Boto3 CloudFront 对象使用计数

Question

希望计算我的 CloudFront dist 中的所有对象被单独命中的次数，以便我可以生成 excel sheet 来跟踪使用情况统计信息。我一直在查看 CloudFront 的 boto3 文档，但无法确定可以访问该信息的位置。我看到 AWS Cloudfront 控制台生成了 'Popular Objects' 报告。我不确定是否有人知道如何获取 AWS 在 boto3 中为该报告生成的数字？

如果无法通过 Boto3 访问它，是否可以使用 AWS CLI 命令代替？

更新：

这是我最终使用的伪代码，希望它是其他人的起点：

import boto3
import gzip
from datetime import datetime, date, timedelta
import shutil
from xlwt import Workbook

def analyze(timeInterval):
    """
    analyze usage data in cloudfront
    :param domain:
    :param id:
    :param password:
    :return: usage data
    """
    outputList = []
    outputDict = {}

    s3 = boto3.resource('s3', aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=PASSWORD)
    data = s3.Bucket(AWS_STORAGE_BUCKET_NAME)
    count = 0
    currentDatetime = str(datetime.now()).split(' ')
    currentDatetime = currentDatetime[0].split('-')
    currentdatetimeYear = int(currentDatetime[0])
    currentdatetimeMonth = int(currentDatetime[1])
    currentdatetimeDay = int(currentDatetime[2])
    currentDatetime = date(year=currentdatetimeYear, month=currentdatetimeMonth, day=currentdatetimeDay)

    # create excel workbook/sheet that we'll save results to
    wb = Workbook()
    sheet1 = wb.add_sheet('Log Results By URL')
    sheet1.write(0, 1, 'File')
    sheet1.write(0, 2, 'Total Hit Count')
    sheet1.write(0, 3, 'Total Byte Count')

    for item in data.objects.all():
        count += 1
        # print(count, '\n', item)
        # print(item.key)
        datetimeRef = str(item.key).replace(CLOUDFRONT_IDENTIFIER+'.', '')
        datetimeRef = datetimeRef.split('.')
        datetimeRef = datetimeRef[0]
        datetimeRef = str(datetimeRef[:-3]).split('-')
        datetimeRefYear = int(datetimeRef[0])
        datetimeRefMonth = int(datetimeRef[1])
        datetimeRefDay = int(datetimeRef[2])
        datetimeRef = date(year=datetimeRefYear, month=datetimeRefMonth, day=datetimeRefDay)
        # print('comparing', datetimeRef - timedelta(days=1), currentDatetime)
        if timeInterval == 'daily':
            if datetimeRef > currentDatetime - timedelta(days=1):
                pass
            else:
                # file not within datetime restrictions, don't do stuff
                continue
        elif timeInterval == 'weekly':
            if datetimeRef > currentDatetime - timedelta(days=7):
                pass
            else:
                # file not within datetime restrictions, don't do stuff
                continue
        elif timeInterval == 'monthly':
            if datetimeRef > currentDatetime - timedelta(weeks=4):
                pass
            else:
                # file not within datetime restrictions, don't do stuff
                continue
        elif timeInterval == 'yearly':
            if datetimeRef > currentDatetime - timedelta(weeks=52):
                pass
            else:
                # file not within datetime restrictions, don't do stuff
                continue
        print('datetimeRef', datetimeRef)
        print('currentDatetime', currentDatetime)
        print('Analyzing File:', item.key)

        # download the file
        s3.Bucket(AWS_STORAGE_BUCKET_NAME).download_file(item.key, 'logFile.gz')

        # unzip the file
        with gzip.open('logFile.gz', 'rb') as f_in:
            with open('logFile.txt', 'wb') as f_out:
                shutil.copyfileobj(f_in, f_out)

        # read the text file and add contents to a list
        with open('logFile.txt', 'r') as f:
            lines = f.readlines()
            localcount = -1
            for line in lines:
                localcount += 1
                if localcount < 2:
                    continue
                else:
                    outputList.append(line)

        # print(outputList)
        # iterate through the data collecting hit counts and byte size
        for dataline in outputList:
            data = dataline.split('\t')
            # print(data)
            if outputDict.get(data[7]) is None:
                outputDict[data[7]] = {'count': 1, 'byteCount': int(data[3])}
            else:
                td = outputDict[data[7]]
                outputDict[data[7]] = {'count': int(td['count']) + 1, 'byteCount': int(td['byteCount']) + int(data[3])}

    # print(outputDict)
    #  iterate through the result dictionary and write to the excel sheet
    outputDictKeys = outputDict.keys()
    count = 1
    for outputDictKey in outputDictKeys:
        sheet1.write(count, 1, str(outputDictKey))
        sheet1.write(count, 2, outputDict[outputDictKey]['count'])
        sheet1.write(count, 3, outputDict[outputDictKey]['byteCount'])
        count += 1
    safeDateTime = str(datetime.now()).replace(':', '.')

    # save the workbook
    wb.save(str(timeInterval)+str('_Log_Result_'+str(safeDateTime)) + '.xls')


if __name__ == '__main__':
    analyze('daily')

Answer 1

来自Configuring and Using Standard Logs (Access Logs) - Amazon CloudFront：

You can configure CloudFront to create log files that contain detailed information about every user request that CloudFront receives. These are called standard logs, also known as access logs. These standard logs are available for both web and RTMP distributions. If you enable standard logs, you can also specify the Amazon S3 bucket that you want CloudFront to save files in.

日志文件可能很大，但您可以Query Amazon CloudFront Logs using Amazon Athena。

Boto3 CloudFront 对象使用计数

Boto3 CloudFront Object Usage Count

python

amazon-web-services

boto3

amazon-cloudfront