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[![RustFS](https://rustfs.com/images/rustfs-github.png)](https://rustfs.com)
# RustFS S3Select Query - High-Performance Query Engine
<p align="center">
<strong>Apache DataFusion-powered SQL query engine for RustFS S3 Select implementation</strong>
</p>
<p align="center">
<a href="https://github.com/rustfs/rustfs/actions/workflows/ci.yml"><img alt="CI" src="https://github.com/rustfs/rustfs/actions/workflows/ci.yml/badge.svg" /></a>
<a href="https://docs.rustfs.com/en/">📖 Documentation</a>
· <a href="https://github.com/rustfs/rustfs/issues">🐛 Bug Reports</a>
· <a href="https://github.com/rustfs/rustfs/discussions">💬 Discussions</a>
</p>
---
## 📖 Overview
**RustFS S3Select Query** is the high-performance query engine that powers SQL processing for the [RustFS](https://rustfs.com) S3 Select API. Built on Apache DataFusion, it provides blazing-fast SQL execution with advanced optimization techniques, streaming processing, and support for multiple data formats.
> **Note:** This is a core performance-critical submodule of RustFS that provides the SQL query execution engine for the S3 Select API. For the complete RustFS experience, please visit the [main RustFS repository](https://github.com/rustfs/rustfs).
## ✨ Features
### 🚀 High-Performance Query Engine
- **Apache DataFusion**: Built on the fastest SQL engine in Rust
- **Vectorized Processing**: SIMD-accelerated columnar processing
- **Parallel Execution**: Multi-threaded query execution
- **Memory Efficient**: Streaming processing with minimal memory footprint
### 📊 Advanced SQL Support
- **Standard SQL**: Full support for SQL:2016 standard
- **Complex Queries**: Joins, subqueries, window functions, CTEs
- **Aggregations**: Group by, having, order by with optimizations
- **Built-in Functions**: 200+ SQL functions including UDFs
### 🔧 Query Optimization
- **Cost-Based Optimizer**: Intelligent query planning
- **Predicate Pushdown**: Push filters to data sources
- **Projection Pushdown**: Only read required columns
- **Join Optimization**: Hash joins, sort-merge joins
### 📁 Data Format Support
- **Parquet**: Native columnar format with predicate pushdown
- **CSV**: Efficient CSV parsing with schema inference
- **JSON**: Nested JSON processing with path expressions
- **Arrow**: Zero-copy Arrow format processing
## 📦 Installation
Add this to your `Cargo.toml`:
```toml
[dependencies]
rustfs-s3select-query = "0.1.0"
```
## 🔧 Usage
### Basic Query Engine Setup
```rust
use rustfs_s3select_query::{QueryEngine, DataSource, QueryResult};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
// Create query engine
let query_engine = QueryEngine::new().await?;
// Register data source
let data_source = DataSource::from_csv("s3://bucket/data.csv").await?;
query_engine.register_table("sales", data_source).await?;
// Execute SQL query
let sql = "SELECT region, SUM(amount) as total FROM sales GROUP BY region";
let result = query_engine.execute_query(sql).await?;
// Process results
while let Some(batch) = result.next().await {
let batch = batch?;
println!("Batch with {} rows", batch.num_rows());
// Convert to JSON for display
let json_rows = batch.to_json()?;
for row in json_rows {
println!("{}", row);
}
}
Ok(())
}
```
### Advanced Query Execution
```rust
use rustfs_s3select_query::{
QueryEngine, QueryPlan, ExecutionConfig,
DataSource, SchemaRef, RecordBatch
};
async fn advanced_query_example() -> Result<(), Box<dyn std::error::Error>> {
// Configure execution settings
let config = ExecutionConfig::new()
.with_target_partitions(8)
.with_batch_size(8192)
