hdbconnect-arrow

Apache Arrow integration for the hdbconnect SAP HANA driver. Converts HANA result sets to Arrow RecordBatch format, enabling zero-copy interoperability with the entire Arrow ecosystem.

Why Arrow?

Apache Arrow is the universal columnar data format for analytics. By converting SAP HANA data to Arrow, you unlock seamless integration with:

[!TIP] Arrow's columnar format enables vectorized processing — operations run 10-100x faster than row-by-row iteration.

Installation

[dependencies]
hdbconnect-arrow = "0.1"

Or with cargo-add:

cargo add hdbconnect-arrow

[!IMPORTANT] Requires Rust 1.88 or later.

Usage

Basic batch processing

use hdbconnect_arrow::{HanaBatchProcessor, BatchConfig, Result};
use arrow_schema::{Schema, Field, DataType};
use std::sync::Arc;

fn process_results(result_set: hdbconnect::ResultSet) -> Result<()> {
    let schema = Arc::new(Schema::new(vec![
        Field::new("id", DataType::Int64, false),
        Field::new("name", DataType::Utf8, true),
    ]));

    let config = BatchConfig::default();
    let mut processor = HanaBatchProcessor::new(Arc::clone(&schema), config);

    for row in result_set {
        if let Some(batch) = processor.process_row(&row?)? {
            println!("Batch with {} rows", batch.num_rows());
        }
    }

    // Flush remaining rows
    if let Some(batch) = processor.flush()? {
        println!("Final batch with {} rows", batch.num_rows());
    }

    Ok(())
}

Schema mapping

use hdbconnect_arrow::{hana_type_to_arrow, hana_field_to_arrow};
use hdbconnect::TypeId;

// Convert individual types
let arrow_type = hana_type_to_arrow(TypeId::DECIMAL, Some(18), Some(2));
// Returns: DataType::Decimal128(18, 2)

// Convert entire field metadata
let arrow_field = hana_field_to_arrow(&hana_field_metadata);

Custom batch size

use hdbconnect_arrow::BatchConfig;
use std::num::NonZeroUsize;

let config = BatchConfig::new(NonZeroUsize::new(10_000).unwrap());

Ecosystem integration

use datafusion::prelude::*;

let batches = collect_batches_from_hana(result_set)?;
let ctx = SessionContext::new();
ctx.register_batch("hana_data", batches[0].clone())?;

let df = ctx.sql("SELECT * FROM hana_data WHERE amount > 1000").await?;
df.show().await?;

use duckdb::{Connection, arrow::record_batch_to_duckdb};

let conn = Connection::open_in_memory()?;
conn.register_arrow("sales", batches)?;

let mut stmt = conn.prepare("SELECT region, SUM(amount) FROM sales GROUP BY region")?;
let result = stmt.query_arrow([])?;

use polars::prelude::*;

let batch = processor.flush()?.unwrap();
let df = DataFrame::try_from(batch)?;

let result = df.lazy()
    .filter(col("status").eq(lit("active")))
    .group_by([col("region")])
    .agg([col("amount").sum()])
    .collect()?;

use arrow_ipc::writer::FileWriter;
use parquet::arrow::ArrowWriter;
use std::fs::File;

// Arrow IPC (Feather) format
let file = File::create("data.arrow")?;
let mut writer = FileWriter::try_new(file, &schema)?;
writer.write(&batch)?;
writer.finish()?;

// Parquet format
let file = File::create("data.parquet")?;
let mut writer = ArrowWriter::try_new(file, schema.clone(), None)?;
writer.write(&batch)?;
writer.close()?;

use pyo3_arrow::PyArrowType;
use pyo3::prelude::*;

#[pyfunction]
fn get_hana_data(py: Python<'_>) -> PyResult<PyArrowType<RecordBatch>> {
    let batch = fetch_from_hana()?;
    Ok(PyArrowType(batch))
}

// Python: df = pl.from_arrow(get_hana_data())

Features

Enable optional features in Cargo.toml:

[dependencies]
hdbconnect-arrow = { version = "0.1", features = ["async", "test-utils"] }

[!TIP] Enable test-utils in dev-dependencies for unit testing without a HANA connection.

Type mapping

API overview

use hdbconnect_arrow::{MockRow, MockRowBuilder};

let row = MockRowBuilder::new()
    .push_i64(42)
    .push_string("test")
    .push_null()
    .build();

use hdbconnect_arrow::{ArrowConversionError, Result};

fn convert_data() -> Result<()> {
    // ArrowConversionError covers:
    // - Type mismatches
    // - Decimal overflow
    // - Schema incompatibilities
    // - Invalid batch configuration
    Ok(())
}

Performance

The crate is optimized for high-throughput data transfer:

• Zero-copy conversion — Arrow builders write directly to memory without intermediate allocations
• Batch processing — Configurable batch sizes to balance memory usage and throughput
• Decimal optimization — Direct BigInt arithmetic avoids string parsing overhead
• Builder reuse — Builders reset between batches, eliminating repeated allocations

[!NOTE] For large result sets, use LendingBatchIterator to stream data with constant memory usage.

Part of pyhdb-rs

This crate is part of the pyhdb-rs workspace, providing the Arrow integration layer for the Python SAP HANA driver.

Related crates:

• hdbconnect-py — PyO3 bindings exposing Arrow data to Python

Resources

• Apache Arrow — Official Arrow project
• Arrow Rust — Rust implementation
• DataFusion — Query engine built on Arrow
• Powered by Arrow — Projects using Arrow

MSRV policy

[!NOTE] Minimum Supported Rust Version: 1.88. MSRV increases are minor version bumps.

License

Licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE)
• MIT license (LICENSE-MIT)

at your option.