refactor(2-9):将条形图改为折线图

- 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

2-9.py

@@ -1,74 +1,104 @@
 import re
-from pyspark import SparkContext
+import datetime
+from pyspark.sql import SparkSession, Row
+from pyspark.sql import functions as F
 import matplotlib.pyplot as plt
 
-# 初始化 SparkContext
+# 定义日志解析的正则表达式
-sc = SparkContext.getOrCreate()
+APACHE_ACCESS_LOG_PATTERN = '^(\S+) (\S+) (\S+) \[([\w:/]+\s[+-]\d{4})\] "(\S+) (\S+)\s(\S)" (\d{3}) (\S+)'
 
-# 日志匹配的正则表达式
+# 将Apache日志中的时间字符串解析为datetime对象
-LOG_PATTERN = re.compile(
+month_map = {'Jan': 1, 'Feb': 2, 'Mar': 3, 'Apr': 4, 'May': 5, 'Jun': 6, 'Jul': 7,
-    r'^(\S+) (\S+) (\S+) \[([\w:/]+\s[+-]\d{4})\] "(\S+) (\S+)\s*(\S*)\s?" (\d{3}) (\S+)'
+             'Aug': 8, 'Sep': 9, 'Oct': 10, 'Nov': 11, 'Dec': 12}
-)
 
-# 解析日志的函数
-def parse_log_line(line):
-    match = LOG_PATTERN.match(line)
-    if not match:
-        return None
 
-    content_size_str = match.group(9)
+def parse_apache_time(s):
-    content_size = int(content_size_str) if content_size_str.isdigit() else 0
+    """Convert Apache time format into a Python datetime object"""
+    return datetime.datetime(int(s[7:11]),
+                             month_map[s[3:6]],
+                             int(s[0:2]),
+                             int(s[12:14]),
+                             int(s[15:17]),
+                             int(s[18:20]))
 
-    return {
-        'ip': match.group(1),
-        'user_identity': match.group(2),
-        'user_id': match.group(3),
-        'timestamp': match.group(4),
-        'method': match.group(5),
-        'endpoint': match.group(6),
-        'protocol': match.group(7),
-        'status_code': int(match.group(8)),
-        'content_size': content_size
-    }
 
-# 提取小时
+def parseApacheLogLine(logline):
-def extract_hour(log):
+    """Parse a line in the Apache Common Log format"""
-    timestamp = log['timestamp']
+    match = re.search(APACHE_ACCESS_LOG_PATTERN, logline)
-    hour = timestamp.split(":")[1]  # 从时间戳中提取小时
+    if match is None:
-    return hour
+        return (logline, 0)
+    size_field = match.group(9)
+    size = int(size_field) if size_field != '-' else 0
+    return (Row(
+        host=match.group(1),
+        client_identd=match.group(2),
+        user_id=match.group(3),
+        date_time=parse_apache_time(match.group(4)),
+        method=match.group(5),
+        endpoint=match.group(6),
+        protocol=match.group(7),
+        response_code=int(match.group(8)),
+        content_size=size
+    ), 1)
 
-if __name__ == "__main__":
-    # 加载日志文件
-    logFile = "hdfs://master:9000/user/root/apache.access.log.PROJECT"
-    raw_logs = sc.textFile(logFile)
 
-    # 解析并过滤有效日志
+def main():
-    access_logs = raw_logs.map(parse_log_line).filter(lambda x: x is not None).cache()
+    # 创建SparkSession
+    spark = SparkSession.builder \
+        .appName("Apache Log Analysis") \
+        .getOrCreate()
 
-    # 只保留 404 响应代码的日志
+    # 读取日志文件
-    bad_records = access_logs.filter(lambda log: log['status_code'] == 404).cache()
+    logFile = 'apache.access.log.PROJECT'  # 替换为您的日志文件路径
+    rdd = spark.sparkContext.textFile(logFile)
 
-    # 提取每小时的 404 错误记录
+    # 解析日志行
-    hourly_404_counts = bad_records.map(lambda log: (extract_hour(log), 1)) \
+    parsed_logs = rdd.map(parseApacheLogLine)
-                                   .reduceByKey(lambda a, b: a + b) \
-                                   .sortByKey()  # 按小时排序
 
-    # 将结果转换为列表
+    # 过滤出有效日志行
-    hourly_404_counts_list = hourly_404_counts.collect()
+    access_logs = parsed_logs.filter(lambda s: s[1] == 1).map(lambda s: s[0]).cache()
 
-    # 提取小时和对应的 404 次数
+    # 过滤出404响应代码的日志
-    hours = [hour for hour, count in hourly_404_counts_list]
+    access_logs_404 = access_logs.filter(access_logs.response_code == 404)
-    counts = [count for hour, count in hourly_404_counts_list]
 
-    # 使用 matplotlib 绘制折线图
+    # 提取小时信息
+    access_logs_with_hour = access_logs_404.withColumn("hour", F.hour(access_logs_404.date_time))
+
+    # 计算每小时的404响应代码数量
+    hourly_404_counts = access_logs_with_hour.groupBy("hour").count().orderBy("hour")
+
+    # 收集数据并准备绘图
+    hourly_counts = hourly_404_counts.collect()
+
+    # 提取小时和计数
+    hours = [row["hour"] for row in hourly_counts]
+    counts = [row["count"] for row in hourly_counts]
+
+    # 使用Matplotlib绘制折线图
     plt.figure(figsize=(10, 6))
-    plt.plot(hours, counts, marker='o', color='b', linestyle='-', linewidth=2, markersize=6)
+    plt.plot(hours, counts, marker='o', linestyle='-', color='b', label='404 Responses')
-    plt.title('每小时404响应代码数量')
+    plt.title("Hourly 404 Response Code Counts")
-    plt.xlabel('小时')
+    plt.xlabel("Hour of the Day")
-    plt.ylabel('404响应次数')
+    plt.ylabel("Count of 404 Responses")
-    plt.xticks(rotation=45)  # 将小时标签旋转 45 度
+    plt.xticks(range(24))  # 显示24小时
-    plt.tight_layout()
+    plt.grid(True)
+    plt.legend()
     plt.show()
 
-    # 停止 Spark
+    # 使用Matplotlib绘制条形图
-    sc.stop()
+    plt.figure(figsize=(10, 6))
+    plt.bar(hours, counts, color='orange', label='404 Responses')
+    plt.title("Hourly 404 Response Code Counts")
+    plt.xlabel("Hour of the Day")
+    plt.ylabel("Count of 404 Responses")
+    plt.xticks(range(24))  # 显示24小时
+    plt.grid(True)
+    plt.legend()
+    plt.show()
+
+    # 结束SparkSession
+    spark.stop()
+
+
+if __name__ == "__main__":
+    main()