fly6516
c48a91f11e
- 重新编写日志解析逻辑，使用正则表达式匹配日志行 - 添加错误处理和日志文件为空时的处理逻辑- 优化 Top 10 最常访问的端点统计代码- 使用 f-string 改进代码可读性 - 添加 SparkContext 初始化和停止逻辑
49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
import re
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from pyspark import SparkContext
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sc = SparkContext.getOrCreate()
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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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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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logFile = "hdfs://master:9000/user/root/apache.access.log.PROJECT"
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raw_logs = sc.textFile(logFile)
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access_logs = raw_logs.map(parse_log_line).filter(lambda x: x is not None).cache()
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# 加入一个保护，防止 access_logs 空时报错
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if access_logs.isEmpty():
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print("日志文件为空或解析失败")
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else:
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endpoint_counts = (access_logs
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.map(lambda log: (log['endpoint'], 1))
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.reduceByKey(lambda a, b: a + b)
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.sortBy(lambda x: -x[1])
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.take(10))
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print("Top 10 most visited endpoints:")
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for endpoint, count in endpoint_counts:
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print(f"{endpoint}: {count} hits")
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sc.stop()
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