I am using a user-defined function (readByteUFF) to read file, perform transform the content and return a pyspark.sql Row.
I use this function on an rdd (which is a large collection of files that should follow the same pattern) in the following setup:
mapped_rdd = rdd.map(readByteUFF)
df = mapped_rdd.toDF()
df.write.format("delta").mode("append").option("mergeSchema", "true").option("path", "dbfs:/mnt/").saveAsTable("table")
This works well when the files follow the same pattern, but in situations where the the function returns an error or a None the entire thing throws me
org.apache.spark.SparkException: Job aborted.
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<command-3680885025756706> in <module>
8
9 #df.createOrReplaceTempView("table")
---> 10 df.write.format("delta").mode("append").option("mergeSchema", "true").option("path", "dbfs:/mnt/").saveAsTable("table")
/databricks/spark/python/pyspark/sql/readwriter.py in saveAsTable(self, name, format, mode, partitionBy, **options)
775 if format is not None:
776 self.format(format)
--> 777 self._jwrite.saveAsTable(name)
778
779 @since(1.4)
/databricks/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in __call__(self, *args)
1255 answer = self.gateway_client.send_command(command)
1256 return_value = get_return_value(
-> 1257 answer, self.gateway_client, self.target_id, self.name)
1258
1259 for temp_arg in temp_args:
/databricks/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
/databricks/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(
I would like to be able to flag the files which cause problems while processing the remaining files. Is there a way to handle this in the flatMap()-functionality?