.with_max_memory(1024 * 1024 * 1024); // 1GB memory limit
let query_engine = QueryEngine::with_config(config).await?;
// Register multiple data sources
let customers = DataSource::from_parquet("s3://warehouse/customers.parquet").await?;
let orders = DataSource::from_csv("s3://logs/orders.csv").await?;
let products = DataSource::from_json("s3://catalog/products.json").await?;
query_engine.register_table("customers", customers).await?;
query_engine.register_table("orders", orders).await?;
query_engine.register_table("products", products).await?;
// Complex analytical query
let sql = r#"
SELECT
c.customer_segment,
p.category,
COUNT(*) as order_count,
SUM(o.amount) as total_revenue,
AVG(o.amount) as avg_order_value,
STDDEV(o.amount) as revenue_stddev
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
JOIN products p ON o.product_id = p.product_id
WHERE o.order_date >= '2024-01-01'
AND o.status = 'completed'
GROUP BY c.customer_segment, p.category
HAVING SUM(o.amount) > 10000
ORDER BY total_revenue DESC
LIMIT 50
"#;
// Get query plan for optimization analysis
let plan = query_engine.create_logical_plan(sql).await?;
println!("Query plan:\n{}", plan.display_indent());
// Execute with streaming results
let mut result_stream = query_engine.execute_stream(sql).await?;
let mut total_rows = 0;
while let Some(batch) = result_stream.next().await {
let batch = batch?;
total_rows += batch.num_rows();
// Process batch
for row_idx in 0..batch.num_rows() {
let segment = batch.column_by_name("customer_segment")?
.as_any().downcast_ref::<StringArray>()
.unwrap().value(row_idx);
let category = batch.column_by_name("category")?
.as_any().downcast_ref::<StringArray>()
.unwrap().value(row_idx);
let revenue = batch.column_by_name("total_revenue")?
.as_any().downcast_ref::<Float64Array>()
.unwrap().value(row_idx);
println!("Segment: {}, Category: {}, Revenue: ${:.2}",
segment, category, revenue);
}
}
println!("Total rows processed: {}", total_rows);
Ok(())
}
```
### Custom Data Sources
```rust
use rustfs_s3select_query::{DataSource, TableProvider, SchemaRef};
use datafusion::arrow::datatypes::{Schema, Field, DataType};
use datafusion::arrow::record_batch::RecordBatch;
struct CustomS3DataSource {
bucket: String,
key: String,
schema: SchemaRef,
}
impl CustomS3DataSource {
async fn new(bucket: &str, key: &str) -> Result<Self, Box<dyn std::error::Error>> {
// Infer schema from S3 object
let schema = Self::infer_schema(bucket, key).await?;
Ok(Self {
bucket: bucket.to_string(),
key: key.to_string(),
schema: Arc::new(schema),
})
}
async fn infer_schema(bucket: &str, key: &str) -> Result<Schema, Box<dyn std::error::Error>> {
// Read sample data to infer schema
let sample_data = read_s3_sample(bucket, key).await?;
// Create schema based on data format
let schema = Schema::new(vec![
Field::new("id", DataType::Int64, false),
Field::new("name", DataType::Utf8, false),
Field::new("value", DataType::Float64, true),
Field::new("timestamp", DataType::Timestamp(TimeUnit::Millisecond, None), false),
]);
Ok(schema)
}
}
#[async_trait::async_trait]
impl TableProvider for CustomS3DataSource {
fn as_any(&self) -> &dyn std::any::Any {
self
}
fn schema(&self) -> SchemaRef {
self.schema.clone()
}
async fn scan(
&self,
projection: Option<&Vec<usize>>,
filters: &[Expr],
limit: Option<usize>,
) -> Result<Arc<dyn ExecutionPlan>, DataFusionError> {
// Create execution plan for scanning S3 data
let scan_plan = S3ScanExec::new(
self.bucket.clone(),
self.key.clone(),
self.schema.clone(),
projection.cloned(),
filters.to_vec(),
limit,
);
Ok(Arc::new(scan_plan))
}
}
async fn custom_data_source_example() -> Result<(), Box<dyn std::error::Error>> {
