我对Apache Airflow不满意。从字面上看才刚刚开始,我遇到了一个错误。我写了我的第一个数据,并且正在调用Python脚本。当我设置它时,它成功运行并开始工作,并且我计划每天运行一次。我今天来检查它,但dag失败并显示错误消息-HTTP错误404:找不到。
对我来说,一切都是新奇的,因此很抱歉,如果这很容易解决,但是我不明白为什么会出现404错误。我尝试重新启动docker,看看这是否是网络服务器问题,但没有运气。
感谢您的帮助
from airflow.models import DAG
from datetime import datetime, timedelta
from airflow.operators.python_operator import PythonOperator
from covid_cases import covid_data
default_args = {
'owner': 'airflow',
'start_date': datetime(2020, 10, 4),
'retries': 2,
'retry_delay': timedelta(seconds=20)}
dag = DAG(dag_id = 'covid_updates',
default_args = default_args,
schedule_interval = "0 4 * * *")
t1 = PythonOperator(task_id = 'covid_update',
python_callable = covid_data,
dag = dag)
t1
def covid_data():
"""
Overview:
---------
Downloads the USA COVID data directly from John Hopkins CSSEGISandData.
This function merges all data from most current date to earliest date (2020-4-11).
Using this function a user can conduct time series analysis in how COVID
increases/decreases in various states.
Output:
-------
One uncleaned .csv file called "usa_covid_cases.csv"
"""
from datetime import datetime, timedelta
import pandas as pd
from urllib.error import HTTPError
# Set starting index
i = 1
# Earliest dataset available on GitHub
start_date = datetime.strptime('2020-4-11', '%Y-%m-%d').date()
# Pulling today's date minus 1 day due to delay posting on GitHub
today = datetime.now().date() - timedelta(days=i)
# Setting llist to store dataframe file names
file_names = []
# Looping until date is equal to earlist date = Start Date
while not (start_date.day == today.day and start_date.month == today.month and start_date.year == today.year):
# Extracting variables from current date
day = today.day
month = today.month
year = today.year
# Cleaning and converting values for formatting on GitHub URL link
if day < 10:
day = '0' + str(day)
if month < 10:
month = '0' + str(month)
# Setting variable for each url
url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports_us/{}-{}-{}.csv'\
.format(month, day, str(year))
try:
# Reading each url as a datafra,
df = pd.read_csv(url, error_bad_lines=False)
except HTTPError as e:
# handle the error (print, log, etc)
continue
finally:
# Code moved here to prevent an endless loop
# Subtracting the new index to increase 1 less day from the current date
today = datetime.now().date() - timedelta(days=i)
# Saving each dataframe into the empty list
file_names.append(df)
# Increasing the index by 1
i += 1
# Once while loop ends - concat all the files into a single dataframe
new_df = pd.concat(file_names)
# Save output into new csv file
new_df.to_csv('usa_covid_cases.csv')
答案 0 :(得分:1)
从日志中,当您尝试从URL读取数据并且该URL不存在时会发生错误。
pd.read_csv(url, error_bad_lines=False) #Line 50 of covid_data.py
答案 1 :(得分:0)
我刚刚看过GitHub repo,最早的数据集来自 2020年12月12日。
为防止DAG无法从可能的已删除数据集中失败,您可以将pd.read_csv()
包装在try except
块中,如下所示:
# Import the HTTPError (error from you screenshot)
from urllib.error import HTTPError
...
try:
# Reading each url as a datafra,
df = pd.read_csv(url, error_bad_lines=False)
except HTTPError as e:
# handle the error (print, log, etc)
continue
finally:
# Code moved here to prevent an endless loop
# Subtracting the new index to increase 1 less day from the current date
today = datetime.now().date() - timedelta(days=i)
# Saving each dataframe into the empty list
file_names.append(df)
# Increasing the index by 1
i += 1
...