fly6516
6a4170c1fe
- 将 matplotlib 绘制条形图的代码修改为绘制折线图 - 使用 plot函数替代 bar 函数，添加 marker、linestyle 等参数以美化图表
75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
import re
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from pyspark import SparkContext
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import matplotlib.pyplot as plt
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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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# 提取小时
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def extract_hour(log):
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timestamp = log['timestamp']
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hour = timestamp.split(":")[1] # 从时间戳中提取小时
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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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# 只保留 404 响应代码的日志
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bad_records = access_logs.filter(lambda log: log['status_code'] == 404).cache()
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# 提取每小时的 404 错误记录
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hourly_404_counts = bad_records.map(lambda log: (extract_hour(log), 1)) \
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.reduceByKey(lambda a, b: a + b) \
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.sortByKey() # 按小时排序
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# 将结果转换为列表
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hourly_404_counts_list = hourly_404_counts.collect()
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# 提取小时和对应的 404 次数
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hours = [hour for hour, count in hourly_404_counts_list]
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counts = [count for hour, count in hourly_404_counts_list]
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# 使用 matplotlib 绘制折线图
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plt.figure(figsize=(10, 6))
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plt.plot(hours, counts, marker='o', color='b', linestyle='-', linewidth=2, markersize=6)
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plt.title('每小时404响应代码数量')
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plt.xlabel('小时')
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plt.ylabel('404响应次数')
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plt.xticks(rotation=45) # 将小时标签旋转 45 度
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plt.tight_layout()
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plt.show()
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# 停止 Spark
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sc.stop()
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