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Evaluate EQ and IN predicates with Arrow's Bloom reader after footer statistics, following Java Iceberg's inclusive filtering rules. Keep other predicates, missing columns and files without usable field IDs or Bloom metadata inclusive. Hash literals using the file's physical representation, including numeric promotions, temporal types, decimal precision and width, binary/fixed and UUID values. Cache each column's Bloom filter per row group and load only Bloom payloads during planning. Preserve split selection, file-relative row positions and inherited row IDs after pruning.
manuzhang
reviewed
Nov 13, 2026
| BloomVisitor visitor(manifest, row_group, bloom_reader, field_id_to_column_idx_); | ||
| return Visit<bool>(bound_, visitor); | ||
| } catch (const ::parquet::ParquetException& e) { | ||
| return Invalid("Cannot read Parquet Bloom filter: {}", e.what()); |
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Shall we fail on this path? Might it still match?
manuzhang
reviewed
Nov 13, 2026
| auto should_read, stats_filter_->ShouldRead(reader_->manifest(), *row_group)); | ||
| if (should_read && bloom_filter_) { | ||
| auto bloom_reader = | ||
| reader_->parquet_reader()->GetBloomFilterReader().RowGroup(row_group_index); |
Member
|
@zhjwpku Thanks for the PR. I've left some comments inline. BTW, is there a flag to switch on/off this feature? |
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Author
Yeah, maybe we should have some handles for these pruning, let me check how the java impl deal with this. |
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Evaluate EQ and IN predicates with Arrow's Bloom reader after footer statistics, following Java Iceberg's inclusive filtering rules. Keep other predicates, missing columns and files without usable field IDs or Bloom metadata inclusive.
Hash literals using the file's physical representation, including numeric promotions, temporal types, decimal precision and width, binary/fixed and UUID values. Cache each column's Bloom filter per row group and load only Bloom payloads during planning. Preserve split selection, file-relative row positions and inherited row IDs after pruning.