Spark DataFrame write fails if input dataframe has columns in different order than iceberg schema

[ravichinoy]

For this test case, https://github.com/apache/incubator-iceberg/blob/6f28abfa62838d531be4faa93273965665af933d/spark/src/test/java/org/apache/iceberg/spark/source/TestPartitionValues.java

if I replace https://github.com/apache/incubator-iceberg/blob/6f28abfa62838d531be4faa93273965665af933d/spark/src/test/java/org/apache/iceberg/spark/source/TestPartitionValues.java#L135 with

df.select("data", "id").write()

the test case fails with below error,

Cannot write incompatible dataset to table with schema: table { 1: id: optional int 2: data: optional string } Problems:

• data is out of order, before id java.lang.IllegalArgumentException: Cannot write incompatible dataset to table with schema: table { 1: id: optional int 2: data: optional string } Problems:
• data is out of order, before id

However if I set checkOrdering to false in here, https://github.com/apache/incubator-iceberg/blob/949c6a98ac80acec10568070772082c1178eb739/api/src/main/java/org/apache/iceberg/types/CheckCompatibility.java

Result rows should match expected:<[{"id"=1,"data"="a"}, {"id"=2,"data"="b"}, {"id"=3,"data"="c"}, {"id"=4,"data"="null"}]> but was:<[{"id"=1,"data"=""}, {"id"=2,"data"=""}, {"id"=3,"data"=""}, {"id"=4,"data"="
"}]> Expected :[{"id"=1,"data"="a"}, {"id"=2,"data"="b"}, {"id"=3,"data"="c"}, {"id"=4,"data"="null"}] Actual :[{"id"=1,"data"=""}, {"id"=2,"data"=""}, {"id"=3,"data"=""}, {"id"=4,"data"="
"}]

this is because PartitionSpec accessors are being built out of iceberg schema. If we update the code to build the accessors from the input schema, then the re-order test case passes.

this is shown in below PR

https://github.com/apache/incubator-iceberg/pull/745

We are trying to understand if there is any specific reason to set checkOrdering to false by default and not expose it as a parameter and build the accessors in PartitionSpec from table schema instead of input schema.

And If possible , we would like to enable checkOrdering as a configurable parameter so that it can be turned off and write jobs do not have to use the same ordering as Iceberg Table.

[holdenk]

Was this resolved in https://github.com/apache/iceberg/pull/745 ?

[amitmittal5]

Hello, is this issue resolved? I am still getting this issue in iceberg 1.4.2 while trying to write in iceberg format to ADLS using spark-streaming.

[amitmittal5]

Hello, is this issue resolved? I am still getting this issue in iceberg 1.4.2 while trying to write in iceberg format to ADLS using spark-streaming.

It was actually resolved earlier but I overlooked. To ignore the column ordering, add the spark write config "spark.sql.iceberg.check-ordering" as "false". It is used in org.apache.iceberg.spark.source.SparkWriteBuilder.validateOrMergeWriteSchema(), if it is not provided, the default is true and checks for ordering.

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