hudi-datafusion

A native Rust library for Apache Hudi, with bindings to Python

The hudi-rs project aims to broaden the use of Apache Hudi for a diverse range of users and projects.

Example usage

[!NOTE] These examples expect a Hudi table exists at /tmp/trips_table, created using the quick start guide.

Python

Read a Hudi table into a PyArrow table.

from hudi import HudiTable

hudi_table = HudiTable("/tmp/trips_table")
records = hudi_table.read_snapshot()

import pyarrow as pa
import pyarrow.compute as pc

arrow_table = pa.Table.from_batches(records)
result = arrow_table.select(
    ["rider", "ts", "fare"]).filter(
    pc.field("fare") > 20.0)
print(result)

Rust

cargo new my_project --bin && cd my_project
cargo add tokio@1 datafusion@39
cargo add hudi --features datafusion

use std::sync::Arc;

use datafusion::error::Result;
use datafusion::prelude::{DataFrame, SessionContext};
use hudi::HudiDataSource;

#[tokio::main]
async fn main() -> Result<()> {
    let ctx = SessionContext::new();
    let hudi = HudiDataSource::new("/tmp/trips_table").await?;
    ctx.register_table("trips_table", Arc::new(hudi))?;
    let df: DataFrame = ctx.sql("SELECT * from trips_table where fare > 20.0").await?;
    df.show().await?;
    Ok(())
}

Work with cloud storage

Ensure cloud storage credentials are set properly as environment variables, e.g., AWS_*, AZURE_*, or GOOGLE_*. Relevant storage environment variables will then be picked up. The target table's base uri with schemes such as s3://, az://, or gs:// will be processed accordingly.

Contributing

Check out the contributing guide for all the details about making contributions to the project.