Crate aisle 

Metadata-driven Parquet pruning for Rust.

Aisle evaluates DataFusion predicates against Parquet metadata (row-group statistics, page indexes, bloom filters) to determine which data to skip before reading, dramatically reducing I/O for selective queries.

Why Aisle?

The Problem: Parquet readers typically apply filters after reading data, wasting I/O on irrelevant row groups and pages.

The Solution: Aisle evaluates predicates against metadata before reading:

• Row-group pruning using min/max statistics
• Page-level pruning using column/offset indexes
• Bloom filter checks for definite absence (high-cardinality columns)

The Result: 70-99% I/O reduction for selective queries without modifying the Parquet format.

Quick Start

use std::sync::Arc;

use aisle::PruneRequest;
use arrow_schema::{DataType, Field, Schema};
use bytes::Bytes;
use datafusion_expr::{col, lit};
use parquet::{
    arrow::arrow_reader::ParquetRecordBatchReaderBuilder, file::metadata::ParquetMetaDataReader,
};

// 1. Load metadata (without reading data)
let metadata = ParquetMetaDataReader::new().parse_and_finish(&parquet_bytes)?;

// 2. Define schema and filter predicate
let schema = Arc::new(Schema::new(vec![
    Field::new("user_id", DataType::Int64, false),
    Field::new("age", DataType::Int64, false),
]));

let predicate = col("user_id")
    .gt_eq(lit(1000i64))
    .and(col("age").lt(lit(30i64)));

// 3. Prune row groups using metadata
let result = PruneRequest::new(&metadata, &schema)
    .with_predicate(&predicate)
    .enable_page_index(false) // Row-group level only
    .enable_bloom_filter(false) // No bloom filters
    .prune();

println!(
    "Kept {} of {} row groups",
    result.row_groups().len(),
    metadata.num_row_groups()
);

// 4. Apply pruning to Parquet reader
let reader = ParquetRecordBatchReaderBuilder::try_new(parquet_bytes.clone())?
    .with_row_groups(result.row_groups().to_vec()) // Skip irrelevant row groups!
    .build()?;

// Read only the relevant data (70-99% I/O reduction!)
for batch in reader {
    // Process matching rows...
}

Key Features

• Row-group pruning: Skip entire row groups using min/max statistics
• Page-level pruning: Skip individual pages within row groups
• Bloom filter support: Definite absence checks for point queries (=, IN)
• DataFusion expressions: Use familiar col("x").eq(lit(42)) syntax
• Conservative evaluation: Never skips data that might match (safety first)
• Async-first API: Optimized for remote storage (S3, GCS, Azure)
• Non-invasive: Works with upstream parquet crate, no format changes
• Best-effort compilation: Uses supported predicates even if some fail

Main API Entry Points

Synchronous API

Use PruneRequest for the builder-style API:

let result = PruneRequest::new(metadata, schema)
    .with_predicate(&col("id").gt(lit(100i64)))
    .enable_page_index(true)
    .prune();

let kept_row_groups = result.row_groups();
let page_selection = result.row_selection();

Async API with Bloom Filters

Use PruneRequest::prune_async() for async pruning with bloom filter support:

use aisle::PruneRequest;
use datafusion_expr::{col, lit};
use parquet::arrow::async_reader::ParquetRecordBatchStreamBuilder;
use tokio::fs::File;

let file = File::open("data.parquet").await?;
let mut builder = ParquetRecordBatchStreamBuilder::new(file).await?;

let predicate = col("user_id").eq(lit(12345i64));

let result = PruneRequest::new(builder.metadata(), builder.schema())
    .with_predicate(&predicate)
    .enable_bloom_filter(true)  // Check bloom filters
    .enable_page_index(true)
    .prune_async(&mut builder).await;

println!("Kept {} row groups", result.row_groups().len());

Custom Bloom Filter Provider

Implement AsyncBloomFilterProvider for optimized bloom filter loading:

use aisle::AsyncBloomFilterProvider;
use parquet::bloom_filter::Sbbf;

struct CachedBloomProvider {
    // Your cache/storage implementation
}

impl AsyncBloomFilterProvider for CachedBloomProvider {
    async fn bloom_filter(&mut self, row_group: usize, column: usize) -> Option<Sbbf> {
        // Load from cache or fetch from storage
    }
}

