import re
import datetime
from pyspark.sql import SparkSession, Row
from pyspark.sql import functions as F
import matplotlib.pyplot as plt

# 定义日志解析的正则表达式
APACHE_ACCESS_LOG_PATTERN = '^(\S+) (\S+) (\S+) \[([\w:/]+\s[+-]\d{4})\] "(\S+) (\S+)\s(\S)" (\d{3}) (\S+)'

# 将Apache日志中的时间字符串解析为datetime对象
month_map = {'Jan': 1, 'Feb': 2, 'Mar': 3, 'Apr': 4, 'May': 5, 'Jun': 6, 'Jul': 7,
             'Aug': 8, 'Sep': 9, 'Oct': 10, 'Nov': 11, 'Dec': 12}


def parse_apache_time(s):
    """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]))


def parseApacheLogLine(logline):
    """Parse a line in the Apache Common Log format"""
    match = re.search(APACHE_ACCESS_LOG_PATTERN, logline)
    if match is None:
        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)


def main():
    # 创建SparkSession
    spark = SparkSession.builder \
        .appName("Apache Log Analysis") \
        .getOrCreate()

    # 读取日志文件
    logFile = 'apache.access.log.PROJECT'  # 替换为您的日志文件路径
    rdd = spark.sparkContext.textFile(logFile)

    # 解析日志行
    parsed_logs = rdd.map(parseApacheLogLine)

    # 过滤出有效日志行
    access_logs = parsed_logs.filter(lambda s: s[1] == 1).map(lambda s: s[0]).cache()

    # 过滤出404响应代码的日志
    access_logs_404 = access_logs.filter(access_logs.response_code == 404)

    # 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', linestyle='-', color='b', 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()

    # 使用Matplotlib绘制条形图
    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()