当月目录
    current_month_directory = yesterday.strftime("%Y.%m")

    # 生成 CSV 文件名，包含当月目录
    #csv_filename = f"dwh_balance_flow_infos_{yesterday_str}.csv"
    csv_filename = f"bos_balance_flow_{yesterday_str}.csv"

    # 检查本地文件是否已存在
    if os.path.exists(csv_filename):
        print(f"File {csv_filename} already exists locally. Skipping database query")
        # 初始化 S3 上传器
        s3_uploader = S3Uploader(AWS_ACCESS_KEY, AWS_SECRET_KEY, S3_BUCKET_NAME)

        # 构建 S3 对象键
        s3_object_key = f"{current_month_directory}/{csv_filename}"

        # 检查文件是否已存在于 S3 中
        if s3_uploader.file_exists_in_s3(s3_object_key):
            print(f"File {csv_filename} already exists in S3. Skipping upload.")

            # 发送通知到Lark
            file_size = os.path.getsize(csv_filename)
            file_size_mb = file_size / (1024 * 1024)  # Convert bytes to megabytes
            message = f"S3路径：/{s3_object_key} {file_size_mb:.2f} MB 检测已存在"
            send_notification("只读库bos_balance_flow备份成功:", message)
        else:
            # 上传文件至 S3
            s3_uploader.upload_to_s3(csv_filename, s3_object_key)

    else:
        # 使用 with 语句连接数据库
        with pymysql.connect(host=DB_HOST, user=DB_USER, password=DB_PASSWORD, database=DB_NAME) as connection:
            # 使用 pandas 读取数据库数据
            sql_query = f"SELECT * FROM bos_balance_flow  WHERE created_at >= '{yesterday_str} 00:00:00.000' AND created_at < '{yesterday_str} 23:59:59.999'"
            #sql_query = f"SELECT * FROM dwh_balance_flow_infos WHERE created_at >= '{yesterday_str} 00:00:00.000' AND created_at < '{yesterday_str} 23:59:59.999'"
            df = pd.read_sql_query(sql_query, connection)

        # 保存数据到 CSV 文件
        df.to_csv(csv_filename, index=False)

        # 初始化 S3 上传器
        s3_uploader = S3Uploader(AWS_ACCESS_KEY, AWS_SECRET_KEY, S3_BUCKET_NAME)

        # 构建 S3 对象键
        s3_object_key = f"{current_month_directory}/{csv_filename}"

        # 检查文件是否已存在于 S3 中
        if s3_uploader.file_exists_in_s3(s3_object_key):
            print(f"File {csv_filename} already exists in S3. Skipping upload !!")

            # 发送通知到Lark
            file_size = os.path.getsize(csv_filename)
            file_size_mb = file_size / (1024 * 1024)  # Convert bytes to megabytes
            message = f"S3路径：/{s3_object_key} {file_size_mb:.2f} MB 检测已存在"
            send_notification("只读库bos_balance_flow备份成功:", message)

        else:
            # 上传文件至 S3
            s3_uploader.upload_to_s3(csv_filename, s3_object_key)


            # 发送通知到Lark
            file_size = os.path.getsize(csv_filename)
            file_size_mb = file_size / (1024 * 1024)  # Convert bytes to megabytes
            message = f"S3路径：/{s3_object_key} {file_size_mb:.2f} MB"
            send_notification("只读库bos_balance_flow备份成功:", message)


if __name__ == "__main__":
    main()

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原始发表：2023-12-22，如有侵权请联系 cloudcommunity@tencent.com 删除

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