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Prefetch
========

.. image:: ../_static/prefetch.svg
   :alt: File Layout
   :align: center
   :width: 100%

In Paimon C++, we use a multi-producer, single-consumer model to optimize file
reading. The core idea is to split a file into row-based read ranges and assign
them to multiple reader threads (producers). Each reader thread owns an
independent result queue that holds its processed RecordBatches. In the main
reader thread (the consumer), we sort the heads of all queues by the read
range's starting row and select the RecordBatch with the smallest starting row
to ensure globally ordered results.

Read Range Splitting Strategy
=============================

Designing an efficient ReadRange splitting strategy requires balancing two key
objectives:

- Minimize read amplification: Ensure the data fetched from storage is used
  effectively, avoiding unnecessary I/O overhead.
- Reduce ReadRange span: Ideally, the size of a ReadRange should match a single
  read batch size to enable fine-grained parallel control.

Below we detail how these strategies are applied to Parquet and ORC.

Parquet
========

Parquet files are organized into RowGroups and Pages. Since C++ Parquet does
not support row-level seeking, prefetching can only be done at the RowGroup
level. This naturally avoids read amplification, but introduces a new
challenge: if a file contains only a small number of RowGroups, parallelism is
severely limited. Therefore, we recommend users reduce RowGroup size when
writing Parquet files to increase opportunities for parallel processing.

Another critical difference is the read behavior compared to ORC. ORC strictly
returns RecordBatches aligned to Stripe boundaries, whereas C++ Parquet may
return a RecordBatch containing data from multiple RowGroups. This can lead to
output order confusion during parallel reads. We modified C++ Parquet internals
to return results strictly aligned to RowGroup boundaries, matching ORC's
behavior. With this change, parallel reading no longer requires complex seek
operations, improving overall read efficiency.

ORC
===

ORC files are organized into Stripes. Paimon C++ generates row ranges from
Stripe metadata, the selected columns, and the configured natural read size.
Prefetch is enabled when the estimated compressed size of the selected columns
exceeds the configured threshold; otherwise the reader uses the normal
single-reader path.