PySpark, setting spark conf values in a function and catching for errors

[Mich Talebzadeh <mi...@gmail.com>]

Hi,


I have multiple routines that are using Spark for Google BigQuery that set
these configuration values. I have decided to put them in a PySpark
function as below with spark as an input.


def setSparkConfSet(spark):

    try:

        spark.conf.set("GcpJsonKeyFile",
config['GCPVariables']['jsonKeyFile'])

        spark.conf.set("BigQueryProjectId",
config['GCPVariables']['projectId'])

        spark.conf.set("BigQueryDatasetLocation",
config['GCPVariables']['datasetLocation'])

        spark.conf.set("google.cloud.auth.service.account.enable", "true")

        spark.conf.set("fs.gs.project.id",
config['GCPVariables']['projectId'])

        spark.conf.set("fs.gs.impl",
"com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem")

        spark.conf.set("fs.AbstractFileSystem.gs.impl",
"com.google.cloud.hadoop.fs.gcs.GoogleHadoopFS")

        spark.conf.set("temporaryGcsBucket",
config['GCPVariables']['tmp_bucket'])

    except:

        print(f"""Could not set spark config variables, quitting""")

        sys.exit(1)


Two questions


Is it necessary to catch any error or simply call the function in the main
routine or do the error handling in the module that is calling this
function?


Thanks



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