reading multiple csv files using pathos.multiprocessing

[Prototype998]

I'm using PySpark and Pathos to read numerous CSV files and create many DF, but I keep getting this problem.

code for the same:-

from pathos.multiprocessing import ProcessingPool

def readCsv(path):

  return spark.read.csv(path,header=True)

csv_file_list = [file[0][5:] for file in dbutils.fs.ls("/databricks-datasets/COVID/coronavirusdataset/") if file[1].endswith(".csv")]

pool = ProcessingPool(2)

results = pool.map(readCsv, csv_file_list)

[AmanSehgal]

You actually don't need to filter `.csv` files like that.

You can use `pathGlobFilter` to do a regex match for selecting files that matches provided regular expression.

df = spark.read.option("pathGlobFilter","*.csv").csv(upload_path)

[Vidula]

Hi @Punit Chauhan​ 

Hope all is well! Just wanted to check in if you were able to resolve your issue and would you be happy to share the solution or mark an answer as best? Else please let us know if you need more help. 

We'd love to hear from you.

Thanks!

[Prototype998]

@Ajay Pandey​ @Rishabh Pandey​ 

[Rishabh-Pandey · Accepted solution]

hey @Punit Chauhan​ refer this code

from multiprocessing.pool import ThreadPool
pool = ThreadPool(5)
notebooks = ['dim_1', 'dim_2']
pool.map(lambda path: dbutils.notebook.run("/Test/Threading/"+path, timeout_seconds= 60, arguments={"input-data": path}),notebooks)

[Prototype998]

thanks @Rishabh Pandey​