Ok I found an excellent YouTube tutorial that provides a lot more detail about the setup than the original tutorial: https://www.youtube.com/playlist?list=PLf0swTFhTI8pYbd8mr36LiYIOOY2xw5Iu I am now getting this error: WARNING: An illegal reflective access operation has occurred WARNING: Illegal reflective access by org.apache.hadoop.security.authentication.util.KerberosUtil (file:/C:/spark-2.3.0-bin-hadoop2.7/jars/hadoop-auth-2.7.3.jar) to method sun.security.krb5.Config.getInstance() WARNING: Please consider reporting this to the maintainers of org.apache.hadoop.security.authentication.util.KerberosUtil WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations WARNING: All illegal access operations will be denied in a future release 2018-03-15 19:42:54 WARN NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). 2018-03-15 19:42:54 WARN Utils:66 - Service 'SparkUI' could not bind on port 4040. Attempting port 4041. Traceback (most recent call last): File "E:/DataScience/pySpark/HelloSpark/main.py", line 13, in <module> print ("Number of elements: " + str(counts.count())) File "E:\DataScience\pySpark\HelloSpark\venv\lib\site-packages\pyspark\rdd.py", line 1056, in count return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum() File "E:\DataScience\pySpark\HelloSpark\venv\lib\site-packages\pyspark\rdd.py", line 1047, in sum return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add) File "E:\DataScience\pySpark\HelloSpark\venv\lib\site-packages\pyspark\rdd.py", line 921, in fold vals = self.mapPartitions(func).collect() File "E:\DataScience\pySpark\HelloSpark\venv\lib\site-packages\pyspark\rdd.py", line 824, in collect port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd()) File "E:\DataScience\pySpark\HelloSpark\venv\lib\site-packages\py4j\java_gateway.py", line 1160, in __call__ answer, self.gateway_client, self.target_id, self.name) File "E:\DataScience\pySpark\HelloSpark\venv\lib\site-packages\py4j\protocol.py", line 320, in get_return_value format(target_id, ".", name), value) py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe. : java.lang.IllegalArgumentException at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source) at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source) at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source) at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:46) at org.apache.spark.util.FieldAccessFinder$anon$3$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:449) at org.apache.spark.util.FieldAccessFinder$anon$3$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:432) at scala.collection.TraversableLike$WithFilter$anonfun$foreach$1.apply(TraversableLike.scala:733) at scala.collection.mutable.HashMap$anon$1$anonfun$foreach$2.apply(HashMap.scala:103) at scala.collection.mutable.HashMap$anon$1$anonfun$foreach$2.apply(HashMap.scala:103) at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230) at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40) at scala.collection.mutable.HashMap$anon$1.foreach(HashMap.scala:103) at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732) at org.apache.spark.util.FieldAccessFinder$anon$3.visitMethodInsn(ClosureCleaner.scala:432) at org.apache.xbean.asm5.ClassReader.a(Unknown Source) at org.apache.xbean.asm5.ClassReader.b(Unknown Source) at org.apache.xbean.asm5.ClassReader.accept(Unknown Source) at org.apache.xbean.asm5.ClassReader.accept(Unknown Source) at org.apache.spark.util.ClosureCleaner$anonfun$org$apache$spark$util$ClosureCleaner$clean$14.apply(ClosureCleaner.scala:262) at org.apache.spark.util.ClosureCleaner$anonfun$org$apache$spark$util$ClosureCleaner$clean$14.apply(ClosureCleaner.scala:261) at scala.collection.immutable.List.foreach(List.scala:381) at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$clean(ClosureCleaner.scala:261) at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:159) at org.apache.spark.SparkContext.clean(SparkContext.scala:2292) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2066) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2092) at org.apache.spark.rdd.RDD$anonfun$collect$1.apply(RDD.scala:939) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:363) at org.apache.spark.rdd.RDD.collect(RDD.scala:938) at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:153) at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala) at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.base/java.lang.reflect.Method.invoke(Method.java:564) 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:214) at java.base/java.lang.Thread.run(Thread.java:844) Process finished with exit code 1 ... 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