Supported Predicates

Aisle supports a conservative subset of DataFusion expressions:

Type	Example	Row-Group	Page-Level	Bloom Filter
Equality	col("x").eq(lit(42))	✓	✓	✓
Inequality	col("x").not_eq(lit(42))	✓	✓	✗
Comparisons	col("x").lt(lit(100))	✓	✓	✗
Range	col("x").between(lit(10), lit(20))	✓	✓	✗
Set membership	col("x").in_list(vec![...])	✓	✓	✓
Null checks	col("x").is_null()	✓	✓	✗
String prefix	col("name").like(lit("prefix%"))	✓	✓	✗
Logical AND	a.and(b)	✓	✓ (best-effort)	✓
Logical OR	a.or(b)	✓	✓ (all-or-nothing)	✓
Logical NOT	a.not()	✓	✓ (exact only)	✗
Type casting	cast(col("x"), DataType::Int64)	✓ (no-op only)	✓	✓

Unsupported predicates are logged in CompileResult::errors() but don’t prevent pruning with supported parts.

Page-Level Pruning

Enable page indexes for finer-grained pruning within row groups:

let result = PruneRequest::new(metadata, schema)
    .with_predicate(&col("id").gt(lit(100i64)))
    .enable_page_index(true)  // Enable page-level pruning
    .prune();

// Apply both row-group and page-level selections
if let Some(row_selection) = result.row_selection() {
    // Use with ParquetRecordBatchReaderBuilder
    // reader.with_row_groups(...).with_row_selection(row_selection)
}

Error Handling

Aisle uses best-effort compilation. Unsupported predicates are logged but don’t block pruning:

let result = PruneRequest::new(metadata, schema)
    .with_predicate(&col("complex_expr").gt(lit(100i64)))
    .prune();

// Check compilation results
let compile_result = result.compile_result();
if compile_result.error_count() > 0 {
    eprintln!(
        "Warning: {} unsupported predicates",
        compile_result.error_count()
    );
    for error in compile_result.errors() {
        eprintln!("  - {}", error);
    }
}

// Still prune using supported predicates!
println!(
    "Successfully compiled {} predicates",
    compile_result.prunable_count()
);

Performance

Aisle can dramatically reduce I/O for selective queries:

Query Type	Selectivity	I/O Reduction
Point query (id = 12345)	0.001%	~99.9%
Range query (date BETWEEN ...)	2%	~98%
Multi-column filter	10%	~90%

Performance Factors:

• Row group size (larger → better statistics granularity)
• Predicate selectivity (lower → more pruning)
• Column cardinality (bloom filters help high-cardinality)
• Page index availability (Parquet 1.12+)

Overhead: Metadata evaluation is typically <1ms per row group.

When to Use Aisle

Good fit:

• Selective queries (reading <20% of data)
• Large Parquet files (>100MB, multiple row groups)
• Remote storage (S3, GCS) where I/O is expensive
• High-cardinality point queries

Not needed:

• Full table scans (no pruning benefit)
• Small files (<10MB, single row group)
• Already using a query engine with built-in pruning (DataFusion, DuckDB)

Examples

See the repository examples:

• basic_usage.rs: Row-group pruning with metadata
• async_usage.rs: Async API with bloom filters

Structs

CompileResult

Result of compiling a DataFusion expression into pruning IR.

PruneOptions

Options for controlling metadata pruning behavior

PruneOptionsBuilder

Builder for PruneOptions

PruneRequest

Builder for one-shot metadata pruning operations.

PruneResult

Result of metadata pruning

Pruner

Reusable pruning façade for a fixed schema.

Enums

CompileError

Errors that can occur during predicate compilation

Traits

AsyncBloomFilterProvider

Async bloom filter provider trait for custom I/O strategies.

Functions

roaring_to_row_selection

Convert a RoaringBitmap back to a RowSelection

row_selection_to_roaring

Convert a RowSelection to a RoaringBitmap