Kkkaneki-k commented on code in PR #6697:
URL: https://github.com/apache/paimon/pull/6697#discussion_r2634096487
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paimon-spark/paimon-spark-3.2/src/main/scala/org/apache/paimon/spark/PaimonBaseScanBuilder.scala:
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@@ -71,7 +77,28 @@ abstract class PaimonBaseScanBuilder
filter =>
val predicate = converter.convertIgnoreFailure(filter)
if (predicate == null) {
- postScan.append(filter)
+ val rowTypeWithRowId = new RowType(
+ false,
+ Collections.singletonList(new DataField(-1, ROW_ID.name(),
DataTypes.BIGINT())))
+ val converterWithRowId = new SparkFilterConverter(rowTypeWithRowId)
+ val newPredicate = converterWithRowId.convertIgnoreFailure(filter)
Review Comment:
> 1. It should pass the filter containing _ROW_ID as `pushedDataFilters` to
Paimon, while still adding it to the postFilter for Spark to handle. Therefore,
almost no changes are needed in Paimon's Spark connector—except possibly update
`new SparkFilterConverter(rowType)` with requiredSchema or rowType with row id.
> 2. Yes, that’s exactly where I intend to put it. CC @JingsongLi
>
> ReadBuilderImpl:
>
> ```java
> private InnerTableScan configureScan(InnerTableScan scan) {
> scan.withFilter(filter)
> .withReadType(readType)
> .withPartitionFilter(partitionFilter)
> // calculate rowRanges from filter
> .withRowRanges(rowRanges);
> }
> ```
>
> I believe users would much prefer writing filters that include _ROW_ID
when using the ReadBuilder API, rather than having to understand what List
rowRanges is and convert to it.
Thanks for your reply! I think your point is correct, and I will modify my
code based on your suggestions.
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