title: Pyspark + Avro type conversion problems after transformation
body: <p>I use Structured Streaming to read Avro records from a Kafka topic A, do some transformations and write as Avro to another Kafka topic B. I use <a href="https://spark.apache.org/docs/3.0.3/sql-data-sources-avro.html#to_avro-and-from_avro" rel="nofollow noreferrer">those functions</a> for serializing and deserializing the Avro records.</p>
  <p>I faced another exception (parsing error with method &quot;FAILFAST&quot;) originally while reading from the second topic B with the specified schema in a separate job. I wondered how this could happen because the schemas were exactly the same.</p>
  <p>To discover more details, I came up with the following experimental setup to reproduce the exception. I found out that the serialized Avro records (with the exact same schema, just some easy transformations) can't be serialized with <code>to_avro</code>:</p>
  <pre><code>from pyspark.sql.types import StringType, Row, StructField, StructType, LongType, StringType, ArrayType
  
  from pyspark.sql import functions as func
  from pyspark.sql.avro import functions as afunc
  
  avro_schema = &quot;&quot;&quot;
  {
      &quot;fields&quot;: [
          {
              &quot;name&quot;: &quot;field_1&quot;,
              &quot;type&quot;: [
                  &quot;string&quot;,
                  &quot;null&quot;
              ]
          },
          {
              &quot;name&quot;: &quot;field_2&quot;,
              &quot;type&quot;: [
                  &quot;long&quot;,
                  &quot;null&quot;
              ]
          },
          {
              &quot;name&quot;: &quot;field_3&quot;,
              &quot;type&quot;: [
                  {
                      &quot;items&quot;: [
                          &quot;string&quot;,
                          &quot;null&quot;
                      ],
                      &quot;type&quot;: &quot;array&quot;
                  },
                  &quot;null&quot;
              ]
          }
      ],
      &quot;name&quot;: &quot;bla&quot;,
      &quot;namespace&quot;: &quot;bla&quot;,
      &quot;type&quot;: &quot;record&quot;
  }
  &quot;&quot;&quot;
  
  # Create example DF
  df = spark.createDataFrame(
      [Row(&quot;bla&quot;, 42, [&quot;bla&quot;, &quot;blubb&quot;])],
      StructType(
          [
              StructField(&quot;field_1&quot;, StringType(), True),
              StructField(&quot;field_2&quot;, LongType(), True),
              StructField(&quot;field_3&quot;, ArrayType(StringType()), True),
          ]
      ),
  )
  
  # Serialize columns to Avro record
  df_avro = df.select(afunc.to_avro(func.struct(df.columns), avro_schema))
  
  # Now, change values in DF to some arbitrary value
  df_changed_1 = df.withColumn(&quot;field_2&quot;, func.lit(998877665544332211))
  df_changed_2 = df.withColumn(&quot;field_2&quot;, func.lit(None)) # To enforce nullability in schema for field_2
  df_changed = df_changed_1.unionByName(df_changed_2)
  
  # Again, serialize columns to Avro record with same schema as before
  df_changed_avro = df_changed.select(afunc.to_avro(func.struct(df_changed.columns), avro_schema))
  
  # Schemas are exactly identical, which makes sense because nothing changed in the schema itself, only the values
  df.schema
  df_changed.schema
  
