Spark：如何使用子集日期读取多个 s3 文件

Posted 2023-04-18

【中文标题】Spark：如何使用子集日期读取多个 s3 文件

【英文标题】：Spark: How to read multiple s3 files using subset date

【发布时间】：2017-05-16 10:06:20

【问题描述】：

我在 EMR 上使用 spark 2.1，我的文件按日期存储：

s3://test/2016/07/01/file.gz
s3://test/2016/07/02/file.gz
...
...
s3://test/2017/05/15/file.gz

我只想读取上个月的数据。 我尝试了这两种解决方案，但不符合我的需求：

How to read multiple gzipped files from S3 into a single RDD

pyspark select subset of files using regex/glob from s3

这是我的脚本：

from_dt = '2017/01/01'
to_dt = '2017/01/31'
datetime_object = datetime.datetime.strptime(from_dt, '%Y/%m/%d')
datetime_object_2 = datetime.datetime.strptime(to_dt, '%Y/%m/%d')

from datetime import date, timedelta

d1 = datetime_object  # start date
d2 = datetime_object_2  # end date

delta = d2 - d1         # timedelta
date_range = []

for i in range(delta.days + 1):
    a = (d1 + timedelta(days=i))
    a = a.strftime('%Y/%m/%d').replace("-","/")
    date_range.append(a)

d = str(date_range).replace('[','').replace(']','').replace('\'',"")

print d
'2017/01/01, 2017/01/02, 2017/01/03, 2017/01/04, 2017/01/05, 2017/01/06, 2017/01/07, 2017/01/08, 2017/01/09, 2017/01/10, 2017/01/11, 2017/01/12, 2017/01/13, 2017/01/14, 2017/01/15, 2017/01/16, 2017/01/17, 2017/01/18, 2017/01/19, 2017/01/20, 2017/01/21, 2017/01/22, 2017/01/23, 2017/01/24, 2017/01/25, 2017/01/26, 2017/01/27, 2017/01/28, 2017/01/29, 2017/01/30, 2017/01/31'

DF1 = spark.read.csv("s3://test/"+d+"/*", sep='|', header='true')

DF1.count()
output : 7000

当我手动输入路径时，我没有得到相同的结果：

DF2 = spark.read.csv("s3://test/2017/01/*/*", sep='|', header='true')

DF2.count()
output : 230000

【参考方案1】：

我发现了错误： 日期范围必须是一个字符串，日期之间没有空格。

d = str(date_range).replace('[','').replace(']','').replace('\'',"").replace('\'',"")
print d
output: '2017/01/01,2017/01/02,2017/01/03,2017/01/04,2017/01/05,2017/01/06,2017/01/07,2017/01/08,2017/01/09,2017/01/10,2017/01/11,2017/01/12,2017/01/13,2017/01/14,2017/01/15,2017/01/16,2017/01/17,2017/01/18,2017/01/19, 2017/01/20,2017/01/21,2017/01/22,2017/01/23,2017/01/24,2017/01/25,2017/01/26, 2017/01/27,2017/01/28,2017/01/29,2017/01/30,2017/01/31'