feat: 添加 2-8.py 文件并实现每小时 404 错误统计
- 新增 2-8.py 文件,实现从日志文件中解析和统计每小时的 404 错误数量 - 使用 Spark 进行日志处理和分析 - 添加日志解析函数和提取小时函数 - 实现从 HDFS 读取日志数据并进行过滤和聚合 - 最后输出每小时的 404 错误数量
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2-8.py
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2-8.py
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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_hour(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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hour = full_date.split(':')[1] # 提取小时
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return hour
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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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badRecords = access_logs.filter(lambda log: log['status_code'] == 404).cache()
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# 每小时 404 错误次数统计
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hourRecorded = (
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badRecords
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.map(lambda log: (extract_hour(log), 1))
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.reduceByKey(lambda a, b: a + b)
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.sortByKey() # 按小时顺序排序
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.cache()
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)
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# 获取结果并打印
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result = hourRecorded.collect()
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print("每小时的 404 错误数量:")
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for hour, count in result:
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print("小时 {}: {} 次 404 错误".format(hour, count))
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# 停止 Spark
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sc.stop()
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