Below is the code that I am running. I get an error for unresolved attributes. Can anyone point me in the right direction? Running from pyspark shell using yarn "MASTER=yarn-client pyspark"
Error is below code: # Import SQLContext and data types from pyspark.sql import * # sc is an existing SparkContext. sqlContext = SQLContext(sc) # The result of loading a parquet file is also a SchemaRDD. # Try loading all data that you have parquetFile = sqlContext.parquetFile("/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.0.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.1.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.10.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.11.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.2.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.3.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.4.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.5.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.6.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.7.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.8.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.9.parq,/user/hive/warehouse/impala_new_4/key=20141001/f1448ca083a5e224-159572f61b50d7a3_854675293_data.0.parq,/user/hive/warehouse/impala_new_4/key=20141001/f1448ca083a5e224-159572f61b50d7a3_854675293_data.1.parq,/user/hive/warehouse/impala_new_4/key=20141001/f1448ca083a5e224-159572f61b50d7a3_854675293_data.2.parq,/user/hive/warehouse/impala_new_4/key=20141001/f1448ca083a5e224-159572f61b50d7a3_854675293_data.3.parq,/user/hive/warehouse/impala_new_4/key=20141001/f1448ca083a5e224-159572f61b50d7a3_854675293_data.4.parq") # Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerTempTable("parquetFileone") results = sqlContext.sql("SELECT * FROM parquetFileone where key=20141001 ") #print results for result in results.collect(): print result Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/opt/cloudera/parcels/CDH-5.2.1-1.cdh5.2.1.p0.12/lib/spark/python/pyspark/sql.py", line 1615, in collect rows = RDD.collect(self) File "/opt/cloudera/parcels/CDH-5.2.1-1.cdh5.2.1.p0.12/lib/spark/python/pyspark/rdd.py", line 678, in collect bytesInJava = self._jrdd.collect().iterator() File "/opt/cloudera/parcels/CDH-5.2.1-1.cdh5.2.1.p0.12/lib/spark/python/pyspark/sql.py", line 1527, in _jrdd self._lazy_jrdd = self._jschema_rdd.javaToPython() File "/opt/cloudera/parcels/CDH-5.2.1-1.cdh5.2.1.p0.12/lib/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py", line 538, in __call__ File "/opt/cloudera/parcels/CDH-5.2.1-1.cdh5.2.1.p0.12/lib/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py", line 300, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling o29.javaToPython. : org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Unresolved attributes: *, tree: Project [*] Filter ('key = 20141001) Subquery parquetFileone ParquetRelation /user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.0.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.1.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.10.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.11.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.2.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.3.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.4.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.5.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.6.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.7.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.8.parq,/user/hive/warehouse/impala_new_4/key=20141001/69446344000a3a17-c90aac1f33a0fbc_875501925_data.9.parq,/user/hive/warehouse/impala_new_4/key=20141001/f1448ca083a5e224-159572f61b50d7a3_854675293_data.0.parq,/user/hive/warehouse/impala_new_4/key=20141001/f1448ca083a5e224-159572f61b50d7a3_854675293_data.1.parq,/user/hive/warehouse/impala_new_4/key=20141001/f1448ca083a5e224-159572f61b50d7a3_854675293_data.2.parq,/user/hive/warehouse/impala_new_4/key=20141001/f1448ca083a5e224-159572f61b50d7a3_854675293_data.3.parq,/user/hive/warehouse/impala_new_4/key=20141001/f1448ca083a5e224-159572f61b50d7a3_854675293_data.4.parq, Some(Configuration: core-default.xml, core-site.xml, yarn-default.xml, yarn-site.xml, mapred-default.xml, mapred-site.xml, hdfs-default.xml, hdfs-site.xml), org.apache.spark.sql.SQLContext@2c76fd82, [] at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$$anonfun$apply$1.applyOrElse(Analyzer.scala:72) at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$$anonfun$apply$1.applyOrElse(Analyzer.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:165) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:183) at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at scala.collection.Iterator$class.foreach(Iterator.scala:727) at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47) at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273) at scala.collection.AbstractIterator.to(Iterator.scala:1157) at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265) at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157) at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252) at scala.collection.AbstractIterator.toArray(Iterator.scala:1157) at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildrenDown(TreeNode.scala:212) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:168) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:156) at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:70) at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:68) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1$$anonfun$apply$2.apply(RuleExecutor.scala:61) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1$$anonfun$apply$2.apply(RuleExecutor.scala:59) at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51) at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60) at scala.collection.mutable.WrappedArray.foldLeft(WrappedArray.scala:34) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1.apply(RuleExecutor.scala:59) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1.apply(RuleExecutor.scala:51) at scala.collection.immutable.List.foreach(List.scala:318) at org.apache.spark.sql.catalyst.rules.RuleExecutor.apply(RuleExecutor.scala:51) at org.apache.spark.sql.SQLContext$QueryExecution.analyzed$lzycompute(SQLContext.scala:397) at org.apache.spark.sql.SQLContext$QueryExecution.analyzed(SQLContext.scala:397) at org.apache.spark.sql.SchemaRDD.javaToPython(SchemaRDD.scala:412) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:606) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379) at py4j.Gateway.invoke(Gateway.java:259) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:207) at java.lang.Thread.run(Thread.java:745) -- *MAGNE**+**I**C* *Sam Flint* | *Lead Developer, Data Analytics*