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
This commit is contained in:
parent
6a4170c1fe
commit
5190f1fb90
142
2-9.py
142
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
|
||||
sc = SparkContext.getOrCreate()
|
||||
# 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+)'
|
||||
|
||||
# 日志匹配的正则表达式
|
||||
LOG_PATTERN = re.compile(
|
||||
r'^(\S+) (\S+) (\S+) \[([\w:/]+\s[+-]\d{4})\] "(\S+) (\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}
|
||||
|
||||
# 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)
|
||||
content_size = int(content_size_str) if content_size_str.isdigit() else 0
|
||||
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]))
|
||||
|
||||
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 extract_hour(log):
|
||||
timestamp = log['timestamp']
|
||||
hour = timestamp.split(":")[1] # 从时间戳中提取小时
|
||||
return hour
|
||||
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)
|
||||
|
||||
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
|
||||
access_logs = raw_logs.map(parse_log_line).filter(lambda x: x is not None).cache()
|
||||
def main():
|
||||
# 创建SparkSession
|
||||
spark = SparkSession.builder \
|
||||
.appName("Apache Log Analysis") \
|
||||
.getOrCreate()
|
||||
|
||||
# 只保留 404 响应代码的日志
|
||||
bad_records = access_logs.filter(lambda log: log['status_code'] == 404).cache()
|
||||
# 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)
|
||||
|
||||
# 提取每小时的 404 错误记录
|
||||
hourly_404_counts = bad_records.map(lambda log: (extract_hour(log), 1)) \
|
||||
.reduceByKey(lambda a, b: a + b) \
|
||||
.sortByKey() # 按小时排序
|
||||
# 解析日志行
|
||||
parsed_logs = rdd.map(parseApacheLogLine)
|
||||
|
||||
# 将结果转换为列表
|
||||
hourly_404_counts_list = hourly_404_counts.collect()
|
||||
# 过滤出有效日志行
|
||||
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
|
||||
hours = [hour for hour, count in hourly_404_counts_list]
|
||||
counts = [count for hour, count in hourly_404_counts_list]
|
||||
# 过滤出404响应代码的日志
|
||||
access_logs_404 = access_logs.filter(access_logs.response_code == 404)
|
||||
|
||||
# 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.title('每小时404响应代码数量')
|
||||
plt.xlabel('小时')
|
||||
plt.ylabel('404响应次数')
|
||||
plt.xticks(rotation=45) # 将小时标签旋转 45 度
|
||||
plt.tight_layout()
|
||||
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()
|
||||
|
||||
# 停止 Spark
|
||||
sc.stop()
|
||||
# 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()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user