61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
import re
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from pyspark import SparkContext
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# 初始化 SparkContext
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sc = SparkContext.getOrCreate()
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# 日志匹配的正则表达式
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LOG_PATTERN = re.compile(
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r'^(\S+) (\S+) (\S+) \[([\w:/]+\s[+-]\d{4})\] "(\S+) (\S+)\s*(\S*)\s?" (\d{3}) (\S+)'
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)
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# 解析日志的函数
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def parse_log_line(line):
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match = LOG_PATTERN.match(line)
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if not match:
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return None
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content_size_str = match.group(9)
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content_size = int(content_size_str) if content_size_str.isdigit() else 0
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return {
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'ip': match.group(1),
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'user_identity': match.group(2),
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'user_id': match.group(3),
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'timestamp': match.group(4),
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'method': match.group(5),
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'endpoint': match.group(6),
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'protocol': match.group(7),
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'status_code': int(match.group(8)),
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'content_size': content_size
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}
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def extract_day(log):
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# 时间格式为：10/Oct/2000:13:55:36 -0700
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full_date = log['timestamp']
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day = full_date.split('/')[0] # 只提取日
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return day
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if __name__ == "__main__":
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# 加载日志文件
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logFile = "hdfs://master:9000/user/root/apache.access.log.PROJECT"
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raw_logs = sc.textFile(logFile)
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# 解析并过滤有效日志
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access_logs = raw_logs.map(parse_log_line).filter(lambda x: x is not None).cache()
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# 过滤 404 状态码的日志
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error_404_logs = access_logs.filter(lambda log: log['status_code'] == 404).cache()
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# 每日 404 次数统计
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errDateSorted = (
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error_404_logs
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.map(lambda log: (extract_day(log), 1))
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.reduceByKey(lambda a, b: a + b)
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.sortBy(lambda x: x[1], ascending=False) # 按次数降序排序
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.cache()
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)
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# 获取最多的五天
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top_5_days = errDateSorted
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