Zouxxyy commented on code in PR #6697:
URL: https://github.com/apache/paimon/pull/6697#discussion_r2634021745
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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,
and ensuring that `splitPartitionPredicatesAndDataPredicates` can correctly
recognize this filter.
2. Yes, that’s exactly where I intend to put it. CC @JingsongLi
```scala
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<Range>
rowRanges is and convert to it.
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