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Apache DataFusion SQL Query Engine https://datafusion.apache.org/
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Andrew Lamb 90b55e38b5
 test(parquet): add I/O access tests for parquet pruning, filter pushdown and TopK (#27022) 
## Which issue does this PR close?

- Part of https://github.com/apache/datafusion/issues/26682
- Follow on to #26897

## Rationale for this change

We have several issues / PRs open that contemplate changing the I/O
pattern such as
- https://github.com/apache/datafusion/issues/24393
- https://github.com/apache/datafusion/issues/20324
- https://github.com/apache/datafusion/pull/26387
- https://github.com/apache/datafusion/pull/26418
- https://github.com/apache/datafusion/pull/23492
- https://github.com/apache/datafusion/pull/24086
- https://github.com/apache/datafusion/pull/25811

I want to have a test that clearly shows how those proposals effect the
scan


## What changes are included in this PR?

Add some new tests that show the Parquet I/O patterns. For example I
think this is quite clear now the difference in I/O between filter
pushdown and not
```rust
/// Without filter pushdown, the scan fetches every column of every row group
#[tokio::test]
async fn test_filter_pushdown_io_disabled() {
    let test = filter_pushdown_test(false).await;
    assert_snapshot!(test.query("SELECT * FROM t WHERE val = 7").await, @r"
    --- Query: SELECT * FROM t WHERE val = 7 (1 rows) ---
    Request 1
        Read Parquet footer (offsets 0..1402)
    Request 2
        Read dictionary page and data page 0 for column 'id' (rg 0) (offsets 4..51)
        Read dictionary page and data page 0 for column 'val' (rg 0) (offsets 51..98)
    Request 3
        Read dictionary page and data page 0 for column 'id' (rg 1) (offsets 98..145)
        Read dictionary page and data page 0 for column 'val' (rg 1) (offsets 145..192)
    Request 4
        Read dictionary page and data page 0 for column 'id' (rg 2) (offsets 192..239)
        Read dictionary page and data page 0 for column 'val' (rg 2) (offsets 239..286)
    Produce batch (1 rows)
    ");
}

/// **With** filter pushdown, the scan first fetches and evaluates the filter
/// column (`val`) for each row group, and then fetches the remaining columns
/// (`id`) only for the row groups with matching rows (row group 1).
#[tokio::test]
async fn test_filter_pushdown_io_enabled() {
    let test = filter_pushdown_test(true).await;
    assert_snapshot!(test.query("SELECT * FROM t WHERE val = 7").await, @r"
    --- Query: SELECT * FROM t WHERE val = 7 (1 rows) ---
    Request 1
        Read Parquet footer (offsets 0..1402)
    Request 2
        Read dictionary page and data page 0 for column 'val' (rg 0) (offsets 51..98)
    Request 3
        Read dictionary page and data page 0 for column 'val' (rg 1) (offsets 145..192)
    Request 4
        Read dictionary page for column 'id' (rg 1) (offsets 98..124)
        Read data page 0 for column 'id' (rg 1) (offsets 124..145)
    Produce batch (1 rows)
    Request 5
        Read dictionary page and data page 0 for column 'val' (rg 2) (offsets 239..286)
    ");
}
```

## What is the testing strategy for this PR?

CI

## Are there any user-facing changes?

No

---------

Co-authored-by: mohitgurav20 <mohitgurav20@gmail.com>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
2026-11-06 01:14:15 +00:00
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README.md

Apache DataFusion

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Website | API Docs | Chat

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DataFusion is an extensible query engine written in Rust that uses Apache Arrow as its in-memory format.

This crate provides libraries and binaries for developers building fast and feature-rich database and analytic systems, customized for particular workloads. See use cases for examples. The following related subprojects target end users:

"Out of the box," DataFusion offers SQL and DataFrame APIs, excellent performance, built-in support for CSV, Parquet, JSON, and Avro, extensive customization, and a great community.

DataFusion features a full query planner, a columnar, streaming, multi-threaded, vectorized execution engine, and partitioned data sources. You can customize DataFusion at almost all points including additional data sources, query languages, functions, custom operators and more. See the Architecture section for more details.

Here are links to important resources:

What can you do with this crate?

DataFusion is great for building projects such as domain-specific query engines, new database platforms and data pipelines, query languages and more. It lets you start quickly from a fully working engine, and then customize those features specific to your needs. See the list of known users.

Contributing to DataFusion

Please see the contributor guide and communication pages for more information.

We discuss our roadmap via GitHub issues and invite you to join the conversation. The current discussion is the DataFusion 2026 Q3-Q4 Roadmap Discussion.

Crate features

This crate has several features which can be specified in your Cargo.toml.

Default features:

  • nested_expressions: functions for working with nested types such as array_to_string
  • compression: reading files compressed with xz2, bzip2, flate2, and zstd
  • crypto_expressions: cryptographic functions such as md5 and sha256
  • datetime_expressions: date and time functions such as to_timestamp
  • encoding_expressions: encode and decode functions
  • parquet: support for reading the Apache Parquet format
  • sql: support for SQL parsing and planning
  • regex_expressions: regular expression functions, such as regexp_match
  • unicode_expressions: include Unicode-aware functions such as character_length
  • unparser: enables support to reverse LogicalPlans back into SQL
  • recursive_protection: uses recursive for stack overflow protection.

Optional features:

  • avro: support for reading the Apache Avro format
  • backtrace: include backtrace information in error messages
  • parquet_encryption: support for using Parquet Modular Encryption
  • serde: enable arrow-schema's serde feature

DataFusion API Evolution and Deprecation Guidelines

Public methods in Apache DataFusion evolve over time: while we try to maintain a stable API, we also improve the API over time. As a result, we typically deprecate methods before removing them, according to the deprecation guidelines.

Dependencies and Cargo.lock

Following the guidance on committing Cargo.lock files, this project commits its Cargo.lock file.

CI uses the committed Cargo.lock file, and dependencies are updated regularly using Dependabot PRs.