  # Have a look at the DFs values
  df.show()
  df_changed.show()
  
  # Enforce DF evaulation
  df_avro.show() # This works as expected
  df_changed_avro.show() # This produced the exception
  </code></pre>
  <p>The last line produces the following exception:</p>
  <pre><code>org.apache.spark.SparkException: Job aborted due to stage failure: Task 2 in stage 35.0 failed 1 times, most recent failure: Lost task 2.0 in stage 35.0 (TID 109, 192.168.2.117, executor driver): org.apache.spark.sql.avro.IncompatibleSchemaException: Cannot convert Catalyst type LongType to Avro type [&quot;long&quot;,&quot;null&quot;].
          at org.apache.spark.sql.avro.AvroSerializer.newConverter(AvroSerializer.scala:219)
          at org.apache.spark.sql.avro.AvroSerializer.$anonfun$newStructConverter$1(AvroSerializer.scala:239)
          at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
          at scala.collection.Iterator.foreach(Iterator.scala:941)
          at scala.collection.Iterator.foreach$(Iterator.scala:941)
          at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
          at scala.collection.IterableLike.foreach(IterableLike.scala:74)
          at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
          at org.apache.spark.sql.types.StructType.foreach(StructType.scala:102)
          at scala.collection.TraversableLike.map(TraversableLike.scala:238)
          at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
          at org.apache.spark.sql.types.StructType.map(StructType.scala:102)
          at org.apache.spark.sql.avro.AvroSerializer.newStructConverter(AvroSerializer.scala:232)
          at org.apache.spark.sql.avro.AvroSerializer.&lt;init&gt;(AvroSerializer.scala:72)
          at org.apache.spark.sql.avro.AvroSerializer.&lt;init&gt;(AvroSerializer.scala:55)
          at org.apache.spark.sql.avro.CatalystDataToAvro.serializer$lzycompute(CatalystDataToAvro.scala:42)
          at org.apache.spark.sql.avro.CatalystDataToAvro.serializer(CatalystDataToAvro.scala:41)
          at org.apache.spark.sql.avro.CatalystDataToAvro.nullSafeEval(CatalystDataToAvro.scala:54)
          at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
          at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
          at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
          at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:345)
          at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
          at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
          at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
          at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
          at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
          at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
          at org.apache.spark.scheduler.Task.run(Task.scala:127)
          at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:463)
          at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
          at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:466)
          at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
          at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
          at java.base/java.lang.Thread.run(Thread.java:829)Driver stacktrace:
          at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2059)
          at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2008)
          at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2007)
          at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
          at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
          at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
          at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2007)
          at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:973)
          at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:973)
          at scala.Option.foreach(Option.scala:407)
          at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:973)
          at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2239)
          at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2188)
          at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2177)
          at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
          at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:775)
          at org.apache.spark.SparkContext.runJob(SparkContext.scala:2114)
          at org.apache.spark.SparkContext.runJob(SparkContext.scala:2135)
          at org.apache.spark.SparkContext.runJob(SparkContext.scala:2154)
          at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:472)
          at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:425)
          at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)
          at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3627)
          at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2697)
          at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3618)
          at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
          at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
          at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
          at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:767)
          at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
          at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3616)
          at org.apache.spark.sql.Dataset.head(Dataset.scala:2697)
          at org.apache.spark.sql.Dataset.take(Dataset.scala:2904)
          at org.apache.spark.sql.Dataset.getRows(Dataset.scala:300)
          at org.apache.spark.sql.Dataset.showString(Dataset.scala:337)
          at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
          at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
          at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
          at java.base/java.lang.reflect.Method.invoke(Method.java:566)
          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.base/java.lang.Thread.run(Thread.java:829)
  Caused by: org.apache.spark.sql.avro.IncompatibleSchemaException: Cannot convert Catalyst type LongType to Avro type [&quot;long&quot;,&quot;null&quot;].
          at org.apache.spark.sql.avro.AvroSerializer.newConverter(AvroSerializer.scala:219)
          at org.apache.spark.sql.avro.AvroSerializer.$anonfun$newStructConverter$1(AvroSerializer.scala:239)
          at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
          at scala.collection.Iterator.foreach(Iterator.scala:941)
          at scala.collection.Iterator.foreach$(Iterator.scala:941)
          at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
          at scala.collection.IterableLike.foreach(IterableLike.scala:74)
          at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
          at org.apache.spark.sql.types.StructType.foreach(StructType.scala:102)
          at scala.collection.TraversableLike.map(TraversableLike.scala:238)
          at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
          at org.apache.spark.sql.types.StructType.map(StructType.scala:102)
          at org.apache.spark.sql.avro.AvroSerializer.newStructConverter(AvroSerializer.scala:232)
          at org.apache.spark.sql.avro.AvroSerializer.&lt;init&gt;(AvroSerializer.scala:72)
          at org.apache.spark.sql.avro.AvroSerializer.&lt;init&gt;(AvroSerializer.scala:55)
          at org.apache.spark.sql.avro.CatalystDataToAvro.serializer$lzycompute(CatalystDataToAvro.scala:42)
          at org.apache.spark.sql.avro.CatalystDataToAvro.serializer(CatalystDataToAvro.scala:41)
          at org.apache.spark.sql.avro.CatalystDataToAvro.nullSafeEval(CatalystDataToAvro.scala:54)
          at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
          at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
          at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
          at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:345)
          at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
          at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
          at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
          at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
          at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
          at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
          at org.apache.spark.scheduler.Task.run(Task.scala:127)
          at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:463)
          at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
          at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:466)
          at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
          at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
          ... 1 more 
  </code></pre>
  <p>According to the documentation, Spark SQL types can not be converted to Avro union types (at least it's not explicitly <a href="https://spark.apache.org/docs/3.0.3/sql-data-sources-avro.html#supported-types-for-spark-sql---avro-conversion" rel="nofollow noreferrer">written down here</a>), but I wonder why it seem to work for the original Dataframe.</p>
  <p>Am I missing something or is this a Bug? This also happens when I for example add a new field to the original Dataframe (df) and try to serialize it with an enhanced schema with the new field added.</p>