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https://issues.apache.org/jira/browse/HUDI-1494?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joyan Sil updated HUDI-1494:
----------------------------
Status: Open (was: New)
> Apache Hudi example from spark-shell throws error for Spark 2.3.0
> -----------------------------------------------------------------
>
> Key: HUDI-1494
> URL: https://issues.apache.org/jira/browse/HUDI-1494
> Project: Apache Hudi
> Issue Type: Bug
> Components: newbie
> Reporter: Joyan Sil
> Priority: Minor
> Fix For: 0.6.0
>
>
> I am trying to run this example
> ([https://hudi.apache.org/docs/quick-start-guide.html]) using spark-shell.
> The Apache Hudi documentation says "Hudi works with Spark-2.x versions" The
> environment details are:
> Platform: HDP 2.6.5.0-292
> Spark version: 2.3.0.2.6.5.279-2
> Scala version: 2.11.8
> I am using the below spark-shell command (N.B. - The spark-avro version
> doesn't exactly match since I could not find the respective spark-avro
> dependency for Spark 2.3.2)
> spark-shell \
> --packages
> org.apache.hudi:hudi-spark-bundle_2.11:0.6.0,org.apache.spark:spark-avro_2.11:2.4.4,org.apache.avro:avro:1.8.2
> \
> --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
> When I try to write the data I get the below error:
> scala> df.write.format("hudi").
> options(getQuickstartWriteConfigs).
> option(PRECOMBINE_FIELD_OPT_KEY, "ts").
> option(RECORDKEY_FIELD_OPT_KEY, "uuid").
> option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
> option(TABLE_NAME, tableName).
> mode(Overwrite).
> save(basePath)
>
> 20/12/27 06:21:15 WARN HoodieSparkSqlWriter$: hoodie table at
> [file:/u/users/j0s0j7j/tmp/hudi_trips_cow|file://u/users/j0s0j7j/tmp/hudi_trips_cow]
> already exists. Deleting existing data & overwriting with new data.
> java.lang.NoSuchMethodError:
> org.apache.avro.Schema.createUnion([Lorg/apache/avro/Schema;)Lorg/apache/avro/Schema;
> at
> org.apache.hudi.spark.org.apache.spark.sql.avro.SchemaConverters$.toAvroType(SchemaConverters.scala:185)
> at
> org.apache.hudi.spark.org.apache.spark.sql.avro.SchemaConverters$$anonfun$5.apply(SchemaConverters.scala:176)
> at
> org.apache.hudi.spark.org.apache.spark.sql.avro.SchemaConverters$$anonfun$5.apply(SchemaConverters.scala:174)
> at scala.collection.Iterator$class.foreach(Iterator.scala:893)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
> at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
> at org.apache.spark.sql.types.StructType.foreach(StructType.scala:99)
> at
> org.apache.hudi.spark.org.apache.spark.sql.avro.SchemaConverters$.toAvroType(SchemaConverters.scala:174)
> at
> org.apache.hudi.AvroConversionUtils$.convertStructTypeToAvroSchema(AvroConversionUtils.scala:77)
> at
> org.apache.hudi.HoodieSparkSqlWriter$.write(HoodieSparkSqlWriter.scala:132)
> at org.apache.hudi.DefaultSource.createRelation(DefaultSource.scala:125)
> at
> org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:46)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
> at
> org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:86)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
> at
> org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
> at
> org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
> at
> org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:654)
> at
> org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:654)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
> at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:654)
> at
> org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:273)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:267)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:225)
> ... 68 elided
> To me it looks the correct avro version is not added to the classpath or
> picked up.
> Can anyone please suggest a work-around? I am stuck at this for quite
> sometime now.
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