I'll add that simple type promotion is done automatically when the types
are compatible (i.e. Int -> Long).
On Tue, May 5, 2015 at 5:55 PM, Michael Armbrust <mich...@databricks.com>
wrote:
> You need to add a select clause to at least one dataframe to give them the
> same schema before you can union them (much like in SQL).
>
> On Tue, May 5, 2015 at 3:24 AM, Wilhelm <niznik.pa...@gmail.com> wrote:
>
>> Hey there,
>>
>> 1.) I'm loading 2 avro files with that have slightly different schema
>>
>> df1 = sqlc.load(file1, "com.databricks.spark.avro")
>> df2 = sqlc.load(file2, "com.databricks.spark.avro")
>>
>> 2.) I want to unionAll them
>>
>> nfd = dfs1.unionAll(dfs2)
>>
>> 3.) Getting the following error
>>
>>
>> ---------------------------------------------------------------------------
>> Py4JJavaError Traceback (most recent call
>> last)
>> <ipython-input-190-a86d9adbea83> in <module>()
>> 17
>> 18
>> ---> 19 nfd = dfs1.unionAll(dfs2)
>> 20
>> 21
>>
>> /home/hadoop/spark/python/pyspark/sql/dataframe.pyc in unionAll(self,
>> other)
>> 669 This is equivalent to `UNION ALL` in SQL.
>> 670 """
>> --> 671 return DataFrame(self._jdf.unionAll(other._jdf),
>> self.sql_ctx)
>> 672
>> 673 def intersect(self, other):
>>
>> /home/hadoop/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:
>>
>> /home/hadoop/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 o76196.unionAll.
>> : org.apache.spark.sql.AnalysisException: unresolved operator 'Union ;
>> at
>>
>> org.apache.spark.sql.catalyst.analysis.CheckAnalysis.failAnalysis(CheckAnalysis.scala:37)
>> at
>>
>> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$apply$3.apply(CheckAnalysis.scala:97)
>> at
>>
>> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$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.unionAll(DataFrame.scala:641)
>> at sun.reflect.GeneratedMethodAccessor36.invoke(Unknown Source)
>> 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)
>>
>> ---------------------------------------------------------------------------
>>
>> 4.) Is it possible to automatically merge 2 DFs with different schemas
>> like
>> that? Am I doing sth. wrong?
>>
>> Much appreciated!
>>
>>
>>
>>
>> --
>> View this message in context:
>> http://apache-spark-user-list.1001560.n3.nabble.com/Two-DataFrames-with-different-schema-unionAll-issue-tp22765.html
>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>
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>>
>
