flashJd commented on PR #9113:
URL: https://github.com/apache/hudi/pull/9113#issuecomment-1623593792
> > How about follow the spark behavior? We should respect the spark
configure: `spark.sql.sources.partitionOverwriteMode`, if it's `Static`,
overwrite the whole table, otherwise if `dynamic`, overwrite the changed
partitions.
> > It appeals this is also how iceberg works.
>
> Agreed, I will check the logic to respect the spark configure
@boneanxs @KnightChess @danny0405
1) I try to respect the spark configure
`spark.sql.sources.partitionOverwriteMode`, but found it's supported in
datasource v2 as HoodieInternalV2Table only support V1_BATCH_WRITE
TableCapability thus can't extend interface `SupportsDynamicOverwrite`
2) For iceberg, I found it repsect the spark config as implement the
interface `SupportsDynamicOverwrite`, but it also set
it's own configure to control the static/dynamic overwrite semantics,
https://github.com/apache/iceberg/blob/1f1ec4be478feae79b04bcea3e9a8556d8076054/spark/v3.4/spark/src/main/java/org/apache/iceberg/spark/SparkWriteConf.java#L106
3) As the v2 BATCH_WRITE not supported now, we can first use hooide config
`hoodie.datasource.write.operation = insert_overwrite_table/insert_overwrite`
to implement the static/dynamic overwrite semantics, then respect spark
configure when v2 write support.
what about you think?
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