feat: 添加日志解析和404 错误统计功能
- 新增日志解析函数 parse_log_line,用于解析 Apache 日志 - 添加过滤 404 错误的函数 filter_404 - 实现从 HDFS 读取日志、解析、过滤和统计 404 错误的完整流程- 打印 404 错误记录的数量
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2-1.py
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47
2-1.py
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import re
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from pyspark import SparkContext
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sc = SparkContext.getOrCreate()
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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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# 日志模式
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LOG_PATTERN = re.compile(r'^(\S+) (\S+) (\S+) \[([\w:/]+\s[+-]\d{4})\] "(\S+) (\S+)\s*(\S*)\s?" (\d{3}) (\S+)')
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# 解析日志
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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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# 过滤出包含 404 响应代码的日志
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def filter_404(log):
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return log['status_code'] == 404
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# 解析并过滤日志
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parsed_logs = raw_logs.map(parse_log_line).filter(lambda x: x is not None)
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# 过滤出 404 错误记录
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error_404_logs = parsed_logs.filter(filter_404).cache()
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# 统计 404 错误的数量
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count_404 = error_404_logs.count()
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# 打印 404 错误记录数量
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print(f"日志中共有 {count_404} 条 404 响应代码记录。")
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sc.stop()
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