pyspark parse fixed width text file

Question

Trying to parse a fixed width text file.

my text file looks like the following and I need a row id, date, a string, and an integer:

00101292017you1234
00201302017 me5678

I can read the text file to an RDD using sc.textFile(path). I can createDataFrame with a parsed RDD and a schema. It's the parsing in between those two steps.

Answer 1

Spark's substr function can handle fixed-width columns, for example:

df = spark.read.text("/tmp/sample.txt")
df.select(
    df.value.substr(1,3).alias('id'),
    df.value.substr(4,8).alias('date'),
    df.value.substr(12,3).alias('string'),
    df.value.substr(15,4).cast('integer').alias('integer')
).show()

will result in:

+---+--------+------+-------+
| id|    date|string|integer|
+---+--------+------+-------+
|001|01292017|   you|   1234|
|002|01302017|    me|   5678|
+---+--------+------+-------+

Having splitted columns you can reformat and use them as in normal spark dataframe.

Answer 2

I want to automate this process as number of columns will be different for different files

df.value.substr(1,3).alias('id'),
df.value.substr(4,8).alias('date'), 
df.value.substr(12,3).alias('string'),
df.value.substr(15,4).cast('integer').alias('integer')

I created a Python function to generate this on the basis of schema file but now when I am appending it with df.select("my automated string").show it's throwing an error analysis exception