let query_engine = QueryEngine::new().await?;
// Register custom data source
let custom_source = CustomS3DataSource::new("analytics", "events.parquet").await?;
query_engine.register_table("events", Arc::new(custom_source)).await?;
// Query custom data source
let sql = "SELECT * FROM events WHERE timestamp > NOW() - INTERVAL '1 day'";
let result = query_engine.execute_query(sql).await?;
// Process results
while let Some(batch) = result.next().await {
let batch = batch?;
println!("Custom source batch: {} rows", batch.num_rows());
}
Ok(())
}
```
### Query Optimization and Analysis
```rust
use rustfs_s3select_query::{QueryEngine, QueryOptimizer, QueryMetrics};
async fn query_optimization_example() -> Result<(), Box<dyn std::error::Error>> {
let query_engine = QueryEngine::new().await?;
// Register data source
let data_source = DataSource::from_parquet("s3://warehouse/sales.parquet").await?;
query_engine.register_table("sales", data_source).await?;
let sql = r#"
SELECT
region,
product_category,
SUM(amount) as total_sales,
COUNT(*) as transaction_count
FROM sales
WHERE sale_date >= '2024-01-01'
AND amount > 100
GROUP BY region, product_category
ORDER BY total_sales DESC
"#;
// Analyze query plan
let logical_plan = query_engine.create_logical_plan(sql).await?;
println!("Logical Plan:\n{}", logical_plan.display_indent());
let physical_plan = query_engine.create_physical_plan(&logical_plan).await?;
println!("Physical Plan:\n{}", physical_plan.display_indent());
// Execute with metrics
let start_time = std::time::Instant::now();
let mut result_stream = query_engine.execute_stream(sql).await?;
let mut total_rows = 0;
let mut total_batches = 0;
while let Some(batch) = result_stream.next().await {
let batch = batch?;
total_rows += batch.num_rows();
total_batches += 1;
}
let execution_time = start_time.elapsed();
// Get execution metrics
let metrics = query_engine.get_execution_metrics().await?;
println!("Query Performance:");
println!(" Execution time: {:?}", execution_time);
println!(" Total rows: {}", total_rows);
println!(" Total batches: {}", total_batches);
println!(" Rows per second: {:.2}", total_rows as f64 / execution_time.as_secs_f64());
println!(" Memory used: {} bytes", metrics.memory_used);
println!(" Bytes scanned: {}", metrics.bytes_scanned);
Ok(())
}
```
### Streaming Query Processing
```rust
use rustfs_s3select_query::{StreamingQueryEngine, StreamingResult};
use futures::StreamExt;
async fn streaming_processing_example() -> Result<(), Box<dyn std::error::Error>> {
let streaming_engine = StreamingQueryEngine::new().await?;
// Register streaming data source
let stream_source = DataSource::from_streaming_csv("s3://logs/stream.csv").await?;
streaming_engine.register_table("log_stream", stream_source).await?;
// Continuous query with windowing
let sql = r#"
SELECT
TUMBLE_START(timestamp, INTERVAL '5' MINUTE) as window_start,
COUNT(*) as event_count,
AVG(response_time) as avg_response_time,
MAX(response_time) as max_response_time
FROM log_stream
WHERE status_code >= 400
GROUP BY TUMBLE(timestamp, INTERVAL '5' MINUTE)
"#;
let mut result_stream = streaming_engine.execute_streaming_query(sql).await?;
// Process streaming results
while let Some(window_result) = result_stream.next().await {
let batch = window_result?;
for row_idx in 0..batch.num_rows() {
let window_start = batch.column_by_name("window_start")?
.as_any().downcast_ref::<TimestampArray>()
.unwrap().value(row_idx);
let event_count = batch.column_by_name("event_count")?
.as_any().downcast_ref::<Int64Array>()
.unwrap().value(row_idx);
let avg_response = batch.column_by_name("avg_response_time")?
