Error when trying to load 30GB SAS file with Pyspark - python-3.x

Question

I am trying to replicate what was done in this article Loading Big SAS files
What I am doing is starting up a jupyter notebook and running the code below. I keep getting a Java load error and I can't figure out why.
Spark Version:2.4.6
Scala Version:2.12.2
Java Version:1.8.0_261
import findspark
findspark.init()
from pyspark.sql.session import SparkSession
spark = SparkSession.builder.\
config("spark.jars.packages","saurfang:spark-sas7bdat:2.0.0-s_2.11")\
.enableHiveSupport().getOrCreate()
df=spark.read.format('com.github.saurfang.sas.spark')\
.load(r'D:\IvyDB\opprcd\opprcd2019.sas7bdat')
Error I always get is below
Py4JJavaError: An error occurred while calling o163.load.
: java.util.concurrent.TimeoutException: Timed out after 60 sec while reading file metadata, file might be corrupt. (Change timeout with 'metadataTimeout' paramater)
at com.github.saurfang.sas.spark.SasRelation.inferSchema(SasRelation.scala:189)
at com.github.saurfang.sas.spark.SasRelation.(SasRelation.scala:62)
at com.github.saurfang.sas.spark.SasRelation$.apply(SasRelation.scala:43)
at com.github.saurfang.sas.spark.DefaultSource.createRelation(DefaultSource.scala:209)
at com.github.saurfang.sas.spark.DefaultSource.createRelation(DefaultSource.scala:42)
at com.github.saurfang.sas.spark.DefaultSource.createRelation(DefaultSource.scala:27)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:341)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:239)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:227)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:174)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)

Answer 1

In our case, we were able to fix this issue by adding Parso library into pyspark. Parso is one of the requirements in Spark SAS Data Source.