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https://issues.apache.org/jira/browse/BEAM-11721?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17275494#comment-17275494
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Tao Li commented on BEAM-11721:
-------------------------------
I tried to use parquet-tools to inspect the parquet file used in my test which
is created with spark code. Here is the meta info:
creator: parquet-mr version 1.10.1 (build
815bcfa4a4aacf66d207b3dc692150d16b5740b9)
extra: org.apache.spark.sql.parquet.row.metadata =
\{"type":"struct","fields":[{"name":"numbers","type":{"type":"array","elementType":"integer","containsNull":true},"nullable":true,"metadata":{}}]}
file schema: spark_schema
--------------------------------------------------------------------------------
numbers: OPTIONAL F:1
.list: REPEATED F:1
..element: OPTIONAL INT32 R:1 D:3
As you can see the file schema is "spark_schema" and ".list" and ".element" are
used to define the schema.
In comparison, if we use beam's ParquetIO to generate parquet files using avro,
the meta info is:
creator: parquet-mr version 1.10.0 (build
031a6654009e3b82020012a18434c582bd74c73a)
extra: parquet.avro.schema =
\{"type":"record","name":"topLevelRecord","fields":[{"name":"numbers","type":["null",{"type":"array","items":"int"}],"doc":""}]}
extra: writer.model.name = avro
file schema: topLevelRecord
--------------------------------------------------------------------------------
numbers: OPTIONAL F:1
.array: REPEATED INT32 R:1 D:2
As you can see, the file schema is "topLevelRecord" and it's using ".array" to
define the schema.
I am not an expert on parquet. Just wondering if this difference could be the
cause to the failure when using ParquetIO to read spark created parquet files.
> Cannot read array values with ParquetIO
> ---------------------------------------
>
> Key: BEAM-11721
> URL: https://issues.apache.org/jira/browse/BEAM-11721
> Project: Beam
> Issue Type: Bug
> Components: io-java-parquet
> Affects Versions: 2.25.0
> Reporter: Tao Li
> Priority: P0
> Attachments: from-spark.snappy.parquet, schema.avsc
>
>
> Hi Beam community,
> I am seeing an error when reading an array field using ParquetIO. I was
> using beam 2.25. Both direct runner and spark runner testing is seeing this
> issue. This is a blocker issue to me for the beam adoption, so a prompt help
> would be appreciated.
> Below is the schema tree as a quick visualization. The array field name is
> "list" and the element type is int.
>
> root |
> -- numbers: array (nullable = true) | |
> -- element: integer (containsNull = true)
>
> The beam code is very simple:
> pipeline.apply(ParquetIO.read(avroSchema).from(parquetPath));
>
> Here is the error when running that code:
>
> {noformat}
> Exception in thread "main"
> org.apache.beam.sdk.Pipeline$PipelineExecutionException:
> java.lang.ClassCastException: org.apache.avro.generic.GenericData$Record
> cannot be cast to java.lang.Number
> at
> org.apache.beam.runners.direct.DirectRunner$DirectPipelineResult.waitUntilFinish(DirectRunner.java:353)
> at
> org.apache.beam.runners.direct.DirectRunner$DirectPipelineResult.waitUntilFinish(DirectRunner.java:321)
> at
> org.apache.beam.runners.direct.DirectRunner.run(DirectRunner.java:216)
> at
> org.apache.beam.runners.direct.DirectRunner.run(DirectRunner.java:67)
> at org.apache.beam.sdk.Pipeline.run(Pipeline.java:317)
> at org.apache.beam.sdk.Pipeline.run(Pipeline.java:303)
> Caused by: java.lang.ClassCastException:
> org.apache.avro.generic.GenericData$Record cannot be cast to java.lang.Number
> at
> org.apache.avro.generic.GenericDatumWriter.writeWithoutConversion(GenericDatumWriter.java:156)
> at
> org.apache.avro.generic.GenericDatumWriter.write(GenericDatumWriter.java:82)
> at
> org.apache.avro.generic.GenericDatumWriter.writeArray(GenericDatumWriter.java:234)
> at
> org.apache.avro.generic.GenericDatumWriter.writeWithoutConversion(GenericDatumWriter.java:136)
> at
> org.apache.avro.generic.GenericDatumWriter.write(GenericDatumWriter.java:82)
> at
> org.apache.avro.generic.GenericDatumWriter.writeField(GenericDatumWriter.java:206)
> at
> org.apache.avro.generic.GenericDatumWriter.writeRecord(GenericDatumWriter.java:195)
> at
> org.apache.avro.generic.GenericDatumWriter.writeWithoutConversion(GenericDatumWriter.java:130)
> at
> org.apache.avro.generic.GenericDatumWriter.write(GenericDatumWriter.java:82)
> at
> org.apache.avro.generic.GenericDatumWriter.write(GenericDatumWriter.java:72)
> at
> org.apache.beam.sdk.coders.AvroCoder.encode(AvroCoder.java:317)
> at org.apache.beam.sdk.coders.Coder.encode(Coder.java:136)
> at
> org.apache.beam.sdk.util.CoderUtils.encodeToSafeStream(CoderUtils.java:82)
> at
> org.apache.beam.sdk.util.CoderUtils.encodeToByteArray(CoderUtils.java:66)
> at
> org.apache.beam.sdk.util.CoderUtils.encodeToByteArray(CoderUtils.java:51)
> at
> org.apache.beam.sdk.util.CoderUtils.clone(CoderUtils.java:141)
> at
> org.apache.beam.sdk.util.MutationDetectors$CodedValueMutationDetector.<init>(MutationDetectors.java:115)
> at
> org.apache.beam.sdk.util.MutationDetectors.forValueWithCoder(MutationDetectors.java:46)
> at
> org.apache.beam.runners.direct.ImmutabilityCheckingBundleFactory$ImmutabilityEnforcingBundle.add(ImmutabilityCheckingBundleFactory.java:112)
> at
> org.apache.beam.runners.direct.ParDoEvaluator$BundleOutputManager.output(ParDoEvaluator.java:301)
> at
> org.apache.beam.repackaged.direct_java.runners.core.SimpleDoFnRunner.outputWindowedValue(SimpleDoFnRunner.java:267)
> at
> org.apache.beam.repackaged.direct_java.runners.core.SimpleDoFnRunner.access$900(SimpleDoFnRunner.java:79)
> at
> org.apache.beam.repackaged.direct_java.runners.core.SimpleDoFnRunner$DoFnProcessContext.output(SimpleDoFnRunner.java:413)
> at
> org.apache.beam.repackaged.direct_java.runners.core.SimpleDoFnRunner$DoFnProcessContext.output(SimpleDoFnRunner.java:401)
> at
> org.apache.beam.sdk.io.parquet.ParquetIO$ReadFiles$ReadFn.processElement(ParquetIO.java:646)
> {noformat}
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