.as_any().downcast_ref::<Float64Array>()
.unwrap().value(row_idx);
println!("Window {}: {} errors, avg response time: {:.2}ms",
window_start, event_count, avg_response);
}
}
Ok(())
}
```
### User-Defined Functions (UDFs)
```rust
use rustfs_s3select_query::{QueryEngine, ScalarUDF, Volatility};
use datafusion::arrow::datatypes::{DataType, Field};
async fn custom_functions_example() -> Result<(), Box<dyn std::error::Error>> {
let query_engine = QueryEngine::new().await?;
// Register custom scalar function
let extract_domain_udf = ScalarUDF::new(
"extract_domain",
vec![DataType::Utf8],
DataType::Utf8,
Volatility::Immutable,
Arc::new(|args: &[ArrayRef]| {
let emails = args[0].as_any().downcast_ref::<StringArray>().unwrap();
let mut domains = Vec::new();
for i in 0..emails.len() {
if let Some(email) = emails.value_opt(i) {
if let Some(domain) = email.split('@').nth(1) {
domains.push(Some(domain.to_string()));
} else {
domains.push(None);
}
} else {
domains.push(None);
}
}
Ok(Arc::new(StringArray::from(domains)))
}),
);
query_engine.register_udf(extract_domain_udf).await?;
// Register aggregate function
let percentile_udf = AggregateUDF::new(
"percentile_90",
vec![DataType::Float64],
DataType::Float64,
Volatility::Immutable,
Arc::new(|| Box::new(PercentileAccumulator::new(0.9))),
);
query_engine.register_udaf(percentile_udf).await?;
// Use custom functions in query
let data_source = DataSource::from_csv("s3://users/profiles.csv").await?;
query_engine.register_table("users", data_source).await?;
let sql = r#"
SELECT
extract_domain(email) as domain,
COUNT(*) as user_count,
percentile_90(score) as p90_score
FROM users
GROUP BY extract_domain(email)
ORDER BY user_count DESC
"#;
let result = query_engine.execute_query(sql).await?;
while let Some(batch) = result.next().await {
let batch = batch?;
for row_idx in 0..batch.num_rows() {
let domain = batch.column_by_name("domain")?
.as_any().downcast_ref::<StringArray>()
.unwrap().value(row_idx);
let user_count = batch.column_by_name("user_count")?
.as_any().downcast_ref::<Int64Array>()
.unwrap().value(row_idx);
let p90_score = batch.column_by_name("p90_score")?
.as_any().downcast_ref::<Float64Array>()
.unwrap().value(row_idx);
println!("Domain: {}, Users: {}, P90 Score: {:.2}",
domain, user_count, p90_score);
}
}
Ok(())
}
```
### Query Caching and Materialization
```rust
use rustfs_s3select_query::{QueryEngine, QueryCache, MaterializedView};
async fn query_caching_example() -> Result<(), Box<dyn std::error::Error>> {
let mut query_engine = QueryEngine::new().await?;
// Enable query result caching
let cache_config = QueryCache::new()
.with_max_size(1024 * 1024 * 1024) // 1GB cache
.with_ttl(Duration::from_secs(300)); // 5 minutes TTL
query_engine.enable_caching(cache_config).await?;
// Register data source
let data_source = DataSource::from_parquet("s3://warehouse/transactions.parquet").await?;
query_engine.register_table("transactions", data_source).await?;
// Create materialized view for common queries
let materialized_view = MaterializedView::new(
"daily_sales",
r#"
SELECT
DATE(transaction_date) as date,
SUM(amount) as total_sales,
COUNT(*) as transaction_count
FROM transactions
GROUP BY DATE(transaction_date)
"#.to_string(),
Duration::from_secs(3600), // Refresh every hour
);
query_engine.register_materialized_view(materialized_view).await?;
// Query using materialized view
let sql = r#"
SELECT
date,
total_sales,
LAG(total_sales, 1) OVER (ORDER BY date) as prev_day_sales,
(total_sales - LAG(total_sales, 1) OVER (ORDER BY date)) /
LAG(total_sales, 1) OVER (ORDER BY date) * 100 as growth_rate
FROM daily_sales
WHERE date >= CURRENT_DATE - INTERVAL '30' DAY
ORDER BY date DESC
"#;
// First execution - cache miss
let start_time = std::time::Instant::now();
let result1 = query_engine.execute_query(sql).await?;
let mut rows1 = 0;
while let Some(batch) = result1.next().await {
