Spark：如何使用子集日期读取多个s3文件

Question

I am using spark 2.1 on EMR, my files are stored by date:

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

I would like to read only last month of data. I tried with those two solutions but it didn't match with my needs:

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

pyspark select subset of files using regex/glob from s3

Here is my script:

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

When i do the same thing putting manually the path i didn't get the same result:

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

DF2.count()
output : 230000

Answer 1

I found the error: the range of date must be a string without space between dates.

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}'