import re
from pyspark import SparkContext

sc = SparkContext.getOrCreate()

LOG_PATTERN = re.compile(r'^(\S+) (\S+) (\S+) \[([\w:/]+\s[+-]\d{4})\] "(\S+) (\S+)\s*(\S*)\s?" (\d{3}) (\S+)')


def parse_log_line(line):
    match = LOG_PATTERN.match(line)
    if not match:
        return None

    content_size_str = match.group(9)
    content_size = int(content_size_str) if content_size_str.isdigit() else 0

    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
    }


logFile = "hdfs://master:9000/user/root/apache.access.log.PROJECT"
raw_logs = sc.textFile(logFile)
access_logs = raw_logs.map(parse_log_line).filter(lambda x: x is not None).cache()

# 加入一个保护，防止 access_logs 空时报错
if access_logs.isEmpty():
    print("日志文件为空或解析失败")
else:
    endpoint_counts = (access_logs
                       .map(lambda log: (log['endpoint'], 1))
                       .reduceByKey(lambda a, b: a + b)
                       .sortBy(lambda x: -x[1])
                       .take(10))

    print("Top 10 most visited endpoints:")
    for endpoint, count in endpoint_counts:
        print(f"{endpoint}: {count} hits")

sc.stop()