You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Vishal Donderia (JIRA)" <ji...@apache.org> on 2019/07/30 12:01:00 UTC
[jira] [Resolved] (SPARK-28563) Spark 2.4 | Reading all the data
inside partition like directory.
[ https://issues.apache.org/jira/browse/SPARK-28563?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Vishal Donderia resolved SPARK-28563.
-------------------------------------
Resolution: Not A Bug
> Spark 2.4 | Reading all the data inside partition like directory.
> -------------------------------------------------------------------
>
> Key: SPARK-28563
> URL: https://issues.apache.org/jira/browse/SPARK-28563
> Project: Spark
> Issue Type: Bug
> Components: Input/Output
> Affects Versions: 2.4.1
> Reporter: Vishal Donderia
> Priority: Blocker
>
> We have upgraded your cluster from Spark 2.3 to 2.4 and currently, we are observing different behavior while reading data.
>
> In Spark 2.3
> spark.read.('basePath','output/model').orc('output/model/abc=4')
> Expected: We will get "abc" column in schema
> Similarly:
> spark.read.('basePath','output/model/abc=4').orc('output/model/abc=4')
> Expected : It will only read data inside parition abc=4 and abc will not be part of schema even "output/model" has different schema of files inside
> In Spark2.4
> spark.read.('basePath','output/model/abc=4').orc('output/model/abc=4')
> It is trying to get the schema from "output/model/" instead of output/model/abc=4 and job is getting failed because of different schema
> {code}
> For partitioned table directories, data files should only live in leaf directories.
> And directories at the same level should have the same partition column name.
> Please check the following directories for unexpected files or inconsistent partition column names:
> at scala.Predef$.assert(Predef.scala:170)
> at org.apache.spark.sql.execution.datasources.PartitioningUtils$.resolvePartitions(PartitioningUtils.scala:364)
> at org.apache.spark.sql.execution.datasources.PartitioningUtils$.parsePartitions(PartitioningUtils.scala:165)
> at org.apache.spark.sql.execution.datasources.PartitioningUtils$.parsePartitions(PartitioningUtils.scala:100)
> at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex.inferPartitioning(PartitioningAwareFileIndex.scala:131)
> at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.partitionSpec(InMemoryFileIndex.scala:71)
> at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex.partitionSchema(PartitioningAwareFileIndex.scala:50)
> at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:144)
> at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:373)
> at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:223)
> at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211)
> at org.apache.spark.sql.DataFrameReader.orc(DataFrameReader.scala:662)
> 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:745)
> {code}
>
>
>
--
This message was sent by Atlassian JIRA
(v7.6.14#76016)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org