Implement Predicate Filter and Pushdown API in orc-rust Similar to parquet-rs

[suxiaogang223]

I've been researching how parquet-rs implements predicate pushdown and would like to introduce a similar approach in orc-rust.

Predicate Pushdown in parquet-rs

In parquet-rs, predicate pushdown is handled using a selection mechanism, allowing efficient filtering of records before they are fully loaded into Arrow arrays.

Key Components:

1. Selection Mechanism (VecDeque<RowSelector>)
   Used in ParquetRecordBatchReader to track which records should be read or skipped.

    let front = selection.pop_front().unwrap();
    if front.skip {
        let skipped = match self.array_reader.skip_records(front.row_count) {
            Ok(skipped) => skipped,
            Err(e) => return Some(Err(e.into())),
        };
    
        if skipped != front.row_count {
            return Some(Err(general_err!(
                "failed to skip rows, expected {}, got {}",
                front.row_count,
                skipped
            )
            .into()));
        }
        continue;
    }

2. Batch Processing (ArrayReader)

   • Implements three key methods:

     fn read_records(&mut self, batch_size: usize) -> Result<usize>;
     fn consume_batch(&mut self) -> Result<ArrayRef>;
     fn skip_records(&mut self, num_records: usize) -> Result<usize>;

   • Allows efficient processing of batch data without unnecessary record loading.

3. Predicate Evaluation (evaluate_predicate)

   • How it works:
     • Uses array_reader and an existing selection to construct a temporary ParquetRecordBatchReader.
     • Iterates through all batches, applying the predicate to filter records.
     • Generates a refined selection that determines which records should be included.
     • A second pass over the Parquet file is performed using this refined selection, skipping filtered records.
   • Key advantage:
     • Since Arrow’s batch structure is immutable, filtering is done via selection, avoiding expensive in-place modifications.
     • This mechanism can also be leveraged for lazy materialization.

Code Example from ParquetRecordBatchReaderBuilder::build()

if let Some(filter) = filter.as_mut() {
    for predicate in filter.predicates.iter_mut() {
        if !selects_any(selection.as_ref()) {
            break;
        }

        let array_reader =
            build_array_reader(self.fields.as_deref(), predicate.projection(), &reader)?;

        selection = Some(evaluate_predicate(
            batch_size,
            array_reader,
            selection,
            predicate.as_mut(),
        )?);
    }
}

Proposal for orc-rust

I propose implementing a similar predicate pushdown API in orc-rust, which would:

1. Introduce a selection mechanism to allow skipping records early in the ORC read pipeline.
2. Implement an ArrayReader equivalent in ORC to handle batch-wise filtering, skipping, and reading.
3. Modify ORC reader API to accept predicate filters and process them in an iterative, selection-based manner.
4. Ensure efficient two-pass filtering:
   • First pass: Evaluate predicates on a temporary reader and compute selection.
   • Second pass: Re-read ORC file using selection, skipping unnecessary records.

Would love to get feedback on whether this approach makes sense for orc-rust, or if there are ORC-specific constraints that require adjustments.

[progval]

There are also column statistics and bloom filters, which are read before ParquetRecordBatchReader is constructed IIRC.

It sounds like this would work for ORC too; both formats are pretty similar.

[suxiaogang223]

I’ll explore how ORC handles these too.

There are also column statistics and bloom filters, which are read before ParquetRecordBatchReader is constructed IIRC.

It sounds like this would work for ORC too; both formats are pretty similar.

[ASiegeLion]

hi,Is there any progress on this issue?

[suxiaogang223]

Still on working🤓

[ASiegeLion]

Excuse me, is there anything I can help you with this PR? We are looking forward to this feature.