From c48a91f11e6a6d607542559940254ca4796f95ac Mon Sep 17 00:00:00 2001
From: fly6516 <fly6516@outlook.com>
Date: Mon, 14 Apr 2025 01:49:05 +0800
Subject: [PATCH] =?UTF-8?q?refactor(1-1.py):=20=E9=87=8D=E6=9E=84=E6=97=A5?=
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- 重新编写日志解析逻辑，使用正则表达式匹配日志行
- 添加错误处理和日志文件为空时的处理逻辑- 优化 Top 10 最常访问的端点统计代码- 使用 f-string 改进代码可读性
- 添加 SparkContext 初始化和停止逻辑
---
 1-1.py | 49 +++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 43 insertions(+), 6 deletions(-)

diff --git a/1-1.py b/1-1.py
index f08f1af..b27440b 100644
--- a/1-1.py
+++ b/1-1.py
@@ -1,11 +1,48 @@
-from log_analysis_step2 import access_logs
+import re
+from pyspark import SparkContext
 
-not200 = access_logs.filter(lambda log: log.response_code != 200)
+sc = SparkContext.getOrCreate()
 
-endpointCountPairTuple = not200.map(lambda log: (log.endpoint, 1))
+LOG_PATTERN = re.compile(r'^(\S+) (\S+) (\S+) \[([\w:/]+\s[+-]\d{4})\] "(\S+) (\S+)\s*(\S*)\s?" (\d{3}) (\S+)')
 
-endpointSum = endpointCountPairTuple.reduceByKey(lambda a, b : a + b)
 
-topTenErrURLs = endpointSum.takeOrdered(10, lambda s: -1 * s[1])
+def parse_log_line(line):
+    match = LOG_PATTERN.match(line)
+    if not match:
+        return None
 
-print('Top Ten failed URLs: %s' % topTenErrURLs)
+    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()