71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
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import re
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import matplotlib.pyplot as plt
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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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LOG_PATTERN = re.compile(r'^(\S+) (\S+) (\S+) \[([\w:/]+\s[+-]\d{4})\] "(\S+) (\S+)\s*(\S*)\s?" (\d{3}) (\S+)')
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# 解析日期和主机
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def parse_day_ip(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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ip = match.group(1)
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full_time = match.group(4)
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day = full_time.split('/')[0] # "10"
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return (day, ip)
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# 1️⃣ 每天总请求数
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def parse_day(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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full_time = match.group(4)
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day = full_time.split('/')[0]
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return (day, 1)
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# 得到每天总请求数
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dailyRequests = raw_logs.map(parse_day).filter(lambda x: x is not None) \
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.reduceByKey(lambda a, b: a + b)
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# 得到每天不同主机数（如 1.3 中的逻辑）
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day_ip_pairs = raw_logs.map(parse_day_ip).filter(lambda x: x is not None)
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dailyUniqueHosts = day_ip_pairs.distinct() \
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.map(lambda x: (x[0], 1)) \
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.reduceByKey(lambda a, b: a + b)
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# 2️⃣ 合并两个 RDD，并计算平均值（用整数除法）
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dailyReqJoinHost = dailyRequests.join(dailyUniqueHosts)
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avgDailyReqPerHost = dailyReqJoinHost.map(
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lambda x: (x[0], x[1][0] // x[1][1]) # (day, total_requests // unique_hosts)
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).sortByKey().cache() # ⚠️ 缓存结果供后续使用
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# 收集数据并准备绘图
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daysWithAvg = []
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avgs = []
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for day, avg in avgDailyReqPerHost.collect():
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daysWithAvg.append(day)
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avgs.append(avg)
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# 绘制折线图
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plt.figure(figsize=(10, 6))
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plt.plot(daysWithAvg, avgs, marker='o', linestyle='-', color='b', label='Average Requests per Host')
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plt.xlabel('Day')
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plt.ylabel('Average Requests per Host')
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plt.title('Average Daily Requests per Host')
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plt.xticks(rotation=45)
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plt.tight_layout()
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plt.legend()
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plt.show()
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sc.stop()
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