[jira] [Commented] (HUDI-1079) Cannot upsert on schema with Array of Record with single field

["Adrian Tanase (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/HUDI-1079?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17152761#comment-17152761 ] 

Adrian Tanase commented on HUDI-1079:
-------------------------------------

[~vinothchandar], [~uditme] - would you mind helping triage this issue. Seems related to 2 other recent improvements on spark to avro schemas. Thank you in advance!

> Cannot upsert on schema with Array of Record with single field
> --------------------------------------------------------------
>
>                 Key: HUDI-1079
>                 URL: https://issues.apache.org/jira/browse/HUDI-1079
>             Project: Apache Hudi
>          Issue Type: Bug
>          Components: Spark Integration
>    Affects Versions: 0.5.3
>         Environment: spark 2.4.4, local 
>            Reporter: Adrian Tanase
>            Priority: Major
>
> I am trying to trigger upserts on a table that has an array field with records of just one field.
>  Here is the code to reproduce:
> {code:scala}
>   val spark = SparkSession.builder()
>       .master("local[1]")
>       .appName("SparkByExamples.com")
>       .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
>       .getOrCreate();
>   // https://sparkbyexamples.com/spark/spark-dataframe-array-of-struct/
>   val arrayStructData = Seq(
>     Row("James",List(Row("Java","XX",120),Row("Scala","XA",300))),
>     Row("Michael",List(Row("Java","XY",200),Row("Scala","XB",500))),
>     Row("Robert",List(Row("Java","XZ",400),Row("Scala","XC",250))),
>     Row("Washington",null)
>   )
>   val arrayStructSchema = new StructType()
>       .add("name",StringType)
>       .add("booksIntersted",ArrayType(
>         new StructType()
>           .add("bookName",StringType)
> //          .add("author",StringType)
> //          .add("pages",IntegerType)
>       ))
>     val df = spark.createDataFrame(spark.sparkContext.parallelize(arrayStructData),arrayStructSchema)
> {code}
> Running insert following by upsert will fail:
> {code:scala}
>   df.write
>       .format("hudi")
>       .options(getQuickstartWriteConfigs)
>       .option(DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY, "name")
>       .option(DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY, "name")
>       .option(DataSourceWriteOptions.TABLE_TYPE_OPT_KEY, "COPY_ON_WRITE")
>       .option(HoodieWriteConfig.TABLE_NAME, tableName)
>       .mode(Overwrite)
>       .save(basePath)
>   df.write
>       .format("hudi")
>       .options(getQuickstartWriteConfigs)
>       .option(DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY, "name")
>       .option(DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY, "name")
>       .option(HoodieWriteConfig.TABLE_NAME, tableName)
>       .mode(Append)
>       .save(basePath)
> {code}
> If I create the books record with all the fields (at least 2), it works as expected.
> Another way to test is by changing the generated data in the tips to just the amount, by dropping the currency on the tips_history field, tests will start failing:
>  [https://github.com/apache/hudi/compare/release-0.5.3...aditanase:avro-arrays-upsert?expand=1]
> I have narrowed this down to this block in the parquet-avro integration: [https://github.com/apache/parquet-mr/blob/master/parquet-avro/src/main/java/org/apache/parquet/avro/AvroRecordConverter.java#L846-L875]
> Which always returns false after trying to decide whether reader and writer schemas are compatible. Going throught that code path makes me thing it's related to the fields being optional, as the inferred schema seems to be (null, string) with default null instead of (string, null) with no default.
> At this point I'm lost, tried to figure something out based on this [https://github.com/apache/hudi/pull/1406/files] but I'm not sure where to start.



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