Struct ArrowColumnWriter

Source

pub struct ArrowColumnWriter { /* private fields */ }

Expand description

Encodes ArrowLeafColumn to ArrowColumnChunk

ArrowColumnWriter instances can be created using an ArrowRowGroupWriterFactory;

Note: This is a low-level interface for applications that require fine-grained control of encoding (e.g. encoding using multiple threads), see ArrowWriter for a higher-level interface

§Example: Encoding two Arrow Array’s in Parallel

// The arrow schema
let schema = Arc::new(Schema::new(vec![
    Field::new("i32", DataType::Int32, false),
    Field::new("f32", DataType::Float32, false),
]));

// Compute the parquet schema
let props = Arc::new(WriterProperties::default());
let parquet_schema = ArrowSchemaConverter::new()
  .with_coerce_types(props.coerce_types())
  .convert(&schema)
  .unwrap();

// Create parquet writer
let root_schema = parquet_schema.root_schema_ptr();
// write to memory in the example, but this could be a File
let mut out = Vec::with_capacity(1024);
let mut writer = SerializedFileWriter::new(&mut out, root_schema, props.clone())
  .unwrap();

// Create a factory for building Arrow column writers
let row_group_factory = ArrowRowGroupWriterFactory::new(&writer, Arc::clone(&schema));
// Create column writers for the 0th row group
let col_writers = row_group_factory.create_column_writers(0).unwrap();

// Spawn a worker thread for each column
//
// Note: This is for demonstration purposes, a thread-pool e.g. rayon or tokio, would be better.
// The `map` produces an iterator of type `tuple of (thread handle, send channel)`.
let mut workers: Vec<_> = col_writers
    .into_iter()
    .map(|mut col_writer| {
        let (send, recv) = std::sync::mpsc::channel::<ArrowLeafColumn>();
        let handle = std::thread::spawn(move || {
            // receive Arrays to encode via the channel
            for col in recv {
                col_writer.write(&col)?;
            }
            // once the input is complete, close the writer
            // to return the newly created ArrowColumnChunk
            col_writer.close()
        });
        (handle, send)
    })
    .collect();

// Start row group
let mut row_group_writer: SerializedRowGroupWriter<'_, _> = writer
  .next_row_group()
  .unwrap();

// Create some example input columns to encode
let to_write = vec![
    Arc::new(Int32Array::from_iter_values([1, 2, 3])) as _,
    Arc::new(Float32Array::from_iter_values([1., 45., -1.])) as _,
];

// Send the input columns to the workers
let mut worker_iter = workers.iter_mut();
for (arr, field) in to_write.iter().zip(&schema.fields) {
    for leaves in compute_leaves(field, arr).unwrap() {
        worker_iter.next().unwrap().1.send(leaves).unwrap();
    }
}

// Wait for the workers to complete encoding, and append
// the resulting column chunks to the row group (and the file)
for (handle, send) in workers {
    drop(send); // Drop send side to signal termination
    // wait for the worker to send the completed chunk
    let chunk: ArrowColumnChunk = handle.join().unwrap().unwrap();
    chunk.append_to_row_group(&mut row_group_writer).unwrap();
}
// Close the row group which writes to the underlying file
row_group_writer.close().unwrap();

let metadata = writer.close().unwrap();
assert_eq!(metadata.file_metadata().num_rows(), 3);