[GitHub] [incubator-iceberg] andrei-ionescu commented on issue #575: After writing Iceberg dataset with nested partitions it cannot be read anymore

[GitBox <gi...@apache.org>]

andrei-ionescu commented on issue #575: After writing Iceberg dataset with nested partitions it cannot be read anymore
URL: https://github.com/apache/incubator-iceberg/issues/575#issuecomment-547481132
 
 
   The steps are:
   
   1. Have a schema and its json data
   2. Add a simple partition and a nested partition
   3. Write the data in `iceberg` format using Iceberg data frame writer 
   4. Read the data into a new Iceberg dataset
   
   The last step fails with the error above. 
   
   Here is some code to explain the issue (extracted from the [gist above](https://gist.github.com/andrei-ionescu/b3e5f5345df3166af7562a830d3dc57d)).
   
   ```scala
   Schema nestedSchema = new Schema(
       optional(1, "id", Types.IntegerType.get()),
       optional(2, "data", Types.StringType.get()),
       optional(3, "nestedData", Types.StructType.of(
           optional(4, "id", Types.IntegerType.get()),
           optional(5, "moreData", Types.StringType.get())))
   );
   
   File parent = temp.newFolder("parquet");
   File location = new File(parent, "test");
   
   HadoopTables tables = new HadoopTables(new Configuration());
   PartitionSpec spec = PartitionSpec.builderFor(nestedSchema)
       .identity("id")
       .identity("nestedData.moreData")
       .build();
   Table table = tables.create(nestedSchema, spec, location.toString());
   
   List<String> jsons = Lists.newArrayList(
       "{ \"id\": 1, \"data\": \"a\", \"nestedData\": { \"id\": 100, \"moreData\": \"p1\"} }",
       "{ \"id\": 2, \"data\": \"b\", \"nestedData\": { \"id\": 200, \"moreData\": \"p1\"} }",
       "{ \"id\": 3, \"data\": \"c\", \"nestedData\": { \"id\": 300, \"moreData\": \"p2\"} }",
       "{ \"id\": 4, \"data\": \"d\", \"nestedData\": { \"id\": 400, \"moreData\": \"p2\"} }"
   );
   Dataset<Row> df = spark
       .read()
       .schema(SparkSchemaUtil.convert(nestedSchema))
       .json(spark.createDataset(jsons, Encoders.STRING()));
   
   // TODO: incoming columns must be ordered according to the table's schema
   df.select("id", "data", "nestedData").write()
       .format("iceberg")
       .mode("append")
       .save(location.toString());
   
   table.refresh();
   
   Dataset<Row> result = spark.read()
       .format("iceberg")
       .load(location.toString());
   ```
   

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