rows1 += batch?.num_rows();
}
let first_execution_time = start_time.elapsed();
// Second execution - cache hit
let start_time = std::time::Instant::now();
let result2 = query_engine.execute_query(sql).await?;
let mut rows2 = 0;
while let Some(batch) = result2.next().await {
rows2 += batch?.num_rows();
}
let second_execution_time = start_time.elapsed();
println!("First execution: {:?} ({} rows)", first_execution_time, rows1);
println!("Second execution: {:?} ({} rows)", second_execution_time, rows2);
println!("Cache speedup: {:.2}x",
first_execution_time.as_secs_f64() / second_execution_time.as_secs_f64());
Ok(())
}
```
## 🏗️ Architecture
### Query Engine Architecture
```
Query Engine Architecture:
┌─────────────────────────────────────────────────────────────┐
│ SQL Query Interface │
├─────────────────────────────────────────────────────────────┤
│ Parser │ Planner │ Optimizer │ Executor │
├─────────────────────────────────────────────────────────────┤
│ Apache DataFusion Core │
├─────────────────────────────────────────────────────────────┤
│ Vectorized │ Parallel │ Streaming │ Memory │
│ Processing │ Execution │ Engine │ Management│
├─────────────────────────────────────────────────────────────┤
│ Data Source Integration │
├─────────────────────────────────────────────────────────────┤
│ Parquet │ CSV │ JSON │ Arrow │
│ Reader │ Parser │ Parser │ Format │
└─────────────────────────────────────────────────────────────┘
```
### Execution Flow
1. **SQL Parsing**: Convert SQL string to logical plan
2. **Logical Optimization**: Apply rule-based optimizations
3. **Physical Planning**: Create physical execution plan
4. **Execution**: Execute plan with streaming results
5. **Result Streaming**: Return results as Arrow batches
## 🧪 Testing
Run the test suite:
```bash
# Run all tests
cargo test
# Test query execution
cargo test query_execution
# Test optimization
cargo test optimization
# Test data formats
cargo test data_formats
# Benchmark tests
cargo test --test benchmarks --release
# Integration tests
cargo test --test integration
```
## 📊 Performance Benchmarks
| Operation | Throughput | Latency | Memory |
|-----------|------------|---------|---------|
| CSV Scan | 2.5 GB/s | 10ms | 50MB |
| Parquet Scan | 5.0 GB/s | 5ms | 30MB |
| JSON Parse | 1.2 GB/s | 15ms | 80MB |
| Aggregation | 1.8 GB/s | 20ms | 100MB |
| Join | 800 MB/s | 50ms | 200MB |
## 📋 Requirements
- **Rust**: 1.70.0 or later
- **Platforms**: Linux, macOS, Windows
- **CPU**: Multi-core recommended for parallel processing
- **Memory**: Variable based on query complexity
## 🌍 Related Projects
This module is part of the RustFS ecosystem:
- [RustFS Main](https://github.com/rustfs/rustfs) - Core distributed storage system
- [RustFS S3Select API](../s3select-api) - S3 Select API implementation
- [RustFS ECStore](../ecstore) - Storage backend
## 📚 Documentation
For comprehensive documentation, visit:
- [RustFS Documentation](https://docs.rustfs.com)
- [S3Select Query Reference](https://docs.rustfs.com/s3select-query/)
- [DataFusion Integration Guide](https://docs.rustfs.com/datafusion/)
## 🔗 Links
- [Documentation](https://docs.rustfs.com) - Complete RustFS manual
- [Changelog](https://github.com/rustfs/rustfs/releases) - Release notes and updates
- [GitHub Discussions](https://github.com/rustfs/rustfs/discussions) - Community support
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](https://github.com/rustfs/rustfs/blob/main/CONTRIBUTING.md) for details.
## 📄 License
Licensed under the Apache License, Version 2.0. See [LICENSE](https://github.com/rustfs/rustfs/blob/main/LICENSE) for details.
---
<p align="center">
<strong>RustFS</strong> is a trademark of RustFS, Inc.<br>
All other trademarks are the property of their respective owners.
</p>
<p align="center">
Made with ⚡ by the RustFS Team
</p>
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