Bazof

Query tables in object storage as of event time.

Bazof is a lakehouse format with time-travel capabilities.

Project Structure

The Bazof project is organized as a Rust workspace with multiple crates:

• bazof: The core library providing the lakehouse format functionality
• bazof-cli: A CLI utility demonstrating how to use the library
• bazof-datafusion: DataFusion integration for SQL queries

Getting Started

To build all projects in the workspace:

cargo build --workspace

Using the CLI

The bazof-cli provides a command-line interface for interacting with bazof:

# Scan a table (current version)
cargo run -p bazof-cli -- scan --path ./test-data --table table0

# Scan a table as of a specific event time
cargo run -p bazof-cli -- scan --path ./test-data --table table0 --as-of "2024-03-15T14:30:00"

DataFusion Integration

The bazof-datafusion crate provides integration with Apache DataFusion, allowing you to:

1. Register Bazof tables in a DataFusion context
2. Run SQL queries against Bazof tables
3. Perform time-travel queries using the AsOf functionality

Example

use bazof_datafusion::BazofTableProvider;
use datafusion::prelude::*;

async fn query_bazof() -> Result<(), Box<dyn std::error::Error>> {
    let ctx = SessionContext::new();
    
    let event_time = Utc.with_ymd_and_hms(2019, 1, 17, 0, 0, 0).unwrap();
    
    let provider = BazofTableProvider::as_of(
        store_path.clone(), 
        local_store.clone(), 
        "ltm_revenue".to_string(),
        event_time
    )?;
    
    ctx.register_table("ltm_revenue_jan17", Arc::new(provider))?;
    
    let df = ctx.sql("SELECT key as symbol, value as revenue FROM ltm_revenue_jan17 WHERE key IN ('AAPL', 'GOOG') ORDER BY key").await?;
    df.show().await?;
}

Run the example:

cargo run --example query_example -p bazof-datafusion

If you install the CLI with cargo install --path crates/bazof-cli, you can run it directly with:

bazof-cli scan --path ./test-data --table table0

Project Roadmap

Bazof is under development. The goal is to implement a data lakehouse with the following capabilities:

• Atomic, non-concurrent writes (single writer)
• Consistent reads
• Schema evolution
• Event time travel queries
• Handling late-arriving data
• Integration with an execution engine

Milestone 0

• Script/tool for generating sample kv data set
• Key-value reader
• DataFusion table provider

Milestone 1

• Multiple columns support
• Single row, key-value writer
• Document spec
• Delta -> snapshot compaction
• Metadata validity checks

Milestone 2

• Streaming in scan
• Schema definition and evolution
• Late-arriving data support