Parquet Errors in PySpark using CDH 5.4 and Spark 1.3.0 with Parquet Table in HIVE

Question

I’m running CDH 5.4 using Spark 1.3.0 with Spark on YARN support. When I create a simple parquet table in HIVE and then try to perform a transformation or aggregation to it using PySpark it throws this error message to me. Any thoughts? To reproduce the problem do the following…

HIVE:

CREATE TABLE IF NOT EXISTS TestTable_Parquet(
Investment_Id int,
Identifier string,
Package_Id int,
AsOfDate timestamp
) STORED AS PARQUET
;
INSERT INTO TABLE TestTable_Parquet
VALUES (1, "id1", 1, "2015-01-01")
;

PySpark:

test = sqlCtx.table("testtable_parquet")
test.filter(test.identifier == "id1")

Error

Py4JJavaError                             Traceback (most recent call last)
<ipython-input-3-690105998113> in <module>()
      1 test = sqlCtx.table("testtable_parquet")
----> 2 test.filter(test.identifier == "id1")

/usr/lib/spark/python/pyspark/sql/dataframe.py in filter(self, condition)
    627             jdf = self._jdf.filter(condition)
    628         elif isinstance(condition, Column):
--> 629             jdf = self._jdf.filter(condition._jc)
    630         else:
    631             raise TypeError("condition should be string or Column")

/usr/lib/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in __call__(self, *args)
    536         answer = self.gateway_client.send_command(command)
    537         return_value = get_return_value(answer, self.gateway_client,
--> 538                 self.target_id, self.name)
    539 
    540         for temp_arg in temp_args:

/usr/lib/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    298                 raise Py4JJavaError(
    299                     'An error occurred while calling {0}{1}{2}.\n'.
--> 300                     format(target_id, '.', name), value)
    301             else:
    302                 raise Py4JError(

Py4JJavaError: An error occurred while calling o34.filter.
: org.apache.spark.sql.AnalysisException: resolved attributes identifier missing from investment_id,identifier,package_id,asofdate;
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis.failAnalysis(CheckAnalysis.scala:37)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$apply$3.apply(CheckAnalysis.scala:93)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$apply$3.apply(CheckAnalysis.scala:43)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:88)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis.apply(CheckAnalysis.scala:43)
at org.apache.spark.sql.SQLContext$QueryExecution.assertAnalyzed(SQLContext.scala:1069)
at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)
at org.apache.spark.sql.DataFrame.logicalPlanToDataFrame(DataFrame.scala:157)
at org.apache.spark.sql.DataFrame.filter(DataFrame.scala:508)
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)

Answer 1

After a bit of playing, the solution to the problem appears to be running this set conf command first, this is only required when you want Spark to speak to Hive:

sqlCtx.setConf("spark.sql.hive.convertMetastoreParquet", "false")